Barros, Ana P. Reply to comment by Qingyuan Han on "Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP)." JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 0148-0227, JUL 31, 2010, DI 10.1029/2009JD013562
Sun, XM, Barros, AP, An Evaluation of the Statistics of Rainfall Extremes in Rain Gauge Observations, and Satellite-Based and Reanalysis Products Using Universal Multifractals, JOURNAL OF HYDROMETEOROLOGY, APR 2010, V 11(2), 388-404, DI 10.1175/2009JHM1142.1
Abstract: Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone-National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]-for the western United States (west of 105 degrees W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40 degrees N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.
Barros, AP, Prat, OP, Testik, FY, Size distribution of raindrops, NATURE PHYSICS, 1745-2473, APR 2010, 6(4), 232-232, ISI:000277281600002
Tao, K, Barros, AP, Using Fractal Downscaling of Satellite Precipitation Products for Hydrometeorological Applications, JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, MAR 2010, 27(3), 409-427, DI 10.1175/2009JTECHA1219.1
Abstract: The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e. g., gridded satellite precipitation products at resolution L x L) and the high resolution (l x l; L >> l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (similar to 25-km grid spacing) to the same resolution as the NCEP stage IV products (similar to 4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent beta, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR) probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km(2)) in the location of peak rainfall intensities for the cases studied.
Prat, OP, Barros, AP, Exploring the Transient Behavior of Z-R Relationships: Implications for Radar Rainfall Estimation, JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, OCT 2009, 48(10), 2127-2143, DI 10.1175/2009JAMC2165.1
Abstract: The objective of this study is to characterize the signature of dynamical microphysical processes on reflectivity-rainfall (Z-R) relationships used for radar rainfall estimation. For this purpose, a bin model with explicit microphysics was used to perform a sensitivity analysis of the shape parameters of the drop size distribution (DSD) as a function of time and rainfall regime. Simulations show that coalescence is the dominant microphysical process for low to moderate rain intensity regimes (R < 20 mm h(-1)) and that the rain rate in this regime is strongly dependent on the spectral properties of the DSD (i.e., the shape). The time to equilibrium for light rainfall is at least twice as long as in the case of heavy rainfall (1 h for stratiform vis-a-vis 30 min for thunderstorms). For high-intensity rainfall (R > 20 mm h(-1)), collision-breakup dynamics dominate the evolution of the raindrop spectra. The time-dependent Z-R relationships produced by the model converge to a universal Z-R relationship for heavy intensity rainfall (A = 1257; b similar to 1) centered on the region of Z-R space defined by the ensemble of over 100 empirical Z-R relationships. Given the intrinsically transient nature of the DSD for light rainfall, it is proposed that the vertical raindrop spectra and corresponding rain rates should be modeled explicitly by a microphysical model. A demonstration using a multicolumn simulation of a Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) overpass over Darwin for a stratiform event during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) is presented.
Barros, AP, Chiao, S, Lang, TJ, Burbank, D, Putkonen, J; Editors: Willett, SD; Hovius, N; Brandon, MT; Fisher, DM; From weather to climate-Seasonal and interannual variability of storms and implications for erosion processes in the Himalaya; TECTONICS, CLIMATE, AND LANDSCAPE EVOLUTION, Geological Society of America Special Papers, Geological-Society-of-America Penrose Conference, JAN 13-17, 2003, Taroko Natl Park, TAIWAN; 2006, 398, 17-38, ISI:000271216300003
Abstract: The extent to which orography may be a product of climate-erosion interactions is largely unknown. One grand challenge is to quantify the precipitation regimes of mountainous regions at the spatial and temporal scales relevant for investigating the interplay of erosion and tectonics in active orogens. In this paper, our objective is to synthesize recent research integrating numerical model simulations, satellite data, and surface observations in the Himalaya to elucidate the role of weather and climate in mountain evolution. We focus on the seasonal and interannual space-time variability of precipitation in the Great Himalayas by studying two preeminent storm regimes in detail-monsoon onsets and depressions in general, and wintertime Western Disturbances (cold season events). High-resolution simulations of heavy precipitation storms for two monsoon onset conditions (1999 and 2001) and one wintertime storm (2000) are used to illustrate the complex patterns of interaction between the mountains and the atmosphere, and to show how these affect the spatial distribution of precipitation. Along with observations from an existing ground-based network, these simulations provide unique insights into the space-time features of seasonal and interannual variability of precipitation. Our analysis indicates that the trajectory of monsoon storms during onset events exerts a strong control on the precipitation amounts and rainfall penetration into the rain shadow. Spatial variability of subsequent storm tracks in any given year helps explain the interannual variability of monsoon precipitation. Both observational data and our simulations define striking spatial variability in precipitation on upwind and downwind flanks of ridges that project into obliquely impinging storms. Specifically, as southeasterly monsoon winds encounter north-south oriented ridges, forced lifting of moist air enhances precipitation on the upwind flanks, whereas less precipitation occurs on downwind flanks. This variability is observed at spatial scales as short as similar to 10 km-a distance equivalent to the spacing of major ridge crests. Because infrequent, singular storm events appear to control the mass input to glaciers, and may determine the frequency and spatial distribution of landslides, these findings provide physically based insight into decoupling high-frequency (seasonal to interannual time scales) from low-frequency (multidecadal to centennial and longer time scales) signals in the interpretation of climate and erosion records in the Himalayas. Furthermore, this research suggests that integrative studies aimed at unraveling the role of climate in landscape evolution must include consideration of storm frequency and intensity along with spatial variability at scales consistent with regional climate forcing.
Prat, OP, Barros, AP, Combining a Rain Microphysical Model and Observations: Implications for Radar Rainfall Estimation, 2009 IEEE RADAR CONFERENCE, VOLS 1 AND 2, 805-808, 2009 IEEE Radar Conference, MAY 04-08, 2009, Pasadena, CA; ISI:000268721800162
Abstract: A bin-model was used to characterize the signature of dynamical microphysical processes on Z-R relationships used for radar rainfall estimation. The sensitivity analysis performed shows that coalescence is the dominant microphysical process for low to moderate rain intensity regimes (R < 20mm h(-1)), and that rain rate in this regime is strongly dependent on the spectral properties of the DSD (i.e. the shape). For high intensity rainfall (R > 20mm h(-1)), collision-breakup dynamics dominate the evolution of the raindrop spectra. Analysis of the time-dependent Z-R relationships produced by the model suggests convergence to a universal ZR relationship for heavy intensity rainfall. Conversely, the model results show that Z-R relationships severely underestimate reflectivity in the light rainfall regime.
Yildiz, O, Barros, AP, Evaluating spatial variability and scale effects on hydrologic processes in a midsize river basin, SCIENTIFIC RESEARCH AND ESSAYS, APR 2009, 4(4), 217-225, ISI:000265885300004
Abstract: The impact of spatial variability and scale on the dynamics of hydrologic processes in the Monongahela river basin of USA was investigated using a physically based spatially distributed hydrologic model developed by Yildiz (2001). The hydrologic model simulations were performed at 1 and 5 km spatial scales for a 5 month period from April through August of 1993. Effects of spatial variability in topography, vegetation and hydrogeology and of spatial scale were evaluated through comparisons of the simulated and observed streamflows for the prescribed resolutions at different locations across the river basin. The evaluation of observed and simulated streamflows using the statistical measures of mean, standard deviation, coefficient of variation, root mean square error and bias showed that model statistics of streamflow followed closely the spatial patterns of those of existing observations, that is, the model captured the space-time features of the 1993 flood across the basin. The changes in the nature of the rainfall-runoff response due to changes in the spatial resolution of the model indicated that there was also a change in governing physical processes at different resolutions. Here, this change was expressed in terms of the relative contributions of surface and subsurface flows.
Giovannettone, JP, Barros, AP, Probing Regional Orographic Controls of Precipitation and Cloudiness in the Central Andes Using Satellite Data, JOURNAL OF HYDROMETEOROLOGY, FEB 2009, 10(1), 167-182, DI 10.1175/2008JHM973.1
Abstract: Data obtained from NOAA's Geostationary Operational Environmental Satellite (GOES) and NASA's Tropical Rainfall Measuring Mission (TRMM) satellites were used to investigate the relationships between topography, large-scale circulation, and the climatology of precipitation and cloudiness in the Andes specifically over Peru and the Altiplano Plateau-at diurnal, seasonal, and interannual time scales. The spatial variability of cloudiness was assessed through empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Results indicate that landform is the principal agent of the space-time variability of moist atmospheric processes in the Andes, with the first mode explaining up to 70% of all observed variability. These results substantiate the differences between "continental'' (Andes and Himalayas) and "maritime'' (Western Cordillera) orographic precipitation regimes, reflecting the degree to which upwind landmasses modulate moisture transport toward and across mountain barriers. GOES brightness temperatures show that afternoon convective activity during the rainy season is more intense on wet hydrometeorological years such as 2001, whereas the space-time structure of nighttime cloudiness at the foothills and outlets of deep interior valleys does not change during the monsoon and from one year to another independently of large-scale conditions. This suggests that daytime cloud formation and precipitation is strongly dependent on large-scale moisture transport. Interactions between mesoscale and ridge valley circulations, which are locked to the topography, determine the space-time organization of clouds and precipitation at nighttime. This leads to strong clustering of precipitation features associated with enhanced convection at high elevations along the ridges and near the headwaters of the major river systems in the TRMM data.
AU Giovannettone, JP, Barros, AP, A Remote Sensing Survey of the Role of Landform on the Organization of Orographic Precipitation in Central and Southern Mexico, JOURNAL OF HYDROMETEOROLOGY, DEC 2008, 9(6), 1267-1283, DI 10.1175/2008JHM947.1
Abstract: Data from NASA's TRMM satellite and NOAA's GOES satellites were used to survey the orographic organization of cloud precipitation in central and southern Mexico during the monsoon with two main objectives: 1) to investigate large-scale forcing versus local landform controls, and 2) to compare the results with previous work in the Himalayas. At large scales, the modes of spatial variability of cloudiness were estimated using the empirical orthogonal function (EOF) analysis of GOES brightness temperatures. Terrain modulation of synoptic-scale high-frequency variability (3-5- and 6-9-day cycles normally associated with the propagation of easterly waves) was found to cause higher dispersion in the EOF spectrum, with the first mode explaining less than 30% of the spatial variability in central and southern Mexico as opposed to 50% and higher in the Himalayas. A detailed analysis of the first three EOFs for 1999, an average La Ni a year with above average rainfall, and for 2001, a weak La Ni a year with below average rainfall, shows that landform (mountain peaks and land-ocean contrast) and large-scale circulation (moisture convergence) alternate as the key controls of regional hydrometeorology in dry and wet years, or as active and break (midsummer drought) phases of the monsoon, respectively. The diurnal cycle is the dominant time scale of variability in 2001, as it is during the midsummer drought in all years. Strong variability at time scales beyond two weeks is only present during the active phases of the monsoon. At the river basin scale, the data show increased cloudiness over the mountain ranges during the afternoon, which moves over the low-lying regions at the foot of the major orographic barriers [the Sierra Madre Occidental (SMO)/Sierra Madre del Sur (SMS) and Trans-Mexican Volcanic Belt (TMVB)], specifically the Balsas and the Rio de Santiago basins at nighttime and in the early morning. At the ridge-valley scale (similar to 100-200 km), robust day-night (ridge-valley) asymmetries suggest strong local controls on cloud and precipitation, with convective activity along the coastal region of the SMO and topographically forced convection at the foothills of headwater ridges in the Altiplano and the SMS. These day-night spatial shifts in cloudiness and precipitation are similar to those found in the Himalayas at the same spatial scales.
Barros, AP, Tao, K, A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous, TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery, JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, NOV 2008, 25(11), 1901-1920, DI 10.1175/2008JTECHA1019.1
Abstract: A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June-September) over a 3-yr period of 1999-2001 was conducted to assess the algorithm's performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2-5 mm h(-1) and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e. g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.
Prat, OP, Barros, AP, Williams, CR, An Intercomparison of Model Simulations and VPR Estimates of the Vertical Structure of Warm Stratiform Rainfall during TWP-ICE, JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, NOV 2008, 47(11), 2797-2815, DI 10.1175/2008JAMC1801.1
Abstract: A model of rain shaft microphysics that solves the stochastic advection-coalescence-breakup equation in an atmospheric column was used to simulate the evolution of a stratiform rainfall event during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) in Darwin, Australia. For the first time, a dynamic simulation of the evolution of the drop spectra within a one-dimensional rain shaft is performed using realistic boundary conditions retrieved from real rain events. Droplet size distribution (DSD) retrieved from vertically pointing radar (VPR) measurements are sequentially imposed at the top of the rain shaft as boundary conditions to emulate a realistic rain event. Time series of model profiles of integral parameters such as reflectivity, rain rate, and liquid water content were subsequently compared with estimates retrieved from vertically pointing radars and Joss-Waldvogel disdrometer (JWD) observations. Results obtained are within the VPR retrieval uncertainty estimates. Besides evaluating the model's ability to capture the dynamical evolution of the DSD within the rain shaft, a case study was conducted to assess the potential use of the model as a physically based interpolator to improve radar retrieval at low levels in the atmosphere. Numerical results showed that relative improvements on the order of 90% in the estimation of rain rate and liquid water content can be achieved close to the ground where the VPR estimates are less reliable. These findings raise important questions with regard to the importance of bin resolution and the lack of sensitivity for small raindrop size (< 0.03 cm) in the interpretation of JWD data, and the implications of using disdrometer data to calibrate radar algorithms.
Barros, AP, Prat, OP, Shrestha, P, Testik, FY, Bliven, LF, Revisiting Low and List (1982): Evaluation of raindrop collision parameterizations using laboratory observations and modeling, JOURNAL OF THE ATMOSPHERIC SCIENCES, SEP 2008, 65(9), 2983-2993, DI 10.1175/2008JAS2630.1
Abstract: Raindrop collision and breakup is a stochastic process that affects the evolution of drop size distributions (DSDs) in precipitating clouds. Low and List have remained the obligatory reference on this matter for almost three decades. Based on a limited number of drop sizes (10), Low and List proposed generalized parameterizations of collisional breakup across the raindrop spectra that are standard building blocks for numerical models of rainfall microphysics. Here, recent laboratory experiments of drop collision at NASA's Wallops Island Facility (NWIF) using updated high-speed imaging technology with the objective of assessing the generality of Low and List are reported. The experimental fragment size distributions (FSDs) for the collision of selected drop pairs were evaluated against explicit simulations using a dynamical microphysics model (Prat and Barros, with parameterizations based on Low and List updated by McFarquhar). Onetoone comparison of the FSDs shows similar distributions; however, the model was found to underestimate the fragment numbers observed in the smallest diameter range (e. g., D < 0.2 mm), and to overestimate the number of fragments produced when small drops (diameter DS >= 1mm) and large drops (diameter DL >= 3mm) collide. This effect is particularly large for fragments in the 0.5-1.0-mm range, and more so for filament breakup (the most frequent type of breakup observed in laboratory conditions), reflecting up to 30% uncertainty in the left-hand side of the FSD (i.e., the submillimeter range). For coalescence, the NWIF experiments confirmed the drop collision energy cutoff (E-T) estimated by Low and List (i. e., E-T > 5.0 mu J). Finally, the digital imagery of the laboratory experiments was analyzed to determine the characteristic time necessary to reach stability in relevant statistical properties. The results indicate that the temporal separation between particle (i.e., single hydrometeor) and population behavior, that is, the characteristic time scale to reach homogeneity in the NWIF raindrop populations, is 160 ms, which provides a lower bound to the governing time scale in population-based microphysical models.
Barros, AP, Bowden, GJ, Toward long-lead operational forecasts of drought: An experimental study in the Murray-Darling River Basin, JOURNAL OF HYDROLOGY, AUG 15 2008, 357(3-4), 349-367, DI 10.1016/j.jhydrol.2008.05.026
Abstract: Resiliency and effectiveness in water resources management of drought is strongly depend on advanced knowledge of drought onset, duration and severity. The motivation of this work is to extend the lead time of operational drought forecasts. The research strategy is to explore the predictability of drought severity from space-time varying indices of large-scale climate phenomena relevant to regional hydrometeorology (e.g. ENSO) by integrating linear and non-linear statistical data models, specifically self-organizing maps (SOM) and multivariate linear regression analysis. The methodology is demonstrated through the step-by-step development of a model to forecast monthly spatial patterns of the standard precipitation index (SPI) within the Murray-Darling Basin (MDB) in Australia up to 12 months in advance. First, the rationale for the physical hypothesis and the exploratory data analysis including principal components, wavelet and partial mutual information analysis to identify and select predictor variables are presented. The focus is on spatial datasets of precipitation, sea surface temperature anomaly (SSTA) patterns over the Indian and Pacific Oceans, temporal and spatial gradients of outgoing longwave radiation (OLR) in the Pacific Ocean, and the far western Pacific wind-stress anomaly. Second, the process of model construction, calibration and evaluation is described. The experimental forecasts show that there is ample opportunity to increase the lead time of drought forecasts for decision support using parsimonious data models that capture the governing climate processes at regional scale. OLR gradients proved to be dispensable predictors, whereas SPI-based predictors appear to control predictability when the SSTA in the region [87.5 degrees N-87.5 degrees S; 27.5 degrees E-67.5 degrees W] and eastward wind-stress anomalies in the region [4 degrees N-4 degrees S; 130 degrees E-160 degrees E) are small, respectively, +/- 1 degrees and +/- 0.01 dyne/cm(2), that is when ENSO activity is weak. The areal averaged 12-month lead-time forecasts of SPI in the MDB explain up to 60% of the variance in the observations (r > 0.7). Based on a threshold SPI of -0.5 for severe drought at the regional scale and for a nominal 12-month lead time, the forecast of the timing of onset is within 0-2 months of the actual threshold being met by the observation, thus effectively a 10-month lead time forecast at a minimum. Spatial analysis suggests that forecast errors can be attributed in part to a mismatch between the spatial heterogeneity of rainfall and raingauge density in the observational. network. Forecast uncertainty on the other hand appears associated with the number of redundant predictors used in the forecast model. (c) 2008 Elsevier B.V. All rights reserved.
Tao, K, Barros, AP, Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP), JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, JUL 29 2008, 113 (D14), DI 10.1029/2007JD009337
Abstract: The manuscript presents results from an exploratory study of metrics to describe the dynamical evolution of atmospheric moisture fields for interpretive studies. Specifically, we conduct diagnostic data analysis of the life cycle (initiation, growth, and decay) of instabilities of index fields of atmospheric moisture that can be related to cloudiness and rainfall from the NCEP North American Regional Reanalysis (NARR) and International Satellite Cloud Climatology Product (ISCCP) data in the southeast United States in 2000-2001. The two principal metrics consist of spatial fields of finite size Lyapunov exponents (FSLE, a measure of the growth rate of instability) and localization length (the characteristic time scale of instability, or alternatively a proxy measure of the local memory of the process). The results reveal (1) consistency among the spatial fields of the seasonal rates of instability growth (FSLE); (2) differences in the dynamical range of perturbation growth in the ISCCP observations (broad) as compared to the NARR (narrow), especially in the smaller perturbation/ fast dynamics regime, which suggests stronger coupling of the physical processes in the NARR; and (3) stronger interseasonal and intraseasonal variability in the manifestation of nonlinear dynamics (e. g., strong summer/fall contrasts) in the NARR as compared to the satellite data. Larger disparities (r(2) similar to 02-0.3) occur over the ocean on the west (summer) and eastern (fall) margins of the Gulf of Mexico and over land in the Trinity river basin (Texas) and at the foothills of the Ouachita mountains where agriculture and large freshwater reservoirs (large free water surfaces) exist and where the 2000 drought attained exceptional intensity. Whereas the differences over the ocean may be attributed to the subgrid-scale representation of warm core rings and the loop current over the Gulf of Mexico, the differences are likely associated with the representation of land-atmosphere interactions in the model over land.
Bhushan, S, Barros, AP, A numerical study to investigate the relationship between moisture convergence patterns and orography in central Mexico, JOURNAL OF HYDROMETEOROLOGY, DEC 2007, 8(6), 1264-1284, DI 10.1175/2007JHM791.1
Abstract: This study examines small-scale orographic effects on atmospheric moisture convergence at the ridge valley scale in the Grande de Santiago River basin in central Mexico during a major monsoon storm on 13-14 August 1999. The simulation was performed using a coupled land-cloud resolving model on three nested grids (at 12-, 3-, and 1-km resolutions). The specific objective is to investigate the physical mechanisms that explain the regional space-time organization of orographic precipitation and cloudiness identified in the region from satellite data. The overarching goals of the research were 1) to characterize the effects of landform and topography-flow geometry relationships on the spatial distribution of precipitation and clouds, especially with regard to the role of mountain winds and lateral drainage flows, and 2) to assess the influence of land-atmosphere interactions (specifically latent and sensible heat fluxes) on moisture convergence patterns during monsoon storms. The model results indicate that large-scale moisture convergence dominates the distribution of total water in the troposphere during monsoon storms, which is modulated by topographically induced gravity waves and thermodynamic gradients associated with the land-sea contrast in the coastal zone. In the Grande de Santiago River basin, mountain-plain differences in thermodynamic response control mesoscale moisture convergence patterns leading to nocturnal buildup in the valleys. At the ridge-valley scale, strong convergence and strong winds (up to 15 m s(-1)) occur on the lee side of ridges oriented perpendicularly to the impinging synoptic flow with the development of transient flow conditions (from supercritical to subcritical) in the valley, independently of the time of day. In turn, this localized hydraulic-jump-like circulation drives strong return winds (i.e., cold outflows in the downvalley direction) that converge in the central lowlands preceding nighttime rainfall, lifting warm moist air at the mouth of the valley, thus initiating nocturnal convection. Simulated moisture convergence patterns are clustered along the ridges and against the foot slopes at the outlet of the north-south oriented catchments consistent with the space-time distribution of satellite observations of precipitation and clouds in the region overall, and with precipitation features detected during the simulated event in particular.
Prat, OP, Barros, AP, Robust numerical solution of the stochastic collection-breakup equation for warm rain (vol 46, pg 1480, 2007), JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
Prat, OP, Barros, AP, A robust numerical solution of the Stochastic collection-breakup equation for warm rain, JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, SEP 2007, 46(9), 1480-1497, DI 10.1175/JAM2544.1
Abstract: The focus of this paper is on the numerical solution of the stochastic collection equation-stochastic breakup equation (SCE-SBE) describing the evolution of raindrop spectra in warm rain. The drop size distribution (DSD) is discretized using the fixed-pivot scheme proposed by Kumar and Ramkrishna, and new discrete equations for solving collision breakup are presented. The model is evaluated using established coalescence and breakup parameterizations (kernels) available in the literature, and in that regard this paper provides a substantial review of the relevant science. The challenges posed by the need to achieve stable and accurate numerical solutions of the SCE-SBE are examined in detail. In particular, this paper focuses on the impact of varying the shape of the initial DSD on the equilibrium solution of the SCE-SBE for a wide range of rain rates and breakup kernels. The results show that, although there is no dependence of the equilibrium DSD on initial conditions for the same rain rate and breakup kernel, there is large variation in the time that it takes to reach steady state. This result suggests that, in coupled simulations of in-cloud motions and microphysics and for short time scales (<30 min) for which transient conditions prevail, the equilibrium DSD may not be attainable except for very heavy rainfall. Furthermore, simulations for the same initial conditions show a strong dependence of the dynamic evolution of the DSD on the breakup parameterization. The implication of this result is that, before the debate on the uniqueness of the shape of the equilibrium DSD can be settled, there is critical need for fundamental research including laboratory experiments to improve understanding of collisional mechanisms in DSD evolution.
Chiao, S, Barros, AP, A numerical study of the hydrometeorological dryline in Northwest India during the monsoon, JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN, FEB 2007, 85A, 337-361, ISI:000246144200019
Abstract: This objective of this study is to elucidate the processes that govern the space-time persistence of the hydrometeorological dryline in Northwest India. The working hypothesis is that orographic forcing and land-atmosphere interactions via soil moisture and vegetation processes lock the hydrometeorological line to the Aravalli range and the Thar Desert (a.k.a. the Great Indian Desert). For this purpose, simulations of active and break phases of the 2001 monsoon season were conducted using a mesoscale model (MM5). During the active phases of the monsoon, southeasterly depressions from the Bay of Bengal propagate over northern India, maintaining sustained convergence of moist available energy east of the Aravalli range, leading to increased rainfall and cloudiness patterns consistent with deep convective activity. Drier air originating from the Arabian Sea in the Western Indian Ocean is constrained to the west. During monsoon break phases, moisture convergence from the Bay of Bengal to the Northern India Convergence Zone (NICZ) decreases dramatically, weakening regional circulations east of the Aravalli range. This allows ventilation of the central portion of the NICZ through penetration of westerly dry air, leading to reduced rainfall, lower soil wetness, decrease of latent heat fluxes, and finally lower CAPE and humidity in the lower troposphere. Whereas the inland propagation of monsoon depressions from the Bay of Bengal triggers the onset (demise) of active and break periods, the sustainability of either regime requires strong feedbacks between humidity and stability in the lower troposphere and the surface energy balance: negative in the case of monsoon breaks, positive in the case of active periods. This study shows that, albeit relatively modest (<600 m average elevation), the Aravalli provides sufficient lift (upwind) and descend (downwind) to organize the spatial distribution of updrafts westward (active phase) and eastward (break phase) of the topographic divide in such a way that low level updrafts are nearly suppressed over the Thar Desert. Sensitivity experiments with modified soil and vegetation cover show that daytime latent heat fluxes (and evapotranspiration) play an important role in the spatial orgnization of CAPE and in triggering light rainfall processes in the semi-arid regions of northwest India, whereas the occurrence of heavy rainfall to the east of the Aravalli range is controlled by large-scale monsoon dynamics.
Testik, FY, Barros, AP, Toward elucidating the microstructure of warm rainfall: A survey, REVIEWS OF GEOPHYSICS, MAY 12 2007, 45(2), 2003, DI 10.1029/2005RG000182
Abstract: Quantitative measurement, estimation, and prediction of precipitation remains one of the grand challenges in the hydrological and atmospheric sciences with far-reaching implications across the natural sciences. Although the roots of current research activity in this topic go back to the beginning of the twentieth century, advances in radar technology and in numerical modeling have provided the impetus for prolific research in the area of cloud and precipitation physics over the last 50 years. As radar rainfall measurements progressively became the staple of hydrometeorological observing systems, cloud and precipitation microphysics emerged as increasingly preeminent areas of research. Here we present a synthesis of the state of the science with respect to the physical dynamics of hydrometeors and, specifically, the transient processes that affect the temporal evolution of rainfall microstructure and that are directly relevant to the quantitative interpretation of radar rainfall measurements and explicit numerical simulations. The focus of our survey is on raindrop morphodynamics (equilibrium raindrop shape and raindrop oscillations), drop-drop interactions ( bounce, coalescence, and breakup), and the dynamical evolution of raindrop size distributions in precipitating clouds.
Yildiz, O, Barros, AP, Elucidating vegetation controls on the hydroclimatology of a mid-latitude basin, JOURNAL OF HYDROLOGY, FEB 15 2007, 333(2-4), 431-448, DI 10.1016/j.jhydrol.2006.09.010
Abstract: The rote of vegetation controls on the hydrological response to climatic variability of a mid-latitude watershed characterized by complex terrain and complex geology was assessed using a coupled surface-groundwater hydrological model. To separate infiltration and runoff production mechanisms from vegetation processes, the study was conducted with respect to both the representation of vegetation processes and the soil hydraulic properties for two different hydroclimatic regimes. The model was applied to simulate the warm season hydrological regime in the Monongahela River Basin in 1988, a major drought year, and in 1993, a wet hydrological year. Sensitivity analysis was conducted using the fractional factorial design method. Time-varying vegetation cover characteristics were directly assimilated to the model from satellite observations, and model simulations of streamflow at the outlet of various catchments were compared against observations to assess the model's ability to capture basic patterns of space-time seasonal variability within the basin. The results show that the physical controls expressed by different parameters and parameter interactions change across the basin with land-use, topography and geology on the one hand, and vary significantly between the spring and summer seasons. This is consistent with the notion of highly non-linear river-basin systems where nonstationarity emerges from the interactions among the spatially variable landscape and the temporally variable climate forcing. Above all, one key finding of this study is to elucidate the governing role of vegetation, specifically as described by Fractional Vegetation Cover and Leaf Area Index parameters in the space-time variability of hydrological response in the Monongahela River Basin for the two hydroclimatological regimes, and especially the linkage between areal. extent of vegetation and runoff production during drought. Because vegetation dynamics modulate the water and energy budgets via evapotranspiration and surface albedo, and this control is especially critical during the spring-summer transition which coincides with the greening season in mid-latitudes, we argue that these processes have far reaching implications for the predictive stability of physically-based models for hydrological change studies, and propose the notion of model calibration conditional on climate regime for operational hydrology.
Barros, AP, Environmental informatics - Long-lead flood forecasting using Bayesian neural networks, Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vols 1-5, IEEE International Joint Conference on Neural Networks (IJCNN 2005), JUL 31-AUG 04, 2005, Montreal, CANADA
Abstract: Neural Networks (NNs) are especially useful in exploratory data analysis to uncover and, or elucidate empirical relationships among data. Parameter estimation, the so-called "training" of neural networks is a variation of standard maximum likelihood estimation, whereby the optimal set of model parameters (the NN weights) maximizes the fit to the calibration (training) data set. In our previous applications of neural networks in hydrometeorology, we focused on the development of complex architectures of neural networks adapted to the characteristics of the available data (multisensor, multiresolution mix of ground-based and satellite observations). These architectures consist of large structures of simpler networks built to embody clearly defined hypothesis of functional relationships that are consistent with the underlying physical processes (rainfall and flood forecasting, wind, temperature and moisture profiles in the atmosphere, temporal evolution of cloud and storm morphologies). One challenge we have not addressed previously is how to quantify the uncertainty in NN-based forecasts or estimates. We begin to address this question through the use of Bayesian Neural Networks (BNNs) for long-lead flood forecasting
Testik, FY, Barros, AP, Bliven, LF, Field observations of multimode raindrop oscillations by high-speed imaging, JOURNAL OF THE ATMOSPHERIC SCIENCES, OCT 2006, 63(10), 2663-2668, ISI:000241554500015
Abstract: Periodic oscillations of raindrops falling at terminal velocity in natural rain are visualized for the first time by high-speed imaging. These images show the existence of an oscillation mode with the same frequency as the fundamental harmonic, but with shape different than that predicted by linear theory. These oscillations cause a lateral drift with a speed of approximately 20%-30% of the drop terminal velocity and without a preferred direction. These experimental observations serve as an insightful illustration of the potential benefit of applying high-speed imaging technology to investigate the dynamical microstructure of rainfall at the raindrop scale.
Wu, SL, Bras, RL, Barros, AP, Sensitivity of channel profiles to precipitation properties in mountain ranges, JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE, MAR 28 2006, 111(F1), DI 10.1029/2004JF000164
Abstract: The stream power erosion law, which describes the erosion rate as a function of channel discharge and gradient, has often been used for modeling landscape evolution in regions dominated by fluvial processes. However, most previous studies utilizing the stream power erosion law simply use drainage area as a surrogate for channel discharge. Despite its convenience this simplification has important shortcomings. Specifically, it ignores the effects of precipitation properties on channel discharge and hence erosion rate, and it ignores the interactions between mountain ranges and precipitation properties. By using the stream power erosion law together with the geomorphoclimatic instantaneous unit hydrograph we provide a method for linking the landscape evolution and precipitation properties directly. Our results demonstrate that the channel profile is sensitive not only to the total precipitation but also to precipitation properties like the rainfall frequency, intensity, duration, and their distribution in space. The channel profile is most sensitive to the variation of rainfall intensity and less sensitive to rainfall frequency and duration. Shorter and more intense rainfall could lead to significantly higher erosion rate and flatter channel profiles compared to longer and less intense rainfall. The spatial variation of precipitation can also influence the evolution of channel profile. Even if the total precipitation remains spatially homogeneous, different spatial behavior of rainfall intensity and rainfall duration may lead to different steady state river profiles. The channel profile tends to be flatter under the conditions of increasing rainfall intensity and decreasing rainfall duration with elevation and vice versa.
Gebremichael, M, Barros, AP, Evaluation of MODIS gross primary productivity (GPP) in tropical monsoon regions, REMOTE SENSING OF ENVIRONMENT, JAN 30 2006, 100(2), 150-166, DI 10.1016/j.rse.2005.10.009
Abstract: Near real-time vegetation indices derived from MODIS (MODerate resolution Imaging Spectroradiometer) observations (http://modis.gsfc.nasa.gov) provide a first opportunity to monitor ecohydrological systems globally at a spatial resolution consistent with biophysical processes at the field scale. Here, we present work toward the quantitative estimation of the uncertainty associated with MODIS Gross Primary Productivity (GPP), an end-product that depends on several MODIS derived vegetation indices. GPP products, available at 8-day and 1-km resolutions, were evaluated in two representative tropical ecosystems: a mixed forest site in the humid tropics (the Marsyandi river basin in the Nepalese Himalayas), and an open shrubland site in a semi-arid region (the Sonora river basin in northern Mexico). The MODIS-GPP products were compared against simulations made with a process-based biochemical-hydrology model driven by flux tower meteorological observations. Whereas the temporal march of vegetation indices and GPP products is consistent between the model and the algorithm, our study indicates that that there is a positive bias in the case of the mixed forest biome in the Marsyandi basin, and a negative bias in the case of open shrublands in the Sonora basin. We examined the error contribution from the DAO meteorological data used in the standard MODIS GPP products. The bias between the GPP estimates using DAO and tower meteorology is - 2.77 gC/m(2)/day (i.e., -77% of the mean of the tower-based GPP) in the Marsyandi, and 0.33 gC/m2/day (i.e., 18% of the mean of the tower-based GPP) in Sonora. Analysis of the temporal evolution of the discrepancies between the model and the MODIS algorithm points to the need for examining the light use efficiency parameterization, especially with regard to the representation of nonlinear functional dependencies on vapor pressure deficit (VPD), photosynthetically available radiation (PAR), and seasonal evolution of the productive capacity of vegetation as influenced by water stress.
Garcia-Quijano, JF, Barros, AP, Incorporating canopy physiology into a hydrological model: photosynthesis, dynamic respiration, and stomatal sensitivity, ECOLOGICAL MODELLING, JUN 10 2005, 185(1), 29-49, DI 10.1016/j.ecolmodel.2004.08.024
Abstract: Vegetation modulates the effects of climate variability through soil-vegetation-atmosphere (SVAT) interactions. A quantitative understanding of such interactions requires the proper integration of the water cycle and photosynthesis. While biochemical models have widely been used to estimate primary production, the effects of water stress on transpiration and carbon assimilation rates, and its feedbacks into the water cycle are not generally represented. The objective of this study is to investigate the limiting effects of soil moisture and evaporative demand on photosynthesis, and to understand its interactions with other hydrological processes. Our approach consists of integrating a physically based land surface hydrological model (LSHM) with a biochemical model for leaf photosynthesis and a substrate-structure separation model for respiration, including parameterizations of the diurnal cycle of Rubisco concentration and species-specific stomatal conductance (resistance). Exploratory simulations to evaluate the model against results from previous studies indicated that the model captures basic processes of canopy physiology well. Sensitivity analysis shows that water stress at sub-daily time-scales is an important limiting factor of photosynthesis, thus constraining carbon assimilation. On the other hand, the results further suggest that the biological control of transpiration via stomatal sensitivity is only significant under soil water stress conditions. Overall, the integrated model is capable of estimating not only carbon assimilation, but also the length of the growing season, as well as feedbacks between vegetation and soil hydrology processes.
Barros, AP, Kim, G, Williams, E, Nesbitt, SW, Probing orographic controls in the Himalayas during the monsoon using satellite imagery, NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2004, 4(1), 29-51, UT ISI:000223409900004
Abstract: The linkages between the space-time variability of observed clouds, rainfall, large-circulation patterns and topography in northern India and the Himalayas were investigated using remote sensing data. The research purpose was to test the hypothesis that cloudiness patterns are dynamic tracers of rainstorms, and therefore their temporal and spatial evolution can be used as a proxy of the spatial and temporal organization of precipitation and precipitation processes in the Himalayan range during the monsoon. The results suggest that the space-time distribution of precipitation, the spatial variability of the diurnal cycle of convective activity, and the terrain (landform and altitudinal gradients) are intertwined at spatial scales ranging from the order of a few kms (1-5 km) up to the continental-scale. Furthermore, this relationship is equally strong in the time domain with respect to the onset and intra-seasonal variability of the monsoon. Infrared and microwave imagery of cloud fields were analyzed to characterize the spatial and temporal evolution of mesoscale convective weather systems and short-lived convection in Northern India, the Himalayan range, and in the Tibetan Plateau during three monsoon seasons (1999, 2000 and 2001). The life cycle of convective systems suggests landform and orographic controls consistent with a convergence zone constrained to the valley of the Ganges and the Himalayan range, bounded in the west by the Aravalli range and the Garhwal mountains and in the East by the Khasi Hills and the Bay of Bengal, which we call the Northern India Convergence Zone (NICZ). The NICZ exhibits strong nighttime activity along the south-facing slopes of the Himalayan range, which is characterized by the development of shortlived convection (1-3 h) aligned with protruding ridges between 1:00 and 3:00 AM. The intra-annual and inter-annual variability of convective activity in the NICZ were assessed with respect to large-scale synoptic conditions, monsoon activity in the Bay of Bengal, and the modulating role of orography. Empirical orthogonal function (EOF) and canonical correlation (CC) analysis suggest that joint modes of variability of monsoon weather and topography, which we call orographic land-atmosphere interactions, modulate the space-time variability of cloudiness in the region. Finally, scaling analysis of cloudiness suggests three different scaling regimes of orographic land-atmosphere interactions: 1) a synoptic-scale regime (greater than or equal to 70-80km); 2) an orographic meso-beta regime (30-70 km) associated with the succession of wide valleys and bulky terrain features; and 3) an orographic meso-a regime (less than or equal to30 km) associated with the complex succession of protruding south-facing ridges and narrow valleys that characterize the Himalayan foothills between altitudes of 3 000 and 5 000 m elevations.
Lang, TJ, Barros, AP, Winter storms in the central Himalayas, JOURNAL OF THE METEOROLOGICAL SOCIETY OF JAPAN, JUN 2004, 82(3), 829-844, ISI:000223245100001
Abstract: Based on observations from a hydrometeorological network on the eastern slopes of the Annapurna Range, nearly all the annual precipitation at low elevations (< 2000 in MSL) in Nepal is in liquid form, even during the winter. However, high elevations (> 3000 in MSL) can receive up to 40% of their annual precipitation as snowfall during the winter, with the highest altitude stations (similar to4000 in MSL and above) having the most total winter precipitation (which can exceed 100 cm). Significant snowstorms are associated with terrain-locked low-pressure systems that form when an upper-level disturbance passes over the notch formed by the Himalayas and Hindu Kush mountains (the so-called Western Disturbances), causing upper-level SW flow over central Nepal and orographically forced precipitation. Based on these results, a 30-year (1973-2002) climatology of these notch depressions is developed and reveals that significant interannual variability in central Himalayan winter storms exists. Weak but statistically significant correlation between notch depressions and the Polar/Eurasia teleconnection pattern was found, suggesting that the strength of the circumpolar vortex may affect the number of depressions passing through the Himalayan region. A typical snow event (11 February 2000) was the subject of an observational and modeling case study. Local precipitation (snow and rain) and other meteorological observations, as well as satellite (Meteosat-5 and TRMM) and NCEP/NCAR Reanalysis data were used, along with a cloud-resolving model with realistic topography. This study shows that significant wintertime precipitation only occurs in the central Himalayas when the large-scale flow evolves to a favorable geometry with respect to the mountains.
Douglas, EM, Barros, AP, Probable maximum precipitation estimation using multifractals: Application in the eastern United States (vol 4, pg 1012, yr, 2004), JOURNAL OF HYDROMETEOROLOGY
Magagi, R, Barros, AP, Estimation of latent heating of rainfall during the onset of the Indian monsoon using TRMM PR and radiosonde data, JOURNAL OF APPLIED METEOROLOGY, FEB 2004, 43(2), 328-349, ISI:000189265200008
Abstract: The objective of this study is to estimate the vertical structure of the latent heating of precipitation in the vicinity of the Himalayas. Based on a cloud physics parameterization and the thermodynamic equilibrium equation, a simple algorithm is proposed to estimate latent heating from a combination of radiosonde and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data, specifically, the radar reflectivity and the rain-rate estimates. An evaluation of the algorithm against 6-hourly areal averages from diagnostic budget studies during the South China Sea Monsoon Experiment (SCMEX) suggests that the algorithm captures well the vertical structure of latent heating between the top of the moist layer and the cloud-top detrainment layer. The retrieval algorithm was applied systematically over the Indian subcontinent and Tibetan plateau within a region comprising 15degrees-32degreesN and 70degrees-95degreesE during June, the month of monsoon onset, for three different years (1999, 2000, and 2001). The estimated latent heating profiles exhibit large spatial and temporal variability in the magnitude and position of maximum latent heating within the same TRMM overpass, and from one year to the next. This reflects the presence of convective activity with varying degrees of organization during the monsoon, and also the interannual variability of large-scale conditions. Along the Himalayan range, the diurnal cycle of latent heating profiles suggests more intense convective activity in the early morning and during nighttime (1-km difference in the height of maximum latent heating), consistent with the diurnal cycle of rainfall observations and cloudiness. The height of maximum latent heating at stations in the Indian subcontinent varies over a wide range, reflecting a mix of stratiform and convective precipitation systems, respectively, 5.7 +/- 2, 3.8 +/- 1.5, and 4.8 +/- 1.7 km MSL, for 1999, 2000, and 2001. Overall, the peak production of latent heating is roughly at the effective terrain elevation of the Himalayan range with regard to synoptic circulation and orographic enhancement effects. The Tibetan plateau behaves as an elevated heat source with maximum heating produced at 7-8 km MSL. Average values of the maximum latent heating ranged between 1.3 and 1.6 K day(-1) per unit rainfall (1 cm day(-1)), with maximum values of up to 10 K day(-1).
Douglas, EM, Barros, AP, Probable maximum precipitation estimation using multifractals: Application in the eastern United States, JOURNAL OF HYDROMETEOROLOGY, Spring Meeting of the American-Geophysical-Union, 2002, WASHINGTON, D.C., DEC 2003, 4(6), 1012-1024, ISI:000187534900003
Abstract: Probable maximum precipitation (PMP) is the conceptual construct that defines the magnitude of extreme storms used in the design of dams and reservoirs. In this study, the value and utility of applying multifractal analysis techniques to systematically calculate physically meaningful estimates of maximum precipitation from observations in the eastern United States is assessed. The multifractal approach is advantageous because it provides a formal framework to infer the magnitude of extreme events independent of empirical adjustments, which is called the fractal maximum precipitation (FMP), as well as an objective estimate of the associated risk. Specifically, multifractal (multiscaling) behavior of maximum accumulated precipitation at daily (327 rain gauges) and monthly (1400 rain gauges) timescales, as well as maximum accumulated 6-hourly precipitable water fluxes for the period from 1950 to 1997 were characterized. Return periods for the 3-day FMP estimates in this study ranged from 5300 to 6200 yr. The multifractal parameters were used to infer the magnitude of extreme precipitation consistent with engineering design criterion (e.g., return periods of 106 yr), the design probable maximum precipitation (DPMP). The FMP and DPMP were compared against PMP estimates for small dams in Pennsylvania using the standard methodology in engineering practice (e.g., National Weather Service Hydrometeorological Reports 51 and 52). The FMP estimates were usually, but not always, found to be lower than the standard PMP (FMP/PMP ratios ranged from 0.5 to 1.0). Furthermore, a high degree of spatial variability in these ratios points to the importance of orographic effects locally, and the need for place-based FMP estimates. DMP/PMP ratios were usually greater than one (0.96 to 2.0), thus suggesting that DPMP estimates can provide a bound of known risk to the standard PMP.
Barros, AP, Lang, TJ, Monitoring the monsoon in the Himalayas: Observations in central Nepal, June 2001, MONTHLY WEATHER REVIEW, JUL 2003, 131(7), 1408-1427, ISI:000183763400015
Abstract: The Monsoon Himalayan Precipitation Experiment (MOHPREX) occurred during June 2001 along the south slopes of the Himalayas in central Nepal. Radiosondes were launched around the clock from two sites, one in the Marsyandi River basin on the eastern footslopes of the Annapurna range, and one farther to the southwest near the border with India. The flights supported rainfall and other hydrometeorological observations (including surface winds) from the Marsyandi network that has been operated in this region since the spring of 1999. The thermodynamic profiles obtained from the soundings support the observed nocturnal maximum in rainfall during the monsoon, with total column moisture and instability maximized just before rainfall peaks. Coinciding with the appearance of a monsoon depression over central India, the onset of the monsoon in this region was characterized by a weeklong weakening of the upper-level westerlies, and an increase in moisture and convective instability. The vertical structure of convection during the project was intense at times, and frequent thunder and lightning were observed. This is suggestive of monsoon break convection, which is expected to be predominant since the monsoon had not fully matured by the end of the month. Comparisons of the MOHPREX data with the NCEP - NCAR reanalysis data reveal that upper-level winds are characterized relatively well by the reanalysis, taking into account the coarse model topography. However, moisture is severely underestimated, leading to significant underestimation of rainfall by the reanalysis. The interaction of the ambient monsoon flow with the south slopes of the Himalayas, modulated by the diurnal variability of atmospheric state, is suggested as the primary cause of the nocturnal peak in rainfall.
Van der Aalst, WMP, Ter Hofstede, AHM, Kiepuszewski, B, Barros, AP, Workflow patterns, DISTRIBUTED AND PARALLEL DATABASES, JUL 2003, 14(1), 5-51, ISI:000181689000001
Abstract: Differences in features supported by the various contemporary commercial workflow management systems point to different insights of suitability and different levels of expressive power. The challenge, which we undertake in this paper, is to systematically address workflow requirements, from basic to complex. Many of the more complex requirements identified, recur quite frequently in the analysis phases of workflow projects, however their implementation is uncertain in current products. Requirements for workflow languages are indicated through workflow patterns. In this context, patterns address business requirements in an imperative workflow style expression, but are removed from specific workflow languages. The paper describes a number of workflow patterns addressing what we believe identify comprehensive workflow functionality. These patterns provide the basis for an in-depth comparison of a number of commercially available workflow management systems. As such, this paper can be seen as the academic response to evaluations made by prestigious consulting companies. Typically, these evaluations hardly consider the workflow modeling language and routing capabilities, and focus more on the purely technical and commercial aspects.
Lang, TJ, Barros, AP, AMS, On the mechanisms determining the spatial variability of heavy precipitation in the Himalayas, 16TH CONFERENCE ON HYDROLOGY, JAN 13-17, 2002, ORLANDO, FL, 2002, 9-9, ISI:000179336300003
Kim, G, Barros, AP, AMS, Spatial and tmeporal organization of convective activity in the hymalayan region during the Asian monsoon, 16TH CONFERENCE ON HYDROLOGY, JAN 13-17, 2002, ORLANDO, FL, 2002, 66-67, ISI:000179336300017
Barros, AP, Gordon, SJ, Assessing the linkages among climate variability, land-use change and the sedimentary regime of the Upper Chesapeake BayEdited by Brebbia, CA; COASTAL ENVIRONMENT - ENVIRONMENTAL PROBLEMS IN COASTAL REGIONS IV, ENVIRONMENTAL STUDIES SERIES, 4th International Conference on Environmental Problems in Coastalm Regions, SEP 16-18, 2002, RHODES, GREECE, 2002, 8, 183-192, ISI:000179556600017
Abstract: The objective of this study was to assess the effects of climate variability and landuse change in the Susquehanna river basin on the sedimentary regime of the Upper Chesapeake Bay, USA. Historical precipitation, stream discharge, land use, suspended sediment and dam reservoir capacity data were used to interpret regional patterns of erosion and sediment transport on annual, decadal and centennial time-scales. Sediment accumulation patterns in the Upper Chesapeake Bay were inferred from the analysis of four piston cores including C-14 dating, magnetic susceptibility and laser particle analysis techniques. Core magnetic susceptibility profiles were found to be a useful tool for stratigraphic correlation on the order of centimeters. On annual to decadal time-scales, a strong relationship was found between sediment yield and regional rainfall-runoff patterns and land surface erodibility. However, on centennial time-scales, our research suggests that anthropogenic land -use change (e.g., clear-cutting of native forests) has caused up to an order of magnitude increase in sediment accumulation rates-in the Upper Chesapeake Bay. As a result, the signature of extreme hydrologic events such as large floods and droughts, and thus Anatural@ climate variability, wag "aliased" from the estuarine sedimentation patterns over the last 150 years. The construction of major dams around 1930 on the Lower Susquehanna River has resulted in a significant decrease in coarse fraction of sediment inputs into the estuary, further reducing our ability to separate flood episodes from average conditions. SN 1462-6098
Kim, G, Barros, AP, Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data, REMOTE SENSING OF ENVIRONMENT, DEC 2002, 83(3), 400-413, ISI:000179326000004
Abstract: Previous work showed that remotely sensed soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observations and hydrometeorological forcing (Remote Sens. Environ. 81 (2002) 1). Specifically, it was determined that this multiscaling behavior is consistent with the scaling of soil hydraulic properties and vegetation cover, while the multifractal behavior is associated with the temporal evolution of soil moisture fields. Here, we apply these findings by directly incorporating information on the spatial structure of soil texture and vegetation water content to the spatial interpolation of remotely sensed soil moisture data. A downscaling model is presented which consists of a modified fractal interpolation method based on contraction mapping. This methodology is different from other fractal interpolation schemes because it generates unique fractal surfaces. It is different from other contraction mapping models because it includes spatially and temporally varying scaling functions as opposed to single-valued scaling factors. The scaling functions are linear combinations of the spatial distributions of ancillary data. The model is demonstrated by downscaling soil moisture fields from 10 to 1 km resolution using remote-sensing data from the Southern Great Plains 1997 (SGP'97) field experiment.
Barros, AP, Hwu, WJ, A study of land-atmosphere interactions during summertime rainfall using a mesoscale model, JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, JUL 2002, 107, DI 10.1029/2000JD000254
Abstract:  A coupled mesoscale model (e g., MM5V3) was used to investigate the mechanisms of land-atmosphere interaction during summertime rainfall in the southern Great Plains. Numerical experiments were conducted for two storms and for clear weather conditions using three distinct physical parameterizations of convective processes. Analysis of the dynamics of moisture and energy states in the boundary layer for all model grid cells revealed the existence of dual limit cycles in the model's phase space of Bowen ratio and relative humidity. The scaling behavior of these limit cycles is consistent with self-similarity of the second kind (i.e., in the sense of intermediate asymptotics). Specifically, the dual limit cycles suggest two distinct regimes of land-atmosphere interactions: (1) a divergent (splitting) trajectory of increasing diurnal variations of Bowen ratio and relative humidity for persistently dry weather conditions and (2) a convergent (merging) trajectory of rapidly decreasing Bowen ratio and increasing relative humidity for wet weather conditions. At the onset of rainfall the convergent cycle exhibits intermittent instability and collapses into a very limited region of high relative humidity and low Bowen ratio, the rainfall attractor. The position of the attractor in phase space is determined by surface controls of land-atmosphere interactions (e. g., soil moisture availability in our applications): That is, land surface heterogeneity determines the dynamic range of land-atmosphere interactions. Regionally, the footprint of these two regimes of land-atmosphere interactions is apparent in the correlation between Bowen ratio and the vertical distribution of relative humidity in the troposphere. In dry weather the correlation varies periodically from positive at night to negative during the day and generally does not penetrate into the upper air. Under wet conditions, however, the correlation is strong and persistently positive throughout the troposphere. These results support the argument that increased surface evaporation during rainfall affects stability conditions in the boundary layer, and thus convective activity in the model, therefore establishing a positive feedback mechanism of land-atmosphere interactions consistent with the rainfall attractor.
Devonec, E, Barros, AP, Exploring the transferability of a land-surface hydrology model, JOURNAL OF HYDROLOGY, AUG 30 2002, 265(1-4), 258-282, ISI:000178071000017
Abstract: The utility of hydrological models as a research tool is strongly linked to their ability to capture changes in regional hydrologic regimes in response to changes in climate forcing, or to simulate the hydrologic regimes of distinct climatic regions: that is, model transferability. To assess the transferability of an existing land-surface hydrology model (LSHM), three case-studies were conducted without changing the model's physical parameterizations and without the calibration of model parameters. A 1D implementation of the LSHM was used with data sets from Cabauw in the Netherlands (field plot scale, one year), and from Valdai in Russia (small catchment scale, 18 years). Simulations of runoff, latent and sensible heat fluxes, soil moisture and soil temperature, and snow accumulation and melt were compared against observations at hourly, daily, monthly, annual and inter-annual time scales. The model can reproduce well the monthly, seasonal and interannual variability of the hydrological regime in response to applied forcing, especially regarding snow accumulation patterns, and the timing and duration of melting. The results also show that, where shallow watertable fluctuations are important in determining the mechanisms of runoff generation (i.e. Valdai), the dynamic interaction between the saturated and the unsaturated zones is an essential hydrologic process that cannot be ignored. Continental-scale simulations were performed using a 1degreesx1degrees global data set to assess the model's ability to capture seasonal cycles and interannual variability of hydrological variables across diverse climatic regions in the continental USA before and during the 1988 drought. The results obtained for the three case-studies suggest that the model can be used to study the range of variability caused by environmental change on mid-latitude hydrological regimes. Further work must be conducted for and and semiarid regions.
Kim, G, Barros, AP, Space-time characterization of soil moisture from passive microwave remotely sensed imagery and ancillary data, REMOTE SENSING OF ENVIRONMENT, AUG 2002, 81(2-3), 393-403, ISI:000177153000019
Abstract: The statistical structure of soil moisture fields was examined using large-scale images (40 x 250 km) obtained during the Southern Great Plains 1997 (SGP'97) hydrology experiment. In particular, empirical scaling analysis was conducted to investigate the linkages between the spatial and temporal variability of soil moisture, and landscape characteristics including terrain, soils, and vegetation. The results show that the soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observation and hydrometeorological forcing. A break in statistical symmetry (multiscaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the alpha- and beta-scale ranges, respectively. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay content. The multifractal behavior is associated with the temporal evolution of drying and wetting regimes, reflecting the nonlinear character of soil moisture dynamics. Finally, Empirical Orthogonal Function (EOF) analysis was conducted to explain the relationship between the spatial structure of estimated soil moisture and that of ancillary data including topography, soil texture, and vegetation cover. Topography appears to dominate the spatial structure of soil moisture only during and immediately after rainfall. In interstorm periods, the spatial evolution of soil moisture is closely associated with the spatial variability of soil hydraulic properties when the soil is above field capacity, while vegetation dominates the evolution of soil moisture fields through evapotranspiration as the landscape dries down.
Lang, TJ, Barros, AP, An investigation of the onsets of the 1999 and 2000 monsoons in central Nepal, MONTHLY WEATHER REVIEW, MAY 2002, 130(5), 1299-1316, ISI:000174782300012
Abstract: The Marsyandi River basin in the central Nepalese Himalayas is a topographically complex region, with strong spatial gradients of precipitation over various timescales. A meteorological network consisting of 20 stations was installed at a variety of elevations (528-4435 m) in this region, and measurements of rainfall were made during the 1999 and 2000 summer monsoons. The onsets of the 1999 and 2000 monsoons in central Nepal were examined at different spatial scales by using a combination of rain gauge, Meteosat-5, Tropical Rainfall Measuring Mission (TRMM), ECMWF analysis, and Indian radiosonde data. At the network, the onsets manifested themselves as multiday rain events, which included a mixture of stratiform and convective precipitation. Moist and unstable upslope flow was associated with the occurrence of heavy rainfall. During each onset, 2-day rainfall reached as high as 462 mm, corresponding to 10%-20% of the monsoon rainfall. Differences among rain gauges were up to a factor of 8, reflecting the role of small-scale terrain features in modulating rainfall amounts. At the larger scale, the onsets were associated with monsoon depressions from the Bay of Bengal that moved close enough to the Himalayas to cause the observed upslope flow from the winds on their eastern flank. During the 1999 onset, convection in this eastern flank collided with the mountains in the vicinity of the network. In 2000 no major collision occurred, and 33%-50% less rain than 1999 fell. Analysis of observations for a 5-yr period (1997-2001) suggests that the interannual variability of the monsoon onset along the Himalayan range is linked to the trajectories and strength of these depressions.
Bindlish, R, Barros, AP, Subpixel variability of remotely sensed soil moisture: An inter-comparison study of SAR and ESTAR, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, FEB 2002, 40(2), 326-337, ISI:000174615100010
Abstract: The representation of subpixel variability in soil moisture estimates from passive microwave data was investigated through sensitivity analysis and by comparison against the spatial structure of soil moisture fields derived from radar data. This work shows that the subpixel variability not represented in brightness temperature fields is directly associated with the spatial organization of soil hydraulic properties and the spatial distribution of vegetation. The significant implication of this result is that the physical connection between soil moisture estimates at the pixel scale and local values within the pixel weakens strongly as the sensor resolution decreases. Subsequently, the application of scaling and fractal interpolation principles to downscale passive microwave data to the spatial resolution of radar data was investigated as a means to recover spatial structure. In particular, ESTAR soil moisture data was successfully downscaled from 200 to 40 m using only one radar frequency (e.g., L-band). This application suggests that the combined use of active and passive single-band microwave remote-sensing of soil moisture is a viable approach to improve the spatial resolution of soil moisture remote-sensing. SN 0196-2892
Bindlish, R, Barros, AP, Including vegetation scattering effects in a radar based soil moisture estimation model Edited by Owe, M; Braubaker, K; Ritchie, J; Rango, A, REMOTE SENSING AND HYDROLOGY 2000, SE IAHS PUBLICATION, International Symposium on Remote Sensing and Hydrology 2000, APR 02-07, 2000, SANTA FE, NM, 2001, 267-354, ISI:000172971600072
Abstract: Previously, the IEM (Integral Equation Model) was successfully used in conjunction with an inversion model to retrieve soil moisture using multi-frequency and multi-polarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR), without the need to prescribe time-varying land surface attributes as constraining parameters. The retrieved values were compared against in situ observations from the Washita '94 field experiment. The RMS error in the estimated soil moisture was of the order of 0.05 cm(-3) cm(-3), which is comparable to the effect of noise in the SAR data. The IEM was originally developed for scattering from a bare soil surface, and therefore the vegetation scattering effects are not explicitly incorporated in the model. In this study, we couple a semi-empirical vegetation scattering model, modified after the water-cloud model, to the existing radar based soil moisture inversion model. This approach allows for the explicit representation of vegetation backscattering effects without the need to specify a large number of parameters. Although the use of this parameterization resulted in modest improvements (roughly 4% overall), it does provide a general framework that can be used for other applications.
Guzel, H, Barros, AP, Using acoustic emission testing to monitor kinetic energy of raindrop and rainsplash erosion, Edited by Ascough, JC; Flanagan, DC; SOIL EROSION RESEARCH FOR THE 21ST CENTURY, PROCEEDINGS, International Symposium on Soil Erosion Research for the 21st Century, JAN 03-05, 2001, HONOLULU, HI; 2001, 525-528, ISI:000174171700136
Abstract: In regions of the globe where rainfall intensity is high (e.g., tropical and subtropical regions), detachment of soil particles by raindrop impact is the key trigger mechanism of soil erosion. One of the common measure of raindrop impact is kinetic energy. The objective of this research is to develop a reliable technology to measure the kinetic energy of rainfall and monitor rainsplash effects in field conditions. For this purpose, we have investigated the use of acoustic emission testing (AET) to measure stress waves produced by raindrops impacting upon a receiving surface. The tests were conducted in the laboratory under still air conditions, and will be replicated in a wind runnel simulating a wide range of boundary-layer conditions. To calibrate the instrument, the force and energy produced by single raindrop impact on a ceramic plate, as well as raindrop velocities, were measured first in controlled laboratory experiments for different raindrop sizes and rainfall intensities. Stress wave properties (amplitude, frequency, and decay), and raindrop/rainfall attributes are subsequently analyzed to derive generalized calibration relationships. A rainfall maker to simulate distributed rainfall has also been built, and the technology is now being tested for distribution of rain. Preliminary results from the calibration of AET are presented here.
Barros, AP, ter Hofstede, AHM, Szyperski, C, IEEE COMPUTER SOCIETY, IEEE COMPUTER SOCIETY, Retrofitting workflows for B2B component assembly, PROCEEDINGS - INTERNATIONAL COMPUTER SOFTWARE & APPLICATIONS CONFERENCE, 25th Annual International Computer Software and Applications Conference (COMPSAC 2001), OCT 08-12, 2001, CHICAGO, IL, 123-128, ISI:000172264100017
Abstract: Sudden and significant demand for B2B process automation has seen the entry of workflow management Systems (WFMS) into the component arena. Workflows offer highly expressive and graphical process control constructs for the coordinative component assembly, however, current provisions seem more suitable for internal process pipelines in single organizations, built without future reuse in mind. In this paper, we identify, particular areas of workflow legacy which obstruct flexible reuse and composition Under B2B assembly. New abstractions are identified for the tighter multi-lateral coupling of workflows such that: synchronization is possible across encapsulated workflows boundaries and external interactions occur through blackbox interfaces. Against the stifled efforts of loosely-coupled WFMS interoperability, a top-down architectural strategy is charted, where regulation can occur "above" workflow components - at a higher-tier workflow component framework - accepting heterogeneous WFMSs as "plug-ins".
Kuligowski, RJ, Barros, AP, Combined IR-microwave satellite retrieval of temperature and dewpoint profiles using artificial neural networks, JOURNAL OF APPLIED METEOROLOGY, 2001, 40(11), 2051-2067, ISI:000171798000016
Abstract: Radiance measurements from satellites offer the opportunity to retrieve atmospheric variables at much higher spatial resolution than is presently afforded by in situ measurements (e.g., radiosondes). However, the accuracy of these retrievals is crucial to their usefulness, and the ill-posed nature of the problem precludes a straightforward solution. A number of retrieval approaches have been investigated, including empirical techniques, coupling with numerical weather prediction models, and data analysis techniques such as regression. In this paper, artificial neural networks are used to retrieve vertical temperature and dewpoint profiles from infrared and microwave brightness temperatures from a polar-orbiting satellite. This approach allows retrievals to be performed even in cloudy conditions-a limitation of infrared-only retrievals. In a direct comparison of this technique with results from the operational Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) retrievals, it was found that the neural-network temperature retrievals had larger errors than the ATOVS retrievals (though generally smaller than the first guess used in the ATOVS retrievals) but that the dewpoint retrievals showed consistent improvement over the comparable ATOVS retrievals.
Kuligowski, RJ, Barros, AP, Blending multiresolution satellite data with application to the initialization of an orographic precipitation model, JOURNAL OF APPLIED METEOROLOGY, 2001, 40(9), 1592-1606, ISI:000170554600004
Abstract: The use of multisensor, multifrequency satellite data to specify initial conditions for numerical weather prediction (NWP) models offers a unique opportunity to improve the depiction of small-scale processes in the atmosphere through a myriad of data assimilation approaches. The authors previously developed an algorithm to retrieve temperature and dewpoint profiles from a combination of infrared [high-resolution infrared radiation sounder (HIRS), 18-20-km resolution] and microwave [Advanced Microwave Sounding Unit-A (AMSU-A), 48-km resolution] data, using collocated radiosondes. Besides (and separately from) the estimation problem, one key question in the context of model initialization is how to blend multiresolution data to generate fields at the spatial resolution of the NWP model of interest. In this paper, a fractal downscaling technique is proposed to blend multiresolution satellite data and generate brightness temperature fields at 1-km resolution. The downscaled HIRS and AMSU-A data subsequently can be processed by the retrieval algorithm to derive temperature and dewpoint fields at the same resolution. The utility of these products as an initial condition for NWP models was assessed in the context of regional quantitative precipitation forecasting (QPF) applications using a limited-area orographic precipitation model nested with a mesoscale model. Results from the simulation of a wintertime storm in the Pocono Mountains of the mid-Atlantic region show improvement in QPF skill when the satellite-derived initial conditions were used. However, the disparity between the sparse times when the satellite data are available (12-h intervals) vis-a-vis the hourly import of boundary conditions from the host model lessens the impact of improved initial conditions. This result suggests that gains in QPF skill are linked to the availability of relevant remote sensing data at time intervals consistent with the useful memory of initial conditions in NWP models.
Kim, G, Barros, AP, Quantitative flood forecasting using multisensor data and neural networks, JOURNAL OF HYDROLOGY, JUN 1 2001, 246(1-4), 45-62, ISI:000168909900004
Abstract: Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction output and rainfall and radiosonde data. The objective of this study was to modify the existing artificial neural network model to include the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems, and convective cloud clusters as input. The convective classification and automated tracking system was used to identify and quantify storm properties such as lift: time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships among weather systems, rainfall production and streamflow response in the study area. Here, we present results from the application of the quantitative flood forecasting model in four watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. The areal extent of the watersheds ranges from 750 to 8700 km(2). The reduction in the mean-squared error of the peak streamflow with respect to persistence was up to 60% for the 24 h lead-time forecasts. For the 18 h lead-time forecasts, the number of successful forecasts for streamflow peaks in the upper 5% percentile was consistently above 60%, and close to 80-90%.
Bindlish, R, Barros, AP, Parameterization of vegetation backscatter in radar-based, soil moisture estimation, REMOTE SENSING OF ENVIRONMENT, APR 2001, 76(1), 130-137, ISI:000168061400011
Abstract: The integral Equation Model (IEM) was previously used in conjunction with an inversion model to retrieve soil moisture using multifrequency and multipolarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR). Convergence rates well above 90%, and small RMS errors were attained, for both vegetated and bare soil areas, using radar data collected during Washita 1994. However, the IEM was originally developed to describe the scattering from bare soil surfaces only, and, therefore, vegetation backscatter effects are not explicitly incorporated in the model. in this study, the problem is addressed by introducing a simple, semiempirical, vegetation scattering parameterization to the multifrequency, soil moisture inversion algorithm. The parameterization was formulated in the framework of the water-cloud model and relies on the concept of a land-cover (land-use)-based dimensionless vegetation correlation length to represent the spatial variability of vegetation across the landscape and radar-shadow effects (vegetation layovers). An application of the modified inversion model to the Washita 1994 data lead to a decrease of 32% in the RMSE, while the correlation coefficient between ground-based and SAR-derived soil moisture estimates improved from 0.84 to 0.95.
Barros, AP, Colello, JD, Surface roughness for shallow overland flow on crushed stone surfaces, JOURNAL OF HYDRAULIC ENGINEERING-ASCE, JAN 2001, 127(1), 38-52, ISI:000165874000004
Abstract: Results of laboratory experiments carried out to determine the effective surface roughness for shallow overland flow as a function of the runoff rate, roughness element height, and underlying soil condition are presented. This work was conducted to extent the understanding of the mechanics of shallow overland flows based on the relationships between the Reynolds number, Froude number, and surface resistance over a wide range of conditions. Results exhibit a strong dependence of the effective roughness on the ratio between the depth of flow and the height of the roughness element, and a strong inverse relationship was found between Manning's it (and the Darcy-Weisbach friction factor f) and the Froude number. Three distinct subcritical flow regimes were identified: (1) Submerged flow (R < 300); (2) sheet flow (R > 1,200); and (3) transitional (partially submerged) flow (300 < R < 1,200). Analysis and synthesis of laboratory data allowed relationships between the Reynolds and Froude numbers, and Manning's n, which are suitable for practical engineering applications, to be established in this study.
Barros, AP, Putkonen, J, Burbank, DW, Chang, ATC, Measurement and analysis of orographic precipitation in the Himalayas - First results from the TRMM hydrometeorological network in central Nepal, 15TH CONFERENCE ON HYDROLOGY, JAN 09-14, 2000, LONG BEACH, CA; 339-339, ISI:000168561100100
Kuligowski, RJ, Barros, AP, Producing satellite retrievals for NWP model initialization using artificial neural networks, SECOND CONFERENCE ON ARTIFICIAL INTELLIGENCE, FEB 09-14, 2000, LONG BEACH, CA, 2000, p. 72, ISI:000168377900014
Barros, AP, Joshi, M, Putkonen, J, Burbank, DW, A study of the 1999 monsoon rainfall in a mountainous region in central Nepal using TRMM products and rain gauge observations, GEOPHYSICAL RESEARCH LETTERS, NOV 15 2000, 27(22), 3683-3686, ISI:000165418200020
Abstract: Raingauge data from the 1999 monsoon were compared with precipitation derived from the precipitation radar (PR) and the microwave imager instruments on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The raingauges are part of a new hydrometeorological network installed in the Marsyandi river basin, which extends from the edge of the Tibetan Plateau to the Gangetic basin. TRMM-derived precipitation showed better detection of rain at low altitude stations as compared with high elevation stations, with good scores for the Pn product for rain rates > 0.5 mm/hr. The 3D PR rain rates suggest strong interaction between mesoscale convective systems and steep terrain at elevations of 1-2 km, which is consistent with the very high rainfall measured at those locations. Analysis of the raingauge data shows that even at altitudes as high as 4,000 m the cumulative monsoon rainfall is comparable to the highest amount recorded in the Indian subcontinent.
Bindlish, R, Barros, AP, Disaggregation of rainfall for one-way coupling of atmospheric and hydrological models in regions of complex terrain, GLOBAL AND PLANETARY CHANGE, JUL 2000, 25(1-2), 111-132, ISI:000088333500008
Abstract: The objective of this work is to incorporate orographic effects in the disaggregation of rainfall fields from atmospheric to hydrological models. For this purpose, a downscaling methodology based on the principles of fractal interpolation is proposed. The new orographic rainfall disaggregation scheme takes into account the spatial characteristics of topography and wind fields over the domain, and how these relate to the spatial variability of rainfall in the region. We illustrate the methodology for two simulated precipitation events in Pennsylvania, and the results are compared against observations at 200 raingauges within the region. Disaggregation of mesoscale meteorological model (MM5) rainfall fields from 4-km down to 1-km resolution using the orographic scheme resulted in average improvements of quantitative precipitation forecasts about 50% of total precipitation amounts. An overall improvement in the temporal correlation between observed and predicted hourly rainfall was also obtained ton the average, the correlation coefficient increased from 0.2 to 0.5), thus suggesting that the methodology may be used effectively to improve the timing of precipitation forecasts. The effect of spatial variability of precipitation on the hydrologic response of a large watershed was also assessed. In particular, MM5 output and disaggregated rainfall fields were used to force a distributed hydrological model of the watershed of the West Branch of the Susquehanna River (14,710 km(2) areal extent). Improvements of 30% were obtained in the prediction of peak streamflow and runoff volumes just by including small-scale (1 km(2)) orographic effects on the space-time variability of rainfall. The intrinsic relationship between rainfall forcing and "optimal'' hydrological parameters obtained through calibration is discussed.
Bindlish, R, Barros, AP, Multifrequency soil moisture inversion from SAR measurements with the use of IEM, REMOTE SENSING OF ENVIRONMENT, JAN 2000, 71(1), 67-88, ISI:000084570200006
Abstract: This study focuses on the development of a consistent methodology for soil-moisture inversion from synthetic aperture radar (SAR) data with the use of the integral equation model (IEM), developed by A. K. Fung and colleagues, without the need to prescribe time-varying land-surface attributes as constraining parameters. Specifically, the dependence of backscatter coefficients obtained from synthetic aperture radar (SAR) on the soil dielectric constant, surface-roughness height, and correlation length was investigated. The IEM was used in conjunction with an inversion model to retrieve soil moisture by using multifrequency and multipolarization data (L-, C-, and X-bands) simultaneously. The results were cross validated with gravimetric observations obtained during the Washita '94 field experiment in the Little Washita Watershed, Oklahoma. The average error in the estimated soil moisture was of the order of 3.4%, which is comparable to that expected due to noise in the SAR data. The retrieval algorithm performed very well for low incidence angles and over bare soil fields, and it deteriorated slightly for vegetated areas and overall for very dry soil conditions. Although the original IEM model was developed for bare soil conditions only, one important result of this study was the fact that the retrieval algorithm performed well for vegetated conditions, as demonstrated by the fact that the convergence ratio varied between 92% (dry conditions) and 98% (wet conditions) of all pixels for all days of the experiment. The sensitivity of soil-moisture estimates to spatial aggregation of remote-sensing data before and after the retrieval also was investigated. The results suggest that there is potential to improve the operational utility of high-resolution SAR data for soil-moisture monitoring by compressing the SAR data (pre-aggregation) to a spatial resolution at least one order of magnitude above that of measurement.
Kuligowski, RJ, Barros, AP, High-resolution short-term quantitative precipitation forecasting in mountainous regions using a nested model, JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 6th International Conference of Precipitation on Predictability of Rainfall at the Various Scales, JUN 29-JUL 01, 1998
CL MAUNA LANI BAY, HAWAII, DEC 27 1999, 104(D24), 31553-31564, ISI:000084692900048
Abstract: In mountainous regions, the spatial and temporal distribution of precipitation is strongly influenced by local orography. The resolution of operational numerical weather prediction (NWP) models has been enhanced significantly in recent years but has yet to reach the level necessary to capture fully the influences of high-relief topography on precipitation and flow dynamics. Furthermore, the parameterizations of precipitation mechanisms and cloud microphysics in these models have been developed on the basis of observational and field campaigns generally carried out far away from complex terrain and thus may not represent the physics associated with orographic precipitation. Here we attempt to address both issues by nesting a small-scale physically based orographic precipitation model (OPM) at 1-km resolution within the fifth-generation Penn State/National Center for Atmospheric Research Mesoscale Model (PSU/NCAR MM5) at 12-km resolution. This approach is investigated by simulating six storms in the Pocono Mountains of Pennsylvania. Some improvement over the MM5 was achieved, such as an increase in hourly threat score. The reliability of the MM5 and OPM forecasts for more intense, less frequent events (exceeding 4 mm/h) was shown to be significant, through the threat scores for these amounts indicate a need for additional improvement. This study suggests that further improvements in nested modeling applications are constrained by the degree to which the host model can provide boundary and initial conditions that represent the actual state of the atmosphere. One possible solution for this problem is the adaptive assimilation of remotely sensed data to provide initial and updated moisture and temperature fields throughout a forecast period.
ter Hofstede, AHM, Barros, AP, Specifying complex process control aspects in workflows for exception handling, 6TH INTERNATIONAL CONFERENCE ON DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, PROCEEDINGS, 6th International Conference on Database Systems for Advanced Applications, APR 19-21, 1999, HSINCHU, TAIWAN, 53-60, ISI:000079917900007
Abstract: Contemporary specification languages of workflow management systems focus on capturing process execution semantics. Constructs are offered that allow the specification of sequential execution, iteration, choice, parallelism and synchronisation. While in workflow modelling it is absolutely imperative that exceptions are dealt with properly, virtually no support for the specification of exception handling is offered at the conceptual level. Typically, exceptions and recovery strategies need to be defined using the programming primitives of the specific workflow management systems used. In this paper we propose a number of conceptual modelling primitives that can be used for the specification of exception handling in workflows. These primitives are illustrated using some real-life examples. A formal semantics is assigned to precisely define their meaning and demonstrating how they can be incorporated in a typical process modelling language.
Brennan, KE, Barros, AP; Edited by Lemmela, R; Helenius, N; The utility of seasonal to interannual climate predictions for water management: A drought forecasting model for the Ohio river basin, PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON CLIMATE AND WATER, VOLS 1-3, AUG 17-20, 1998, ESPOO, FINLAND, 1998, 333-342, ISI:000078339600034
Abstract: Critical science issues in drought research include the need for specific, objective criteria to define the initiation of drought, and the predictability of drought attributes (duration and severity) with lead time adequate for effective water management. The use of climate forecasts depends on whether state-of-the-art regional climate models can produce dependable monthly, seasonal, and interannual forecasts of relevant climate variables (precipitation, temperature, potential evaporation and runoff) for use in hydrologic studies. Although climate models may be "imperfect", the key issue is to determine if the uncertainty in climate model predictions is compatible with the risk levels accepted in water resources management. In this paper, we focus on the predictability of drought at the regional scale in the Ohio River Basin. Historical records of precipitation, surface temperature, streamflow, and PDSI (Palmer drought Severity Index), as well as evapotranspiration estimates obtained from 4-DDA (4-Dimensional Data Analysis) were used to define a hydroclimatology of the region on monthly, seasonal and interannual time-scales. Drought events were subsequently classified not only as a function of drought attributes, but also according to antecedent and concurrent hydrometeorological patterns and time of the year. It was found that the time rate of change of the spatial variability of monthly precipitation was highly correlated with the occurrence of extreme drought with recurrence periods of 7-8 years. These results are subsequently used to formulate a simple multilinear regression drought forecast model for the basin. Independent testing of the model shows that the three month (one season) and six-month (two seasons) lead-time forecasts are highly reliable and provide adequate lead times for effective water resource management.
Kuligowski, RJ, Barros, AP, Using artificial neural networks to estimate missing rainfall data, JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, DEC 1998, 34(6), 1437-1447, ISI:000078134800016
Abstract: Missing rainfall data from a time series or a spatial field of observations can present a serious obstacle to data analysis, modeling studies and operational forecasting in hydrology. Numerous schemes for replacing missing data have been proposed, ranging from simple weighted averages of data points that are nearby in time and space to complex statistically-based interpolation methods and function fitting schemes. This paper presents a technique for replacing missing spatial data using a backpropagation neural network applied to concurrent data from nearby gauges. Tests performed on a sample of gauges in the Middle Atlantic region of the United States show that this technique produces results that compare favorably to simple techniques such as arithmetic and distance-weighted averages of the values from nearby gauges, and also to linear optimization methods such as regression.
Kuligowski, RJ, Barros, AP, Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks, WEATHER AND FORECASTING, DEC 1998, 13(4), 1194-1204, ISI:000077663200019
Abstract: Although the resolution of numerical weather prediction models continues to improve, many of the processes that influence precipitation are still not captured adequately by the scales of present operational models, and consequently precipitation forecasts have not yet reached the level of accuracy needed for hydrologic forecasting. Postprocessing of model output to account for local differences can enhance the accuracy and usefulness of these forecasts. Model Output Statistics have performed this important function for a number of years via regression techniques; this paper presents an alternate approach that uses artificial neural networks to produce 6-h precipitation forecasts for specific locations. Tests performed on four locations in the middle Atlantic region of the United States show that the accuracy of the forecasts produced using neural networks compares favorably with those generated using linear regression, especially for heavier precipitation amounts.
Kuligowski, RJ, Barros, AP, AMER METEOROL SOC, From NWP to QPF estimation using artificial neural networks, 1st Conference on Artificial Intelligence at the 78th American-Meteorological-Society Annual Meeting, JAN 11-16, 1998, PHOENIX, AZ, ISI:000076779400001
Barros, AP, Kuligowski, RJ, Orographic effects during a severe wintertime rainstorm in the Appalachian mountains, MONTHLY WEATHER REVIEW, OCT 1998, 126(10), 2648-2672, ISI:000076306400007
Abstract: The evolution of precipitation features during a severe wintertime rainfall and flooding event associated with a cold front that crossed the central Appalachians on 19 January 1996 is illustrated through the analysis of radiosonde, rainfall, and streamflow gauge data, and WSR-88D images. Striking evidence of the linkage between heavy precipitation cells and orography was obtained by tracking the movement of the center of mass of storm precipitation, which closely followed the contours of regional orographic features. Higher intensity precipitation cells were consistently located windward of the orographic crest, and the trajectory described by the center of mass of precipitation was also consistent with the spatial arrangement of the river basins where hazardous flooding occurred. Persistent, low-intensity (less than or equal to 5 mm h(-1)) rainfall was registered in these basins during the 12-h period that preceded the arrival of frontal storm activity. It is argued that this prefrontal precipitation had a critical impact on watershed rainfall-runoff response and snowpack conditioning during and after the passage of the front. The intent here is to investigate the links between the observed space-time variability of rainfall and the influence of terrain features on mesoscale circulations in the lee side of the Appalachians. In particular, the viability of orographic mechanisms such as forced ascent, lee-wave interference, and precipitation scavenging of shallow orographic clouds was assessed using simple models and the available meteorological and hydrological data.
Kuligowski, RJ, Barros, AP, Experiments in short-term precipitation forecasting using artificial neural networks, MONTHLY WEATHER REVIEW, FEB 1998, 126(2), 470-482, ISI:000071793000012
Abstract: Accurate, timely. site-specific forecasts of precipitation are important for accurately predicting streamflow and flash floods in small drainage basins However, presently available numerical weather prediction models do not generally provide forecasts with the accuracy and/or resolution appropriate for this task. A wide variety of approaches to small-scale, short-term precipitation forecasting have been investigated by numerous authors; this paper describes a simple precipitation forecasting model based on artificial neural networks. The model uses the radiosonde-based 700-hPa wind direction and antecedent precipitation data from a rain gauge network to generate short-term (0-6 h) precipitation forecasts for a target location. The performance of the model is illustrated for a gauge in eastern Pennsylvania.
Barros, AP, ter Hofstede, AHM, Proper, HA; Edited by Olive, A; Pastor, JA; Towards real-scale business transaction workflow modelling, ADVANCED INFORMATION SYSTEMS ENGINEERING, LECTURE NOTES IN COMPUTER SCIENCE, 9th International Conference on Advanced Information Systems Engineering (CAiSE 97), JUN 16-20, 1997, BARCELONA, SPAIN, 1997, 1250, 437-450, ISI:000074027600031
Abstract: While the specification languages of workflow management systems focus on process execution semantics, the successful development of workflows relies on a fuller conceptualisation of business processing including the treatment of time, document transfer, and workflow use and reuse. For this, a wellspring of modelling techniques, paradigms and informal-formal method extensions which address broader enterprise modelling and communication (based on speech-act theory), is available. However, the characterisations - indeed the cognition - of workflows still appears coarse. In this paper, we provide the complementary, empirical insight of a real-scale business transaction workflow. The development of the workflow model follows a set of principles which we believe address workflow modelling suitability. Through the principles, advanced considerations including temporal constraints, message construction and deconstruction together with asynchronous and synchronous modes of messaging, service encapsulation, and complex decision and exception handling are motivated. By illustrating the suitability principles and with it the inherent complexity of business transaction domains, we offer timely insights into workflow specification extension.
Barros, AP, Evans, JL, Designing for climate variability, JOURNAL OF PROFESSIONAL ISSUES IN ENGINEERING EDUCATION AND PRACTICE, APR 1997, 123(2), 62-65, ISI:A1997WU52400005
Abstract: Hurricanes, blizzards, and floods. The atmosphere has visited extreme weather in the United States over the last five years. Is this climate change or climatic variability? Is the distinction important in the context of infrastructure design and planning? To address these questions, the meaning and relevance of the notions of climate change and climate variability must be clearly established in the context of natural hazards, and infrastructure engineering. Multidecadal time scales (whether natural or anthropogenically induced) have been firmly identified in the climate record. Compelling evidence of this variability has been found in data sources as diverse as tree rings, and cores taken from coral reefs, sediments, and glaciers across the globe. It is therefore submitted that the criteria used in engineering design of lifeline infrastructures, land-use planning, and water resources management among others must incorporate this knowledge.
Bindlish, R, Barros, AP, Aggregation of digital terrain data using a modified fractal interpolation scheme, COMPUTERS & GEOSCIENCES, OCT 1996, 22(8), 907-917, ISI:A1996VY61200007
Abstract: A modified fractal interpolation scheme that can be used to produce consistent digital terrain models at different spatial resolutions is introduced in this paper. The focus of the paper is on the aggregation of fine resolution terrain data to coarser scales (up-scaling), although the algorithm is equally useful for disaggregation purposes (down-scaling). Applications with digital elevation models (DEMs) for California and for Pennsylvania are used to illustrate the methodology. Comparisons with alternative approaches such as sampling of fine resolution DEMs, the use of mean and envelope orography representations, and the Cressman-type objective analysis are presented. The modified fractal interpolation scheme preserves well both the spatial structure of the elevations and orographic gradients and performed consistently better than other algorithms, especially for high level aggregation (coarse discretization).
Barros, AP, An evaluation of model parameterizations of sediment pathways: A case study for the Tejo estuary, CONTINENTAL SHELF RESEARCH, NOV 1996, 16(13), 1725-1749, ISI:A1996VD99700005
Abstract: A simple sediment transport model was used to reproduce the evolution of sedimentary features in the Tejo estuary dyer a 50-yr period. The parameterizations of fundamental processes of sediment dynamics such as erosion, deposition and flocculation were evaluated by conducting sensitivity analysis of model simulations to changes in specific parameters and their interactions. The analysis of the sediment budget for model simulations indicated that deposition and erosion mechanisms are sufficient to describe long-term bathymetric changes in the Tejo. The results suggest that erosion is the controlling mechanism in the classic representations of sediment pathways used in the model, while deposition is highly dependent on the prevailing erosion regime. At low to average concentrations of suspended sediments, dynamic feedbacks between deposition and erosion mechanisms control the sediment regime, but for high concentrations (erosional regime) advection determines the net export or accretion in the estuary. The short-term variability of the sediment budget along the longitudinal axis of the estuary is directly related to flocculation effects, which contribute to a significant increase of the instantaneous rates of removal of suspended sediments in the maximum turbidity zone, especially during peak flood and ebb flows. The role of erosion thresholds was also investigated with the purpose of assessing feedback effects among bottom and water-column processes. This study showed that modeling of bottom consolidation processes needed for a realistic representation of the
time-space evolution of bed erosion thresholds is essential to replicate sediment processes consistent with in situ observations.
BARROS, AP, ADAPTIVE MULTILEVEL MODELING OF LAND-ATMOSPHERE INTERACTIONS, JOURNAL OF CLIMATE, SEP 1995, 8(9), 2144-2160, ISI:A1995RT01100002
Abstract: Adaptive multilevel methods allow full coupling of atmospheric and land surface hydrological models by preserving consistency between the large-scale (atmospheric) and the regional (land) components. The methodology was investigated for three case studies involving the coupling of models with different levels of complexity and different spatial resolutions. The first case study consisted of coupling two simple models. One model provided the potential and the other the rotational components of atmospheric wind fields, which were used to drive a 3D orographic precipitation model used to investigate the long-term precipitation for the Olympic Mountains in Washington State. In the second case study, intermittent coupling ( every 4 hours) of three versions of the orographic precipitation model operating at 40-m, 60-m, and 80-km resolution, respectively, was established to replicate the precipitation patterns of specifically chosen storms as they evolved across the central Sierra Nevada region. The third case study consisted of coupling the orographic precipitation model (40-km resolution) to a 1D model describing mass and energy balance conditions at the land surface for the northern and central Sierra Nevada region. Numerical coupling of the precipitation and the land surface models was implemented on a 2D finite-element mesh with 10-km resolution. One contribution of this study was the long-term simulation of the intra-annual dynamics of the hydrological cycle in a mountainous environment.
BARROS, AP, AMER METEOROL SOC, Use of remote sensing data of surface soil moisture to extract subsurface moisture profiles, Conference on Hydrology, at the 75th AMS Annual Meeting, JAN 15-20, 1995, DALLAS, TX; 1995, 171-172, ISI:A1995BD28W00043
BARROS, AP, LETTENMAIER, DP, INCORPORATION OF AN EVAPORATIVE COOLING SCHEME INTO A DYNAMIC-MODEL OF OROGRAPHIC PRECIPITATION, MONTHLY WEATHER REVIEW, DEC 1994, 122(12), 2777-2783; ISI:A1994PW05800009
Abstract: A simple evaporative cooling scheme was incorporated into a dynamic model to estimate orographic precipitation in mountainous regions. The orographic precipitation model is based on the transport of atmospheric moisture and the quantification of precipitable water across a 3D representation of the terrain from the surface up to 250 hPa. Advective wind fields are computed independently and boundary conditions are extracted from radiosonde data. Precipitation rates are obtained through calibration of a spatially distributed precipitation efficiency parameter. The model was applied to the central Sierra Nevada. Results show a gain of the order of 20% in threat-score coefficients designed to measure the forecast ability of the model. Accuracy gains are largest at high elevations and during intense storms associated with warm air masses.
BARROS, AP, LETTENMAIER, DP, DYNAMIC MODELING OF OROGRAPHICALLY INDUCED PRECIPITATION, REVIEWS OF GEOPHYSICS, AUG 1994, 32(3), 265-284, ISI:A1994PH87000002
Abstract: Local orography governs the triggering of cloud formation and the enhancement of processes such as condensation and hydrometeor nucleation and growth in mountainous regions. Intense, lengthy precipitation events are typical upwind of the topographic divide, with sharply decreasing magnitude and duration on the lee side. Differences in mean annual precipitation of several hundred percent between windward slopes of orographic barriers and adjacent valleys or lee side slopes are not unusual. Because much of the streamflow in areas such as the western United States is derived from mountainous areas that are remote and often poorly instrumented, modeling of orographic precipitation has important implications for water resources management. Models of orographically induced precipitation differ by their treatment of atmospheric dynamics and by the extent to which they rely on bulk parameterization of cloud and precipitation physics. Adiabatic ascent and a direct proportionality between precipitation efficiency and orographically magnified updrafts are the most frequent assumptions in orographic precipitation modeling. Space-time discretization (i.e., resolution) is a major issue because of the high spatial variability of orographic precipitation. For a specific storm, relative errors as large as 50 to 100% are common in the forecast/hindcast of precipitation intensity and can be even larger in the case of catastrophic storms. When monthly or seasonal timescales are used to evaluate model performance, the magnitude of such errors decreases dramatically, reaching values as low as 10 to 15%. Current research is focusing on the development of data assimilation techniques to incorporate radar and satellite observations, and on the development of aggregation and disaggregation methodologies to address the implications of modeling a multiscale problem at restricted spatial and temporal resolutions.
BARROS, AP, LETTENMAIER, DP, MULTISCALE AGGREGATION AND DISAGGREGATION OF PRECIPITATION FOR REGIONAL HYDROCLIMATOLOGICAL STUDIES
SO MACROSCALE MODELLING OF THE HYDROSPHERE, IAHS PUBLICATIONS, International Symposium on Macroscale Modelling of the Hydrosphere, JUL 21, 1993
CL YOKOHAMA, JAPAN, 1993, 214-183, ISI:A1993BB28H00018
BARROS, AP, LETTENMAIER, DP, DYNAMIC MODELING OF THE SPATIAL-DISTRIBUTION OF PRECIPITATION IN REMOTE MOUNTAINOUS AREAS, MONTHLY WEATHER REVIEW, APR 1993, 121(4), 1195-1214, ISI:A1993KX21700015
Abstract: Precipitation in remote mountainous areas dominates the water balance of many water-short areas of the globe, such as western North America. The inaccessibility of such environments prevents adequate measurement of the spatial distribution of precipitation and, hence, direct estimation of the water b alance from observations of precipitation and runoff. Resolution constraints in atmospheric models can likewise result in large biases in prediction of the water balance for grid cells that include highly diverse topography. Modeling of the advection of moisture over topographic barriers at a spatial scale sufficient to resolve the dominant topographic features offers one method of better predicting the spatial distribution of precipitation in mountainous areas. A model is described herein that simulates Lagrangian transport of moist static energy and total water through a 3D finite-element grid, where precipitation is the only scavenging agent of both variables. The model is aimed primarily at the reproduction of the properties of high-elevation precipitation for long periods of time, but it operates at a time scale (during storm periods) of 10 min to 1 h and, therefore, is also able to reproduce the distribution of storm precipitation with an accuracy that may make it appropriate for the forecasting of extreme events. The model was tested by application to the Olympic Mountains, Washington, for a period of eight years (1967-74). Areal average precipitation, estimated through use of seasonal and annual runoff, was reproduced with errors in the 10%-15% range. Similar accuracy was achieved using point estimates of monthly precipitation from snow courses and low-elevation precipitation gauges.