by Ajay Bankar, V. Rakesh & Smrati Purwar
This study explores the impact of assimilating diverse observational data on forecasting extreme rainfall events (EREs) using a three dimensional variational (3D-Var) assimilation approach. It focuses on 38 EREs across three meteorological divisions in Karnataka, India, using a high-resolution (03-km) Weather Research and Forecasting (WRF) model with three nested domains. Five distinct experiments were conducted, including a Control experiment without assimilation, and subsequent experiments integrating observations from various sources like atmospheric profiles from Atmospheric InfraRed Sounder (AIRS) and Moderate resolution Imaging Spectroradiometer (MODIS) satellites and radiosondes, ocean surface wind observations from Advanced Scatterometer (ASCAT), Special Sensor Microwave Imager (SSMI), and WindSAT satellites and buoys, ground observations from Karnataka State Natural Disaster Monitoring Centre (KSNDMC), as well as a combined assimilation experiment with all available observations. The accuracy of rainfall forecasts is evaluated by comparing model outputs with high-resolution telemetric rain-gauge (TRG; 6480 stations) data and other meteorological parameters against telemetric weather station (TWS; 860 stations) data from KSNDMC. Assimilation experiments show positive improvements over control experiment in predicting rainfall. Results consistently indicate underprediction of rainfall in the intricate topographical region of the Western Ghats (WG) across all experiments, contrasting with overprediction along the coastal areas of Karnataka. The experiment involving Ocean Winds showcased a substantial 40 % reduction in rainfall overprediction (above 2 mm threshold). Both Ocean Winds and Station Data assimilation notably enhanced rainfall prediction accuracy over most of the regions in Karnataka, with Ocean Winds exhibiting the highest improvement (53 %), closely followed by Station Data (50 %). Importantly, assimilating Ocean Winds and Station Data aided in reducing overprediction, while assimilating Satellite Profiles reduced underprediction in the interior part of Karnataka but increased overprediction over the coastal region compared to the control experiment. Frequency of occurrence of rainfall is considerably enhanced along the coastline in all 3D-Var experiments. Bias score indicates maximum improvement in assimilation using Ocean Winds and Station Data. Simulation of basic meteorological parameters also improved with assimilation particularly during the day hours. The results underscore the crucial role of assimilation of satellite and in-situ observations in improving forecast accuracy of EREs during the monsoon season.
by Priyanshi Singhai, Arindam Chakraborty, Kaushik Jana, Kavirajan Rajendran, Sajani Surendran and Kathy Pegion
An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. Here, we highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version 2 (CFSv2). While all ensemble members often agree on the sign of predicted El Niño Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in some of the individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO signals significantly influences moisture convergence and Indian summer monsoon rainfall (ISMR). This stronger influence of ENSO on seasonal predictions increases ENSO–ISMR correlation in ensemble mean seasonal hindcasts. Thus, this discrepancy in the ENSO–ISMR relationship is not present in the individual ensemble members, considered individually or together (without averaging) as independent realizations. Therefore, adequate care should be taken while evaluating physical mechanisms of teleconnection in ensemble mean predictions that can often be skewed due to constructive or destructive superposition of different impacts.
by K. Silpa and Anil Earnest
This study investigates the seismic behavior and deformation characteristics in the outer-arc region off the Nicobar trench using stress inversion and teleseismic finite-fault modeling. The spatial and depth-wise variations in stress field in this region indicate variations in post-seismic deformation scenarios along the arc. The lateral shear, similar to the north Wharton basin, is the major deformational field in the Nicobar segment, favoring earthquake faulting along ∼ESE or ∼NNE nodal planes here. Shallow oblique-normal faulting and deeper oblique-reverse regimes are observed in the Nias region. These observations suggest that the plate bending effects directly influence the ongoing tectonics in the Nias region. Moreover, variations in the stress fields across the arc could be indicative of an uncoupled plate interface. Our finite-fault modeling analysis indicates dominant ∼ESE-WNW fault plane orientations for selected events from this region. This suggests the possibility of faulting within similarly oriented active shear structures within the northern Wharton Basin, contrasting the prevailing ∼N-S faulting pattern in that area. Besides, the centroid depths of most of these earthquakes are typically within the 600°C isotherm. However, the seismic slip may extend deeper by rupturing the crust, occasionally reaching upper mantle depths.
by Payoshni Samantray and Krushna Chandra Gouda
This comprehensive study aims to investigate the cloudburst events that occurred in the Indian Himalayan Region (IHR) during the year 2022, focusing on their characteristics, structure and dynamics. Cloudburst events have been observed in different states of the Himalayan region, with their unique geographical features, and are susceptible to extreme weather phenomena, which pose significant challenges to the local communities and infrastructure. A majority of cloudburst events occur within the folds of valleys of the Indian Himalayas, where elevations range from 325 to 4073 m. As the year 2022 witnessed frequent cloudburst events in the IHR (Himachal Pradesh recorded around 24 cloudburst events, Uttarakhand recorded 18 and Jammu and Kashmir recorded 24 events), an attempt is being made for the comprehensive analysis of the role of atmospheric dynamics to result in such extreme events in the valley region. The analysis clearly advocates that certain atmospheric phenomena, such as frontal boundaries like temperature, humidity, convective available potential energy, convective inhibition, or atmospheric disturbances, can act as triggers for cloud burst events. The spatial distribution of different thermodynamic parameters during the previous day, event day and the day after the cloudburst events are also analysed to quantify the role of atmospheric dynamics in the temporal distribution. This study has highlighted the importance of low-level jets and moisture transport in forming the convective systems that lead to cloudbursts over the Himalayan region due to wind patterns.
by Siva Sai Kumar Rajana, Sampad Kumar Panda, Sridevi Jade, Chiranjeevi G. Vivek
The occurrence characteristics of Equatorial F-region Irregularities (EFIs) along the Indian longitude sector are investigated using GPS-derived ROTI observations from 2010 to 2023, under both quiet and disturbed geomagnetic conditions. The analysis of the results emphasizes a strong dependence of EFIs occurrence on solar cycle variation. Additionally, the EFIs are more intense at the EIA regions under maximum solar activity periods attributed to the high ambient electron density in the F-layer. The occurrence of EFIs is found to be severe and frequently observed in equinox seasons in contrast to solstice seasons. Also, the occurrence of EFIs exhibits equinoctial asymmetry with more intense EFIs in the spring equinox than autumn equinox during the peak and declining periods of the solar cycle. Conversely, severe EFIs are observed during autumn equinox than spring equinox in the ascending phase of the solar cycle. The seasonal occurrence probability of EFIs is highest in the equinox seasons, following the winter solstice, and summer solstice in high solar activity conditions. Investigations of EFIs during two prominent geomagnetic storm events (17 March 2015 and 08 September 2017) during the study period show that the storm-induced Prompt Penetration Electric Fields (PPEFs) modulated the regular intensity of dusk time Pre-Reversal Enhancement (PRE), resulting in poleward expansion of ionospheric irregularities up to mid-latitude regions with spatial time lags. However, storm-induced westward electric fields and oscillating IMF Bz created Counter Electrojet (CEJ) like conditions, suppressing the dusk time ionospheric irregularities over dip equatorial and low-latitude regions on 17 March 2013. This study provides a comprehensive understanding of how ionospheric irregularities manifest under varying conditions, thereby supporting further research on substantiating the spatiotemporal EFIs characteristics and their predictions over the region.
- Assessment of long-term spatio-temporal variability of hot extremes and associated physical mechanism over India
- Homogeneity Adjustment of Surface Temperature Data and Study of the Climate Variability Over Coastal Odisha by a Climate Departure Index
- Assessment of Country-Specific GHG Emissions from Road Transport Sector in India
- Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming