Reach Scale Hydrology

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Surface meteorological forcing downscaled from NLDAS-2 and StageIV over the continental United States

3-km (1/32°) CONUS Meteorological Forcing

This 3-km (1/32°), hourly meteorological forcing data for CONUS was developed by downscaling North American Land Data Assimilation System 2 (NLDAS-2) data in combination with several higher resolution products. The precipitation combines the Stage IV and Stage II radar/gauge products with NLDAS-2, the shortwave radiation combines GOES Surface and Insolation Product (GSIP) with NLDAS-2 (GSIP product discontinued on 2018-01-02), while the other field variables are downscaled from NLDAS-2.

Downscaling Procedure

Here we use the 12 km (1/8°) National Data Assimilation System phase 2 (NLDAS‐2) product [Xia et al., 2012] as the backbone and blend in finer resolution products for different variables when available, including the 4 km Stage IV and Stage II radar/gauge products and the Level 2 shortwave radiation product from the GOES Surface and Insolation Products (GSIP). A gap‐filling procedure is performed on Stage IV hourly data together with a daily rescaling to match the daily total from NLDAS‐2 at 12 km. The GSIP Level 2 data, validated at 45 min past the hour, are first gridded to 3 km (1/32°) resolution and then adjusted for timing based on solar angles. Other 3 km (1/32°) forcing fields were downscaled from the 12 km (1/8°) NLDAS‐2 data with adjustment for elevation effects and physical consistency [Cosgrove et al., 2003]: air temperature (fixed lapse rate of −6.5 K/km), pressure (hydrostatic), specific humidity (adjustment according to both downscaled temperature and pressure), longwave radiation (radiative temperature adjusted according to the lapse rate), and wind speed (bilinear interpolation).


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File Format

All data is stored in self-explained NetCDF format. The NetCDF variables include:

  • lwdown: long_name = "Downward Longwave Radiation" ; units = "W/m^2"

  • precip: long_name = "Precipitation" ; units = "kg/m^2/s"

  • psurf: long_name = "Pressure" ; units = "Pa"

  • spfh: long_name = "Specific Humidity" ; units = "1"

  • swdown: long_name = "Downward Shortwave Radiation" ; units = "W/m^2"

  • tair: long_name = "Air Temperature" ; units = "K"

  • wind: long_name = "Wind Speed" ; units = "m/s"


The dataset is first described in this paper:

Pan, M., Cai, X., Chaney, N.W., Entekhabi, D.,Wood, E.F.,2016. An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations. Geophys. Res. Lett. 43,9662–9668.

Some of the application papers are as follows:

Beck, H.E., Pan, M., Roy, T., Weedon, G.P., Pappenberger, F., Van Dijk, A.I., Huffman, G.J., Adler, R.F. and Wood, E.F., 2019. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrology and Earth System Sciences, 23(1), pp.207-224,

Cai, X., Pan, M., Chaney, N.W., Colliander, A., Misra, S., Cosh, M.H., Crow, W.T., Jackson, T.J. and Wood, E.F., 2017. Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper‐resolution model. Water Resources Research, 53(4), pp.3013-3028,

Sadri, S., Wood, E.F. and Pan, M., 2018. Developing a drought-monitoring index for the contiguous US using SMAP. Hydrology and Earth System Sciences, 22(12), pp.6611-6626,

Peng, B., Guan, K., Pan, M. and Li, Y., 2018. Benefits of seasonal climate prediction and satellite data for forecasting US maize yield. Geophysical Research Letters, 45(18), pp.9662-9671,

Vergopolan, N., Chaney, N.W., Beck, H.E., Pan, M., Sheffield, J., Chan, S. and Wood, E.F., 2020. Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates. Remote Sensing of Environment, 242, p.111740,

Contact Ming Pan for questions.