A new paradigm for short-range forecasting of severe weather over the Indian region

Roy, Soma Sen; Sharma, Pradeep; Sen, B.; Devi, K. Sathi; Devi, Sunitha; Gopal, Neetha K.; Kumar, Naresh; Mishra, Krishna; Katyar, Shobhit; Singh, Surendra Pratap; Balakrishnan, Shibin; Singh, Charan; Srivastava, Kuldeep; Lotus, Sonam; Paul, Surender; Singh, Bikram; Gupta, Jancy; Bandopadhyay, Subhajit; Das, G. K.; Shankar, Anand; Kotal, S. D.; Biswas, H. R.; Shaw, S.; Das, Sunit; PHUKAN, RANJAN; Nagarathna, K.; Balachandran, S.; Puviarasan, N.; Stella, Stefania; Bibraj, R.; Mini, V. K.; Rahul, M.R.; Agnihotri, Geeta; Sarkar, Jayanta; Mohanty, Monalisa; Singh, Veer; Hosalikar, K. S.; Nitha, T.S.; Sahu, Mohan Lal; Kumari, B. Padma; Kashyapi, A.; Singh, Manmohan; Singh, H. A. K.; Sharma, Radhey Shyam; Raha, G. N.; Reddy, Y. Krishnamohan; Ramesh, K. J.; Mohapatra, M.

doi:10.1007/s00703-021-00788-z

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…Pada Gambar 5 dapat dilihat bahwa negara terbanyak dan paling dominan yang membahas mengenai klasifikasi -prediksi curah hujan adalah India diikuti oleh Amerika Serikat dan China pada posisi kedua dan ketiga terbanyak, sedangkan Indonesia juga termasuk lima besar (berada di posisi ke-empat) negara terbanyak artikel membahas topik klasifikasi -prediksi curah hujan. India menjadi negara terbanyak yang membahas mengenai klasifikasi -prediksi curah hujan [46]- [52] juga dikarenakan banyaknya kasus hidrometeorologi yang terjadi seperti banjir dan hujan es [53]. Untuk sebaran subject area artikel dengan topik rainfall classificationprediction ditunjukkan oleh Gambar 7.…”

Section: Gambar 4 Novelty Atau Kebaruan Topik Rainfall Classificationunclassified

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Riza,

Nuryadi

2023

Digitalzone

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This Research aims to review the machine learning methods used for classifying or predicting rainfall, using various features from existing data. The study used a bibliometric approach to search for metadata related to rainfall classification and prediction studies using machine learning keywords in Scopus journals. There found 94 metadata articles stored in a Comma Separated Values (CSV) file. The data in this article used published articles from 2014 to 2023 with relevant topics. The study provides information on the latest machine learning methods used for classifying or predicting rainfall. The findings of the study include an increase in published articles by 221.43% from 2018 to 2022. The article titled "An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives" by Cramer S., Kampouridis M, Freitas A.A, and Alexandridis A.K received the highest citation count of 129. The study also classified 15 keywords into 3 clusters, with common and fewer keywords. India emerged as the country with the most publications on classifying or predictiong rainfall, and the subject areas of computer science and engineering dominated the distribution of articles. Developing the use of deep learning methods and adding feature extraction algorithms in selecting features used to model data can improve the efficiency and accuracy of the rainfall classification - prediction process. The development of research data using radar images with the type of image processing research can also be maximised for research related to classification - prediction of rainfall using machine learning methods.

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Section: Gambar 4 Novelty Atau Kebaruan Topik Rainfall Classificationunclassified

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Riza,

Nuryadi

2023

Digitalzone

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Section: Ifs Lightning Forecast Verificationmentioning

confidence: 99%

“…For each day and for both observation and forecasted data, if the total lightning flash density is greater than zero in one bin, we denote it as 'yes' and 'no' if the total lightning in a bin is zero. The corresponding contingency table [33] is illustrated in Table 1. We have then calculated four skill scores using the contingency table to evaluate the forecast for Day 1, Day 2, and Day 3 lead times (Table 1).…”

Section: Ifs Lightning Forecast Verificationmentioning

confidence: 99%

“…These products use combinations of high-resolution satellite data, IITM lightning sensors, RADAR observations, high-resolution mesoscale models, and outputs from the Global Forecast System (GFS, at 12.5 km spatial resolution). All these efforts have significantly improved accuracy for nowcasting and short-range forecasting [33]. To address the disaster management aspect of lightning, the Lightning Resilient India Campaign was launched in 2019 to reduce lightning deaths by 80% by 2022.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of ECMWF Lightning Flash Forecast over Indian Subcontinent during MAM 2020

et al. 2022

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During the pre-monsoon season (March–April–May), the eastern and northeastern parts of India, Himalayan foothills, and southern parts of India experience extensive lightning activity. Mean moisture, surface and upper-level winds, the sheared atmosphere in the lower level, and high positive values of vertically integrated moisture flux convergence (VIMFC) create favorable conditions for deep convective systems to occur, generating lightning. From mid-2018, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) operationally introduced lightning flash density on a global scale. This study evaluates the ECMWF lightning forecasts over India during the pre-monsoon season of 2020 using the Indian Institute of Tropical Meteorology (IITM) Lightning Location Network (LLN) observation data. Qualitative and quantitative analysis of the ECMWF lightning forecast has shown that the lightning forecast with a 72-h lead time can capture the spatial and temporal variation of lightning with a 90% skill score.

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“…The India Meteorological Department (IMD) has a wide network of weather radars with a proposal to more than double it to cover the entire country by 2025 for rainfall observation and accurate nowcasting [17]. The IMD has been using various expert systems that utilize weather radar data for nowcasting.…”

Section: Introductionmentioning

confidence: 99%

Operational Assessment of High Resolution Weather Radar Based Precipitation Nowcasting System

Raj,

Sahoo,

Puviarasan

et al. 2024

Atmosphere

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North East Monsoon (NEM) is the major source of rainfall for the south-eastern parts of peninsular India. Short time rainfall prediction data (i.e., nowcasting) are based on the observations from Doppler weather radars which has a high spatial and temporal resolution. This study focuses on the short-term ensemble prediction system using weather radar data to predict precipitation during the NEM and is the first of its kind in the Indian region to make an assessment of the operational performance of the prediction system. Six rainfall events have been studied for the assessment of short-term prediction system where the precipitation systems are different and include a tropical storm observed over different days during the 2022 NEM season. To assess the performance of the system, Fractional Skill Scores (FSS) at a 1 km window have been computed for a lead time of 0–2 h for all the rainfall events with more than 750 samples using different optical flow methods and ensemble sizes. The best average skill score and maximum skill score obtained at a 2 h lead time is 0.65 and 0.78 for tropical storms, 0.5 and 0.78 for stratiform and 0.15 and 0.38 for convective precipitation. It has found that the performance of the model is best for precipitation systems that are widespread and have a longer life period.

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A new paradigm for short-range forecasting of severe weather over the Indian region

Cited by 9 publications

References 23 publications

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Bibliometric Study : Rainfall Classification - Prediction using Machine Learning Methods

Evaluation of ECMWF Lightning Flash Forecast over Indian Subcontinent during MAM 2020

Operational Assessment of High Resolution Weather Radar Based Precipitation Nowcasting System

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