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T. N. Krishnamurti
Arindam Chakraborty
, and
A. K. Mishra


Recently the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) project office made available a new product called the convective–stratiform heating (CSH). These are the datasets for vertical profiles of diabatic heating rates (the apparent heat source). These observed estimates of heating are obtained from the TRMM satellite’s microwave radiances and the precipitation radar. The importance of such datasets for defining the vertical distribution of heating was largely the initiative of Dr. W.-K. Tao from NASA’s Goddard Laboratory. The need to examine how well some of the current cumulus parameterization schemes perform toward describing the amplitude and the three-dimensional distributions of heating is addressed in this paper. Three versions of the Florida State University (FSU) global atmospheric model are run that utilize different versions of cumulus parameterization schemes; namely, modified Kuo parameterization, simple Arakawa–Schubert parameterization, and Zhang–McFarlane parameterization. The Kuo-type scheme used here relies on moisture convergence and tends to overestimate the rainfall generally compared to the TRMM estimates. The other schemes used here show only a slight overestimate of rain rates compared to TRMM; those invoke mass fluxes that are less stringent in this regard in defining cloud volumes. The mass flux schemes do carry out a total moisture budget for a vertical column model and include all components of the moisture budget and are not limited to the horizontal convergence of moisture. The authors carry out a numerical experimentation that includes over a hundred experiments from each of these models; these experiments differ only in their use of the cumulus parameterization. The rest of the model physics, resolution, and initial states are kept the same for each set of 117 forecasts. The strategy for this experimentation follows the authors’ previous studies with the FSU multimodel superensemble. This includes a 100-day training and a 17-day forecast phase, both of which include a large number of forecast experiments. The training phase provides a useful statistical database for tagging the systematic errors of the respective models. The forecast phase is designed to minimize the collective bias errors of these member models. In these forecasts the authors also include the ensemble mean and the multimodel superensemble. In this paper the authors examine model errors in their representations of the heating (amplitude, vertical level of maximum, and the geographical distributions). The main message of this study is that some cumulus parameterization schemes overestimate the amplitude of heating, whereas others carry lower values. The models also exhibit large errors in the placement of the vertical level of maximum heating. Some significant errors were also found in the geographical distributions of heating. The ensemble mean largely mimics the model features and also carries some large errors. The superensemble is more selective in reducing the three-dimensional collective bias errors of the models and provides the best short range forecasts, through hour 60, for the heating. This study shows that it is possible to diagnose some of the modeling errors in the heating for individual member models and that information can be important for correcting such features.

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