Browsing by Author "Anumeha Dube"

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Assessing Spatial Accuracy of Lightning Forecasts Over India: Supporting Impact-Based Forecasting for Vulnerable Regions
(John Wiley and Sons Ltd, 2025) Harvir Singh; Anumeha Dube; Raghavendra G. Ashrit; John P. George; Vijayapurapu Srinivasa Prasad
Lightning is one of the most hazardous natural phenomena, causing significant damage to life and property. In India, lightning activity peaks during pre-monsoon and monsoon seasons. In 2022, 36% of the deaths from natural disasters were attributed to lightning. Accurate forecasting is critical for preparedness and mitigation, but complex convection processes often lead to spatial mismatches in forecasts. Spatial verification methods offer valuable insights into the accuracy of modeling systems. This study evaluates the performance of a high-resolution (4 km) regional model, operational at the National Centre for Medium Range Weather Forecasting (NCMRWF), that is, NCUMR (NCMRWF Regional Model), in predicting lightning strikes over India during pre-monsoon and monsoon seasons from 2021 to 2024. The Method for Object-Based Diagnostic Evaluation (MODE) was applied to assess the model's ability to predict lightning-prone regions. The primary objectives of this study are (i) to analyze the performance of the NCUMR model in predicting regions affected by lightning and (ii) to determine whether MODE can be used as an effective tool for forecasting lightning-prone areas. Results demonstrate that the NCUMR model is capable of forecasting the spatial structure and distribution of lightning events with reasonable accuracy up to 3 days in advance. On Day 1, more than 88% of lightning objects for thresholds above 5 strikes/day show boundary overlap with observations, with centroid distances for 50% of matched objects remaining below 55 km. For Day 2 lead time, 83%–85% of objects show boundary overlap. On Day 3, although displacement errors increase slightly, over 85% of objects still exhibit zero boundary distance at lower thresholds, and centroid distances remain within 1°–1.5°. For all lead times, 75% of the forecasted objects have area ratios exceeding 0.7, and complexity ratios consistently above 0.7, indicating good structural agreement. While intensity is generally under-forecasted, 90th percentile intensity ratios exceed 0.5 in most cases. The model performs better for lower thresholds and shows improved object correspondence during the monsoon season compared to pre-monsoon. These results confirm the utility of object-based verification using MODE in capturing spatial aspects of lightning forecasts and highlight its potential application for real-time impact-based forecasting and early warning systems. © 2025 The Author(s). Meteorological Applications published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
Assessing the impact of enhanced model resolution on heatwave prediction during June 2023
(Royal Society of Chemistry, 2025) Sakshi Sharma; Harvir Singh; Anumeha Dube; Arun Chakraborty; M. N. Raghavendra Sreevathsa; Raghavendra G. Ashrit; V. S. Prasad
Heatwaves are extreme weather events characterized by prolonged periods of unusually high temperatures, often leading to severe impacts on health, economy, and infrastructure. Global numerical weather prediction (NWP) models are useful for heatwave prediction, but they often underestimate the intensity due to their coarse resolution. To address this, experiments with high-resolution NWP models are required to better capture the intensity of heatwaves. In this study, we conducted an experiment using the National Centre for Medium Range Weather Forecasting (NCMRWF) Global Unified Model (NCUM-G) in two configurations: 12 km (Exp12) and 6 km (Exp06) resolutions, both initialized with identical conditions. To assess their performance, forecasts from the two versions were evaluated against the India Meteorological Department's (IMD) gridded observations using the mean error (ME) for the extreme heatwave over eastern India during 14–19 June 2023. The observed maximum temperature (Tmax) during this event reached 42–46 °C, well above the climatological 32–36 °C, mainly due to a ridge over eastern India and delayed monsoon onset from cyclone Biparjoy. Results show that Exp06 provided superior accuracy at shorter lead times (Day 1 and Day 3), closely capturing the observed heatwave intensity, while Exp12 outperformed Exp06 at longer lead times (Day 5). Regional verification revealed that Exp06 forecasts aligned particularly well with observations over Uttar Pradesh and Bihar, while both models showed comparable performance over Jharkhand and Odisha. These findings highlight the trade-offs between the resolution and forecast range in global models and demonstrate that high-resolution experiments can substantially improve short-range predictions of extreme heatwaves in India. © 2025 The Author(s).
Assessment of bias correction technique to improve ozone reanalysis dataset over India
(Springer Nature, 2025) Tanu Gangwar; Anumeha Dube; V. Abhijith; Sunita Verma
This study provides the first systematic evaluation of three global reanalysis ozone products MERRA-2, CAMS and ERA5 against quality-controlled CPCB ground observations over five climatological regions of India. This study has two primary objectives: (1) to document the performance of these datasets across India’s geographically, climatologically and demographically diverse regions; and (2) to evaluate the effectiveness of various bias-correction methods in improving their agreement with observations. Observed daily mean ozone concentrations ranged from 6.7 to 57.6 µg/m3; all three-reanalysis exhibited regionally coherent biases, with CAMS most prone to overestimation (mean bias: 42.3 to 108.1 µg/m3). Spatial patterns of bias varied by region, with the largest positive departures over the Indo Gangetic Plains (IGP), Western India (WI), Himalayan Region (HR), Central India (CI) and Southern India (SI). Verification metrics like RMSE, MAE, correlation coefficient (r), index of agreement (d) is used to analyse the strengths and weaknesses of each dataset in capturing ozone variability over these regions. To enhance the dataset accuracy bias correction techniques, including Quantile–Quantile (QQ) mapping, Ratio Adjustment Transformation (RAT-add and RAT-multi), and Variance Scaling (Vari), were applied. The RAT-multi method emerged as the most effective, substantially reducing F-Bias, RMSE, and MAE while improving correlation (r) and Index of agreement (d). Notable improvements were observed in CI and IGP, where Corrected MERRA-2 achieved an RMSE of 17.164 µg/m3 and F-Bias ~ 1. In the IGP region, the CAMS ozone dataset was corrected using the RAT-multi method showed statistically significant performance, by achieving improvement of 75.647%. This was followed by WI (72.080%), SI (69.358%), HR (67.313%), and CI showed the least improvement with 65.257%. Challenges persisted in the Himalayan Region due to its complex topography. This study establishes a benchmark for bias correction of reanalysis datasets over India, with corrected CAMS using RAT-multi outperforming others. This study underscores the importance of post-processing reanalysis data to address biases arising from limitations in model physics and parametrization, thereby improving its applicability for regional air quality assessments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Assessment of extreme rainfall events over Kerala using EVA and NCUM-G model forecasts
(Springer, 2023) V. Abhijith; Raghavendra Ashrit; Anumeha Dube; Sunita Verma
Assessment of extreme rainfall events (ERE) is crucial for disaster management. Numerical weather prediction (NWP) model-based predictions often fail to predict the extremes. This could be due to several reasons, including insufficient model resolution to capture the sub-grid scale processes, inadequate high-quality observational data for assimilation, uncertainty in initial conditions and approximations in model physics. Estimation of rainfall for different return periods (RP) using extreme value analysis (EVA) can aid in better decision-making. RP of an event indicates its probability and rarity over the region. The current study shows how EVA can be used to supplement model predictions. This study uses the high-resolution (0.25×0.25) gridded observed rainfall data from India Meteorological Department (IMD), which has been available for 117 years (1901–2017). The generalised extreme value (GEV) distribution is applied with suitable goodness-of-fit tests. Rainfall amounts corresponding to 100-year RP are estimated using EVA over the entire data period (1901–2017) and three epochs (1901–1940, 1941–1980, and 1981–2017). The results indicate increasing rainfall amounts corresponding to 100-year RP. Similarly, rainfall amounts for 25, 50, 100, and 200-year RP over Kerala are computed to compare with the extremely heavy rainfall (≤21 cm/day) amounts reported during JJAS 2018 and 2019. Further, this approach supplements the operational forecasts of NCUM-G model forecasts. © 2023, Indian Academy of Sciences.
How much does a high-resolution global ensemble forecast improve upon deterministic prediction skill for the Indian summer monsoon?
(Springer, 2023) Paromita Chakraborty; Anumeha Dube; Abhijit Sarkar; A.K. Mitra; R. Bhatla; R.S. Singh
The global Ensemble Prediction System (EPS) at NCMRWF (NEPS-G) comprises of 22 perturbed members in addition to the control (CNTL) member at 12 km horizontal resolution. Running this state-of-the-art ensemble configuration employs large computational resources compared to a deterministic system; hence it is crucial to determine if and to what extent it enhances the prediction skill of forecasts over the Indian region. In this study, we attempt to quantify the improvement in the skill of NEPS-G relative to the deterministic forecast for the 2018 Indian summer monsoon season. The ensemble mean shows substantially reduced forecast errors in the monsoon precipitation when verified against the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) data. NEPS-G mean demonstrates an improved skill for forecasts of moderate rainfall categories based on Peirce’s skill score, probability of detection and critical success index. The ensemble mean also shows an enhanced forecast skill at longer lead times, based on the anomaly correlation coefficient for both zonal winds at 850 hPa and precipitation. The model tends to underpredict very light precipitation and overpredict light precipitation. The Symmetric Extremal Dependence Index indicates a reasonable fidelity of the model in predicting heavy to very heavy rainfall. The continuous ranked probability score for NEPS-G is much lower than the mean absolute error of the CNTL forecast. The Relative Operating Characteristic curve of the ensemble distribution relative to CNTL further illustrates the value-addition by NEPS-G model to forecasts at longer lead times. Thus, through this study, the use of large computational resources for running the high-resolution NEPS-G is proved to be justified as it produces more reliable forecasts with longer lead times. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
Improving the Indian Monsoon Data Assimilation and Analysis regional reanalysis and maximum and minimum temperatures over India using macine-learning techniques
(John Wiley and Sons Ltd, 2025) Harvir Singh; Anumeha Dube; Prashant Kumar Srivastava; Raghavendra G. Ashrit; John P. George; Vijayapurapu Srinivasa Prasad
To complement forecasts from numerical weather prediction (NWP) models and to better understand historical weather patterns, reanalysis datasets like IMDAA (Indian Monsoon Data Assimilation and Analysis) are widely used. However, these datasets carry inherent biases from the underlying NWP models. Bias correction techniques can significantly improve their reliability. This study applies multivariate bias correction to location-specific IMDAA 2-m maximum (Tmax) and 2-m minimum (Tmin) temperatures using five machine-learning (ML) methods: Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB) and Convolutional Neural Network (CNN). Overfitting analysis indicates that RF, MLR and CNN show strong generalization, while SVM shows strong overfitting at specific stations and XGB demonstrates moderate overfitting at many locations. Accuracy analysis of the bias correction models shows that mean error (ME) and root-mean-squared error (RMSE) are significantly reduced for all stations. XGB is optimal for Tmax, maintaining low bias and variability, while RF shows the best performance for Tmin, especially in coastal and peninsular regions. Categorical verification is carried out using the Equitable Threat Score (ETS), Heidke Skill Score (HSS), and Peirce Skill Score (PSS) for Tmax (>35, 38 and 40°C) and Tmin (>28, 30, and 35°C), which confirms statistically significant improvements (95% confidence intervals, CI) for lower Tmax thresholds (>35°C). Maximum improvement is seen in Tmin where the scores change from negative to positive for all thresholds and regions. Finally, Relative Economic Value (REV) is computed which demonstrates that bias correction increases forecast value, particularly at lower thresholds. Results indicate that bias correction significantly enhances skill of the reanalysis dataset, with Tmax showing 20%–50% improvement (˜80%–90% along coasts) and Tmin improving by 70%–90%. However, the magnitude of improvement varies by region and threshold, therefore a region-specific bias correction strategy is recommended. © 2025 Royal Meteorological Society.
Object-based forecasting of heat waves over India: A novel approach
(Springer, 2025) Harvir Singh; Anumeha Dube; Raghavendra G. Ashrit; Prashant Kumar Srivastava; V. S. Prasad
Heat waves are recognized as one of the world’s most hazardous natural phenomena. It is well known that in recent times, both the intensity and frequency of heat waves have been increasing globally and in India, resulting in increased casualties. Timely and accurate forecasts of these events can help mitigate disasters and reduce losses due to heat waves. Usually, high-resolution numerical weather prediction (NWP) models can predict the intensity of extreme events accurately. However, these forecasts often suffer from a location mismatch and show limited reliability when verified using traditional methods relying on a grid-wise comparison. New and advanced spatial verification techniques allow the comparison of different aspects of the forecast-observation pair and highlight the actual value and utility of the forecasts. This study utilizes the method for object-based evaluation (MODE) to evaluate the 2 m maximum temperature forecasts from the NCMRWF Unified Model (NCUM) over the Indian land region for three summer seasons (2022–2024). The main objectives of this study are (a) to quantify the errors in the location, intensity, area, and structure of the forecasted objects obtained by using MODE and (b) based on the distribution of the displacement and intensity errors of the forecasted objects, decide if output from MODE can be used for forecasting heat waves over larger areas, which will be highly beneficial for the forecasters to generate an impact-based forecast for a region. To assess the skill of NCUM in predicting Tmax, we compared attributes like centroid distance, area, intensity, and structure of the forecasted and observed objects. It is found that (i) for lower Tmax values, the centroid distance between the forecast and observed objects is small (≤200 km, up to day-5 lead time), and for higher Tmax, this distance is ~250 km. (ii) for lower Tmax values, most of the forecasted objects have an area ratio that is closer to 1, indicating a similar spatial extent of the forecasts and observations; (iii) the intensity of the forecasted and observed objects is quite similar, which is seen from high values of the percentile intensity ratio (~0.99), (iv) the structure of the forecasted objects is predicted with reasonable accuracy which the higher values can see of the complexity ratio. These results demonstrate that the NCUM model has the ability to provide valuable heat wave forecasts in terms of location, area, intensity, and structure up to 5-days in advance, highlighting its potential for use in disaster preparedness and response efforts. © Indian Academy of Sciences 2025.