Spatiotemporal Disaggregation of Remotely Sensed Precipitation for Ensemble Hydrologic Modeling and Data Assimilation

Abstract

Historically, estimates of precipitation for hydrologic applications have largely been obtained using ground-based rain gauges despite the fact that they can contain significant measurement and sampling errors. Remotely sensed precipitation products provide the ability to overcome spatial coverage limitations, but the direct use of these products generally suffers from their relatively coarse spatial and temporal resolution and inherent retrieval errors. A simple ensemble-based disaggregation scheme is proposed as a general framework for using remotely sensed precipitation data in hydrologic applications. The scheme generates fine-scale precipitation realizations that are conditioned on large-scale precipitation measurements. The ensemble approach allows for uncertainty related to the complex error characteristics of the remotely sensed precipitation (undetected events, nonzero false alarm rate, etc.) to be taken into account. The methodology is applied through several synthetic experiments over the southern Great Plains using the Global Precipitation Climatology Project 1° daily (GPCP-1DD) product. The scheme is shown to reasonably capture the land-surface-forcing variability and propagate this uncertainty to the estimation of soil moisture and land surface flux fields at fine scales. The ensemble results outperform a case using sparse ground-based forcing. Additionally, the ensemble nature of the framework allows for simply merging the open-loop soil moisture estimation scheme with modern data assimilation techniques like the ensemble Kalman filter. Results show that estimation of the soil moisture and surface flux fields are further improved through the assimilation of coarse-scale microwave radiobrightness observations.