Nikhil Gajbhiv
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Nikhil Gajbhiv has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Ashwin Vijay Jadhav
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University (SPPU), Pune 411007, India
1 shared publication - B. L. Sudeepkumar
India Meteorological Department Ministry of Earth Sciences Pune India
1 shared publication - Ketaki Belange
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University (SPPU), Pune 411007, India
1 shared publication - P. R. C. Rahul
Indian Institute of Tropical Meteorology (IITM), Pune 411008, India
1 shared publication - Rohini Lakshman Bhawar
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune 411007, India
1 shared publication - Vinay Kumar
Department of Atmospheric Science, Environmental Science and Physics, University of the Incarnate Word, San Antonio, TX 78209, USA
1 shared publication
Latest Publications
- Open Access
- 11 August 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (8) , 957
Evaluation of the Reanalysis and Satellite Surface Solar Radiation Datasets Using Ground-Based Observations over India
Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (Kt). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m2), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m2; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes.
- 11 August 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (8) , 957
Open AccessEvaluation of the Reanalysis and Satellite Surface Solar Radiation Datasets Using Ground-Based Observations over India
Surface solar radiation (SSR) is a critical component of the Earth’s energy balance and plays a pivotal role in climate modelling, hydrological processes, and solar energy planning. In data-scarce regions like India, where dense ground-based radiation networks are limited, reanalysis and satellite-derived SSR datasets are often utilized to fill observational gaps. However, these datasets are subject to systematic biases, particularly under diverse sky and seasonal conditions. This study presents a comprehensive evaluation of four widely used SSR datasets: ERA5, IMDAA, MERRA2, and CERES, against high-quality in situ observations from 27 India Meteorological Department (IMD) stations, for the period 1985–2020. The assessment incorporates multi-scale temporal analysis (daily/monthly), spatial validation, and sky-condition stratification via the clearness index (Kt). The results indicate that CERES exhibits the best overall performance with the lowest RMSE (16.30 W/m2), minimal bias (–2.5%), and strong correlation (r = 0.97; p = 0.01), particularly under partly cloudy conditions. ERA5, with a finer spatial resolution, also performs robustly (RMSE = 20.80 W/m2; MBE = –0.8%; r = 0.94; p = 0.01), showing consistent agreement with observed seasonal cycles, though slightly underestimating SSR during monsoonal cloud cover. MERRA2 shows moderate overestimation (+4.4%) with region-specific bias variability, while IMDAA demonstrates persistent overestimation (+10.2%) across all conditions, highlighting limited sensitivity to atmospheric transparency. Importantly, this study reconciles apparent contradictions between monthly and sky condition-based bias analyses, attributing them to aggregation differences. While reanalysis datasets overestimate SSR during the monsoon on average, they tend to underestimate it under heavily overcast conditions. These insights are critical for guiding the selection and application of SSR datasets in solar energy modelling, SPV system design, and climate diagnostics across India’s heterogeneous atmospheric regimes.