HRRR Forcing for SnowEx17 Simulations

HRRR Forcing for SnowEx17 Simulations over Grand Mesa, CO

Authors: Yueqian Cao and Ana P. Barros

The data can be found here 

This folder contains the 3-km resolution HRRR (High-Resolution Rapid Refresh) forcing data to drive physically-based snow hydrology models for the period 9/2016-6/2017 for the SNOWEx Grand Mesa site. The forcing consists of hourly, 3km surface pressure, 2m specific humidity and air temperature, U- and V-wind components at 10 m, surface downward short and longwave radiation, and precipitation rate fields.

 

The subfolder 1 Hour contains the+01 HRRR forecasts (original dataset).

 

The subfolder 30 Min contains the same data as 1 Hour but linearly interpolated to 30 min.  In addition, missing data in the original HRRR files were filled using linear interpolation as well. The interpolated forcing were used to drive the MSHM (Multilayer Snow Hydrology Model) in Cao and Barros (2019).

 1) 1 Hour subfolder

 

      Each file is stored in .mat format and contains a 7270Ï43 matrix.  The row represents the time series from 9/1/2016 0:00 UTC to 6/30/2017 21:00 UTC; while each column is the variable for   each of the 43 grids (ordered from left to the right; central geolocations provided in Table 1) over the region of study (39°N ~ 39.1°N, 108.2°W ~ 107.8°W). The data files are organized as follows:  

 

  • B16.9~17.6_f01.mat: Specific Humidity at 2 m above ground (kg/kg)
  • C16.9~17.6_f01.mat: Surface Downward Longwave Radiation Flux (W/m2)
  • D16.9~17.6_f01.mat: Surface Downward Shortwave Radiation Flux (W/m2)
  • P16.9~17.6_f01.mat: Precipitation Rate (kg/m2/s)
  • Pressure16.9~17.6_f01.mat: Surface Pressure (Pa)
  • T16.9~17.6_f01.mat: Air Temperature at 2 m above ground (K)
  • UWind16.9~17.6_f01.mat: U-Component of Wind at 10 m above ground (m/s)
  • VWind16.9~17.6_f01.mat: V-Component of Wind at 10 m above ground (m/s)

 2) 30 Min subfolder

 

      Each file is stored in .csv format and contains a 14539Ï43 matrix. The same conventions are followed as for the hourly data sets.  The precipitation rate was classified as rain or snowfall as described in Section 3 below. The data files are organized as follows:

 

  • B16.9_17.6Interp_f01.csv: Specific Humidity at 2 m above ground (kg/kg)
  • C16.9_17.6Interp_f01.csv: Surface Downward Longwave Radiation Flux (W/m2)
  • D16.9_17.6Interp_f01.csv: Surface Downward Shortwave Radiation Flux (W/m2)
  • F16.9_17.6Interp_f01.csv: Windspeed at 10 m above ground (m/s)
  • Pressure16.9_17.6Interp_f01.csv: Surface Pressure (Pa)
  • Rainfall16.9_17.6Interp_f01.csv: Rainfall Rate (kg/m2/s)
  • Snowfall16.9_17.6Interp_f01.csv: Snowfall Rate (kg/m2/s)
  • T16.9_17.6Interp_f01.csv: Air Temperature at 2 m above ground (K)

 3) Data Processing

 

  • Rain-Snow Classification -After interpolating the 1-hr precipitation rate data 30 min, the precipitation was classified as rain or snow according to the value of the 30 min air temperature, respectively above or below 273.15K.  

 

  • Total Wind at 10 m height and 30 min temporal resolution was calculated from the U-Component () and V-Component () of wind also interpolated to 30 min: .

 

                                                Table 1 – HRRR Grid Coordinates  

ID

Latitude [°N]

Longitude [E°]

1

39.0024

-108.013

2

39.0055

-107.978

3

39.0085

-107.944

4

39.0116

-107.909

5

39.0146

-107.875

6

39.0176

-107.84

7

39.0207

-107.806

8

39.0137

-108.189

9

39.0168

-108.155

10

39.0199

-108.12

11

39.023

-108.086

12

39.0261

-108.051

13

39.0292

-108.017

14

39.0323

-107.982

15

39.0353

-107.948

16

39.0384

-107.913

17

39.0414

-107.879

18

39.0444

-107.844

19

39.0475

-107.81

20

39.0405

-108.193

21

39.0436

-108.159

22

39.0467

-108.124

23

39.0498

-108.09

24

39.0529

-108.055

25

39.056

-108.021

26

39.0591

-107.986

27

39.0621

-107.952

28

39.0652

-107.917

29

39.0682

-107.883

30

39.0713

-107.848

31

39.0743

-107.814

32

39.0673

-108.197

33

39.0704

-108.163

34

39.0735

-108.128

35

39.0766

-108.094

36

39.0797

-108.059

37

39.0828

-108.025

38

39.0859

-107.99

39

39.0889

-107.956

40

39.092

-107.921

41

39.095

-107.887

42

39.0981

-107.852

43

39.0972

-108.167

 

 4) Data Availability

 

Current Date - August 2019

 

Data Availability– September 2016 - June 2017

 

 

References

 

Cao, Y., and Barros, A.P., 2019: Understanding the Impact of Forcing Uncertainty on Snowpack Predictability – Results from SnowEx17. Water Resources Research, in preparation for submission.