Seismo-ionospheric anomalies associated with the 2025 Mw 7.7 Myanmar earthquake: a case study
by Siva Sai Kumar Rajana, Sambit Prasanajit Naik, Sampad Kumar Panda, Chiranjeevi G. Vivek, Sridevi Jade
Quantifying weather-rice crop production relationships across different physiographical zones in Odisha, India
by Puja Sahu, P. K. Mohanty & Krushna Chandra Gouda
Climate change has direct effect on rice crop production, resulting in an impact on food security in tropical countries like India. The coastal state of Odisha, situated in the country's eastern region, also suffered from crop production/yield due to the regional climate change. In this research work, the multi-source long term data analysis is being carried out to quantify the climate-crop relation in the state of Odisha. The analysis of rainfall, temperature, Kharif crop (rice) yield and production for the past three decades in Odisha at district scale is carried out. The study based on the cluster analysis of different physiographical regions i.e. coastal plain (CP), river catchment (RC) and mountainous zone (MZ). The region wise climatology, interannual variability, and trend analysis of both rainfall and temperature and the rain/temperature days are computed using the data over 30 (1993–2022) years. The variability of annual and monsoon rainfall is comparatively higher in the CP and RC than in MZ. The monsoon rainfall trend seems to be decreasing in the CP region, increasing in the MZ and no trend over RC. The analysis infers a negligible (0.01 °C/year) temperature trend in the state. The correlation result suggested that the Kharif rice crop yield & production well correlated with the summer monsoon rainfall over RC and MZ at 90% confidence levels. The rice production in the state shows increasing trend in RC as compared to other two regions. The results suggest a good correlation between the rainfall and rice crop production observed in RC and MZ regions. Similarly, temperature also correlates better with the Kharif crop production and yield in CP as compared to other regions. The district scale analysis also indicated that strong correlation (95%) of rice crop yield with temperature is observed in 10 districts and the relation of crop production and rainfall is strong in 12 districts. The impact of large-scale process like ENSO on the district scale Kharif yield/production also assessed. The analysis signifies the global, local, and direct impact of climate patterns on rice production at district and regional scales. This study opens a scope for the potential use of the integrated approach to address the impact of climate change on a state's agriculture and food sector.
Multi-Instrument Observations of Unseasonal Post-Sunset Equatorial Plasma Bubbles During Two Moderate Geomagnetic Storms in May and June 2024 Over East/Southeast Asia
by Sampad Kumar Panda, Siva Sai Kumar Rajana, Chiranjeevi G Vivek, Ramkumar Vankadara, Punyawi Jamjareegulgarn
This study investigated an unseasonal development of post-sunset EPBs in summer solstice period over the East/Southeast Asian longitude region by using multi-instrument observations during two consecutive moderate geomagnetic storm events (16-17 May 2024 and 28-29 June 2024). The results indicate, formation of EPBs during non-climatological plasma bubble season is primarily driven by sustained southward oriented IMF-Bz in the storm main phase, which facilitated the penetration of eastward electric fields into the equatorial ionosphere. These electric fields uplifted the F-region plasma to altitudes favourable for irregularities growth. Also, noteworthy hemispheric asymmetry is noticed in the formation of EPBs, manifesting more intense occurrence in Southern Hemisphere during the geomagnetic storm of 16-17 May 2024 and extended up to ∼20oS magnetic latitude. During the 28-29 June 2024 geomagnetic storm, EPBs are more prominent in the Northern Hemisphere and reached beyond EIA region up to ∼22oN magnetic latitude. In brief, the EPBs developed initially over the 100°E longitude sector, exhibited eastward drift and thereafter extended to 120°E longitude region during the 16-17 May 2024 geomagnetic storm. During the 28-29 June 2024 geomagnetic storm, EPBs formed over the 120°E longitude region and later drifted to 140°E longitude sector. These findings highlight the dominant role of storm-time electrodynamics in triggering EPBs and emphasize the need for continuous regional monitoring of EPBs to mitigate space weather impacts on satellite-based communication and GNSS systems.
WRF Model Forecast Evaluation of Extreme Rainfall Events over Karnataka during the Indian Summer Monsoon
by Ajay Bankar, Rakesh V, and Smrati Purwar
The skill of the Weather Research and Forecasting (WRF) model in predicting extreme rainfall events (EREs) is investigated for two different initialization times: 1200 UTC (12-hour lead time) and 0000 UTC (zero lead time). A high resolution (3-km) WRF model configuration is used to simulate 38 EREs in Karnataka, a state in southwest India. These EREs were identified by analyzing daily accumulated rainfall recorded by telemetric rain-gauge (TRG) stations over the region, specifically during the prominent rainy season in the state (June to November) for the period 2015–2019. EREs were defined using the 99.993rd percentile threshold for each subdivision and year. The model forecasts of rainfall and other meteorological variables were verified against high density TRG (6480 stations) and telemetric weather station (860 stations) observations throughout the Karnataka. The model performance was assessed through computation of quantitative error measures and statistical skill scores. Consistently, both sets of experiments reveal a propensity of WRF model to overestimate rainfall in the coastal region, with simulations initialized at 0000 UTC displaying a more pronounced wet bias compared to those at 1200 UTC. However, simulations initialized at 0000 UTC are more skillful than 1200 UTC in predicting the frequency of rainfall occurrences. Validation of other meteorological variables indicates that the model tends to underpredict temperature and overpredict relative humidity, especially during night hours, while it overpredicts wind speed over the region. Quantification of the model errors against in-situ measurements are crucial before deploying these models for operational applications and a deeper understanding of these errors will aid in further improvements to the model.
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