Correction of real-time satellite precipitation with satellite soil moisture observations

摘要

Rainfall and soil moisture are two key elements in modeling the interactionsbetween the land surface and the atmosphere. Accurate and high-resolutionreal-time precipitation is crucial for monitoring and predicting the onset offloods, and allows for alert and warning before the impact becomes adisaster. Assimilation of remote sensing data into a flood-forecasting modelhas the potential to improve monitoring accuracy. Space-borne microwaveobservations are especially interesting because of their sensitivity tosurface soil moisture and its change. In this study, we assimilate satellitesoil moisture retrievals using the Variable Infiltration Capacity (VIC) landsurface model, and a dynamic assimilation technique, a particle filter, toadjust the Tropical Rainfall Measuring Mission Multi-satellite PrecipitationAnalysis (TMPA) real-time precipitation estimates. We compare updatedprecipitation with real-time precipitation before and after adjustment andwith NLDAS gauge-radar observations. Results show that satellite soilmoisture retrievals provide additional information by correcting errors inrainfall bias. The assimilation is most effective in the correction of mediumrainfall under dry to normal surface conditions, while limitedegativeimprovement is seen over wet/saturated surfaces. On the other hand,high-frequency noises in satellite soil moisture impact the assimilation byincreasing rainfall frequency. The noise causes larger uncertainty in thefalse-alarmed rainfall over wet regions. A threshold of 2 mm day?1soil moisture change is identified and applied to the assimilation, whichmasked out most of the noise.