• SUSHANT KUMAR

Articles written in Journal of Earth System Science

• Bias correction of maximum temperature forecasts over India during March–May 2017

In recent times, instances of intense heat waves have increased over the Indian subcontinent. This increase in temperature has an adverse effect on human health and the economy. Over India, such high temperatures are usually seen during the months of March–May (summer). For weather forecasters, it is a challenging job to accurately predict the timing and intensity of this anomalous high temperature. The difficulty in the accurate prediction of weather is increased because of the presence of systematic biases inthe models. These biases are present because of improper parameterizations or model physics. For increasing the reliability or accuracy of a forecast it is essential to remove these biases by using a process called post-processing. In this study the biases in the surface temperature maximum are corrected using two methods, namely, the moving average and the decaying average. One of the main advantages of both the methods is that they do not require a large amount of past data for calibration and they take into account the most recent behaviour of the forecasting system. Verification, for maximum surface temperature during March–May 2017, was carried out in order to decide upon the method giving the best temperature forecast. It was found that both the bias correction methods lead to a decrease in the mean error in maximum surface temperature ($T_{max}$). However, the decaying average method showed a higher decrease in the mean error. Scores obtained from a contingency table like POD, FAR and PSS, showed that for $T_{max}$, the decaying average method outperforms the forecasts, i.e., raw and moving average in terms of having high POD and PSS and a low FAR.

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

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

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

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.

• Probabilistic forecasting of Super Cyclone ‘Amphan’ using NCMRWF global and regional ensemble prediction systems

Probabilistic forecasting of tropical cyclone (TC) from ensemble prediction systems provides flowdependent uncertainty associated with the model forecast and helps in better decision making. NCMRWF global and regional ensemble prediction systems (NEPS-G and NEPS-R) have been used in forecasting the intensity and track of the Super Cyclone ‘Amphan’, which hit eastern India and Bangladesh in May 2020. Observation shows very heavy rainfall (${\ge}$11.5 cm/day) over West Bengal and Bangladesh on 20th May 2020. NEPS-R predicted very heavy rainfall in day-2 forecasts with a probability in the range of 50–70%. NEPS-G also could predict very heavy rainfall in day-2 forecast with probability lying in the same range but over a small area. In its day-5 forecast also, NEPS-G was able to predict very heavy rainfall with a probability lying in the range of 30–50%. The prediction of time and magnitude of the maximum intensity by NEPS-R was predicted better from the initial condition of 00 UTC 16th May 2020. Prediction of intensi-fication is better in NEPS-R forecast. Both NEPS-G and NEPS-R are under-dispersive in 10 m maximum wind forecasting. The RMSE-spread relationship of maximum wind speed is better in the NEPS-R forecast till 72 hrs forecast lead time. The reliability of both NEPS-Gand NEPS-R strike probability forecasts is good and NEPS-G shows better reliability at lower probability values. The mean direct position error (DPE) from NEPS-G does not exceed 350 kmin day-5 and 270km in day-3 forecast lead time. The meanDPEof NEPS-R forecast is about 175 km in day-3 forecast lead time. Both NEPS-G and NEPS-R predicted early landfall, but the error in the landfall time does not exceed 3 hrs within the last 72 hrs before landfall. NEPS-R at short range and NEPS-G at longer range show reasonable skill in prediction of tropical cyclone ‘Amphan’.

• # Journal of Earth System Science

Volume 132, 2023
All articles
Continuous Article Publishing mode

• # Editorial Note on Continuous Article Publication

Posted on July 25, 2019