Hydrologic Prediction

Usage of Remote-Sensing Data

A significant amount of CHRS's efforts focuses on the utilization of remote sensing information to improve hydrometeorlogic predictions. CHRS scientists use EOS data extensively for enhancement of the mesoscale model, data assimilation, land-surface hydrologic modeling.

A major component of our research involves the (direct or indirect) assimilation of ground-based and satellite remotely sensed data into the semi-arid land-atmosphere mesoscale weather forecasting system for adjustment of the land surface and atmospheric model parameters.

The goal of assimilation is to provide a dynamically consistent simulation/forecast that represents an optimal combination of the information contained in the model (summarizing the dynamics/physics of the system and the past history) and the information contained in the observations (reflecting the actual state of the system including factors and uncertainties not properly represented in the model). Successful assimilation will result in unbiased estimates of the model states and their trajectory rate of change, thereby extending the reliability of the short-term model forecast. Satellite information useful to constrain atmospheric cloud and radiation fields includes the multiple sensor soundings of atmospheric temperature and water vapor profiles, while satellite information about the surface water and energy processes includes multi-channel microwave sensor data related to precipitation, surface vegetation, temperature, and soil moisture. We intend to implement an integrated monitoring/assimilation technique based on this information to obtain estimates of the important surface and atmospheric parameters required by the mesoscale model, including rainfall, snowfall, atmospheric moisture, temperature, surface vegetation, and soil moisture. (Table 2) lists EOS products used in our various studies.