National Centre for Medium Range Weather Forecasting

About: National Centre for Medium Range Weather Forecasting is a government organization based out in Noida, India. It is known for research contribution in the topics: Monsoon & Weather Research and Forecasting Model. The organization has 176 authors who have published 368 publications receiving 4749 citations.

Medium Range Prediction of Summer Monsoon Activities over India vis-a-vis Their Correspondence with the Observational Features

Abstract: A detailed examination on various aspects of the southwest monsoon over the Indian subcontinent using analyses and forecasts of the NCMRWF global data assimilation and forecast system for the years 1994 and 1996 is carried out in this study. Objective procedures developed by Ramesh et al. (1996) based on the monsoon 1995 data are employed in the present work. It is found that all the deterministic features of the summer monsoon viz., the onset, its advancement, stagnation / revival and the withdrawal, can reasonably by prognosticated after employing the objective methodologies and monitoring daily variations of certain derived quantities from the large scale analyses and forecasts. Further, a good correspondence of the observed large scale / synoptic scale circulation features and the observed rainfall etc. with the deterministic characteristics of the summer monsoon show a good prospect for real time prognosis of the important summer monsoon activities over the Indian subcontinent.

Comparison of statistical and dynamical downscaling methods for seasonal-scale winter precipitation predictions over north India

Abstract: The main aim of the present study is to analyze the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over North India. For this purpose, a Canonical Correlation Analysis (CCA) based statistical downscaling approach and dynamical downscaling (at 30 km) with an optimized configuration of the Regional Climate Model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200hPa, temperature at 200hPa and sea surface temperatures) over 3 different domains were This article is protected by copyright. All rights reserved. This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/joc.5897 A cc ep te d A rti cl e selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Further, two bias correction methods namely Quantile Mapping (QM) and Mean Bias-remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA based statistical downscaling. Overall, the results indicate that the QM based bias corrected downscaled RegCM model is a useful tool for wintertime seasonal scale precipitation prediction over North India.

Assimilation of INSAT-3D imager water vapour clear sky brightness temperature in the NCMRWF’s assimilation and forecast system

Abstract: This paper describes the direct assimilation of water vapour (WV) clear sky brightness temperatures (CSBTs) from the INSAT-3D imager in the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) assimilation and forecast system INSAT-3D imager WV CSBTs show a systematic bias of 2–3 K compared to the data simulated from the model first guess fields in the pre-assimilation study The bias in the INSAT-3D imager WV CSBTs is removed using a statistical bias correction prior to assimilation The impact of INSAT-3D imager WV channel CSBTs is investigated through different approaches: (i) single observation experiments and (ii) global assimilation experiments using the hybrid-four-dimensional variational technique Single observation experiments of channels of the same frequency from different instruments like the INSAT-3D imager and sounder, and the Meteosat visible and infrared imager (MVIRI) onboard Meteosat-7, show the INSAT-3D imager and MVIRI WV channels have a similar impact on the analysis increment Global assimilation clearly shows the positive impact of the INSAT-3D imager WV CSBTs on the humidity and upper tropospheric wind fields, whereas the impact on the temperature field, particularly over the tropics, is neutral Validation of model forecasted parameters with the in situ radio sonde observations also showed the positive impact of assimilation on the humidity and wind fields INSAT-3D imager WV CSBTs have been assimilated operationally in NCUM since August 2018

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

Abstract: 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.