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- Author or Editor: Sulochana Gadgil x
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Abstract
A simple objective method for delineation of the ITCZ from daily 2.5 degree data on satellite measured outgoing longwave radiation and albedo is described. The method involves identification of grid points with a large fraction of deep convective clouds by imposition of a bispectral threshold and subsequent filtering to retain organized large-scale convection. The thresholds are derived so as to make the delineation by the objective method as close as possible to that from subjective scans by Sikka and Gadgil. The results of the objective method are similar to those obtained by McBride by subjective analysis of the winter monsoon region and Murakami's identification of convective regions based on pixel data.
Abstract
A simple objective method for delineation of the ITCZ from daily 2.5 degree data on satellite measured outgoing longwave radiation and albedo is described. The method involves identification of grid points with a large fraction of deep convective clouds by imposition of a bispectral threshold and subsequent filtering to retain organized large-scale convection. The thresholds are derived so as to make the delineation by the objective method as close as possible to that from subjective scans by Sikka and Gadgil. The results of the objective method are similar to those obtained by McBride by subjective analysis of the winter monsoon region and Murakami's identification of convective regions based on pixel data.
Abstract
The outgoing longwave radiation (OLR) fluxes derived from NOAA-SR (1974–78) are found to he consistently higher than those from NOAA-7 (1982 onward) over a large part of the tropical belt. Analysis of the variation of the mean July–August OLR and the rainfall over the Indian region suggests that the lower values of OLR in the latter period cannot be attributed to more intense convection. Thus, the consistently lower values of OLR in the latter period over a large part of the tropical belt (including the oceanic regions) may be a manifestation of a systematic bias arising from various factors such as changes in instruments, equatorial crossing time, etc. Obviously, if such a bias is present, it has to be removed before the dataset can be used for the study of interannual variations. If the bias is removed by a simple method based on the variation of convection over the entire tropical belt, the OLR variations over the Indian region become consistent with the rainfall variations.
Abstract
The outgoing longwave radiation (OLR) fluxes derived from NOAA-SR (1974–78) are found to he consistently higher than those from NOAA-7 (1982 onward) over a large part of the tropical belt. Analysis of the variation of the mean July–August OLR and the rainfall over the Indian region suggests that the lower values of OLR in the latter period cannot be attributed to more intense convection. Thus, the consistently lower values of OLR in the latter period over a large part of the tropical belt (including the oceanic regions) may be a manifestation of a systematic bias arising from various factors such as changes in instruments, equatorial crossing time, etc. Obviously, if such a bias is present, it has to be removed before the dataset can be used for the study of interannual variations. If the bias is removed by a simple method based on the variation of convection over the entire tropical belt, the OLR variations over the Indian region become consistent with the rainfall variations.
Abstract
An investigation is presented of the daily variation of the maximum cloud zone (MCZ) and the 7W mb trough in the Northern Hemisphere over the Indian longitudes 70–90°E during April–October for 1973–77. It is found that during June–September there are two favorable locations for a MCZ over these longitudes–on a majority of days the MCZ is present in the monsoon zone north of 15°N, and often a secondary MCZ occurs in the equatorial region (0–10°N). The monsoon MCZ gets established by northward movement of the MCZ occurring over the equatorial Indian ocean in April and May. The secondary MCZ appears intermittently, and is characterized by long spells of persistence only when the monsoon MCZ is absent. In each of the seasons studied, the MCZ temporarily disappeared from the mean summer monsoon location (15–28°N) about four weeks after it was established near the beginning of July. It is reestablished by the northward movement of the secondary MCZ, which becomes active during the absence of the monsoon MCZ, in a manner strikingly similar to that observed in the spring to summer transition. A break in monsoon conditions prevails just prior to the temporary disappearance of the monsoon MCZ. Thus we conclude that the monsoon MCZ cannot survive for longer than a month without reestablishment by the secondary MCZ. Possible underlying mechanisms are also discussed.
Abstract
An investigation is presented of the daily variation of the maximum cloud zone (MCZ) and the 7W mb trough in the Northern Hemisphere over the Indian longitudes 70–90°E during April–October for 1973–77. It is found that during June–September there are two favorable locations for a MCZ over these longitudes–on a majority of days the MCZ is present in the monsoon zone north of 15°N, and often a secondary MCZ occurs in the equatorial region (0–10°N). The monsoon MCZ gets established by northward movement of the MCZ occurring over the equatorial Indian ocean in April and May. The secondary MCZ appears intermittently, and is characterized by long spells of persistence only when the monsoon MCZ is absent. In each of the seasons studied, the MCZ temporarily disappeared from the mean summer monsoon location (15–28°N) about four weeks after it was established near the beginning of July. It is reestablished by the northward movement of the secondary MCZ, which becomes active during the absence of the monsoon MCZ, in a manner strikingly similar to that observed in the spring to summer transition. A break in monsoon conditions prevails just prior to the temporary disappearance of the monsoon MCZ. Thus we conclude that the monsoon MCZ cannot survive for longer than a month without reestablishment by the secondary MCZ. Possible underlying mechanisms are also discussed.
Abstract
In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm.
The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a “nature run” were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.
Abstract
In this paper the performance of a multimodel ensemble forecast analysis that shows superior forecast skills is illustrated and compared to all individual models used. The model comparisons include global weather, hurricane track and intensity forecasts, and seasonal climate simulations. The performance improvements are completely attributed to the collective information of all models used in the statistical algorithm.
The proposed concept is first illustrated for a low-order spectral model from which the multimodels and a “nature run” were constructed. Two hundred time units are divided into a training period (70 time units) and a forecast period (130 time units). The multimodel forecasts and the observed fields (the nature run) during the training period are subjected to a simple linear multiple regression to derive the statistical weights for the member models. The multimodel forecasts, generated for the next 130 forecast units, outperform all the individual models. This procedure was deployed for the multimodel forecasts of global weather, multiseasonal climate simulations, and hurricane track and intensity forecasts. For each type an improvement of the multimodel analysis is demonstrated and compared to the performance of the individual models. Seasonal and multiseasonal simulations demonstrate a major success of this approach for the atmospheric general circulation models where the sea surface temperatures and the sea ice are prescribed. In many instances, a major improvement in skill over the best models is noted.
