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.

Evaluation of precipitation forecasts from 3D-Var and hybrid GSI-based system during Indian summer monsoon 2015

Abstract: This paper presents a systematic investigation of medium-range rainfall forecasts from two versions of the National Centre for Medium Range Weather Forecasting (NCMRWF)-Global Forecast System based on three-dimensional variational (3D-Var) and hybrid analysis system namely, NGFS and HNGFS, respectively, during Indian summer monsoon (June–September) 2015. The NGFS uses gridpoint statistical interpolation (GSI) 3D-Var data assimilation system, whereas HNGFS uses hybrid 3D ensemble–variational scheme. The analysis includes the evaluation of rainfall fields and comparisons of rainfall using statistical score such as mean precipitation, bias, correlation coefficient, root mean square error and forecast improvement factor. In addition to these, categorical scores like Peirce skill score and bias score are also computed to describe particular aspects of forecasts performance. The comparison results of mean precipitation reveal that both the versions of model produced similar large-scale feature of Indian summer monsoon rainfall for day-1 through day-5 forecasts. The inclusion of fully flow-dependent background error covariance significantly improved the wet biases in HNGFS over the Indian Ocean. The forecast improvement factor and Peirce skill score in the HNGFS have also found better than NGFS for day-1 through day-5 forecasts.

Stratospheric zonal wind and temperature in relation to summer monsoon rainfall over India

Abstract: The interannual variability of the Indian summer monsoon (June-September) rainfall is examined in relation to the stratospheric zonal wind and temperature fluctuations at three stations, widely spaced apart. The data analyzed are for Balboa. Ascension and Singapore, equatorial stations using recent period (1964-1994) data, at each of the 10.30 and 50 hPa levels. The 10hPa zonal wind for Balboa and Ascension during January and the 30 Hpa zonal wind for Balboa during April are found to be positively correlated with the subsequent Indian summer monsoon rainfall, whereas the temperature at 10 hPa for Ascension during May is negatively correlated with Indian Indian summer monsoon rainfall. The relationship with stratospheric temperatures appears to be the best, and is found to be stable over the period of analysis. Stratospheric temperature is also significantly correlated with the summer monsoon rainfall over a large and coherent region, in the north-west of India. Thus, the 10 Hpa temperature for Ascension in May appears to be useful for forecasting summer monsoon rainfall for not only the whole of India, but also for a smaller region lying to the north-west of India.

Indian summer monsoon simulation studies with different orographic representations in a spectral GCM

Abstract: The current study focuses on two aspects of the representation of orography in a spectral general circulation model (GCM). The most important issue is the reduction of the ripple effect, especially the oceanic surface deformations, which can create tendency for unrealistic oceanic rainfall patches in the model climate. In this study, seven types of digital filters were tried at horizontal resolution equivalent to spectral truncation at T80 to identify the most effective filter. Digital filtering results in much greater reduction in orographic barrier, and hence, the blocking effect compared to mean orography. The second point is retention of the barrier effect. In the past, several attempts have been made to address this. Techniques have been developed in the past to construct an optimal orography, to enable more region-specific enhancement of mountains with less impact over oceans and plains. These techniques include site-specific kernels and the cost-function-minimization. An attempt has been made in the current study to develop a digitally filtered, locally enhanced, optimal orography called the Filtered-Modified Orography (FMO), for its application to Indian summer monsoon simulation. A number of sensitivity experiments have been conducted to determine the possible impacts of FMO on the seasonal simulation and the medium-range prediction of Indian summer monsoon. Results of this study show that two-dimensional (2-D) Lanczos filter is the most effective in reducing the ripples and the associated errors in the orographic representation. FMO is able to enhance the peaks of the Himalayan range and Western Ghats, and in consequence, it produces a better simulation of Indian summer monsoon circulation features and the associated rainfall in the seasonal and medium time scale. This procedure for local enhancement is found to be more beneficial than global enhancement of orography in a spectral GCM so far as the regional weather prediction is concerned. Copyright © 2008 Royal Meteorological Society

Hydrochloric acid emission dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter

Abstract: Abstract. The Winter Fog Experiment (WiFEX) was an intensive field campaign conducted at Indira Gandhi International Airport (IGIA) Delhi, India, in the Indo-Gangetic Plain during the winter of 2017–2018. Here, we report the first comparison in South Asia of the high temporal resolution measurements of NH3 along with water-soluble inorganic ions in PM2.5 (Cl-, NO3-, SO42- and NH4+) and corresponding precursor gases (HCl, SO2, HONO, and HNO3) made at the WiFEX research site, using the Monitor for AeRosols and Gases in Ambient Air (MARGA) and high-resolution simulations with Weather Research and Forecasting model coupled with chemistry (WRF-Chem). The hourly measurements were used to investigate how well the model captures the temporal variation of gaseous and particulate water-soluble species and gas-to-particle partitioning of NH3, using the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) aerosol scheme. The model frequently simulated higher NH3 and lower NH4+ concentrations than the observations, while total NHx values/variability agreed well with the observations. Under the winter conditions of Delhi, high concentrations of hydrochloric acid (HCl) in the ambient air are found to dominate the gas-to-particle partitioning, as NH3 is usually in excess. The default model set-up of WRF-Chem excludes anthropogenic HCl emissions, so sulfuric acid (H2SO4) dominates the gas-to-particle partitioning with NH3 during the simulation period. The sensitivity experiments, including HCl emissions in the model, showed that the inclusion of HCl emissions improves the simulated gas-to-particle conversion rate of ammonia by 24 % (as indicated by NH4+ concentrations) while reducing the bias in gas phase NH3 by 10 %. Nevertheless, even with waste burning HCl emissions included, we find that WRF-Chem still overestimates sulfur dioxide (SO2) and nitrate (NO3−) formation and underestimates sulfate (SO42−), nitrous acid (HONO), nitric acid (HNO3), and HCl concentration in which it interacts, thus limit the gas-to-particle conversion of NH3 to NH4+ in the model. This indicates that modeling of ammonia requires a correct chemistry mechanism with accurate emission inventories for the industrial HCl emissions.