Abstract
Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the onset of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. The assimilation is most effective in the correction of medium rainfall under dry to normal surface conditions, while limited/negative improvement is seen over wet/saturated surfaces. On the other hand, high-frequency noises in satellite soil moisture impact the assimilation by increasing rainfall frequency. The noise causes larger uncertainty in the false-alarmed rainfall over wet regions. A threshold of 2 mm day−1 soil moisture change is identified and applied to the assimilation, which masked out most of the noise.
Highlights
Precipitation is perhaps the most important variable in controlling energy and mass fluxes that dominate climate and the terrestrial hydrological and ecological systems
We propose as part of the work how to improve the generation of rain particles and the bias correction of the satellite soil moisture observations, as well as to enhance the assimilation algorithm to maximize the information that can be gained from using soil moisture alone to adjust precipitation
We present in the paper improvements in the generation of rain particles and the bias-correction of the satellite soil moisture observations, as well as enhancements to the assimilation algorithm to maximize the information that can be gained from using soil moisture alone in adjusting precipitation
Summary
Precipitation is perhaps the most important variable in controlling energy and mass fluxes that dominate climate and the terrestrial hydrological and ecological systems. Crow et al (2003, 2009, 2011) corrected space-borne rainfall retrievals by assimilating remotely sensed surface soil moisture retrievals into an Antecedent Precipitation Index (API) based soil water balance model using a Kalman filter (Kalman, 1960). These studies focused on multi-day aggregation periods and a space aggregated correction at 1◦ resolution for the corrected precipitation totals. One important conclusion by Wanders et al (2015) is that their results showed the limited potential for satellite soil moisture observations for correcting precipitation at high resolution if “all-weather” – i.e., microwave-based – land surface temperatures are not available coincidently, as was the case with AMSR-E.
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