Vesta Afzali Gorooh - Concepedia

Author

Vesta Afzali Gorooh

University of California, Irvine ORCID

Also Known As

Afzali Gorooh, Vesta, Gorooh, Vesta Afzali, Vesta Afzali Gorooh

Publications

591

Citations

H-Index

Concepts

All Affiliations

University of California, Irvine Samueli Institute Universities Space Research Association University of California San Diego Pacific Northwest National Laboratory

About

Vesta Afzali Gorooh is an author at University of California, Irvine specializing in meteorology, earth science, and geography.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	8	8	404
2	Data Science	2	2	61
3	Social Sciences	1	1	24
4	Climate Sciences	1	1	26
5	Computer Science	1	1	37

Vesta Afzali Gorooh

Publications

534

Citations

H-Index

Page 1

	Year	Citations
PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Journal of Hydrometeorology	2020	130
An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast—Part 2: Circulation, Boundary Layer, and Clouds David Painemal, Andrea F. Corral, Armin Sorooshian, Journal of Geophysical Research Atmospheres EngineeringClimate ModelingCloud FieldsOceanographyAtmospheric Model	2021	97
An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast – Part 1: Analysis of Aerosols, Gases, and Wet Deposition Chemistry Andrea F. Corral, Rachel A. Braun, Brian Cairns, Journal of Geophysical Research Atmospheres EngineeringAir QualityOceanographyEarth ScienceAerosol Transport	2021	95
PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation Phu Nguyen, E. J. Shearer, Mohammed Ombadi, Bulletin of the American Meteorological Society Hydrological PredictionEngineeringWeather ForecastingClimate ModelingEarth Science	2019	63
Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS Vesta Afzali Gorooh, Subodh Kalia, Phu Nguyen, Remote Sensing Earth ObservationDeepctc ModelMachine LearningEngineeringEarth Science	2020	37
Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset Hamidreza Mosaffa, Mojtaba Sadeghi, Negin Hayatbini, Remote Sensing EngineeringPrecipitation TrendsWeather ForecastingSpatiotemporal VariationsEarth Science	2020	32
Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019) Mojtaba Sadeghi, E. J. Shearer, Hamidreza Mosaffa, Journal of Hydrology HydrometeorologyMeteorologyHydrologic Remote SensingEngineeringGeography	2021	30
Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale Mojtaba Sadeghi, Ata Akbari Asanjan, Mohammad Faridzad, Remote Sensing A ComparisonEngineeringWeather ForecastingClimate ModelingEarth Science	2019	26
Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data Vesta Afzali Gorooh, Ata Akbari Asanjan, Phu Nguyen, Journal of Hydrometeorology Earth ObservationConvolutional Neural NetworkEngineeringSpatiotemporal Data FusionClimate Modeling	2022	24

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