Abstract
It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.
Highlights
Soil moisture is a critical hydrospheric state variable that often limits the exchanges of water and energy between the atmosphere and land surface, controls the partitioning of rainfall among evaporation, infiltration, and runoff, and may have significant impacts on numerical weather, climate, and hydrologic predictions. e Global Forecast System (GFS) of National Centers of Environmental Prediction (NCEP) of NOAA is the primary weather forecast model that provides up to 16-day weather forecasts for users around the world
Over the east continental United States (CONUS), surface humidity in the GFS control run clearly shows positive bias in daytime and nighttime, which is consistent with the horizontal surface moisture distribution as shown in Figure 1. is bias got reduced in the GFS sensitivity run, but forecast surface humidity in nighttime is still higher than the observation. e surface temperature in the GFS run shows a cold bias of daytime after 4 days of forecast, and the cold bias becomes more obvious with forecast time. is cold bias got reduced in the sensitivity run (Figure 2(d)), indicating that the soil moisture data assimilation can have a good improvement in the surface temperature forecast
It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models [34]
Summary
Soil moisture is a critical hydrospheric state variable that often limits the exchanges of water and energy between the atmosphere and land surface, controls the partitioning of rainfall among evaporation, infiltration, and runoff, and may have significant impacts on numerical weather, climate, and hydrologic predictions. e Global Forecast System (GFS) of National Centers of Environmental Prediction (NCEP) of NOAA is the primary weather forecast model that provides up to 16-day weather forecasts for users around the world. 2. Satellite Soil Moisture Data Products e NCEP GFS, North American Mesoscale System (NAM), and their associated assimilation systems include a land surface model (LSM) component that requires soil moisture data as an input for accurate weather and seasonal climate predictions. Satellite-based global soil moisture observational data products are believed to provide a substantial constraint to the model estimate uncertainties and improve the global and mesoscale model accuracies of weather forecasts These satellite soil moisture data products have not been used by the NCEP numerical weather prediction (NWP) models because either their qualities or their availabilities/formats do not meet the NWP model operation requirements, or algorithms for ingesting the soil moisture data products into the NWP models have not been implemented or tested. In this study, considering that soil moisture variation within nonraining days is small and that the blended soil moisture data from the SMOPS represent only daily soil moisture level, the soil moisture data assimilation is carried out at 00, 06, 12, and 18 UTC cycles in the GFS-GSI system, and only at 00 UTC cycle, the GFS is performed for week one forecast (0–192 hrs) to save computation resources. e SMOPS has used Noah LSM multiple-year grid-wise means and standard deviations to scale surface layer soil moisture retrievals from the individual satellite sensors already before blending [4], and the blended soil moisture data are assumed to have the same climatology as the model simulations of the Noah LSM used in the GFS
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