Abstract
The NASA Soil Moisture Active Passive (SMAP) mission Level<4 Soil Moisture (L4_SM) product provides global, 9<km resolution, 3<hourly surface and root<zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L<band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation<based precipitation. This paper describes and evaluates the use of satellite< and gauge<based precipitation from the NASA Integrated Multi<satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm begin-ning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG<Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge<only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ~0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge<only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation<minus<forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6.
| Original language | English |
|---|---|
| Pages (from-to) | 1699-1723 |
| Number of pages | 25 |
| Journal | Journal of Hydrometeorology |
| Volume | 24 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2023 |
Funding
for this work was provided by the NASA SMAP mission and the SMAP Science Team. Computational resources were provided by the NASA High-End Computing program through the NASA Center for Climate Simulation. We are grateful for those who make the generation and dissemination of the SMAP data products possible, including staff at JPL, GSFC, NSIDC, and NOAA CPC. For the in situ validation data we thank S. Bircher, A. Colliander, Á. González-Zamora, K. H. Jensen, E. Lopez-Baeza, J. Martínez-Fernández, T. Pellarin, M. Thibeault, J. P. Walker, and X. Wu. The USDA is an equal opportunity provider and employer.
| Funders |
|---|
| National Aeronautics and Space Administration |
Keywords
- Kalman filters
- Land surface
- Land surface model
- Precipitation
- Remote sensing
- Soil moisture