National Center for Medium Range Weather Forecasting

Greater Noida, India

National Center for Medium Range Weather Forecasting

Greater Noida, India
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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

Singh S.K.,National Center for Medium Range Weather Forecasting | Prasad V.S.,National Center for Medium Range Weather Forecasting
International Journal of Remote Sensing | Year: 2017

The Sounder for Probing Vertical Profiles of Humidity (SAPHIR) is a sounding instrument of Megha-Tropiques (Indo-French joint satellite); launched by the Indian Space Research Organization on 12 October 2011 with six channels near the absorption band of water vapour at 183 GHz. In the framework of this work, the assimilation scheme has been first modified to enable the SAPHIR radiance observations being used as additional observation type, and second, a methodology has been prepared to remove the radiance pixels significantly affected by clouds. The impact of SAPHIR radiances on analysis as well as forecasts of the National Centre for Medium Range Weather Forecasting-Global Forecast System (NGFS) at T574L64 resolution has been investigated through data assimilation. Measurements from SAPHIR are incorporated into the Gridpoint Statistical Interpolation three-dimensional variational assimilation system to provide the improved initial conditions. To find out the impact, analysis/forecast cycling experiments with and without SAPHIR radiances are performed during the period 10–29 November 2013. The impact of the improvement in term of root mean square error has been clearly evaluated for five parameters, namely, relative humidity, temperature, wind, geopotential height, and specific humidity. It is demonstrated that the assimilation of SAPHIR observations results in a considerable improvement for the five parameters over the global region. During the study period, two tropical cyclones (HELEN, 19–22 November and LEHAR, 23–28 November) were formed over the North Indian Ocean. Impact on specific humidity and track forecast errors of tropical cyclone are also examined. Overall, initial results show the usefulness of SAPHIR radiances in the NGFS. © 2017 Informa UK Limited, trading as Taylor & Francis Group.

Jain S.,National Center for Medium Range Weather Forecasting | Kar S.C.,National Center for Medium Range Weather Forecasting
Journal of Atmospheric and Solar-Terrestrial Physics | Year: 2017

This paper discusses the transport of water vapour in the tropopause region over the Tibetan Plateau, where high water vapour mixing ratio is observed during the Northern Hemisphere (NH) summer-monsoon period. The Weather Research and Forecasting (WRF) model has been used to study the two contrasting cases i.e. when water vapour is high and low at 100 hPa (close to tropopause). The composite distribution of water vapour shows two key results (a) the water vapour appears be transported to the Tibetan plateau region from the extra-tropics under the influence of stronger northwesterly winds and (b) the vertical water vapour flux is relatively higher over the Tibetan Plateau region during the period when water vapour amount at this level is higher. This suggests that in addition to the horizontal transport from the extra-tropics, the local convection occurring over the Tibetan Plateau also contributes to the increase in the water vapour over this region. The differences in the circulation during high and low water vapour cases suggest that a cyclonic circulation difference over the central Indian region limit the transport of water vapour from the Bay of Bengal to the Tibetan Plateau region. © 2017 Elsevier Ltd

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.

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