TY - JOUR
T1 - Combined assimilation of satellite precipitation and soil moisture
T2 - A case study using TRMM and SMOS data
AU - Lin, Liao Fan
AU - Ebtehaj, Ardeshir M.
AU - Flores, Alejandro N.
AU - Bastola, Satish
AU - Bras, Rafael L.
N1 - Publisher Copyright:
© 2017 American Meteorological Society.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
AB - This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).
KW - Atmosphere-land interaction
KW - Data assimilation
KW - Mesoscale models
KW - Numerical weather prediction/forecasting
KW - Precipitation
KW - Soil moisture
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U2 - 10.1175/MWR-D-17-0125.1
DO - 10.1175/MWR-D-17-0125.1
M3 - Article
AN - SCOPUS:85040454148
SN - 0027-0644
VL - 145
SP - 4997
EP - 5014
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 12
ER -