Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 13, 2012
Publication Date: February 28, 2012
Repository URL: http://handle.nal.usda.gov/10113/59879
Citation: Holmes, T.R., Jackson, T.J., Reichle, R., Basara, J. 2012. An assessment of numerical weather prediction (NWP) models surface soil temperature products using ground-based measurements. Water Resources Research. DOI: 10.1029/2011WR010538. Interpretive Summary: A new National Aeronautics and Space Administration (NASA) satellite called SMAP (Soil Moisture Active/Passive) is planned to be launched in 2013 and will measure soil moisture from space with higher accuracy and better resolution then ever before. Unlike similar passive microwave satellites before it, it will not have an onboard means of measuring the temperature of the land surface, an important ingredient for retrieving soil moisture. Instead it will rely on modeled soil temperature fields from numerical weather prediction (NWP) centers. This study assessed the level of accuracy with which this can be obtained from different NWP centers, in comparison to in situ measurements over the state of Oklahoma, US. All temperatures were interpolated to a common depth of 5 cm below the surface to optimize comparability at the input level needed for the soil moisture retrievals. It is found that both the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF) and the Modern-Era Retrospective analysis for Research and Applications (MERRA) from the Global Modeling and Assimilation Office (GMAO) may provide temperature fields at accuracy levels below the level allocated in SMAP’s error budget for the 6 AM morning overpass. The global data assimilation system (GDAS) as used by the National Center for Environmental Prediction (NCEP) provides a better description of the full daily temperature oscillation and may be a better input for the 6 PM SMAP overpass. The findings of this study will help assure that SMAP will deliver on its accuracy goal of soil moisture mapping with likely benefits to society in the form of better agricultural drought monitoring, flood forecasting and weather prediction.
Technical Abstract: Numerical weather prediction (NWP) models developed by various weather centers produce estimates of the soil temperature state. In this study in situ data collected over the state of Oklahoma is used to assess and compare three NWP surface (soil) temperature products. These are 1) the integrated forecast system from the European Center for Medium range Weather Forecasts (ECMWF), 2) the Modern-Era Retrospective analysis for Research and Applications (MERRA) from the Global Modeling and Assimilation Office (GMAO), and 3) the global data assimilation system (GDAS) as used by the National Center for Environmental Prediction (NCEP). Since the motivation for this investigation is related to using the surface soil temperature in retrieving soil moisture from the proposed Soil Moisture Active Passive (SMAP) L-band satellite, the focus is on the characterization of the nominal contributing depth of 0.05 m. Therefore, each NWP set is synchronized to match the mean phase of in situ data at 0.05 m under sod, and may be regarded as the best estimate of the 0.05 m temperature to be obtained from these NWP models. The assessment of accuracy and precision is presented in the context of time of day, which provides insight into the impact of diurnal variation on the ability to accurately model soil temperature. Specific attention is directed to the overpass times of SMAP, which has an early morning pass at around 6 AM local time. Based upon the analysis, the ECMWF and MERRA products have very similar performance metrics, with a root mean square error of 1.7 K at the overpass time for SMAP. These results indicate that either product is satisfactory for SMAP.