Search Results
and moisture), which can be used for heat and moisture budget studies as well as single-column modeling (SCM) and CRM studies of the observed convective cloud systems. Note that providing such large-scale fields was one of the key goals of the TWP-ICE field campaign ( May et al. 2008 ). Section 2 provides details about the data and data analysis issues. The synoptic conditions and associated cloud fields are briefly summarized in section 3 . Section 4 describes characteristics of the analyzed
and moisture), which can be used for heat and moisture budget studies as well as single-column modeling (SCM) and CRM studies of the observed convective cloud systems. Note that providing such large-scale fields was one of the key goals of the TWP-ICE field campaign ( May et al. 2008 ). Section 2 provides details about the data and data analysis issues. The synoptic conditions and associated cloud fields are briefly summarized in section 3 . Section 4 describes characteristics of the analyzed
forecast phase. In the training phase, the models are compared with the observation–analysis data. A multiple linear regression is performed in this phase to estimate the relative performance of the member models. The outcome of this regression is statistical weights assigned to each of the models. These weights are then passed on to the forecast phase to construct the superensemble forecast: where O is the observed mean field during the training phase; a i is the weight for the i th member model
forecast phase. In the training phase, the models are compared with the observation–analysis data. A multiple linear regression is performed in this phase to estimate the relative performance of the member models. The outcome of this regression is statistical weights assigned to each of the models. These weights are then passed on to the forecast phase to construct the superensemble forecast: where O is the observed mean field during the training phase; a i is the weight for the i th member model
availability of latent heating (LH) estimates based on the Tropical Rainfall Measuring Mission (TRMM; Tao et al. 2006 ) provide an unprecedented opportunity to investigate heating structures associated with the MJO convection. By using LH estimates based on an earlier version of a spectral latent heating algorithm ( Shige et al. 2004 ), Morita et al. (2006) examined vertical heating structures of the MJO based on a composite analysis. In their study, two centers of maximum heating at about 3 and 7 km
availability of latent heating (LH) estimates based on the Tropical Rainfall Measuring Mission (TRMM; Tao et al. 2006 ) provide an unprecedented opportunity to investigate heating structures associated with the MJO convection. By using LH estimates based on an earlier version of a spectral latent heating algorithm ( Shige et al. 2004 ), Morita et al. (2006) examined vertical heating structures of the MJO based on a composite analysis. In their study, two centers of maximum heating at about 3 and 7 km
monsoon region (SAMR), where there is a conjunction of high sea surface temperature (SST) and the largest precipitation rates on the planet. In addition, the heating in the monsoon regions has an important influence on the global circulation through the generation of strong teleconnection patterns ( Webster 1994 ; Webster et al. 1998 ). The SAMR is therefore chosen as the focus of the analysis for these reasons. The specific goal of this study is to examine the four-dimensional structure of LH over
monsoon region (SAMR), where there is a conjunction of high sea surface temperature (SST) and the largest precipitation rates on the planet. In addition, the heating in the monsoon regions has an important influence on the global circulation through the generation of strong teleconnection patterns ( Webster 1994 ; Webster et al. 1998 ). The SAMR is therefore chosen as the focus of the analysis for these reasons. The specific goal of this study is to examine the four-dimensional structure of LH over
required for the HH algorithm, as is the cloud-scale velocity, which is obtained by applying a regression method to a CRM-simulated database. The SLH algorithm ( Shige et al. 2004 , 2007 , 2008 , 2009 ) is also based on CRM (i.e., GCE) results. It uses TRMM Precipitation Radar (PR) information (i.e., melting layer, precipitation top height, rain rate and type) to select the heating profiles from a LUT. The PRH algorithm ( Satoh and Noda 2001 ; M. Katsumata et al. 2008, unpublished manuscript) also
required for the HH algorithm, as is the cloud-scale velocity, which is obtained by applying a regression method to a CRM-simulated database. The SLH algorithm ( Shige et al. 2004 , 2007 , 2008 , 2009 ) is also based on CRM (i.e., GCE) results. It uses TRMM Precipitation Radar (PR) information (i.e., melting layer, precipitation top height, rain rate and type) to select the heating profiles from a LUT. The PRH algorithm ( Satoh and Noda 2001 ; M. Katsumata et al. 2008, unpublished manuscript) also
