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...

No abstract

No abstract

A brief summary of the current capabilities of a high resolution global numerical prediction model towards resolving the life cycles of hurricanes is first presented. Next, we illustrate the results of season long integrations for the years 1987 and 1988 using the observed sea surface temperature (SST) anomalies over the global oceans. The model being used here is the FSU atmospheric global spectral model at the horizontal resolution of T42 and with 16 vertical layers. The main emphasis of this study is on hurricane tracks for these and for global warming experiments. The global warming scenarios were modeled using doubled CO 2 and enhanced SST anomalies. The model being atmospheric does not simulate the ocean, and SST anomalies need to be prescribed. It is assumed in these experiments that the SST anomalies of the doubled CO 2 world appear similar to those of the current period but that they are slightly warmer over the global tropics. That is determined using a simple proportionality relationship requiring an enhancement of the global mean SST anomaly over the tropics. Such an enhancement of the SST anomaly of an El Niñ o year 1987 amplifies the SST anomaly for the El Niñ o of the double CO 2 atmosphere somewhat. The La Niñ a SST anomalies were similarly enhanced for the double CO 2 atmosphere during 1988. These hurricane season experiments cover the period June through October for the respective years. It was necessary to define the thresholds for a model simulated hurricane; given such a definition we have compared first the tracks and frequency of storms based on the present day CO 2 simulations with the observed storms for 1987 and 1988. Those comparisons were noted to be very close to the observed numbers of the storms. The doubled CO 2 storms show a significant enhancement of the frequency of storms for the La Niñ a periods, however there was no noticeable change for the El Niñ o experiments. We have also run an experiment using the SST anomalies from a triple CO 2 climate run made at the Max Planck Institut at Hamburg. This experiment simulated some 7 hurricanes over the Atlantic Ocean. The intensity of hurricanes, inferred from maximum winds at 850 mb, show that on the average the storms are slightly more intense for the double CO 2 experiments compared to the storms simulated from current CO 2 conditions. The triple CO 2 storms were slightly stronger in this entire series of experiments.

No abstract