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- Author or Editor: C. M. Kishtawal x
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Abstract
A pronounced continental-scale diurnal mode of the Asian summer monsoon is mapped using data from recent satellites Meteosat-5 and TRMM. These datasets were available at high temporal resolutions. A result that stands out is the diurnal divergent circulation that in the afternoon hours has an ascending lobe over north-central India and has a descending lobe that reaches out radially toward central China, the southern part of China, the equatorial Indian Ocean, and the western Arabian Sea. The reverse circulation is clearly seen during the early morning hours. This diurnal pulsation of continental-scale divergent circulation appears to be an integral part of the monsoon. Another finding relates to the diurnal slowing down and speeding up of the Tibetan high circulations, especially in the southern flanks where the tropical easterly jet resides and exhibits a pulsation of intensity. The amplitude of pulsation was found to reach up to 7 m s−1. Thus this continental-scale change appears to be a pronounced feature. The phase and amplitude of various satellite datasets derived from the 90-min datasets are also displayed to confirm this major mode, that is, the diurnal oscillation of monsoon.
Abstract
A pronounced continental-scale diurnal mode of the Asian summer monsoon is mapped using data from recent satellites Meteosat-5 and TRMM. These datasets were available at high temporal resolutions. A result that stands out is the diurnal divergent circulation that in the afternoon hours has an ascending lobe over north-central India and has a descending lobe that reaches out radially toward central China, the southern part of China, the equatorial Indian Ocean, and the western Arabian Sea. The reverse circulation is clearly seen during the early morning hours. This diurnal pulsation of continental-scale divergent circulation appears to be an integral part of the monsoon. Another finding relates to the diurnal slowing down and speeding up of the Tibetan high circulations, especially in the southern flanks where the tropical easterly jet resides and exhibits a pulsation of intensity. The amplitude of pulsation was found to reach up to 7 m s−1. Thus this continental-scale change appears to be a pronounced feature. The phase and amplitude of various satellite datasets derived from the 90-min datasets are also displayed to confirm this major mode, that is, the diurnal oscillation of monsoon.
Abstract
A surface-based rainfall monitoring network reveals that summer rainfall over Taiwan exhibits a prominent diurnal variation. In this study, an attempt has been made to detect the diurnal variability of Taiwan rainfall using observations from the Tropical Rainfall Measuring Mission (TRMM) satellite. The results show that the diurnal patterns of Taiwan rainfall can be detected with TRMM Microwave Imager (TMI) observations using a satellite observation period of 36 or more days, and detected signals match reasonably with those using continuous surface observations. However, sometimes, because of the unfavorable combination of satellite sampling and the occurrence of some transient regimes in local rainfall, there is a possibility of misinterpreting the diurnal cycle. The TRMM precipitation radar sensor also reveals a diurnal cycle of convective and stratiform rainfall. The convective activity increases during the late afternoon over Taiwan, which may be the effect of convection forced by localized mass convergence caused by the sea breeze. It is of interest that TMI data indicate a significant increase of rainfall over orographic regions during the same time.
Abstract
A surface-based rainfall monitoring network reveals that summer rainfall over Taiwan exhibits a prominent diurnal variation. In this study, an attempt has been made to detect the diurnal variability of Taiwan rainfall using observations from the Tropical Rainfall Measuring Mission (TRMM) satellite. The results show that the diurnal patterns of Taiwan rainfall can be detected with TRMM Microwave Imager (TMI) observations using a satellite observation period of 36 or more days, and detected signals match reasonably with those using continuous surface observations. However, sometimes, because of the unfavorable combination of satellite sampling and the occurrence of some transient regimes in local rainfall, there is a possibility of misinterpreting the diurnal cycle. The TRMM precipitation radar sensor also reveals a diurnal cycle of convective and stratiform rainfall. The convective activity increases during the late afternoon over Taiwan, which may be the effect of convection forced by localized mass convergence caused by the sea breeze. It is of interest that TMI data indicate a significant increase of rainfall over orographic regions during the same time.
Abstract
Monthly mean surface latent heat fluxes (LHFs) over the global oceans are estimated using bulk formula. LHFs are computed using wind speed (U) from the Special Sensor Microwave Imager (SSM/I), sea surface temperature (SST) from the Advanced Very High Resolution Radiometer (AVHRR), and near-surface specific humidity. Near-surface specific humidity (Qa ) is estimated from SSM/I-observed precipitable water (W) and AVHRR-observed SST using a genetic algorithm (GA) approach. The GA-retrieved monthly mean Qa has an accuracy of 0.80 ± 0.32 g kg−1 as compared with surface marine observations based on the Comprehensive Ocean–Atmosphere Data Set (COADS). The GA approach improves upon the surface specific humidity retrieval based on regression, the EOF approach, and is comparable to the artificial neural network technique.
The satellite-derived LHFs are compared with globally distributed surface marine observations to monthly averages of 1° × 1° latitude–longitude bins, during 1988–93. When GA-retrieved Qa is used in the computation of satellite-derived latent heat fluxes (LHFGA) the global mean rmse, bias, and correlation are 22 ± 8 W m−2, 5 W m−2, and 0.85, respectively, for monthly mean latent heat fluxes. The rmses in LHF are larger when Qa is retrieved using regression and EOF approaches.
Abstract
Monthly mean surface latent heat fluxes (LHFs) over the global oceans are estimated using bulk formula. LHFs are computed using wind speed (U) from the Special Sensor Microwave Imager (SSM/I), sea surface temperature (SST) from the Advanced Very High Resolution Radiometer (AVHRR), and near-surface specific humidity. Near-surface specific humidity (Qa ) is estimated from SSM/I-observed precipitable water (W) and AVHRR-observed SST using a genetic algorithm (GA) approach. The GA-retrieved monthly mean Qa has an accuracy of 0.80 ± 0.32 g kg−1 as compared with surface marine observations based on the Comprehensive Ocean–Atmosphere Data Set (COADS). The GA approach improves upon the surface specific humidity retrieval based on regression, the EOF approach, and is comparable to the artificial neural network technique.
The satellite-derived LHFs are compared with globally distributed surface marine observations to monthly averages of 1° × 1° latitude–longitude bins, during 1988–93. When GA-retrieved Qa is used in the computation of satellite-derived latent heat fluxes (LHFGA) the global mean rmse, bias, and correlation are 22 ± 8 W m−2, 5 W m−2, and 0.85, respectively, for monthly mean latent heat fluxes. The rmses in LHF are larger when Qa is retrieved using regression and EOF approaches.
Abstract
With an aim to exploit current satellite observations for determining vertical wind profiles, the authors have carried out a complex empirical orthogonal function (CEOF) analysis of a large number of radiosonde observations of wind fields over the Indian Ocean. This analysis suggests that the first two CEOFs explain more than 80% of the total variance. While the first principal component is highly correlated with the upper-level winds at 250 mb (r = 0.95), the second one is well correlated with the 800-mb winds (r = 0.82). This analysis leads to a retrieval algorithm that ensures the retrieval of vertical profiles of winds, using satellite-tracked cloud motion vector winds. Assuming that accurate measurements of wind are available at the above-mentioned levels, the rms error of retrieval for each component of wind is estimated to range between 2 and 6.5 m s−1 at different levels, which is much lower than the natural variance of wind at these levels. To construct a better visualization of retrieval, the authors have provided retrieved and true wind profiles side by side for three typical synoptic conditions.
Abstract
With an aim to exploit current satellite observations for determining vertical wind profiles, the authors have carried out a complex empirical orthogonal function (CEOF) analysis of a large number of radiosonde observations of wind fields over the Indian Ocean. This analysis suggests that the first two CEOFs explain more than 80% of the total variance. While the first principal component is highly correlated with the upper-level winds at 250 mb (r = 0.95), the second one is well correlated with the 800-mb winds (r = 0.82). This analysis leads to a retrieval algorithm that ensures the retrieval of vertical profiles of winds, using satellite-tracked cloud motion vector winds. Assuming that accurate measurements of wind are available at the above-mentioned levels, the rms error of retrieval for each component of wind is estimated to range between 2 and 6.5 m s−1 at different levels, which is much lower than the natural variance of wind at these levels. To construct a better visualization of retrieval, the authors have provided retrieved and true wind profiles side by side for three typical synoptic conditions.
Abstract
The water vapor winds from the operational geostationary Indian National Satellite (INSAT) Kalpana-1 have recently become operational at the Space Applications Centre (SAC). A series of experimental forecasts are attempted here to evaluate the impact of water vapor winds derived from Kalpana-1 for the track and intensity prediction of two Bay of Bengal tropical cyclones (TCs), Sidr and Nargis, using the Weather Research and Forecasting (WRF) modeling system. The assimilation of water vapor winds has made some impact in the initial position errors as well as track forecasts when compared with the corresponding control experiments for both TCs. However, no statistically significant improvement is noticed in the simulations of TC intensities [i.e., minimum sea level pressure (MSLP) and maximum surface winds forecasts when satellite winds are used for assimilation]. Moreover, the performance of Kalpana-1 winds is evaluated by repeating the same sets of experiments using Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and compared with observed data. The simulation of initial position errors, and track and intensity forecasts using the assimilation of water vapor winds from both satellites are comparable. Though, these results are preliminary with respect to the Kalpana-1 winds, the present study can provide some insight to the WRF model users over the Indian Ocean region.
Abstract
The water vapor winds from the operational geostationary Indian National Satellite (INSAT) Kalpana-1 have recently become operational at the Space Applications Centre (SAC). A series of experimental forecasts are attempted here to evaluate the impact of water vapor winds derived from Kalpana-1 for the track and intensity prediction of two Bay of Bengal tropical cyclones (TCs), Sidr and Nargis, using the Weather Research and Forecasting (WRF) modeling system. The assimilation of water vapor winds has made some impact in the initial position errors as well as track forecasts when compared with the corresponding control experiments for both TCs. However, no statistically significant improvement is noticed in the simulations of TC intensities [i.e., minimum sea level pressure (MSLP) and maximum surface winds forecasts when satellite winds are used for assimilation]. Moreover, the performance of Kalpana-1 winds is evaluated by repeating the same sets of experiments using Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and compared with observed data. The simulation of initial position errors, and track and intensity forecasts using the assimilation of water vapor winds from both satellites are comparable. Though, these results are preliminary with respect to the Kalpana-1 winds, the present study can provide some insight to the WRF model users over the Indian Ocean region.
Abstract
A new regional relationship is derived for wet tropospheric range correction h for radar altimeter and precipitable water (W) over the Indian Ocean using ship observations for the period 1982–91. The W varied over the range of 20–80 mm, thus providing total variability expected over tropical oceans. A fifth-order polynomial between h and W gave an rms error of 2.3 mm when compared with h computed using direct relation. Model results have also been compared with an earlier relation over the Indian Ocean and with the global relation, and it has been found that the present model yields the lowest rms error in h values over the Indian Ocean. Comparisons with earlier models show a factor of 2 improvement in the accuracy of the correction over this region.
The W values have also been derived using the NOAA High-Resolution Infrared Sounder data for the years 1980, 1981, and 1984. These monthly mean W (3-yr averaged) values have been used to study the variabilities in W. The Indian Ocean depicts large variabilities of W even on a monthly scale. The monthly mean map of h has also been given to get a rough idea about the values of corrections required over this region.
Here h obtained using W from a sample pass of the Seasat Scanning Multichannel Microwave Radiometer (SMMR) and h provided by the Fleet Numeric Oceanographic Center (FNOC) have been compared to point out deviations of FNOC model-derived h values from h obtained from SMMR-derived W. The Special Sensor Microwave/Imager-derived W values for a sample pass over the Arabian Sea and the Bay of Bengal have been used to estimate h values from our derived relation between h and W. The Bay of Bengal exhibits high h values compared to those over the Arabian Sea. This study demonstrates the usefulness of the proposed regional relation between h and W for application to satellite-borne altimeter data, such as the ERS-1 and Topex/Poseidon missions, where an onboard microwave radiometer provides instantaneous W measurements for studying various oceanographic phenomena.
Abstract
A new regional relationship is derived for wet tropospheric range correction h for radar altimeter and precipitable water (W) over the Indian Ocean using ship observations for the period 1982–91. The W varied over the range of 20–80 mm, thus providing total variability expected over tropical oceans. A fifth-order polynomial between h and W gave an rms error of 2.3 mm when compared with h computed using direct relation. Model results have also been compared with an earlier relation over the Indian Ocean and with the global relation, and it has been found that the present model yields the lowest rms error in h values over the Indian Ocean. Comparisons with earlier models show a factor of 2 improvement in the accuracy of the correction over this region.
The W values have also been derived using the NOAA High-Resolution Infrared Sounder data for the years 1980, 1981, and 1984. These monthly mean W (3-yr averaged) values have been used to study the variabilities in W. The Indian Ocean depicts large variabilities of W even on a monthly scale. The monthly mean map of h has also been given to get a rough idea about the values of corrections required over this region.
Here h obtained using W from a sample pass of the Seasat Scanning Multichannel Microwave Radiometer (SMMR) and h provided by the Fleet Numeric Oceanographic Center (FNOC) have been compared to point out deviations of FNOC model-derived h values from h obtained from SMMR-derived W. The Special Sensor Microwave/Imager-derived W values for a sample pass over the Arabian Sea and the Bay of Bengal have been used to estimate h values from our derived relation between h and W. The Bay of Bengal exhibits high h values compared to those over the Arabian Sea. This study demonstrates the usefulness of the proposed regional relation between h and W for application to satellite-borne altimeter data, such as the ERS-1 and Topex/Poseidon missions, where an onboard microwave radiometer provides instantaneous W measurements for studying various oceanographic phenomena.
Abstract
In this study the simulation of a severe rainfall episode over Mumbai on 26 July 2005 has been attempted with two different mesoscale models. The numerical models used in this study are the Brazilian Regional Atmospheric Modeling System (BRAMS) developed originally by Colorado State University and the Advanced Research Weather Research Forecast (WRF-ARW) Model, version 2.0.1, developed at the National Center for Atmospheric Research. The simulations carried out in this study use the Grell–Devenyi Ensemble cumulus parameterization scheme. Apart from using climatological sea surface temperature (SST) for the control simulations, the impact of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST on the simulation of rainfall is evaluated using these two models. The performances of the models are compared by examining the predicted parameters like upper- and lower-level circulations, moisture, temperature, and rainfall. The strength of convective instability is also derived by calculating the convective available potential energy. The intensity of maximum rainfall around Mumbai is significantly improved with TMI SST as the surface boundary condition in both the models. The large-scale circulation features, moisture, and temperature are compared with those in the National Centers for Environmental Prediction analyses. The rainfall prediction is assessed quantitatively by comparing the simulated rainfall with the rainfall from TRMM products and the observed station values reported in Indian Daily Weather Reports from the India Meteorological Department.
Abstract
In this study the simulation of a severe rainfall episode over Mumbai on 26 July 2005 has been attempted with two different mesoscale models. The numerical models used in this study are the Brazilian Regional Atmospheric Modeling System (BRAMS) developed originally by Colorado State University and the Advanced Research Weather Research Forecast (WRF-ARW) Model, version 2.0.1, developed at the National Center for Atmospheric Research. The simulations carried out in this study use the Grell–Devenyi Ensemble cumulus parameterization scheme. Apart from using climatological sea surface temperature (SST) for the control simulations, the impact of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST on the simulation of rainfall is evaluated using these two models. The performances of the models are compared by examining the predicted parameters like upper- and lower-level circulations, moisture, temperature, and rainfall. The strength of convective instability is also derived by calculating the convective available potential energy. The intensity of maximum rainfall around Mumbai is significantly improved with TMI SST as the surface boundary condition in both the models. The large-scale circulation features, moisture, and temperature are compared with those in the National Centers for Environmental Prediction analyses. The rainfall prediction is assessed quantitatively by comparing the simulated rainfall with the rainfall from TRMM products and the observed station values reported in Indian Daily Weather Reports from the India Meteorological Department.
Abstract
A new approach is introduced for determining surface latent heat flux (LHF) and sensible heat flux (SHF) over the global oceans exclusively from satellite observations. Measurements of wind speed (U), sea surface temperature (SST), near surface specific humidity (Qa ), and air–sea temperature difference (ΔT = SST − Ta ) are required for computing these fluxes by bulk formulas. To compute the heat fluxes exclusively from satellite data, U is obtained from Special Sensor Microwave Imager (SSM/I), SST is obtained from Advanced Very High Resolution Radiometer (AVHRR), empirical algorithm proposed earlier is used to compute ΔT, and a new one is developed to estimate Qa . The developed empirical equation for Qa estimations is an extension of the authors’ previous method. Compared to the Comprehensive Ocean–Atmosphere Data Set (COADS), the Qa retrieved by the previous approach had a negative bias of the order of more than 2 g kg−1 over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2 g kg−1 over the Arabian Sea and the Bay of Bengal during summertime. The new empirical equation takes into account these seasonal biases over the Gulf Stream, Kuroshio, and the Arabian Sea. Compared to COADS observations, the Qa retrieved from the developed empirical equation has global mean root mean square error (rmse), bias, and correlation of the order of 0.55, −0.007, and 0.98 g kg−1, respectively.
Compared to COADS, the satellite-derived monthly mean LHF has global mean rmse, bias, and correlation of the order of 20, 6, and 0.97 W m−2, respectively. Likewise, satellite-derived monthly mean SHF has global mean rmse, bias, and correlations of the order of 6, 0.4, and 0.98 W m−2, respectively. The monthly fields show that the spatial patterns and seasonal variability of satellite-derived latent and sensible heat fluxes are generally good in agreement with those of the COADS and earlier satellite-derived fluxes.
Sixteen-year (January 1988–December 2003) datasets of surface heat fluxes and basic input parameters over the global oceans have been constructed using SSM/I and AVHRR data. This dataset has a spatial resolution of 1° × 1° latitude–longitude and temporal resolution of one month. This unique dataset is constructed exclusively from satellite observations, and it can be obtained from the Meteorology and Oceanography Group Space Applications Centre.
Abstract
A new approach is introduced for determining surface latent heat flux (LHF) and sensible heat flux (SHF) over the global oceans exclusively from satellite observations. Measurements of wind speed (U), sea surface temperature (SST), near surface specific humidity (Qa ), and air–sea temperature difference (ΔT = SST − Ta ) are required for computing these fluxes by bulk formulas. To compute the heat fluxes exclusively from satellite data, U is obtained from Special Sensor Microwave Imager (SSM/I), SST is obtained from Advanced Very High Resolution Radiometer (AVHRR), empirical algorithm proposed earlier is used to compute ΔT, and a new one is developed to estimate Qa . The developed empirical equation for Qa estimations is an extension of the authors’ previous method. Compared to the Comprehensive Ocean–Atmosphere Data Set (COADS), the Qa retrieved by the previous approach had a negative bias of the order of more than 2 g kg−1 over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2 g kg−1 over the Arabian Sea and the Bay of Bengal during summertime. The new empirical equation takes into account these seasonal biases over the Gulf Stream, Kuroshio, and the Arabian Sea. Compared to COADS observations, the Qa retrieved from the developed empirical equation has global mean root mean square error (rmse), bias, and correlation of the order of 0.55, −0.007, and 0.98 g kg−1, respectively.
Compared to COADS, the satellite-derived monthly mean LHF has global mean rmse, bias, and correlation of the order of 20, 6, and 0.97 W m−2, respectively. Likewise, satellite-derived monthly mean SHF has global mean rmse, bias, and correlations of the order of 6, 0.4, and 0.98 W m−2, respectively. The monthly fields show that the spatial patterns and seasonal variability of satellite-derived latent and sensible heat fluxes are generally good in agreement with those of the COADS and earlier satellite-derived fluxes.
Sixteen-year (January 1988–December 2003) datasets of surface heat fluxes and basic input parameters over the global oceans have been constructed using SSM/I and AVHRR data. This dataset has a spatial resolution of 1° × 1° latitude–longitude and temporal resolution of one month. This unique dataset is constructed exclusively from satellite observations, and it can be obtained from the Meteorology and Oceanography Group Space Applications Centre.
Abstract
The remotely sensed upper-tropospheric water vapor wind information has been of increasing interest for operational meteorology. A new tracer selection based on a local image anomaly and tracking procedure, itself based on Nash–Sutcliffe model efficiency, is demonstrated here for the estimation of upper-tropospheric water vapor winds both for cloudy and cloud-free regions from water vapor images. The pressure height of the selected water vapor tracers is calculated empirically using a height assignment technique based on a genetic algorithm. The new technique shows encouraging results when compared with Meteosat-5 water vapor winds over the Indian Ocean region. The water vapor winds produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) from Meteosat-5 and the present algorithm are compared with collocated radiosonde observations according to Coordination Group for Meteorological Satellites guidelines. The proposed algorithm shows better accuracy in terms of mean vector difference, rms vector difference, standard deviation, speed bias, number of collocations, and mean speed and mean direction differences. Also it is found that the sensitivity of the spatial consistency check in the quality indicator is not so significant for the improvement of statistics.
Abstract
The remotely sensed upper-tropospheric water vapor wind information has been of increasing interest for operational meteorology. A new tracer selection based on a local image anomaly and tracking procedure, itself based on Nash–Sutcliffe model efficiency, is demonstrated here for the estimation of upper-tropospheric water vapor winds both for cloudy and cloud-free regions from water vapor images. The pressure height of the selected water vapor tracers is calculated empirically using a height assignment technique based on a genetic algorithm. The new technique shows encouraging results when compared with Meteosat-5 water vapor winds over the Indian Ocean region. The water vapor winds produced by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) from Meteosat-5 and the present algorithm are compared with collocated radiosonde observations according to Coordination Group for Meteorological Satellites guidelines. The proposed algorithm shows better accuracy in terms of mean vector difference, rms vector difference, standard deviation, speed bias, number of collocations, and mean speed and mean direction differences. Also it is found that the sensitivity of the spatial consistency check in the quality indicator is not so significant for the improvement of statistics.
Abstract
The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.
Abstract
The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.
