One of the major challenges facing the hydrological and meteorological communities is to make better use, within land surface hydrology and atmospheric boundary layer models, of a rapidly growing ensemble of observations. Operational sensors provide relevant observations, but the repeat cycle and spatial resolution often do not meet the needs of watershed to regional-scale models. Data available from experimental ground-based and aircraft instruments are potentially useful for model input and/or validation, but are often of limited spatial and temporal extent. It is therefore important to develop systems in which both in situ and remote observations are assimilated with model estimates of surface energy and water fluxes and boundary layer properties. By using intensive data sets including ground-based, aircraft and satellite observations such as those collected during short-term field experiments, two important advances can be made. First, we can gain a better understanding of model physics, thus improving our ability to estimate and forecast surface and boundary layer processes. Second, we can learn much about the spatial, temporal and radiometric attributes of a remote sensor system that result in useful data. This information is valuable for designing the next generation of satellite-borne sensors.
The Southern Great Plains '97 field experiment (SGP 97) provided a rich suite of ground- and aircraft-based measurements of soil moisture, surface meteorology, boundary layer fluxes, and related variables. This data set will be extremely useful in developing and validating strategies for assimilating disparate forms of information, particularly remotely-sensed data, into land surface and boundary layer models. An overview of modeling activities being carried out by SGP 97 participants, and others utilizing the data, will be presented. The presentation will encompass watershed-scale hydrology models, regional-scale hydrology models, land surface energy and water flux models, boundary layer models, and possibly others. Emphasis will be placed on those efforts which combine data from several sources and employ novel assimilation schemes