by K. Shimna, R. K. Archana, C. Vineeth, Gang Lu, T. K. Pant and M. S. M. Vijayan
During the recovery phase of the Gannon Superstorm on 11 May 2024, the thermosphere-ionosphere system experienced dramatic changes marked by an unusual redistribution of plasma density. The peak electron density (Nmax) from Global-scale Observations of the Limb and Disk (GOLD), ground-based Vertical Total Electron Content (VTEC), and topside VTEC from Swarm C satellite revealed an enhanced electron density at the equator and a complete disappearance of the Equatorial Ionization Anomaly (EIA) crests. This absence of EIA crests was linked to a strong daytime Counter Electrojet (CEJ) caused by disturbance dynamo effects observed in ground magnetic data. The unusually strong CEJ suppressed the ExB vertical drift, thereby eliminating the fountain effect. Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) simulations further indicated the presence of equatorward converging meridional winds from both hemispheres. This converging wind hindered the diffusion of plasma from the dip equatorial rΣO/N2 ratio along the equator was observed during the recovery phase, further contributing to this phenomenon. The present study, therefore, for the first time, demonstrates the combined effects of storm-induced changes in equatorial electrodynamics, meridional winds, and significant compositional changes during the recovery phase of a superstorm, and their influence on plasma distributions. The corroborative evidence from model simulations further strengthened these observational findings.
by Smrutishree Lenka, K. C. Gouda, MOHAN T. S., Rani Devi and V. S. Prasad
This study examines the mechanisms involved in the onset and progression of the monsoon over the Indian subcontinent by analyzing key dynamical and thermodynamical processes. Additionally, it investigates the characteristics of monsoon onset during various ENSO phases. To achieve this, we utilized long-term observations and ERA5 reanalysis data to conduct a comprehensive study on the onset and related characteristics.
Results indicate that, a robust connection between the thermal gradient in both zonal and meridional directions and the corresponding vertical wind shear both across the active monsoon shear domain (MSD) and Kerala domain (KD). It is found that, this relationship varies across different time scales i.e., annual, seasonal (ISM-length), and onset pentad (p0), underscoring that the intensity of monsoon convection hinges on the thermal gradient and wind shear. The analysis concludes that presence of weak vertical wind shear, creates a stable state and the presence of a significant thermal gradient, characterized by minimal variations in wind speed and direction at different altitudes, are critical factors that facilitate the monsoon onset. Further, El Niño tends to increase wind shear over the Indian subcontinent and leads to a delayed monsoon onset. Elevated geopotential height during an El Niño year indicates the existence of a high-pressure region, which is an unfavorable condition for the onset of the monsoon. Analysis of temperature tendency equation exhibit a robust diabatic heating which is linked to a Gill-like Rossby wave response, initiating thermodynamic interactions with midlatitude westerly jet. Furthermore, this study also gives an indication of the influence of central pacific (CP) warming events and its influence on monsoon onset characteristics, which needs further exploration.
by V. Rakesh, V. Arun Kumar & K. V. Ramesh
In this study, we present machine learning model support vector machine (SVM) and deep learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), long- and short-term time series network (LSTNet), and temporal convolution network (TCN) to estimate leaf area index (LAI) utilizing Sentinel-2 multispectral imagery within the Kosi watershed located in the central Himalayan region is based on various vegetation indices. In situ LAI data were collected through a random sampling method using a plant canopy analyzer LI-COR over a continuous field survey period of 25 days for model training and validation. The current study evaluated four variants of the LSTM model, with the bidirectional LSTM emerging as the most effective in estimating LAI. The LAI derived using the Rendvi index was found to be superior compared to those obtained from other vegetation indices. The bidirectional LSTM (BiLSTM) model outperformed the SVM model in LAI retrieval based on all accuracy metrics computed. Specifically, the R2 and mean absolute percentage error (MAPE) values for the BiLSTM model were 0.99 and 0.29, respectively, while the SVM model achieved corresponding values of 0.93 and 0.56. Additionally, the performance of the BiLSTM in predicting LAI surpassed that of other deep learning models such as GRU (R2 = 0.99, MAPE = 0.82), LSTNet (R2 = 0.99, MAPE = 0.74), and TCN (R2 = 0.99, MAPE = 0.39). We compared the performance of the BiLSTM with the SVM model and the moderate-resolution imaging spectroradiometer (MODIS) LAI product by validating against in situ LAI measurements. The LAI retrieved using the BiLSTM model (R2 = 0.99) demonstrated superior agreement with in situ measurements compared to the MODIS LAI product (R2 = 0.0003). Furthermore, seasonal changes in vegetation and interannual variations in LAI over the Kosi watershed were more accurately depicted in the high-resolution LAI retrieved with BiLSTM from Sentinel-2 compared to the MODIS LAI product.
by Smrati Purwar, Ajay Bankar, V Rakesh, Suelynn Choy, Haobo Li, G N Mohapatra & Yuriy Kuleshov
This study delves into the assessment of the impact of assimilating two kinds of Global Navigation Satellite System (GNSS) observations (ground-based (GB) and space-based) in the prediction of extreme rainfall events (EREs). We consider seven distinct sources of GNSS radio occultation (GNSS-RO) observations as GNSS space-based observations, with six of them (COSMIC1, KOMPSAT5, MetOPA, MetOPB, TerraSARX, TanDEMX) being combined to form a group of Global Positioning System (GPS)-RO observations, while COSMIC-2 (C2), which receives signals from GPS and GLObal NAvigation Satellite System (GLONASS), is handled independently. The study is conducted over the southern state of India, Karnataka, for 16 EREs that occurred between October 2019 and December 2020. The zenith tropospheric delay (ZTD) from International GNSS Service (IGS) GB stations and refractivity data from GNSS-RO (GPS-RO and C2) are assimilated in the Weather Research and Forecasting (WRF) model for forecasting these EREs. The results indicate that the assimilation of ZTD from GNSS-GB observations does not significantly improve the forecast due to an insufficient number of observations, whereas the improvement through the assimilation of refractivity profiles from GPS-RO observations is significant even with a smaller number of observations, due to the high vertical resolution of the profiles. The assimilation of GNSS-GB and GPS-RO observations improves the humidity, temperature, and winds at different vertical levels which leads to improvement in the rainfall prediction, particularly in the interior part of Karnataka. Furthermore, the assimilation of C2 observations proves highly advantageous, resulting in significant improvements across forecasted weather parameters and rainfall when compared to GNSS-GB and GPS-RO. These improvements in weather parameters due to assimilation result in a substantial improvement in rainfall by 17% for assimilation of C2 and 21% for assimilation of all GNSS observations (GNSS-GB, GPS-RO, and C2) across most of the regions, including coastal and south interior parts of Karnataka. This study delves into the assessment of the impact of assimilating two kinds of Global Navigation Satellite System (GNSS) observations (ground-based (GB) and space-based) in the prediction of extreme rainfall events (EREs). We consider seven distinct sources of GNSS radio occultation (GNSS-RO) observations as GNSS space-based observations, with six of them (COSMIC1, KOMPSAT5, MetOPA, MetOPB, TerraSARX, TanDEMX) being combined to form a group of Global Positioning System (GPS)-RO observations, while COSMIC-2 (C2), which receives signals from GPS and GLObal NAvigation Satellite System (GLONASS), is handled independently. The study is conducted over the southern state of India, Karnataka, for 16 EREs that occurred between October 2019 and December 2020. The zenith tropospheric delay (ZTD) from International GNSS Service (IGS) GB stations and refractivity data from GNSS-RO (GPS-RO and C2) are assimilated in the Weather Research and Forecasting (WRF) model for forecasting these EREs. The results indicate that the assimilation of ZTD from GNSS-GB observations does not significantly improve the forecast due to an insufficient number of observations, whereas the improvement through the assimilation of refractivity profiles from GPS-RO observations is significant even with a smaller number of observations, due to the high vertical resolution of the profiles. The assimilation of GNSS-GB and GPS-RO observations improves the humidity, temperature, and winds at different vertical levels which leads to improvement in the rainfall prediction, particularly in the interior part of Karnataka. Furthermore, the assimilation of C2 observations proves highly advantageous, resulting in significant improvements across forecasted weather parameters and rainfall when compared to GNSS-GB and GPS-RO. These improvements in weather parameters due to assimilation result in a substantial improvement in rainfall by 17% for assimilation of C2 and 21% for assimilation of all GNSS observations (GNSS-GB, GPS-RO, and C2) across most of the regions, including coastal and south interior parts of Karnataka.
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.
- Identification of malaria vulnerable zones in North East Region of India: Spatiotemporal assessment through geographical information system and self-organizing maps
- WRF Model Forecast Evaluation of Extreme Rainfall Events over Karnataka during the Indian Summer Monsoon
- Regional assessment of rainfall indices for drought conditions during the monsoon season in Odisha
- Influence of large-scale climate modes (ENSO, IOD and PJ) on marine heatwave characteristics in the Indian ocean region