Sarat C. Kar

Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3

The regional climate model (RegCM3) from the Abdus Salam International Centre for Theoretical Physics has been used to simulate the Indian summer monsoon for three different monsoon seasons such as deficit (1987), excess (1988) and normal (1989). Sensitivity to various cumulus parameterization and closure schemes of RegCM3 driven by the National Centre for Medium Range Weather Forecasting global spectral model products has been tested. The model integration of the nested RegCM3 is conducted using 90 and 30-km horizontal resolutions for outer and inner domains, respectively. The India Meteorological Department gridded rainfall (1° × 1°) and National Centre for Environment Prediction (NCEP)–Department of Energy (DOE) reanalysis-2 of 2.5° × 2.5° horizontal resolution data has been used for verification. The RegCM3 forced by NCEP–DOE reanalysis-2 data simulates monsoon seasons of 1987 and 1988 reasonably well, but the monsoon season of 1989 is not represented well in the model simulations. The RegCM3 runs driven by the global model are able to bring out seasonal mean rainfall and circulations well with the use of the Grell and Anthes–Kuo cumulus scheme at 90-km resolution. While the rainfall intensity and distribution is brought out well with the Anthes–Kuo scheme, upper air circulation features are brought out better by the Grell scheme. The simulated rainfall distribution is better with RegCM3 using the MIT-Emanuel cumulus scheme for 30-km resolution. Several statistical analyses, such as correlation coefficient, root mean square error, equitable threat score, confirm that the performance of MIT-Emanuel scheme at 30-km resolution is better in simulating all-India summer monsoon rainfall. The RegCM3 simulated rainfall amount is more and closer to observations than that from the global model. The RegCM3 has corrected its driven GCM in terms of rainfall distribution and magnitude over some parts of India during extreme years. This study brings out several weaknesses of the RegCM model which are documented in this paper.

Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.

Impact of Land Surface Processes on the South Asian Monsoon Simulations Using WRF Modeling System

The Weather Research and Forecasting model has been used to examine the role of land surface processes on Indian summer monsoon simulations. Isolated experiments have been carried out with physical parameterization schemes (land surface and planetary boundary layer) and data assimilation to examine their relative roles in the representation of regional hydroclimate in model simulations. The impact of vegetation green fraction on the model simulations has been extensively studied by replacing the default United States Geological Survey (USGS) vegetation cover data with that of Indian Space Research Organisation (ISRO) data. Results indicate that differences in the treatment of surface processes in the model lead to large differences in precipitation simulation over the Indian domain. Several hydroclimate parameters from the simulations using ISRO and USGS vegetation green fractions were examined. It is seen that the role of vegetation green fraction in these experiments has been to increase latent heat flux to the atmosphere. Two sets of data assimilation experiments were also carried out for an entire year using the same set of observed data but with different land surface parameterization schemes. It is found that evenwhen using the same observed data, the differences in land surface schemes reduce the impact and contribution of observed data being assimilated into the model. The hydroclimate over the region becomes a function of the land surface scheme. This study highlights the importance of vegetation green fraction and land surface schemes in the context of the regional hydroclimate over South Asia.

Assimilation of IRS-P4 (MSMR) meteorological data in the NCMRWF global data assimilation system

Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer (MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system. The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S.

Impact of SSM/I retrieval data on the systematic bias of analyses and forecasts of the Indian summer monsoon using WRF assimilation system

Assimilation and forecast experiments have been carried out in this study using conventional observations as well as total precipitable water and surface wind data retrieved from the Special Sensor for Microwave Imaginary (SSM/I) sensors. The main objectives of this study were to document the bias in short-range predictions of the Weather Research and Forecasting (WRF) version 3.1 model over the Indian region during the summer monsoon season and the impact of SSM/I data. All the experiments were carried out in the monsoon seasons of 2001 as a part of pilot phase studies for the South Asian Regional Reanalysis (SARR) project. It is seen that the model has strong bias in wind forecasts over the Arabian Sea and the Indian Ocean. A cyclonic bias in the forecasts exists over south-west India. Over the equatorial Indian Ocean, a strong southerly bias towards the Bay of Bengal is noticed. The model has a systematic bias to increase moisture over most parts of the equatorial Indian Ocean. Except over the Gangetic plains, the model exhibits dry bias with reduced moisture over most parts of India in 24 hour forecasts. The impact of assimilation of SSM/I products has been to increase the moisture over the Bay of Bengal, where the model has shown dry bias. The moisture content over the equatorial Indian Ocean (western sector) reduced significantly after assimilation of SSM/I data, where the model has a tendency to enhance moisture. Major rainfall zones during the monsoon season are brought out well in 6 hour forecasts by the model; however, the rainfall amount increased over the Bay of Bengal due to the assimilation of SSM/I data. These features are consistent with the moisture and wind differences between the two assimilation experiments. A quantitative verification of model rainfall in terms of equitable threat scores indicate that the accuracy of rainfall products is higher when SSM/I data are assimilated. It is seen that the general pattern of rainfall tendency in 24 hour forecasts remains the same irrespective of whether the forecast initial conditions are with or without SSM/I data. Examination of a case of monsoon depression showed that assimilation of SSM/I data improved the analysis.

The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over Western Himalaya

Climate prediction over the Western Himalaya is a challenging task due to the highly variable altitude and orientation of orographic barriers. Surface characteristics also play a vital role in climate simulations and need appropriate representation in the models. In this study, two land surface parameterization schemes (LSPS), the Biosphere-Atmosphere Transfer Scheme (BATS) and the Common Land Model (CLM, version 3.5) in the regional climate model (RegCM, version 4) have been tested over the Himalayan region for nine distinct winter seasons in respect of seasonal precipitation (three years each for excess, normal and deficit). Reanalysis II data of the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) have been used as initial and lateral boundary conditions for the RegCM model. In order to provide land surface boundary conditions in the RegCM model, geophysical parameters (10 min resolution) obtained from the United States Geophysical Survey were used. The performance of two LSPS (CLM and BATS) coupled with the RegCM is evaluated against gridded precipitation and surface temperature data sets from the India Meteorological Department (IMD). It is found that the simulated surface temperature and precipitation are better represented in the CLM scheme than in the BATS when compared with observations. Further, several statistical analysis such as bias, root mean square error (RMSE), spatial correlation coefficient (CC) and skill scores like the equitable threat score (ETS) and the probability of detection (POD) are estimated for evaluating RegCM simulations using both LSPS. Results indicate that the RMSE decreases and the CC increases with the use of the CLM compared to BATS. ETS and POD also indicate that the performance of the model is better with the CLM than with the BATS in simulating seasonal scale precipitation. Overall, results suggest that the performance of the RegCM coupled with the CLM scheme improves the model skill in predicting winter precipitation (by 15-25%) and temperature (by 10-20%) over the Western Himalaya.

Seasonal prediction skill of winter temperature over North India

The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December–January–February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982–2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.

Dynamical downscaling approach for wintertime seasonal-scale simulation over the Western Himalayas

The performance of RegCM4 for seasonal-scale simulation of winter circulation and associated precipitation over the Western Himalayas (WH) is examined. The model simulates the circulation features and precipitation in three distinct precipitation years reasonably well. It is found that the RMSE decreases and correlation coefficient increases in the precipitation simulations with the increase of model horizontal resolutions. The ETS and POD for the simulated precipitation also indicate that the performance of model is better at 30 km resolution than at 60 and 90 km resolutions. This improvement comes due to better representation of orography in the high-resolution model in which sharp orography gradient in the domain plays an important role in wintertime precipitation processes. A comparison of model-simulated precipitation with observed precipitation at 17 station locations has been carried out. Overall, the results suggest that 30 km model produced better skill in simulating the precipitation over the WH and this model is a useful tool for further regional downscaling studies.

Evaluation of performance of seasonal precipitation prediction at regional scale over India

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982–2008) as well as independent predictions for the period 2009–2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions as well as India as a whole during all the four seasons.

Sensitivity of the Himalayan orography representation in simulation of winter precipitation using Regional Climate Model (RegCM) nested in a GCM

The role of the Himalayan orography representation in a Regional Climate Model (RegCM4) nested in NCMRWF global spectral model is examined in simulating the winter circulation and associated precipitation over the Northwest India (NWI; 23°–37.5°N and 69°–85°E) region. For this purpose, nine different set of orography representations for nine distinct precipitation years (three years each for wet, normal and dry) have been considered by increasing (decreasing) 5, 10, 15, and 20% from the mean height (CNTRL) of the Himalaya in RegCM4 model. Validation with various observations revealed a good improvement in reproducing the precipitation intensity and distribution with increased model height compared to the results obtained from CNTRL and reduced orography experiments. Further it has been found that, increase in height by 10% (P10) increases seasonal precipitation about 20%, while decrease in height by 10% (M10) results around 28% reduction in seasonal precipitation as compared to CNTRL experiment over NWI region. This improvement in precipitation simulation comes due to better representation of vertical pressure velocity and moisture transport as these factors play an important role in wintertime precipitation processes over NWI region. Furthermore, a comparison of model-simulated precipitation with observed precipitation at 17 station locations has been also carried out. Overall, the results suggest that when the orographic increment of 10% (P10) is applied on RegCM4 model, it has better skill in simulating the precipitation over the NWI region and this model is a useful tool for further regional downscaling studies.