• S KARUNASAGAR

      Articles written in Journal of Earth System Science

    • Improved skill of NCMRWF Unified Model (NCUM-G) in forecasting tropical cyclones over NIO during 2015–2019

      SUSHANT KUMAR ANUMEHA DUBE SUMIT KUMAR S INDIRA RANI KULDEEP SHARMA S KARUNASAGAR SAJI MOHANDAS RAGHAVENDRA ASHRIT JOHN P GEORGE ASHIS K MITRA

      More Details Abstract Fulltext PDF

      Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWPmodels enhances the operational forecaster’s confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the premonsoon (April–May,AM)and post-monsoon (October–December,OND)TCseasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.

    • Evaluation of five high-resolution global model rainfall forecasts over India during monsoon 2020

      RAGHAVENDRA ASHRIT MOHAN S THOTA ANUMEHA DUBE KONDAPALLI NIRANJAN KUMAR S KARUNASAGAR SUSHANT KUMAR HARVIR SINGH RAJASEKHAR MEKA R PHANI MURALI KRISHNA ASHIS K MITRA

      More Details Abstract Fulltext PDF

      This study aims to evaluate the performance of five global medium-range operational NWP model rainfall forecasts, namely NCUM, UKMO, IMD GFS, NCEP GFS and ECMWF to provide an intercomparison of rainfall forecasts over India in terms of skill in predicting daily rainfall (24-hr accumulated rainfall). Veri- fication and intercomparison of rainfall forecasts over India during monsoon 2020 (JJAS) are carried out using both (i) standard traditional verification methods (POD, FAR, RMSE, etc.) and (ii) advanced spatial verification methods (MODE, FSS). The evaluation also includes assessment of large-scale mean patterns, temporal evolution of spells during the season, dominant modes using spectral analysis, basin-scale rainfall time series and isolated heavy rainfall cases. Our analysis suggests that some of the key large-scale aspects of monsoon (seasonal mean, active/break spells, and northward propagation) are realistically represented in all the models, with slight discrepancies. In addition, the spectral analysis of rainfall is in association with observed rainfall in Day-1 forecast and deteriorates with lead times. Synoptic variance in NCUM on longer leading times is closer to observations. While the standard categorical verification over India as a whole (spatial averaged) suggests that ECMWF forecast skill is relatively high among the Bve models, the veri-fication over the sub-regions shows mixed results with no clear unique higher performer among the models. In addition, basin-scale verification of rainfall forecasts for five rivers over the Indian subcontinent shows a fairly good amount of skill in terms of CC and RMSE up to Day-3 with comparable scores among the models. The advanced spatial verification metrics, like MODE and FSS, applied to the models show varying skills with different attributes. However, for FSS, forecast skill was high (low) for lower (higher) rainfall thresholds of 20 mm/day (100 mm/day). Though different models with different spatial resolutions show reasonable skill scores for larger regions, for high-impact heavy rainfall events, which are generally localised, the models have very comparable poor skill with no clear edge by a model among the five models.

  • Journal of Earth System Science | News

    • Editorial Note on Continuous Article Publication

      Posted on July 25, 2019

      Click here for Editorial Note on CAP Mode

© 2022-2023 Indian Academy of Sciences, Bengaluru.