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). 2. Superensemble methodology The superensemble methodology combines a set of multimodel forecasts to construct a single consensus forecast ( Krishnamurti et al. 1999 , 2000 ). In this methodology, the models are combined with different weights, which is unlike a simple ensemble construction. The weights of the models are determined based on their past performance. The details of this methodology are as follows. The time line of the model dataset is divided into two parts: a training phase and a
). 2. Superensemble methodology The superensemble methodology combines a set of multimodel forecasts to construct a single consensus forecast ( Krishnamurti et al. 1999 , 2000 ). In this methodology, the models are combined with different weights, which is unlike a simple ensemble construction. The weights of the models are determined based on their past performance. The details of this methodology are as follows. The time line of the model dataset is divided into two parts: a training phase and a
also been detected (e.g., chlorophyll: Waliser et al. 2005 ; ozone: Tian et al. 2007 ; aerosols: Tian et al. 2008 ). The quasi-periodic occurrence of the MJO provides a primary source for the predictability of tropical atmosphere on subseasonal time scales, which may bridge the forecasting gap between medium- to long-range weather forecast and short-term climate prediction (e.g., Waliser et al. 2006 ; Jiang et al. 2008 ). Therefore, the improved understanding of the fundamental features of the
also been detected (e.g., chlorophyll: Waliser et al. 2005 ; ozone: Tian et al. 2007 ; aerosols: Tian et al. 2008 ). The quasi-periodic occurrence of the MJO provides a primary source for the predictability of tropical atmosphere on subseasonal time scales, which may bridge the forecasting gap between medium- to long-range weather forecast and short-term climate prediction (e.g., Waliser et al. 2006 ; Jiang et al. 2008 ). Therefore, the improved understanding of the fundamental features of the
land even without average upward velocity at the midtroposphere over the South America (∼60°W). We also examined this point with the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data (not shown), although midtropospheric vertical velocities with ERA-40 are not downward at deep-heating longitudes over the South America; however, they show very small upward velocities that are distinct from those over the ocean. To examine the above west–east contrast in
land even without average upward velocity at the midtroposphere over the South America (∼60°W). We also examined this point with the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data (not shown), although midtropospheric vertical velocities with ERA-40 are not downward at deep-heating longitudes over the South America; however, they show very small upward velocities that are distinct from those over the ocean. To examine the above west–east contrast in
are also included in this study: the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis (NCEPII; Kanamitsu et al. 2002 ), the Japanese 25-yr reanalysis (JRA25; Onogi et al. 2007 ), the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ); and the Modern Era Retrospective Analysis for Research and Applications (MERRA) from the National Aeronautics and Space Administration (NASA) Global Modeling and
are also included in this study: the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis (NCEPII; Kanamitsu et al. 2002 ), the Japanese 25-yr reanalysis (JRA25; Onogi et al. 2007 ), the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ); and the Modern Era Retrospective Analysis for Research and Applications (MERRA) from the National Aeronautics and Space Administration (NASA) Global Modeling and
, 2007 : Heating structures of the TRMM field campaigns. J. Atmos. Sci. , 64 , 2593 – 2610 . Shibagaki , Y. , and Coauthors , 2006 : Multiscale aspects of convective systems associated with an intraseasonal oscillation over the Indonesian Maritime Continent. Mon. Wea. Rev. , 134 , 1682 – 1696 . Shukla , J. , and D. A. Paolino , 1983 : The Southern Oscillation and long range forecasting of the summer monsoon rainfall over India. Mon. Wea. Rev. , 111 , 1830 – 1837 . Sikka , D. R
, 2007 : Heating structures of the TRMM field campaigns. J. Atmos. Sci. , 64 , 2593 – 2610 . Shibagaki , Y. , and Coauthors , 2006 : Multiscale aspects of convective systems associated with an intraseasonal oscillation over the Indonesian Maritime Continent. Mon. Wea. Rev. , 134 , 1682 – 1696 . Shukla , J. , and D. A. Paolino , 1983 : The Southern Oscillation and long range forecasting of the summer monsoon rainfall over India. Mon. Wea. Rev. , 111 , 1830 – 1837 . Sikka , D. R
the TWP-ICE soundings and background European Centre for Medium-Range Weather Forecasts (ECMWF) analyses using an interpolation scheme described by Cressman (1959) . The Cressman scheme uses a weighting function that depends on the distance between an observation station and an analysis grid point, as well as the difference between observations and the background. The interpolation is carried out for the difference field between observations and the background. If there is no measurement within a
the TWP-ICE soundings and background European Centre for Medium-Range Weather Forecasts (ECMWF) analyses using an interpolation scheme described by Cressman (1959) . The Cressman scheme uses a weighting function that depends on the distance between an observation station and an analysis grid point, as well as the difference between observations and the background. The interpolation is carried out for the difference field between observations and the background. If there is no measurement within a
, 2007) . Briefly, HERB synthesizes ice cloud microphysical property information from VIRS; liquid cloud properties, precipitation profiles, SST, and water vapor retrievals from the TRMM TMI; and vertical profiles of temperature and humidity from the European Center for Medium-Range Weather Forecasts (ECMWF) reanalyses to characterize the three-dimensional structure of clouds and precipitation in the atmosphere. These provide input to a broadband radiative transfer model that simulates vertical
, 2007) . Briefly, HERB synthesizes ice cloud microphysical property information from VIRS; liquid cloud properties, precipitation profiles, SST, and water vapor retrievals from the TRMM TMI; and vertical profiles of temperature and humidity from the European Center for Medium-Range Weather Forecasts (ECMWF) reanalyses to characterize the three-dimensional structure of clouds and precipitation in the atmosphere. These provide input to a broadband radiative transfer model that simulates vertical
resource for scientific research and applications (see a review by Tao et al. 2006 ). Such products enable new insights and investigations into the complexities of convective system life cycles, diabatic heating controls and feedbacks related to mesoscale to synoptic-scale circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. Five different TRMM LH
resource for scientific research and applications (see a review by Tao et al. 2006 ). Such products enable new insights and investigations into the complexities of convective system life cycles, diabatic heating controls and feedbacks related to mesoscale to synoptic-scale circulations and their forecasting, the relationship of tropical patterns of LH to the global circulation and climate, and strategies for improving cloud parameterizations in environmental prediction models. Five different TRMM LH
