In its simplest description, the task at hand is to redesign CPC's Monitoring and Prediction of US Soil Moisture and Associated Land Surface Variables, by replacing the leaky bucket model by the NOAH land surface model. Since the late 1990's the NOAH model, along with VIC and MOSAIC has been run at EMC in real time forward mode. Any of these three models is physically much more complete and interesting than the leaky bucket. Additionally, the spatial and temporal resolution is very much higher (perhaps more than CPC needs) than the Huang et al (1996) model operating on monthly data at 344 US Climate Divisions, and we would be in a position to use the snow and frozen soil variables, which would imply possible applications outside the warm season (where soil moisture effects are thought to be large). The main computational and data effort is to rerun the NOAH model retroactively as far back as is feasible: 1949-present. Except for the precipitation from CPC, all other forcing elements needed in the NOAH model are derived from Global Reanalysis. While better forcing is available for recent years (solar radiation from satellite), the absolute necessity for a homogeneous climate data set forces us to use solar radiation from Reanalysis, and makes our real time effort contingent upon the continuation of Reanalysis/CDAS.

As the first step of the above effort, a 50 year (1949-1998) hourly retroactive LDAS forcing data set was generated, including air temperature, air humidity, surface pressure, wind speed (U,V), surface downward shortwave radiation, surface downward longwave radiation and precipitation. Some unique procedures were developed to prepare this hourly retroactive forcing data in order to make a homogeneous forcing data set at the required spatial and temporal resolutions. The model parameters and fixed fields are derived from existing high resolution vegetation, soil coverage and orography. The preliminary results show that the hourly forcing data set is reasonably good, compared with (scant) observations. At the time of this writing, a few test runs have been done and the Retroactive LDAS Run is about to begin, starting from the first day of 1949. The outputs will provide an improved soil moisture and more associated land surface variable data set, such as snowpack, surface fluxes etc which we never had before. Many interesting data validation and model comparisons are underway. The retroactive run will also provide superior model consistent initial conditions for numerical predictions. It is expected that CPC and the research community would benefit from this improved soil moisture data sets.