GPM

Content which is affiliated solely with the Global Precipitation Measurement Mission.

A spatiotemporal framework to calibrate high-resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

Submitted by LisaN on
Publication Year
Authors
Nasseri, M., G. Schoups, and M. Taheri
Journal
Int'l. J. Climatology
Volume
42(4)
Page Numbers
2169-2194
DOI
10.1002/joc.7358
Mission Affiliation
Major Category

A dynamic and thermodynamic analysis of the 11 December 2017 tornadic supercell in the Highveld of South Africa

Submitted by LisaN on
Publication Year
Authors
Lekoloane, L. E., M.-J. M. Bopape, T. G. Rambuwani, T. Ndarana, S. Landman, P. Mofokeng, M. Gijben, and N. Mohale
Journal
Weather and Climate Dynamics
Volume
2(2)
Page Numbers
373–393
DOI
10.5194/wcd-2-373-2021
Mission Affiliation
Major Category

Validation of GPM IMERG Extreme Precipitation in the Maritime Continent by Station and Radar Data

Submitted by LisaN on
Publication Year
Authors
Da Silva, N. A., B. G. M. Webber, A. J. Matthews, M. M. Feist, T. H. M. Stein, C. E. Holloway, and M. F. A. B. Abdullah
Journal
Earth and Space Science
Volume
8(7)
Page Numbers
e2021EA001738
DOI
10.1029/2021EA001738
Mission Affiliation
Major Category

Results of the Dragon 4 Project on New Ocean Remote Sensing Data for Operational Applications

Submitted by LisaN on
Publication Year
Authors
Gibert, F., J. Boutin, W. Dierking, A. Granados, Y. Li, E. Makhoul, J. Meng, A. Supply, E. Vendrell, J.-L. Vergely, J. Wang, J. Yang, K. Xiang, X. Yin, and X. Zhang
Journal
Rem. Sens.
Volume
13(14)
Page Numbers
2847
DOI
10.3390/rs13142847
Mission Affiliation
Major Category

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Submitted by LisaN on
Publication Year
Authors
Chen, C., B. Hu, and Y. Li
Journal
Hydrology and Earth System Sciences
Volume
25(11)
Page Numbers
5667–5682
DOI
10.5194/hess-25-5667-2021
Mission Affiliation
Major Category

Satellite rainfall products outperform ground observations for landslide prediction in India

Submitted by LisaN on
Publication Year
Authors
Brunetti, M. T., M. Melillo, S. L. Gariano, L. Ciabatta, L. Brocca, G. Amarnath, and S. Peruccacci
Journal
Hydrology and Earth System Sciences
Volume
25(6)
Page Numbers
3267–3279
DOI
10.5194/hess-25-3267-2021
Mission Affiliation
Major Category