Journal & Conference Publications

Megha Lab is dedicated to advancing scientific knowledge through publications in peer-reviewed journals and active participation in leading national and international conferences.

Peer Reviewed Journals

Sl. No. Paper
1.
Upadhyaya, S., Kirstetter, P. E., Kuligowski, R. J. & Searls, M. (2022). Towards Improved Precipitation Estimation with the GOES-16 Advanced Baseline Imager: Algorithm and Evaluation. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.4368.
2.
Upadhyaya, S. A., Kirstetter, P. E., Kuligowski, R. J., & Searls, M. (2022). Exploring the Temporal Information from GEO Satellites for Estimating Precipitation with Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters. https://ieeexplore.ieee.org/document/9819929
3.
Lemma, E., Upadhyaya, S., & Ramsankaran, R. (2022). Meteorological drought monitoring across the main river basins of Ethiopia using satellite rainfall product. Environmental Systems Research, 11(1), 1-15. https://doi.org/10.1186/s40068-022-00251-x
4.
Upadhyaya, S.A., Kirstetter, P.-E., Kuligowski, R.J., Gourley, J.J. and Grams, H. (2021) Classifying precipitation from GEO Satellite Observations: Prognostic Model. Quarterly Journal of the Royal Meteorological Society. https://doi.org/10.1002/qj.4134
5.
Upadhyaya, S.A., Kirstetter, P.-E., Kuligowski, R.J., Searls, M. (2021) Classifying precipitation from GEO Satellite Observations: Diagnostic Model. Quarterly Journal of the Royal Meteorological Society,1–17. http://dx.doi.org/10.1002/qj.4130
6.
Upadhyaya, S., Kirstetter, P. E., Gourley, J. J., & Kuligowski, R. J. (2020). On the propagation of satellite precipitation estimation errors: from passive microwave to infrared estimates. , J. Hydrometeor. https://doi.org/10.1175/JHM-D-19-0293.1
7.
Lemma, E., Upadhyaya, S., & Ramsankaran, RAAJ. (2019). Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia. Int J Remote Sens https://doi.org/10.1080/01431161.2018.1558373
8.
Upadhyaya, S., and Ramsankaran, RAAJ., (2018a). Comprehensive inter-comparison of INSAT multispectral rainfall algorithm estimates and TMPA 3B42-RT V7 estimates across different climate regions of India during southwest monsoon period. Environmental monitoring and assessment, 190(1), 45. https://doi.org/10.1007/s10661-017-6411-7
9.
Upadhyaya, S., and Ramsankaran, RAAJ., (2018b). Error modelling for modified-INSAT multi-spectral rainfall algorithm. Int J Remote Sens, 39(6), 1836-1852. https://doi.org/10.1080/01431161.2017.1415483
10.
Upadhyaya, S., and Ramsankaran, RAAJ., (2016). Modified-INSAT Multispectral Satellite Rainfall Algorithm (M-IMSRA) at Climate Region Scale –Development and Validation, Remote Sens Environ.187, 186-201. https://doi.org/10.1016/j.rse.2016.10.013
11.
Upadhyaya, S., and Ramsankaran, RAAJ., (2014). Support Vector Machine (SVM) Based Rain Area Detection from Kalpana-1 Satellite Data. TC VIII, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. (ISPRS Annals) II-8, 21-27. https://doi.org/10.5194/isprsannals-II-8-21-2014
12.
Upadhyaya, S., and Ramsankaran, RAAJ., (2014). Multi-Index Rain Detection: A New Approach for Regional Rain Area Detection from Remotely Sensed Data, J. Hydrometeor, 15, 2314–2330. https://doi.org/10.1175/JHM-D-14-0006.1