This paper addresses real-time precipitation forecasts from a multianalysis-multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis-multimodel system studied here. In this paper, ''multimodel'' refers to different models whose forecasts are being assimilated for the construction of the superensemble. ''Multianalysis'' refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a ''best'' rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis-multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis-multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually biasremoved models. The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1-3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high for...

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The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 2A12 product consists of unique components configured for land and oceanic precipitation retrievals. This design was based on the vastly different physical characteristics of the retrieval, involving primarily emission over ocean and entirely scattering over land. This paper describes the current status of the TRMM Version 6 (V6) 2A12 product over land and envisioned improvements for TRMM TMI V7 and GPM GMI V1.On a global scale, the 2A12 land algorithm exhibits biases when compared with the TRMM 2A25 (Precipitation Radar (PR) based) and rain gauges. These range from 6 percent for GPCC to 20 percent for 2A25. Closer comparison also reveals regional and seasonal biases, with the largest positive biases found in warm-season convective zones and over semi-arid regions. Some negative biases are found in warm-rain precipitation regimes where scattering at 85 GHz is unable to detect a precipitation signal. On an instantaneous time scale, 2A12 land also produces a positive bias when compared with high-quality radar data from Melbourne, Florida, a TRMM ground validation site. The largest discrepancies occur for rain rates of less than 2 mm h -1 . A number of known "anomalies" are highlighted, including overestimation of rainfall in deep convective systems, underestimation in warm-rain regimes, and a number of features associated with the

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

Satellite observations in the visible, infrared, and microwave spectrum provide a great deal of information on clouds and precipitation as well as the atmosphere in which the clouds are embedded. A major issue is how to use this information to initialize cloudy and precipitating atmospheric regions in NWP models. Most cloud-and/or rain-affected observations are discarded in current data assimilation systems. The major problems are the discontinuous nature, in time and space, of clouds and precipitation, the complex nonlinear and not-well-modeled processes involved in their formation/prediction, and the need for current data assimilation systems to use linearized versions of these nonlinear processes. As a result, cloud/rain-affected radiances are much more difficult to assimilate than clear-sky radiances, which are sensitive to the smoother fields of temperature and water vapor that are controlled by more linear, wellmodeled processes. Since clouds and precipitation often occur in sensitive regions in terms of forecast impact, improvements in their assimilation are likely necessary for continuing significant gains in weather forecasting and, in particular, the prediction of two key weather elements affecting human activities: precipitation and cloudiness (which impacts another key weather factor, surface temperature).In 2005