To generate all the input forcing data required to run iSnobal, the following measured or derived variables are needed
- Air temperature
- Vapor pressure
- Precipitation
- Wind speed and direction
- Cloud factor
This page documents a more detailed description of each of the input variables, the types of input data that can be used for SMRF, and the data format for passing the data to SRMF.
- Air temperature [Celcius]
Measured or modeled air temperature at the surface
- Vapor pressure [Pascals]
Derived from the air temperature and measured relative humidity. Can be calculated using the IPW utility
sat2vp
.- Precipitation [mm]
Instantaneous precipitation with no negative values. If using a weighing precipitation gauge that outputs accumulated precipitation, the value must be converted.
- Wind speed [meters per second]
The measured wind speed at the surface. Typically an average value over the measurement interval.
- Wind direction [degrees]
The measured wind direction at the surface. Typically an average value over the measurement interval.
- Cloud factor [None]
The percentage between 0 and 1 of the incoming solar radiation that is obstructed by clouds. 0 equates to no light and 1 equates to no clouds. The cloud factor is derived from the measured solar radiation and the modeled clear sky solar raditation. The modeled clear sky solar radiation can be calculated using the IPW utility
twostream
.
All types of input data to SMRF are assumed to be point measurements. Therefore, each measurement location must have a X, Y, and elevation associated with it.
Generally, SMRF will be run using measured variables from weather stations in and around the area of interest. Below are some potential websites for finding data for weather stations:
Gridded datasets can be used as input data for SMRF. The typical use will be for downscaling gridded weather model forecasts to the snow model domain in order to produce a short term snowpack forecast. In theory, any resolution can be utilized, but the methods have been tested and developed using Weather Reserach and Forecasting (WRF) at a 1 and 3 km resolution. Each grid point will be used as if it were a weather stations, with it's own X, Y, and elevation. Therefore, the coarse resolution model terrain can be taken into account when downscaling to a higher resolution DEM.
Using WRF as a gridded dataset for SMRF.See Havens et al. (in prep) for more details and further discussion on using WRF for forcing iSnobal.
Each variable requires its own CSV file plus a metadata file. See smrf.data.csv_data
for more information. The variable files must be structured as:
date_time | ID_1 | ID_2 | ... | ID_N |
---|---|---|---|---|
10/01/2008 00:00 | 5.2 | 13.2 | ... | -1.3 |
10/01/2008 01:00 | 6.3 | NAN | ... | -2.5 |
... | ... | ... | ... | ... |
09/30/2009 00:00 | 10.3 | 21.9 | ... | 0.9 |
date_time
must be chronolgical and in any format that pandas.to_datetime()
can parse. Errors will occur on import when pandas cannot parse the string. The best format to use is MM-DD-YYYY HH:mm.
The column headers are the station ID numbers, which uniquely identify each station. The station ID is used throughout SMRF to filter and specify stations, as well as the metadata.
The data for each station is in the column under the station ID. Missing values can be included as either NAN
or blank, which will be converted to NaN
in SMRF. Missing data values will not be included in the distribution calculations.
The metadata CSV file tells SMRF important information about the location for each stations. At a minimum the metadata file must have a primary_id, X, Y, and elevation. The locations must be in UTM and the elevation is in same units as the DEM (typically meters).
primary_id | X | Y | elevation |
---|---|---|---|
ID_1 | 625406 | 4801625 | 1183 |
ID_2 | 586856 | 4827316 | 998 |
... | ... | ... | ... |
ID_N | 641751 | 4846381 | 2310 |
Example data files can be found for WY 2009 for the Boise River Basin in test_data/stationData
.
The MySQL database is more flexible than CSV files but requires more effort to setup. However, SMRF will only import the data and stations that were requested without loading in additional data that isn't required. See smrf.data.mysql_data
for more information.
The data table contains all the measurement data with a single row representing a measurement time for a station. The date column (i.e. date_time
) must be a DATETIME
data type with a unique constraint on the date_time
column and primary_id
column.
date_time | primary_id | var1 | var2 | ... | varN |
---|---|---|---|---|---|
10/01/2008 00:00 | ID_1 | 5.2 | 13.2 | ... | -1.3 |
10/01/2008 00:00 | ID_2 | 1.1 | 0 | ... | -10.3 |
10/01/2008 01:00 | ID_1 | 6.3 | NAN | ... | -2.5 |
10/01/2008 01:00 | ID_2 | 0.3 | 7.1 | ... |
|
The metadata table is the same format as the CSV files, with a primary_id, X, Y, and elevation column. A benefit to using MySQL is that we can use a client
as a way to group multiple stations to be used for a given model run. For example, we can have a client named BRB, which will have all the station ID's for the stations that would be used to run SMRF. Then we can specify the client in the configuration file instead of listing out all the station ID's. To use this feature, a table must be created to hold this information. Then the station ID's matching the client will only be imported. The following is how the table should be setup. Source is used to track where the data is coming from.
station_id | client | source |
---|---|---|
ID_1 | BRB | Mesowest |
ID_2 | BRB | Mesowest |
ID_3 | TUOL | CDEC |
... | ... | ... |
ID_N | BRB | Mesowest |
Please contact Scott Havens (scott.havens@ars.usda.gov) if you'd like to use a MySQL database but need help setting up the database and tables to work with SMRF. We can provide scripts that will help create the database.
Gridded datasets can come in many forms and the smrf.data.loadGrid
module is meant to import gridded datasets. Currently, SMRF can ingest WRF output in the standard wrf_out NetCDF files. SMRF looks for specific variables with the WRF output file and converts them to the related SMRF values. The grid cells are imported as if they are a single measurement station with it's own X, Y, and elevation. The minimum variables required are:
- Times
The date time for each timestep
- XLAT
Latitude of each grid cell
- XLONG
Longitude of each grid cell
- HGT
Elevation of each grid cell
- T2
Air temperature at 2 meters above the surface
- DWPT
Dew point temperature at 2 meters above the surface, which will be used to calculate vapor pressure
- GLW
Incoming thermal radiation at the surface
- RAINNC
Accumulated precipitation
- CLDFRA
Cloud fraction for all atmoshperic layers, the average will be used at the SMRF cloud factor
- UGRD
Wind vector, u component
- VGRD
Wind vector, v component