by Rajasekhar Mopuri, Madhusudhan Rao Kadiri, Kantha Rao Bhimala and Srinivasa Rao Mutheneni
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.
by Puja Sahu, P K Mohanty, S R Padhi & K C Gouda
Drought in Odisha, India, varies significantly due to its diverse physiographical features, such as orography, river basins, lakes, coastlines, and the Eastern Ghats, which influence rainfall distribution and drought vulnerability. Understanding and interpreting these rainfall patterns is essential for inferring regional rainfall variability for drought conditions, which in turn affects sectors like agriculture, health, energy, and disaster management. This study aims to analyze and quantify regional rainfall variability for drought assessment, focusing on identifying suitable indices for monitoring drought at the block and district levels. Using long-term block-level rainfall data, five key drought indices were evaluated: Precipitation Concentration Index (PCI), Standardized Precipitation Index (SPI), Effective Drought Index (EDI), Deciles Index (DI), and Percentage Departure (PD). Statistical analyses were conducted to assess the performance of these indices in capturing rainfall variability across monthly and daily timescales. The study explored how the indices reflect drought severity in districts with varying geographic conditions. PCI was effective for rainfall distribution, while SPI and EDI were the most reliable for monitoring extreme droughts. In contrast, DI and PD did not align well with observed data. The EDI showed significant drought impacts in the blocks of Jajpur, Khordha, and Koraput, highlighting the need for targeted agricultural interventions. SPI and EDI provide robust tools for assessing drought in Odisha, particularly for disaster management and water resource planning at block, district, and state levels. The study's findings offer critical insights for managing drought in vulnerable regions.
by S. Swetha, K. V. Ramesh & V. Rakesh
Marine heatwaves (MHWs), defined as anomalously warm sea surface temperature (SST) events, are analyzed over the Indian Ocean using NOAA OISST V2 (0.25° resolution) from 1981 to 2022, focusing on frequency, heatdays, maximum intensity, and mean intensity. Spatial analysis reveals MHW hotspots in the central-southwestern Indian Ocean, Bay of Bengal, and northern Arabian Sea, with 1–3 events per year lasting 3–15 days. Basin-wide mean maximum and mean intensities are 1.1 °C and 0.9 °C, respectively. Significant upward trends are found in all MHW metrics, including frequency (0.73 events/decade), heatdays (1.7 days/decade), mean intensity (0.11 °C/decade), and maximum intensity (0.14 °C/decade), with the Arabian Sea exhibiting the largest rise in heatdays (17.79 days/decade). A comparison of the periods 1981–2000 and 2001–2022 shows increased frequency and duration in the latter, though with marginally lower intensities. Empirical Orthogonal Function (EOF) analysis explains 28% and 17% of the spatial variance in frequency and duration, respectively. Partial correlation and kernel-based methods identify the El Niño–Southern Oscillation (ENSO), particularly positive Niño3.4 anomalies, as the dominant driver of MHW variability, strongly correlated with frequency (r = 0.59) and intensity (r = 0.5). Granger causality and Convergent Cross Mapping confirm ENSO’s particularly Niño3.4’s causal influence and reveal bidirectional feedback with MHWs heatdays, linked to Walker circulation driven changes in zonal winds and thermocline depth. The Pacific–Japan (PJ) pattern exerts secondary influence, while the Indian Ocean Dipole (IOD) plays a limited role. This underscores the importance of incorporating such drivers in predictive models and regional adaptation strategies.
by Ashish Routray, Shivaji Singh Patel, Devajyoti Dutta, K. B. R. R. Hari Prasad, K. C. Gouda, Rajeev Bhatla, V. S. Prasad
An effort was made to investigate the impact of the assimilation of microwave imager (MI) radiance observations on the simulation of tropical cyclones (TCs) using the high-resolution NCUM-4DVAR assimilation method. Two numerical experiments were performed: a control experiment (CTL) (assimilated global telecommunication system [GTS] observations) and satellite observations (SAT) (assimilated GTS plus global microwave imager [GMI] and special sensor microwave imager and sounder [SSMIS] satellite MI radiance). Assimilation of MI radiance is observed to be capable of depicting the structure, track and intensity of storms. The analysis increments of temperature and geopotential height suggested that the SAT experiment effectively adjusted the core region of TCs and systematically corrected the position in the first guess of the model. The strength of large-scale moisture transport from the underlying oceanic surface and helicity around the storms are well simulated by the SAT with the axisymmetric eye of TCs. The premature intensification of TCs is simulated by CTL, whereas the progression of the intensity of storms is relatively well captured in the SAT experiment. Dry air invading the inner core of a TC can disrupt the energy cycle and reduce storm intensity, as seen in the CTL simulations. Similarly, the negative value of diabatic heating appears around the centre of the storm with an increase of altitudes in CTL simulations, which impedes the intensity of TCs. This feature suggested that CTL simulations do not adequately bring out the intensity and vertical structure of the TCs. The track forecast of storms is considerably improved in SAT simulations compared to CTL. The forecast skill of rainfall is also relatively improved in SAT simulations. Overall assimilation of MI radiances enhanced the model's forecast skill to simulate structure, movement, intensity and precipitation associated with the storms.
- Quantifying CMIP6 models’ performance in simulating marine heatwaves over the Indian Ocean region: A comprehensive analysis of key MHW properties and future projections
- Climate variability and warming in Coastal Odisha: assessing interannual temperature trends and impacts
- Multi-Instrument Analysis of Ionospheric Equatorial Plasma Bubbles over the Indian and Southeast Asian Longitudes During the 19–20 April 2024 Geomagnetic Storm
- Modeling near-surface velocity inversion in a sediment sequence using microtremor HVSR