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- Author or Editor: M. Rajeevan x
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Abstract
Based on the data from Earth Radiation Budget Experiment (ERBE), many investigators have concluded that the net cloud radiative forcing at the top of the atmosphere is small in the deep convective region of the Tropics. This conclusion has been shown to be invalid for the Asian monsoon region during the period June–September. The ERBE data have been used to show that in the Asian monsoon region the net cloud radiative forcing at the top of the atmosphere is negative and its magnitude exceeds 30 W m−2 in 25% of the grids in this region. The large negative net cloud radiative forcing in the Asian monsoon region during June–September has been shown to be on account of the presence of large amount of high clouds and the large optical depth of these clouds. This combination of high cloud amount and high optical depth occurs in the Asian monsoon region only. In the other deep convective regions of the Tropics, high clouds with large optical depths are present, but they do not cover a large area.
Abstract
Based on the data from Earth Radiation Budget Experiment (ERBE), many investigators have concluded that the net cloud radiative forcing at the top of the atmosphere is small in the deep convective region of the Tropics. This conclusion has been shown to be invalid for the Asian monsoon region during the period June–September. The ERBE data have been used to show that in the Asian monsoon region the net cloud radiative forcing at the top of the atmosphere is negative and its magnitude exceeds 30 W m−2 in 25% of the grids in this region. The large negative net cloud radiative forcing in the Asian monsoon region during June–September has been shown to be on account of the presence of large amount of high clouds and the large optical depth of these clouds. This combination of high cloud amount and high optical depth occurs in the Asian monsoon region only. In the other deep convective regions of the Tropics, high clouds with large optical depths are present, but they do not cover a large area.
Abstract
Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951–2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future.
Abstract
Daily rainfall data obtained from 1025 rain gauges spread across the country over 51 years (1951–2001) are subjected to correlation analysis to identify homogeneous rainfall zones over India. In contrast to earlier studies, which were based on seasonal/annual rainfall, the present study identifies homogeneous rainfall regions with the help of seasonal [southwest monsoon (SWM) and northeast monsoon (NEM)] and annual rainfall. India is divided into 26 (20) homogeneous rainfall zones using annual and SWM (NEM) rainfall. The delineated homogeneous regions are compared and contrasted with those defined by earlier studies, employing a variety of schemes. The interseries correlations of rainfall within each zone are found to be better when the zones are identified by the present study than by other studies. The tests that are performed to evaluate coherency of zones reveal that the zones are homogeneous not only at different temporal scales (interannual and intraseasonal) but also in terms of rain amount, rain frequency, and rain type. Although the delineation of coherent zones is done using interannual/seasonal rainfall data, these zones exhibit coherency in rainfall variations at intraseasonal scale. Nevertheless, the degree of homogeneity is different for rainfall variations occurring at different temporal scales. Further, the zones show better coherency in excess rainfall years than in deficit rainfall years. Longer-term utility of the delineated zones is studied by examining delineated zones and their coherency in the first and second half of the total data period. Although the regions remain the same in both the periods, the coherency is reduced in the second half, suggesting that the homogeneity of regions may vary in the future.
Abstract
Southwest monsoon rainfall over India during July 2002 was the lowest since instrumental records of rainfall data have been available. The present study is an attempt to examine some of the probable causes for this unprecedented low rainfall during July. It is found that the strength of the intertropical convergence zone (ITCZ) over the north Indian Ocean and the pre-mei-yu front over the northwest Pacific Ocean during the month of May has significant positive correlation with the July rainfall over India, and it can be used as a precursor for predicting July rainfall over India. The activity of the ITCZ over the north Indian Ocean and pre-mei-yu front in May is an indicator of the strength of first monsoon intraseasonal oscillation (ISO). It was also found that the ITCZ over the north Indian Ocean and pre-mei-yu front were not active during May 2002, probably because of the weak ISO activity during the first half of the monsoon season.
Abstract
Southwest monsoon rainfall over India during July 2002 was the lowest since instrumental records of rainfall data have been available. The present study is an attempt to examine some of the probable causes for this unprecedented low rainfall during July. It is found that the strength of the intertropical convergence zone (ITCZ) over the north Indian Ocean and the pre-mei-yu front over the northwest Pacific Ocean during the month of May has significant positive correlation with the July rainfall over India, and it can be used as a precursor for predicting July rainfall over India. The activity of the ITCZ over the north Indian Ocean and pre-mei-yu front in May is an indicator of the strength of first monsoon intraseasonal oscillation (ISO). It was also found that the ITCZ over the north Indian Ocean and pre-mei-yu front were not active during May 2002, probably because of the weak ISO activity during the first half of the monsoon season.
Abstract
The availability of daily observed rainfall estimates at a resolution of 0.5° × 0.5° latitude–longitude from a collection of over 2100 rain gauge sites over India provided the possibility for carrying out 5-day precipitation forecasts using a downscaling and a multimodel superensemble methodology. This paper addresses the forecast performances and regional distribution of predicted monsoon rains from the downscaling and from the addition of a multimodel superensemble. The extent of rainfall prediction improvements that arise above those of a current suite of operational models are discussed. The design of two algorithms one for downscaling and the other for the construction of multimodel superensembles are both based on the principle of least squares minimization of errors. That combination is shown to provide a robust forecast product through day 5 of the forecast for regional rains over the Indian monsoon region. The equitable threat scores from the downscaled superensemble over India well exceed those noted from the conventional superensemble and member models at current operational large-scale resolution.
Abstract
The availability of daily observed rainfall estimates at a resolution of 0.5° × 0.5° latitude–longitude from a collection of over 2100 rain gauge sites over India provided the possibility for carrying out 5-day precipitation forecasts using a downscaling and a multimodel superensemble methodology. This paper addresses the forecast performances and regional distribution of predicted monsoon rains from the downscaling and from the addition of a multimodel superensemble. The extent of rainfall prediction improvements that arise above those of a current suite of operational models are discussed. The design of two algorithms one for downscaling and the other for the construction of multimodel superensembles are both based on the principle of least squares minimization of errors. That combination is shown to provide a robust forecast product through day 5 of the forecast for regional rains over the Indian monsoon region. The equitable threat scores from the downscaled superensemble over India well exceed those noted from the conventional superensemble and member models at current operational large-scale resolution.
Abstract
The strong cross-equatorial flow in the lower troposphere, widely known as the monsoon low-level jet (MLLJ), plays an important role in the Indian summer monsoon (ISM) rainfall during June–September. Using high-resolution GPS radiosonde observations over Gadanki (13.5°N, 79.2°E), some new aspects of MLLJ have been reported. In the present study it is found that, on average, the MLLJ exists at 710 hPa over southeastern peninsular India, rather than at 850 hPa as reported by earlier studies. It is observed that the ECMWF Re-Analysis (ERA)-Interim data provide better results on the spatial, temporal, and vertical variation of MLLJ. Further, the characteristics of the MLLJ during the active and break spells of ISM are also investigated; higher MLLJ core height and intensity are found during active phases of the Indian monsoon. This study emphasizes the use of high-resolution measurements for studying monsoon dynamics in detail.
Abstract
The strong cross-equatorial flow in the lower troposphere, widely known as the monsoon low-level jet (MLLJ), plays an important role in the Indian summer monsoon (ISM) rainfall during June–September. Using high-resolution GPS radiosonde observations over Gadanki (13.5°N, 79.2°E), some new aspects of MLLJ have been reported. In the present study it is found that, on average, the MLLJ exists at 710 hPa over southeastern peninsular India, rather than at 850 hPa as reported by earlier studies. It is observed that the ECMWF Re-Analysis (ERA)-Interim data provide better results on the spatial, temporal, and vertical variation of MLLJ. Further, the characteristics of the MLLJ during the active and break spells of ISM are also investigated; higher MLLJ core height and intensity are found during active phases of the Indian monsoon. This study emphasizes the use of high-resolution measurements for studying monsoon dynamics in detail.
Abstract
Global positioning system (GPS) radio occultation (RO) data available during 2001–10 have been used to examine the variations in the refractivity during the onset of Indian summer monsoon (ISM) over the east Arabian Sea (5°–15°N, 65°–75°E). An enhancement of 5–10 N-units in the refractivity is observed around 4.8 km (~600 hPa) a few days (9.23 ± 3.6 days) before onset of the monsoon over Kerala, India. This is attributed to moisture buildup over the Arabian Sea during the monsoon onset phase. A sudden increase (1.5–2 K) in mean upper-tropospheric temperature at the time of onset and during the active phase of the monsoon is attributed to convective activity and the release of latent heat. On the day of monsoon onset over Kerala, an appreciable dip in the refractivity is observed that persisted for 1–3 days followed by an enhancement in refractivity with the active phase of the monsoon. An arbitrary value of 128 N-units difference between 4.8 km (~600 hPa) and 16 km (~100 hPa) coupled with a dip in refractivity on the day of monsoon arrival might give an indication of clear transition of atmospheric conditions and the detection of monsoon onset. Further, a good relation is also found between the activity of monsoon and variability in the refractivity.
Abstract
Global positioning system (GPS) radio occultation (RO) data available during 2001–10 have been used to examine the variations in the refractivity during the onset of Indian summer monsoon (ISM) over the east Arabian Sea (5°–15°N, 65°–75°E). An enhancement of 5–10 N-units in the refractivity is observed around 4.8 km (~600 hPa) a few days (9.23 ± 3.6 days) before onset of the monsoon over Kerala, India. This is attributed to moisture buildup over the Arabian Sea during the monsoon onset phase. A sudden increase (1.5–2 K) in mean upper-tropospheric temperature at the time of onset and during the active phase of the monsoon is attributed to convective activity and the release of latent heat. On the day of monsoon onset over Kerala, an appreciable dip in the refractivity is observed that persisted for 1–3 days followed by an enhancement in refractivity with the active phase of the monsoon. An arbitrary value of 128 N-units difference between 4.8 km (~600 hPa) and 16 km (~100 hPa) coupled with a dip in refractivity on the day of monsoon arrival might give an indication of clear transition of atmospheric conditions and the detection of monsoon onset. Further, a good relation is also found between the activity of monsoon and variability in the refractivity.
Abstract
A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.
Abstract
A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.
Abstract
This study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK. Furthermore, the evolution of various model-predicted large-scale and local meteorological parameters corresponding to the predicted MOK date is in good agreement with that of the observation, suggesting the robustness of the devised criterion and the suitability of CFSv2 model for MOK prediction. However, it should be noted that the criterion proposed in the present study can be used only in the dynamical prediction framework, as it necessitates input data on the future evolution of rainfall and low-level wind.
Abstract
This study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK. Furthermore, the evolution of various model-predicted large-scale and local meteorological parameters corresponding to the predicted MOK date is in good agreement with that of the observation, suggesting the robustness of the devised criterion and the suitability of CFSv2 model for MOK prediction. However, it should be noted that the criterion proposed in the present study can be used only in the dynamical prediction framework, as it necessitates input data on the future evolution of rainfall and low-level wind.
Abstract
This study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.
Abstract
This study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.
Abstract
Rain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.
Abstract
Rain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.
