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The objective of this study is to investigate the quality of clouds simulated by the National Centers for Environmental Prediction global forecast system (GFS) model and to examine the causes for some systematic errors seen in the simulations through use of satellite and ground-based measurements. In general, clouds simulated by the GFS model had similar spatial patterns and seasonal trends as those retrieved from passive and active satellite sensors, but large systematic biases exist for certain cloud regimes especially underestimation of low-level marine stratocumulus clouds in the eastern Pacific and Atlantic oceans. This led to the overestimation (underestimation) of outgoing longwave (shortwave) fluxes at the top-of-atmosphere. While temperature profiles from the GFS model were comparable to those obtained from different observational sources, the GFS model overestimated the relative humidity field in the upper and lower troposphere. The cloud condensed water mixing ratio, which is a key input variable in the current GFS cloud scheme, was largely underestimated due presumably to excessive removal of cloud condensate water through strong turbulent diffusion and/or an improper boundary layer scheme. To circumvent the problem associated with modeled cloud mixing ratios, we tested an alternative cloud parameterization scheme that requires inputs of atmospheric dynamic and thermodynamic variables. Much closer agreements were reached in cloud amounts, especially for marine stratocumulus clouds. We also evaluate the impact of cloud overlap on cloud fraction by applying a linear combination of maximum and random overlap assumptions with a de-correlation length determined from satellite products. Significantly better improvements were found for high-level clouds than for low-level clouds, due to differences in the dominant cloud geometry between these two distinct cloud types.

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Department of Defense (D oD), Joint Center for Satellite Data Assimilation (JCSDA) was established in 2000/2001. The goal of the JCSDA is to accelerate the use of observations from earth-orbiting satellites into operational numerical environmental analysis and prediction systems for the purpose of improving weather and oceanic forecasts, seasonal climate forecasts and the accuracy of climate data sets. As a result, a series of data assimilation experiments were undertaken at the JCSDA as part of the preparations for the operational assimilation of AIRS data by its partner organizations 1,2 . Here, for the first time full spatial reso lution radiance data, availab le in real-time from th e AIRS instrument, were used at the JCSDA in data assimilation studies over the globe utilizing the operational NCEP Global Forecast System (GFS). The radiance data from each channel of the instrument were carefully screened for cloud effects and those radiances which were deemed to be clear of cloud effects were used by the GFS forecast system. The result of these assimilation trials has been a first demonstration of significant improvements in forecast skill over both the Northern and Southern Hemisphere compared to the operational system without AIRS data. The experi mental system was designed in a way that rendered it feasible for operational app lication, and that constraint involved us ing the subset of AIRS channels chosen for operational distribution and an analysis methodology close to the current analysis practice, with particular consideration given to time limitations. As a result, operational application of these AIRS data was enabled by the recent NCEP operational upgrade. In addition, because of the improved impact resulting from use of this enhanced d ata set compared to that used operati onally to date, provision of a real-time “warmest field” of view data set has been est ablished for use by international NWP Centers. Keywords: AIRS, data assimilation, satellite meteorology

Full spatial resolution AIRS hyperspectral data have been used in a data assimilation study over the globe utilizing the operational NCEP Global Forecast System (GFS). The result of this assimilation trial has been significant improvements in forecast skill over the Southern Hemisphere and improvement over the Northern Hemisphere.