Current thrusts on TRMM and SSM/I based modeling studies on heavy rains and flooding episodes - KRISHNAMURTI, T. N.; VIJAYA KUMAR, T. S. V.; MACKEY, BRIAN; CUBUCKU, NIHAT; ROSS, ROBERT S.; Adler, R. F. (MAUSAM, 2003)
Our research group at the Florida State University has been using a multianalysis/multimodel approach on real time for the short-range prediction of heavy rains over the tropical belt. The methodology for the construction of the superensemble forecasts follows our recent publications on this topic. Recent improvements in multianalysis/multimodel superensemble forecasts of precipitation have led to much higher skills compared to the member models. This suggested that some useful guidance for regional floods arising from heavy rains might be possible from this approach. These are 1 to 5 day forecasts where the equitable threat scores for rainfall totals in excess of 25 mm/day have been two to three times better for the superensemble compared to the best member model. This study includes forecasts using multimodels from a number of global operational centers and a multianalysis component, which is based on the FSU global spectral model that utilizes TRMM and SSM/I data sets and a number of rain rate algorithms. The differences in the analyses arise from the use of these different rain rate algorithms within physical initialization, which in turn, produces distinct differences among 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 embedded in the multianalysis/multimodel system studied here. The TRMM and the SSM/I rainfall data sets derived from microwave instruments are key to these marked improvements of rainfall forecasts. The statistical biases of the models are determined from a multiple linear regression of these forecasts against a ‘best’ rainfall analysis field, which is based on a TRMM and SSM/I data set that utilizes rain rate algorithms recently developed at NASA Goddard. We also display a sequence of computations that illustrate a “walk-through” of a heavy rain episode. This study specifically deals with recent flood episodes over India, Bangladesh, the United States of America, Mozambique/Madagascar and the Philippines. These results compare the performance of the superensemble against the best and lowest performing model, the ensemble mean and the control experiment (that does not use any TRMM or SSM/I data sets). Overall these results show great promise over the current best models.