• Kuldeep Sharma

      Articles written in Journal of Earth System Science

    • Assessment of Met Office Unified Model (UM) quantitative precipitation forecasts during the Indian summer monsoon: Contiguous Rain Area (CRA) approach

      Kuldeep Sharma Raghavendra Ashrit Elizabeth Ebert Ashis Mitra Bhatla R Gopal Iyengar Rajagopal E N

      More Details Abstract Fulltext PDF

      The operational medium range rainfall forecasts of the Met Office Unified Model (UM) are evaluated over India using the Contiguous Rainfall Area (CRA) verification technique. In the CRA method, forecast and observed weather systems (defined by a user-specified rain threshold) are objectively matched to estimate location, volume, and pattern errors. In this study, UM rainfall forecasts from nine (2007–2015) Indian monsoon seasons are evaluated against 0.5$^{\circ }\times$ 0.5$^{\circ }$ IMD–NCMRWF gridded observed rainfall over India (6.5$^{\circ }{-}$38.5$^{\circ }$N, 66.5$^{\circ }{-}$100.5$^{\circ }$E). The model forecasts show a wet bias due to excessive number of rainy days particularly of low amounts (<1 mm d$^{-1}$). Verification scores consistently suggest good skill the forecasts at threshold of 10 mm d$^{-1}$, while moderate (poor) skill at thresholds of <20 mm d$^{-1}$ (<40 mm d$^{-1}$). Spatial verification of rainfall forecasts is carried out for 10, 20, 40 and 80 mm d$^{-1}$ CRA thresholds for four sub-regions namely (i) northwest (NW), (ii) southwest (SW), (iii) eastern (E), and (iv) northeast (NE) sub-region. Over the SW sub-region, the forecasts tend to underestimate rain intensity. In the SW region, the forecast events tended to be displaced to the west and southwest of the observed position on an average by about 1$^{\circ }$ distance. Over eastern India (E) forecasts of light (heavy) rainfall events, like 10 mm d$^{-1}$ (20 and 40 mm d$^{-1}$) tend to be displaced to the south on an average by about 1$^{\circ }$ (southeast by 1$-2^{\circ }$). In all four regions, the relative contribution to total error due to displacement increases with increasing CRA threshold. These findings can be useful for forecasters and for model developers with regard to the model systematic errors associated with the monsoon rainfall over different parts of India.

    • Unified model rainfall forecasts over India during 2007–2018: Evaluating extreme rains over hilly regions

      KULDEEP SHARMA RAGHAVENDRA ASHRIT SUSHANT KUMAR SEAN MILTON EKKATTIL N RAJAGOPAL ASHIS K MITRA

      More Details Abstract Fulltext PDF

      Prediction of heavy/extreme rains is still a challenge, even for the most advanced state-of-the-art high-resolution Numerical Weather Prediction (NWP) modelling systems. Hydrological models use the rainfall forecasts from the NWP models as input. This study evaluates the performance of the UK Met Office Unified Model (UM) in predicting the rainfall exceeding 80th and 90th percentiles. Such high rainfall amounts occur over the Western Ghats (WGs) and North East (NE) India mainly due to the forced ascent of air parcels. Apart from the significant upgrades in the UM's dynamical core, the model features an increased horizontal grid (40–10 km) and vertical resolution (50–70 levels). The prediction skill of heavy rainfall events improves with an increased horizontal resolution of the model. The probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are the verification metrics used. As per these metrics, model rainfall forecasts have improved during 2007–2018 (increase in CSI from 0.29 to 0.38, POD from 0.45 to 0.55, and decrease in FAR from 0.55 to 0.45). Additionally, to verify extreme and rare events, the symmetric extremal dependence index (SEDI) is also used. SEDI also shows an increase from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE India during the study period, suggesting an improved skill of predicting heavy rains over the mountains. The improved forecast performance is consistent and relatively higher over WGs than over NE states.

    • 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.

  • 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

© 2021-2022 Indian Academy of Sciences, Bengaluru.