Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques
by Gyanendranath Mohapatra, V. Rakesh, Smrati Purwar & A. P. Dimri
Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.
Does increasing the spatial resolution in dynamical downscaling impact climate change projection of Indian summer monsoon, population and GDP?
by C B Jayasankar, K Rajendran and Sajani Surendran
High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) program. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (< − 4 mm/day) and low pattern correlation coefficient (PCC) over the Western Ghats (WG). Mean climatology of Indian summer monsoon (ISM) rainfall simulated by high-resolution CCSM4-WRF outperforms the CORDEX-SA RCMs with low negative biases (~ 1 mm/day) and high PCC (≥ 0.755). This skill of CCSM4-WRF provides better confidence in its future projection at local scale. CCSM4-WRF projects future intensification of monsoon rainfall over most parts of India and reduction over southern WG, which is consistent with recent observed trends, but none of the CORDEX-SA RCMs could simulate this rainfall reduction. For all-India rainfall, ensemble mean of CORDEX-SA models projects an increase by 1.3 ± 0.9 mm/day and CCSM4-WRF projects 0.67 mm/day. Projected changes in socioeconomic variables such as population and gross domestic product (GDP) exhibit future enhancement over most parts of India but with spatial heterogeneity. Shared socioeconomic pathways scenarios show pronounced future population growth over Indian coastal areas and large enhancement in productivity over urban areas. Therefore, climate change projection information of ISM rainfall, together with enhanced future population and GDP, is useful for taking necessary steps for adaptation and mitigation in a sustainable manner.
Prediction of COVID‐19 cases using the weather integrated deep learning approach for India
by Kantha Rao Bhimala, Gopal Krishna Patra, Rajasekhar Mopuri and Srinivasa Rao Mutheneni
Advanced and accurate forecasting of COVID‐19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non‐linear problems. In the present study, the relationship between weather factor and COVID‐19 cases was assessed, and also developed a forecasting model using long short‐term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short‐term (1 day lead) forecast of COVID‐19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium‐range forecast skill (1–7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.
Historical extreme rainfall over the Bangalore city, India, on 14 and 15 August 2017: skill of sub-kilometer forecasts from WRF model
by V. Rakesh, G. N. Mohapatra & Ajay Bankar
Urban extreme rainfall events (UEREs) are one of the weather hazards where modelers continue to struggle for accurate advance prediction despite recent advances in forecasting techniques. Bangalore, one of the prominent IT hubs in India, witnessed many urban flash floods in the recent past; a number of such events happened on 14 and 15 of August 2017, where overnight heavy rains brought several parts of the city to a standstill. In this study, the skill of sub-kilometer resolution forecasts from Weather Research and Forecasting (WRF) model in simulating these UEREs is examined. This study also compared the model performance for forecasts initialized from different forecast cycles in a day. Our examination for two UERE cases showed that forecast initialized at 0600 UTC yielded best results in terms of forecasting spatial distribution and intensity of rainfall. Our analysis also indicated that increasing model resolution further in convection-permitting resolution (< 3 km) has only marginal impact on spatial distribution of rainfall while model skill in simulating rainfall intensity slightly improved with increasing resolution. As expected, simulation by not deploying a cumulus scheme at cloud-resolving resolution do not make a significant difference in spatial distribution; however, inclusion of cumulus scheme improved quantitative precipitation forecast skill. The inferences from this study are useful for modelers working on improving operational forecast skill of UEREs using mesoscale model.
Multiscale Forecasting of High-Impact Weather: Current Status and Future Challenges
by Sharanya J. Majumdar; Juanzhen Sun; Brian Golding; Paul Joe; Jimy Dudhia; Olivier Caumont; Krushna Chandra Gouda; Peter Steinle; Béatrice Vincendon; Jianjie Wang Nusrat Yussouf
Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a ten-year “HIWeather” project to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in Observations; Multiscale Data Assimilation; Multiscale Ensemble Forecasting; and Multiscale Coupled Hazard Modeling is provided.
- An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability
- High‐resolution climate change projection of northeast monsoon rainfall over peninsular India
- Evaluation of the Skill of a Fully-Coupled Atmospheric–Hydrological Model in Simulating Extreme Hydrometeorological Event: A Case Study Over Cauvery River Catchment
- Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2
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