A5035142858- Precipitation Measurement and Analysis
- Meteorological Phenomena and Simulations
- Climate variability and models
- Atmospheric aerosols and clouds
- Tropical and Extratropical Cyclones Research
- Geophysics and Gravity Measurements
- Geochemistry and Geologic Mapping
- Solar Radiation and Photovoltaics
- Atmospheric chemistry and aerosols
- Ocean Waves and Remote Sensing
- Remote-Sensing Image Classification
- Cryospheric studies and observations
- Soil Moisture and Remote Sensing
- Process Optimization and Integration
- Water-Energy-Food Nexus Studies
- Endoplasmic Reticulum Stress and Disease
- Internet of Things and AI
- Remote Sensing in Agriculture
- Air Quality Monitoring and Forecasting
- Ubiquitin and proteasome pathways
- Hydrological Forecasting Using AI
- Network Security and Intrusion Detection
- Climate Change, Adaptation, Migration
- Smart Systems and Machine Learning
- Climate change impacts on agriculture
University of California, San Diego
2019-2024
Scripps Institution of Oceanography
2019-2024
University of California, Irvine
2019-2023
Samueli Institute
2019-2022
UC Irvine Health
2022
Irvine University
2022
United States Army Corps of Engineers
2019
Center for Hydrometeorology and Remote Sensing
2019
Abstract This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. dataset provides hourly, quasi-global, infrared-based estimates at 0.04° × spatial resolution with a short latency (15–60 min). It is intended to supersede PERSIANN–Cloud Classification System (PERSIANN-CCS) previously produced as product of PERSIANN family. We first provide brief description...
Abstract The Western North Atlantic Ocean (WNAO) and adjoining East Coast of America are great importance for atmospheric research have been extensively studied several decades. This broad region exhibits complex meteorological features a wide range conditions associated with gas particulate species from many sources regionally other continents. As Part 1 2‐part paper series, this work characterizes quantities chemistry, including gases, aerosols, wet deposition, by analyzing available...
Abstract The Western North Atlantic Ocean (WNAO) is a complex land‐ocean‐atmosphere system that experiences broad range of atmospheric phenomena, which in turn drive unique aerosol transport pathways, cloud morphologies, and boundary layer variability. This work, Part 2 2‐part paper series, provides an overview the circulation, variability, three‐dimensional structure, precipitation over WNAO; companion (Part 1) focused on chemical characterization aerosols, gases, wet deposition. Seasonal...
Abstract Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; weather, climate, hydrological forecasting. Moreover, real-time precipitation estimation precision is pivotal the monitoring managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data...
Abstract The 2022-2023 winter in the Western U.S., particularly Southern California, experienced unusually wet and cold conditions, prompting vigilant water management. This study chronicles year, highlighting challenges state managers faced as California shifted from extreme drought to elevated flood risks due an unprecedented “weather whiplash” a subsequent record-setting snowpack. By analyzing precipitation temperature data 2002 2023, research highlights anomalous nature of these...
Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided unique dataset to characterize types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of narrow swath coverage low temporal frequency. We use these high-quality observations build Deep Neural...
Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based products with high spatial and temporal resolution long records, as opposed to temporally spatially sparse rain gauge networks, are a suitable alternative analyze trends over Iran. This study analyzes the in annual, seasonal, monthly along contribution of each season month annual Iran 1983–2018 period. For analyses, Mann–Kendall test is applied Precipitation...
Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based estimations are a promising alternative to rain gauges providing homogeneous information. Most satellite-based products suffer from short-term data records, which make them unsuitable various hydrological applications. However, Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks-Climate Data Record...
Abstract Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional (CNNs), along with advancements computational power, open great opportunities to integrate massive amounts of real-time observations characterize spatiotemporal structures surface precipitation. This study aims develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW)...
Abstract This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in postprocessing framework. Enhanced machine model architecture training mechanisms are proposed improve the reliability skill of PQPFs while permitting computationally efficient fitting using short dataset. The methodology is applied 24-h accumulated from an ensemble forecast system recently introduced by Center for Western Weather Water...
Abstract Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted climate modeling studies have been identified several high intensity storms occurring over the last half decade. However, it has difficult to detect historical trends TC at time scales long enough overcome natural variability because of limitations existing observations. We introduce an experimental global high-resolution data record produced using infrared...
Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation over land and coastal regions necessary addressing high degree of spatial heterogeneity water availability demand, resolving extremes that modulate amplify hazards such as flooding landslides. Advancements in computation power along unique spectral data streams from passive meteorological sensors aboard geosynchronous...
Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at University California, Irvine—PERSIANN-CCS (Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other...
Abstract Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative estimation with a rich spatiotemporal resolution is vital for water management early warning systems of landslides these regions. At same time, high-quality near-real-time measurements AR remain challenging due complex topographic features land surface meteorological conditions region: specifically, orographic...
© 2023 American Meteorological Society. This published article is licensed under the terms of default AMS reuse license. For information regarding this content and general copyright information, consult Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Corresponding author: Vesta Afzali Gorooh, vafzaligorooh@ucsd.edu
Abstract. Water is a precious resource and important for human health, agriculture, industry, the environment. When water in short supply, monitoring predicting current future occurrence of precipitation-producing clouds essential. In this study, we investigate cloud microphysical features several convective systems United Arab Emirates (UAE) using multiple data sources, including aircraft measurements, satellite observations, weather radar reanalysis data. The observation dataset from an...
A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on Research Forecast atmospheric model tailored prediction of rivers associated heavy-to-extreme precipitation events over US (West-WRF) is presented. The (WW200En) generated with initial boundary conditions from National Environmental Prediction's Global Ensemble System (GEFS) European Centre Medium-Range Forecasts' Prediction (EPS), 100 unique combinations microphysics, planetary layer, cumulus...
Abstract Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted climate modeling studies have been identified several high intensity storms occurring over the last half decade. However, it has difficult to detect historical trends TC at time scales long enough overcome natural variability because of limitations existing observations. Using a new global high-resolution data record precipitation, we identify general increases...
Abstract In this study, the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Dynamic Infrared Rain-rate model (PDIR) product, a consistently measured 40-year archive of satellite-measured cloud-top infrared temperature data with spatiotemporal resolution 0.04° and 3-hourly as forcing data, is used to investigate trends in TC precipitation 1980 2019 robust linear fitting over multi-year moving averages accumulations volume rates. Trend...