Reach Scale Hydrology
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.
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).
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. https://doi.org/10.1002/2016gl069964.
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, https://doi.org/10.5194/hess-23-207-2019.
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, https://doi.org/10.1002/2016WR019967.
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, https://doi.org/10.5194/hess-22-6611-2018.
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, https://doi.org/10.1029/2018GL079291.
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, https://doi.org/10.1016/j.rse.2020.111740.
Contact Ming Pan email@example.com for questions.