Evaluation of long-term variability of ionospheric total electron content from IRI-2016 model over the Indian sub-continent with a latitudinal chain of dual-frequency geodetic GPS observations during 2002 to 2019
by Siva Sai KumarRajana, T.S.Shrungeshwara, Chiranjeevi G.Vivek, Sampad Kumar Panda and Sridevi Jade
We evaluate the performance of the latest version of International Reference Ionosphere (IRI-2016) by comparing the estimated Total Electron Content (TEC) with the observed values from four geodetic Global Positioning System (GPS) receivers, latitudinally aligned from the equator to low latitudes (−5° to 20° Geomagnetic) in the Indian longitudes (75–95°) from 2002 to 2019. It is observed that IRI-2016 model underestimates and overestimates the GPS TEC depending on the season, solar activity, and geographic location of observation. On a decadal scale, the monthly mean of IRI TEC and GPS TEC show distinct seasonal variation trends for all years with seasonal asymmetry. The bias and RMSE values are low in the ascending and descending phases of solar cycles 23 and 24 compared to high values with significant fluctuations observed during the peak solar activity phase. For the declining phase of solar cycle 24, IRI TEC underestimates the GPS TEC values at EIA crest regions. On an annual scale, IRI TEC agrees better with GPS TEC during the low solar activity years (2010 and 2019) than the high solar activity years (2002 and 2014). Moreover, IRI model is unable to manifest the winter anomaly characteristics during the high solar activity years. On a diurnal scale, the performance of IRI model is poor with high RMSE during daytime hours and it underestimates the GPS TEC at equatorial and low latitude regions during the high solar activity phases. On a seasonal scale, high bias and RMSE are observed during the spring equinox compared to other seasons under the high solar activity phases. On a spatial scale, the bias and RMSE are high with low yearly coefficient of determination () in the EIA crest regions as compared to the equatorial and low latitude regions. High underestimation of IRI model was also observed in November 2011 due to high solar indices. The proportionality relationship between RMSE and solar indices is observed in all the phases of solar activity. Additionally, the mean annual RMSE and values indicate solar activity predominantly affecting the performance of IRI model. This study suggests that the influence of solar and magnetic indices inputs in the present IRI model could be revisited for reflecting the solar activity effects distinctly in the model outputs. In addition, IRI-2016 model requires further improvements for equatorial, low latitude, and EIA crest regions of the Indian sub-continent, specifically during the high solar activity phases.
Source: https://doi.org/10.1016/j.asr.2021.12.005
Evaluation of CMIP6 multi-model ensemble for assessing the intensity and duration of Indian monsoon under future warming scenarios
by Smrutishree Lenka, Krushna Chandra Gouda, Rani Devi & T. S. Mohan
This study evaluates future changes in Indian summer monsoon (ISM) rainfall, seasonal duration, and impact of pre-monsoon temperature rise using the simulations from twelve CMIP6 climate models under multiple Shared Socioeconomic Pathways (SSPs). Analysis indicates that historical simulations (1951–2014) from the models, assessed against India Meteorological Department (IMD) gridded observational data led to the selection of five models that closely capture observed monsoon rainfall patterns within one standard deviation. Analysis indicates that, over central Indian region extreme rise in temperature > 5.4 °C accompanied by a 3.2% increase in monsoon rainfall in the far-future (2075–2100) scenario. Further, the ISM length at each grid point computed using a novel algorithm from the historical analysis reveals that Northwestern India experiences a comparatively shorter monsoon season. Projections under more intense emission scenarios like SSP-370 and SSP-585, exhibits the ISM-length .decrease by nearly 7–8 days over the next 76 years. At the same time, future projections in indicate a substantial rise in ISM rainfall under high-emission scenarios, suggesting an intensification of the monsoon despite a shortened duration. This paradox is closely linked to increasing pre-monsoon temperatures, which enhance atmospheric moisture-holding capacity and lead to more compound extreme events. The study quantifies the sensitivity of monsoon rainfall to pre-monsoon warming, highlighting spatial variability in rainfall response per 1 °C temperature increase and found that that pre-monsoon temperatures continue to be on the higher side in both SSP5-8.5 and the far-feature scenarios. These findings indicate a growing risk of hydro-meteorological extremes such as heatwaves and urban flooding, especially under high-emission futures like SSP3-7.0 and SSP5-8.5. The projected combination of shorter rainy seasons and more intense rainfall events underscores the urgent need for targeted adaptation and resilience planning across sectors and regions in India.
Source: https://doi.org/10.1007/s00382-025-07923-1
Evaluating the Spatial Distribution of WRF-Simulated Rainfall, 2-m Air Temperature, and 2-m Relative Humidity over the Urban Region of Bangalore, India
by Kantha Rao Bhimala, Krushna Chandra Gouda & S. Himesh
The present study evaluates the skill of the Weather Research and Forecasting (WRF) model to simulate high-resolution rainfall, 2-m air temperature (T2m), and 2-m relative humidity (RH2m) over the metropolitan city of Bangalore, India. The novelty of the present study is that the WRF model simulations were carried out for ten different rain intensities during the monsoon season and compared with in situ observations from a high-density rain gauge network (81 rain gauge stations) and automatic weather stations (AWS) located over Bangalore. Our analysis shows that the model underestimated (bias score < 1) rainfall for most (87%) of the stations, and the model accuracy in the forecasting of rainfall was more than 70% for 16% of stations in the city. The RMSE values ranged between 18 and 28 mm/day for most of the rainfall events. Our analysis also found that the underestimation of the convective available potential energy (CAPE < 2000 J/kg) may be a possible reason for the simulation of low-intensity rainfall (< 10 mm/day) in most of the stations in Bangalore. In the case of T2m and RH2m simulations, the model closely matched the observed values [bias: T2m (−1 °C to 1 °C), Rh2m (0–10%)] for most of the AWS, while the model showed cold (−4.5 °C) and moist bias (19%) for the industrial area of Begur station. Proper representation of the urban morphology, air pollution, and anthropogenic heat data in the WRF modeling system may improve the model skill to capture the spatial variability in rainfall, T2m, and RH2m over highly urbanized cities in India.
Effect of large-scale oceanic and atmospheric processes on the Indian summer monsoon
by Smrutishree Lenka, Rani Devi, Chennemkeril Mathew Joseph & Krushna Chandra Gouda
There are several important large-scale oceanic and atmospheric processes like El Niño-Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO), and Indian Ocean Dipole (IOD) which have significant impact on global weather and climate system. This article reviews the mechanism and dynamics of ENSO, MJO, and IOD processes and their impact on global and regional weather and climate particularly the Indian summer monsoon rainfall. Generally, these processes are coupled ocean–atmosphere phenomenon and associated dynamics control the global weather and climate system. Sea surface temperature (SST) anomaly in the central and equatorial pacific region respectively results the warm (El Niño) and cold (La Niña) events and it has strong impacts globally. Similarly, MJO is a dominant phase of intra-seasonal variability in the tropical region and also has significant impacts on the global system like strong wind, convective waves, extreme rainfall, cyclones, and ENSO. The IOD is often termed as the counterpart of pacific El Niño and La Niña in Indian Ocean which mainly measures the SST gradient between Arabian Sea and the eastern Indian Ocean. IOD also linked to ENSO and the shifting warm/cool pool results in the summer monsoon rainfall variability in the India Ocean as well as continental Indian region. All these phenomena have direct impact on the Indian monsoon circulation system, so in this work, these impacts are quantified using the India Meteorological Department (IMD) observed rainfall data over Indian subcontinent as a case study. This work also provides the review of the studies using observation and modelling to understand the dynamics of all the three processes. This review and analysis work will help in understanding the process feedback on the regional rainfall distribution and there is a need of near-future modelling research on these processes and their impacts on weather and climate system and associated sectors.
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