J. Dan Tarpley

Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains

[1] Atmospheric forcing used by land surface models is a critical component of the North American Land Data Assimilation System (NLDAS) and its quality crucially affects the final product of NLDAS and our work on model improvement. A three-year (September 1996-September 1999) retrospective forcing data set was created from the Eta Data Assimilation System and observations and used to run the NLDAS land surface models for this period. We compared gridded NLDAS forcing with station observations obtained from networks including the Oklahoma Mesonet and Atmospheric Radiation Measurement/Cloud and Radiation Testbed at the southern Great Plains. Differences in all forcing variables except precipitation between the NLDAS forcing data set and station observations are small at all timescales. While precipitation data do not agree very well at an hourly timescale, they do agree better at longer timescales because of the way NLDAS precipitation forcing is generated. A small high bias in downward solar radiation and a low bias in downward longwave radiation exist in the retrospective forcing. To investigate the impact of these differences on land surface modeling we compared two sets of model simulations, one forced by the standard NLDAS product and one with station-observed meteorology. The differences in the resulting simulations of soil moisture and soil temperature for each model were small, much smaller than the differences between the models and between the models and observations. This indicates that NLDAS retrospective forcing provides an excellent state-of-the-art data set for land surface modeling, at least over the southern Great Plains region.

Real-time weekly global green vegetation fraction derived from advanced very high resolution radiometer-based NOAA operational global vegetation index (GVI) system

[1] To provide quality-improved and consistent real-time global green vegetation fraction (GVF) data products that are suitable for use in operational numerical weather, climate, and hydrological models, necessary processing steps are applied to the output data stream from the advanced very high resolution radiometer (AVHRR)-based NOAA operational global vegetation index (GVI) system. This paper reviewed the NOAA GVI data and described the algorithm to derive weekly updated real-time GVF from the normalized difference vegetation index (NDVI). The methodology description focuses on algorithm justification in an operational production context. The described algorithm was implemented in the global vegetation processing system (GVPS). The new global GVF data sets include the multiyear GVF weekly climatology and the real-time weekly GVF. Compared to the old 5 year GVF monthly climatology currently used in the operational National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) weather and climate models, the new data sets provide an overall higher vegetation value, real-time surface vegetation information, and numerous other improvements. The new GVF data set quality was partially assured by validation against Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at a few EOS land validation core sites and comparison with another independently processed NDVI data set. Impact of the new GVF data sets in numerical weather prediction (NWP) model was investigated using EMC mesoscale model simulations and concluded overall positive.

Estimating urban temperature bias using polar-orbiting satellite data

Abstract Urban temperature bias, defined to be the difference between a shelter temperature reading of unknown but suspected urban influence and some appropriate rural reference temperature, is estimated through the use of polar-orbiting satellite data. Predicted rural temperatures, based on a method developed using sounding data, are shown to be of reasonable accuracy in many cases for urban bias assessments using minimum temperature data from selected urban regions in the United States in July 1989. Assessments of predicted urban bias were based on comparisons with observed bias, as well as independent measures of urban heat island influence, such as population statistics and urban-rural differences in a vegetation index. This technique provides a means of determining urban bias in regions where few if any rural reference stations are available, or where inhomogeneities exist in land surface characteristics or rural station locations.

A satellite approach for estimating regional land surface energy budget for GCIP/GAPP

[1] Conventional observations cannot provide information on land surface energy fluxes, or information for land surface parameterizations, on a global or regional scale. In this paper, a satellite approach for estimating regional land surface energy budget is developed and implemented to the Mississippi River Basin, which serves as the focus of the World Climate Research Program Global Energy and Water cycles Experiment (GEWEX) Continental Scale International Project (GCIP) and GEWEX Americas Prediction Project (GAPP). The objective of this study is to evaluate the potential of using recently available satellite information to advance current capabilities in determining regional land surface energy budget. The primary forcing parameters in this approach, namely, surface shortwave radiation and skin temperature, are derived from the Geostationary Operational Environmental Satellite (GOES) observations, using inference schemes that are operationally executed at the National Oceanic and Atmospheric Administration National Environmental Satellite Data and Information Service (NESDIS). Shortwave radiation is used to define the absorbed energy at the surface. Diurnal variation of skin temperature is used to define the surface energy partitioning. The real-time NESDIS GOES product covers the continental United States (25°-53°N, 67°-125°W), at a 0.5° spatial resolution and an hourly temporal resolution. Atmospheric conditions of near-surface air temperature, humidity, and wind speed are obtained from the NOAA National Centers for Environmental Prediction (NCEP) Eta model output. A 1-year simulation (May 1997 to May 1998) of the Mississippi River Basin surface energy budget is performed. Model inputs of shortwave radiation and skin temperature, and resulting latent and sensible heat fluxes, are evaluated on various spatial and temporal scales. On a local scale, over the 1-year study period, the RMS difference between estimated and observed monthly shortwave fluxes and latent and sensible heat fluxes are 32, 21, and 20 Wm -2 , respectively. On a regional scale the estimated summertime energy fluxes are of similar pattern and same order of magnitude as the corresponding reanalysis results from NCEP and National Center for Atmospheric Research.