GPM Refereed Publications
Paruelo, J. M., M. Texeira, and F. Tomasel,
: Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems. Agricultural Systems, 214, 103847, doi:10.1016/j.agsy.2023.103847.
Parker, D. J., A. M. Blyth, S. J. Woolnough, A. J. Dougill, C. L. Bain, E. de Coning, M. Diop-Kane, A. Kamga Foamouhouee, B. Lamptey, O. Ndiaye, P. Ruti, Paolo, E. A. Adefisan, et al.,
: The African SWIFT Project: Growing Science Capability to Bring about a Revolution in Weather Prediction. Bull. Amer. Meteor. Soc., 103(2), E349–E369, doi:10.1175/BAMS-D-20-0047.1.
Park, S.-Y., and K.-S. Sunny Lim,
: Implementation of Prognostic Cloud Ice Number Concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics Scheme. JAMES, 15(2), e2022MS003009, doi:10.1029/2022MS003009.
Park, H., J. Hwang, D.-H. Cha, M.-I. Lee, C.-K. Song, J. Kim, S.-H. Park, and D.-K. Lee,
: Does a Scale-Aware Convective Parameterization Scheme Improve the Simulation of Heavy Rainfall Events?. JGR Atmospheres, 129(7), e2023JD039407, doi:10.1029/2023JD039407.
Parc, L., H. Bellenger, L. Bopp, X. Perrot, and D. T. Ho,
: Global ocean carbon uptake enhanced by rainfall. Nature Geoscience, 17(9), 851–857, doi:10.1038/s41561-024-01517-y.
Paramanik, M. M. R., K. M. G. Rabbani, A. Imran, M. J. Islam, and I. M. Syed,
: Prediction of lightning activity over Bangladesh using diagnostic and explicit lightning parameterizations of WRF model. Natural Hazards, 120, 4399–4422, doi:10.1007/s11069-023-06355-6.
Parajuli, S. P., G. L. Stenchikov, A. Ukhov, S. Mostamandi, P. A. Kucera, D. Axisa, W. I. Gustafson Jr., and Y. Zhu,
: Effect of dust on rainfall over the Red Sea coast based on WRF-Chem model simulations. Atmos. Chem. Physics, 22(13), 8659–8682, doi:10.5194/acp-22-8659-2022.
Parajuli, S. P., G. L. Stenchikov, A. Ukhov, H. Morrison, I. Shevchenko, and S. Mostamandi,
: Simulation of a Dust-And-Rain Event Across the Red Sea Using WRF-Chem. JGR Atmospheres, 128(14), e2022JD038384, doi:10.1029/2022JD038384.
Papalexiou, S. M., Y. Markonis, F. Lombardo, A. AghaKouchak, and E. Foufoula-Georgiou,
: Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435-7458, doi:10.1029/2018WR022726.
Papalexiou, S. M., C. R. Rajulapati, K. M. Andreadis, E. Foufoula-Georgiou, M. P. Clark, and K. E. Trenberth,
: Probabilistic Evaluation of Drought in CMIP6 Simulations. Earth's Future, 9(10), e2021EF002150, doi:10.1029/2021EF002150.
Papalexiou, S. M., A. AghaKouchak, K. E. Trenberth, and E. Foufoula-Georgiou,
: Global, Regional, and Megacity Trends in the Highest Temperature of the Year: Diagnostics and Evidence for Accelerating Trends. Earth's Future, 6(1), 71-79, doi:10.1002/2017EF000709.
Papalexiou, S. M., A. AghaKouchak, and E. Foufoula-Georgiou,
: A Diagnostic Framework for Understanding Climatology of Tails of Hourly Precipitation Extremes in the United States. Water Resources Research, 54(9), 6725-6738, doi:10.1029/2018WR022732.
Papageorgiou, E., M. Foumelis, and A. Mouratidis,
: Earth Observation Data Synergy for the Enhanced Monitoring of Ephemeral Water Bodies to Anticipate Karst-Related Flooding. GeoHazards, 4(2), 197-216, doi:10.3390/geohazards4020012.
Papacharalampous, G., H. Tyralis, N. Doulamis, and A. Doulamis,
: Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products. Machine Learning with Applications, 19, 100615, doi:10.1016/j.mlwa.2024.100615.
Papacharalampous, G., H. Tyralis, N. Doulamis, and A. Doulamis,
: Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products. Machine Learning Earth, 1(1), 015004, doi:10.1088/3049-4753/add93b.
Papacharalampous, G., H. Tyralis, N. Doulamis, and A. Doulamis,
: Uncertainty estimation of machine learning spatial precipitation predictions from satellite data. Machine Learning: Science and Technology, 5(3), 035044, doi:10.1088/2632-2153/ad63f3.
Papacharalampous, G., H. Tyralis, N. Doulamis, and A. Doulamis,
: Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data. Rem. Sens., 15(20), 4912, doi:10.3390/rs15204912.
Papacharalampous, G., H. Tyralis, A. Doulamis, and N. Doulamis,
: Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale. Hydrology, 10(2), 50, doi:10.3390/hydrology10020050.
Papa, K.-M., and A. G. Koutroulis,
: Evaluation of precipitation datasets over Greece. Insights from comparing multiple gridded products with observations. Atmos. Res., 315, 107888, doi:10.1016/j.atmosres.2024.107888.
Pantillon, F., S. Davolio, E. Avolio, C. Calvo-Sancho, D. S. Carrió, S. Dafis, E. S. Gentile, J. J. Gonzalez-Aleman, S. Gray, M. M. Miglietta, P. Patlakas, I. Pytharoulis, D. Ricard, A. Ricchi, C. Sanchez, and E. Flaounas,
: The crucial representation of deep convection for the cyclogenesis of Medicane Ianos. Weather and Climate Dynamics, 5(3), 1187–1205, doi:10.5194/wcd-5-1187-2024.