Time filter

Source Type

Joshi S.,National Center for Medium Range Weather Forecasting | Kar S.C.,National Center for Medium Range Weather Forecasting
Theoretical and Applied Climatology | Year: 2017

Coupled ocean atmosphere global climate models are increasingly being used for seasonal scale simulation of the South Asian monsoon. In these models, sea surface temperatures (SSTs) evolve as coupled air-sea interaction process. However, sensitivity experiments with various SST forcing can only be done in an atmosphere-only model. In this study, the Global Forecast System (GFS) model at T126 horizontal resolution has been used to examine the mechanism of El Niño-Southern Oscillation (ENSO) forcing on the monsoon circulation and rainfall. The model has been integrated (ensemble) with observed, climatological and ENSO SST forcing to document the mechanism on how the South Asian monsoon responds to basin-wide SST variations in the Indian and Pacific Oceans. The model simulations indicate that the internal variability gets modulated by the SSTs with warming in the Pacific enhancing the ensemble spread over the monsoon region as compared to cooling conditions. Anomalous easterly wind anomalies cover the Indian region both at 850 and 200 hPa levels during El Niño years. The locations and intensity of Walker and Hadley circulations are altered due to ENSO SST forcing. These lead to reduction of monsoon rainfall over most parts of India during El Niño events compared to La Niña conditions. However, internally generated variability is a major source of uncertainty in the model-simulated climate. © 2017 Springer-Verlag Wien

Litta A.J.,Cochin University of Science and Technology | Mohanty U.C.,Indian Institute of Technology Delhi | Das S.,National Center for Medium Range Weather Forecasting | Mary Idicula S.,Cochin University of Science and Technology
Atmospheric Research | Year: 2012

A common feature of the weather during the pre-monsoon season (March-May) over the east and northeast India is the outburst of severe local storms which have significant socio-economic impact due to loss of lives and properties. Forecasting thunderstorms is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal scales and the inherent non-linearity of their dynamics and physics. In the present study, an attempt has been made to simulate severe local storms that occurred over east India during STORM field experiments 2007, 2009 and 2010, using Non-hydrostatic Mesoscale Model (NMM) and validate the model results with observation. This study shows that the NMM model holds better promise for prediction of thunderstorm with reasonable accuracy. The intensity of rainfall rates is in good agreement with the observation. The model has well captured the stability indices, which act as indicators of severe convective activity. The surface temperature and relative humidity over Kolkata are reasonably well simulated by the NMM model even though one hour time lag or lead exists. The model simulated well the updraft and downdraft over Kolkata, which is an important phenomenon related to thunderstorm life cycle. From the model simulated spatial plots of composite radar reflectivity and cloud top temperature, we can see that the model has also been able to capture the movement of thunder squall. The results of these analyses determined that the 3. km WRF-NMM model has good skill when it comes to the thunderstorm simulation. © 2012 Elsevier B.V.

Prasad V.S.,National Center for Medium Range Weather Forecasting | Singh S.K.,National Center for Medium Range Weather Forecasting
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2016

Megha-Tropiques (MT) is an Indo-French Joint Satellite Mission, launched on 12 October 2011. MT-SAPHIR is a sounding instrument with 6 channels near the absorption band of water vapor at 183 GHz, for studying the water cycle and energy exchanges in the tropics. The main objective of this mission is to understand the life cycle of convective systems that influence the tropical weather and climate and their role in associated energy and moisture budget of the atmosphere in tropical regions. India also has a prestigious space programme and has launched the INSAT-3D satellite on 26 July 2013 which has an atmospheric sounder for the first time along with improved VHRR imager. NCMRWF (National Centre for Medium Range Weather Forecasting) is regularly receiving these new datasets and also making changes to its Global Data Assimilation Forecasting (GDAF) system from time-to-time to assimilate these new datasets. A well planned strategy involving various steps such as monitoring of data quality, development of observation operator and quality control procedures, and finally then studying its impact on forecasts is developed to include new observations in global data analysis system. By employing this strategy observations having positive impact on forecast quality such as MT-SAPHIR, and INSAT-3D Clear Sky Radiance (CSR) products are identified and being assimilated in the Global Data Assimilation and Forecasting (GDAF) system. © 2016 SPIE.

Acharya N.,Indian Institute of Technology Delhi | Kar S.C.,National Center for Medium Range Weather Forecasting | Mohanty U.C.,Indian Institute of Technology Delhi | Kulkarni M.A.,Indian Institute of Technology Delhi | Dash S.K.,Indian Institute of Technology Delhi
Theoretical and Applied Climatology | Year: 2011

The 2009 drought in India was one of the major droughts that the country faced in the last 100 years. This study describes the anomalous features of 2009 summer monsoon and examines real-time seasonal predictions made using six general circulation models (GCMs). El Niño conditions evolved in the Pacific Ocean, and sea surface temperatures (SSTs) over the Indian Ocean were warmer than normal during monsoon 2009. The observed circulation patterns indicate a weaker monsoon in that year over India with weaker than normal convection over the Bay of Bengal and Indian landmass. Skill of the GCMs during hindcast period shows that neither these models simulate the observed interannual variability nor their multi-model ensemble (MME) significantly improves the skill of monsoon rainfall predictions. Except for one model used in this study, the real-time predictions with longer lead (2- and 1-month lead) made for the 2009 monsoon season did not provide any indication of a highly anomalous monsoon. However, with less lead time (zero lead), most of the models as well as the MME had provided predictions of below normal rainfall for that monsoon season. This study indicates that the models could not predict the 2009 drought over India due to the use of less warm SST anomalies over the Pacific in the longer lead runs. Hence, it is proposed that the uncertainties in SST predictions (the lower boundary condition) have to be represented in the model predictions of summer monsoon rainfall over India. © 2011 Springer-Verlag.

Dutta S.K.,National Center for Medium Range Weather Forecasting | Prasad V.S.,National Center for Medium Range Weather Forecasting
Journal of Earth System Science | Year: 2011

An analysis system experiment was conducted for the month of June 2008 with Gridpoint Statistical Interpolation (GSI) analysis scheme using NCMRWF's (National Centre for Medium Range Weather Forecasting) T254L64 model. Global analyses were carried out for all days of the month and respective forecast runs are made up to 120-hr. These analyses and forecasts are inter-compared with the operational T254L64 model outputs which uses Spectral Statistical Interpolation (SSI) analysis scheme. The prime objective of this study is to assess the impact of GSI analysis scheme with special emphasis on Indian summer monsoon as compared to SSI. GSI analysis scheme do have positive impact over India and its surrounding regions. Though not for all but for some fields it is in edge over Spectral Statistical Analysis Scheme. Patterns for the forecast mean error. anomaly correlation and S 1 scores with respect to the respective analyses are same for both GSI and SSI. Both have increasing S 1 scores, decreasing mean errors and anomaly correlation with the advance of forecast days. The vector wind RMSE of the model forecasts with respect to the analyses is lower for GSI at 850 hPa and higher at 250 hPa. But over tropics GSI is better at both levels. The temperature field of GSI has higher correlation and lower RMSE at both 850 and 250 hPa pressure levels. There are improvements in systematic errors for 850 and 200 hPa temperature field in GSI compared to that in SSI. The depression centre in GSI analysis is closer to observation but has produced more intense depression compared to that of SSI. Rainfall forecast of SSI is better at day-1 whereas GSI is closer to the observation at day-5 forecasts valid at the same day. © Indian Academy of Sciences.

Kar S.C.,National Center for Medium Range Weather Forecasting | Acharya N.,Indian Institute of Technology Delhi | Mohanty U.C.,Indian Institute of Technology Delhi | Kulkarni M.A.,Indian Institute of Technology Delhi
International Journal of Climatology | Year: 2012

Rainfall in the month of July in India is decided by large-scale monsoon pattern in seasonal to interannual timescales as well as intraseasonal oscillations. India receives maximum rainfall during July and August. Global dynamic models (either atmosphere only or coupled models) have varying skills in predicting the monthly rainfall over India during July. Multi-model ensemble (MME) methods have been utilized to evaluate the skills of five global model predictions for 1982-2004. The objective has been to develop a prediction system to be used in real time to derive the mean of the forecast distribution of monthly rainfall. It has been found that the weighted multi-model ensemble (MME) schemes have higher skill in predicting July rainfall compared to individual models. Through the MME methods, skill of rainfall predictions improved significantly over eastern parts of India. However, there is a region over India where none of the models or the MME scheme has any useful skill. Similarly, there are few typical years in which the mean distribution of July rainfall cannot be predicted with higher skill using the available statistical post-processing methods. A simple MME probabilistic scheme has been utilized to show that skill of probabilistic predictions improved when the representation of mean of forecast distribution has better skill. © 2011 Royal Meteorological Society.

Osuri K.K.,Indian Institute of Technology Bhubaneswar | Mohanty U.C.,Indian Institute of Technology Bhubaneswar | Routray A.,National Center for Medium Range Weather Forecasting | Niyogi D.,Purdue University
Monthly Weather Review | Year: 2015

The impact on tropical cyclone (TC) prediction from assimilating Doppler weather radar (DWR) observations obtained from the TC inner core and environment over the Bay of Bengal (BoB) is studied. A set of three operationally relevant numerical experiments were conducted for 24 forecast cases involving 5 unique severe/very severe BoB cyclones: Sidr (2007), Aila (2009), Laila (2010), Jal (2010), and Thane (2011). The first experiment (CNTL) used the NCEP FNL analyses for model initial and boundary conditions. In the second experiment [Global Telecommunication System (GTS)], the GTS observations were assimilated into the model initial condition while the third experiment (DWR) used DWR with GTS observations. Assimilation of the TC environment from DWR improved track prediction by 32%-53% for the 12-72-h forecast over the CNTL run and by 5%-25% over GTS and was consistently skillful. More gains were seen in intensity, track, and structure by assimilating inner-core DWR observations as they provided more realistic initial organization/asymmetry and strength of the TC vortex. Additional experiments were conducted to assess the role of warmrain and ice-phase microphysics to assimilate DWR reflectivity observations. Results indicate that the icephase microphysics has a dominant impact on inner-core reflectivity assimilation and in modifying the intensity evolution, hydrometeors, and warm core structure, leading to improved rainfall prediction. This study helps provide a baseline for the credibility of an observational network and assist with the transfer of research to operations over the India monsoon region. © 2015 American Meteorological Society.

Prasad V.S.,National Center for Medium Range Weather Forecasting | Johny C.J.,National Center for Medium Range Weather Forecasting
Journal of Earth System Science | Year: 2016

Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global Forecast System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3D Var. Hybrid experiment made significant improvement in wind forecasts in all the regions on verification against mean analysis. The verification of forecasts with radiosonde observations also show improvement in wind forecasts with the hybrid assimilation. On verification against observations, hybrid experiment shows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational 3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013. © Indian Academy of Sciences.

Kar S.C.,National Center for Medium Range Weather Forecasting | Rana S.,National Center for Medium Range Weather Forecasting | Rana S.,Victoria University of Wellington
Theoretical and Applied Climatology | Year: 2014

The role of El Niño/Southern Oscillation (ENSO) and the mechanism through which ENSO influences the precipitation variability over northwest India and the adjoining (NWIA) region is well documented. In this study, the relative role of North Atlantic Oscillation (NAO)/Arctic Oscillation (AO) and ENSO in modulating the Asian jet stream in the Northern Hemisphere winter and their relative impact on the precipitation variability over the region have been estimated through analysis of observed data. It is seen that interannual variations of NWIA precipitation are largely influenced by ENSO. An empirical orthogonal function (EOF) analysis has been carried out to understand dominant modes of interannual variability of zonal wind at 200 hPa of the Northern Hemisphere. The EOF-1 pattern in the tropical region is similar to that of an ENSO pattern, and the principal component (PC) time series corresponds to the ENSO time series. The EOF-2 spatial pattern resembles that of NAO/AO with correlation of PC time series with AO and NAO being 0.74 and 0.62, respectively. The precipitation anomaly time series over the region of interest has marginally higher correlation with the PC-2 time series as compared to that of PC-1. Regression analysis of precipitation and circulation parameters indicates a larger contribution of the second mode to variability of winds and precipitation over the NWIA. Moisture transport from the Arabian Sea during the active phase of NAO/AO and the presence of a cyclonic anomaly lead to higher precipitation over the NWIA region. © 2013 Springer-Verlag Wien.

Kar S.C.,National Center for Medium Range Weather Forecasting | Mali P.,National Center for Medium Range Weather Forecasting | Routray A.,National Center for Medium Range Weather Forecasting
Pure and Applied Geophysics | Year: 2014

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. © 2014, Springer Basel.

Loading National Center for Medium Range Weather Forecasting collaborators
Loading National Center for Medium Range Weather Forecasting collaborators