Seminars & Events


ECSPrecip Seminar Series 2023: #1

"AGU Precipitation Student Award 2022 winners"

28 March 2023 - 10am EDT

First of the 2023 quarterly seminars organized by the ECSPrecip subcommittee dedicated to the "AGU Precipitation Student Award 2022 winners". Julia Shates (University of Wisconsin-Madison), Mochi Liao (Duke University) and SeungUk Kim (University of Illinois-Urbana Champaign) are three PhD candidates working on precipitation, investigating rain-snow transition levels in the atmosphere, uncertainties on precipitation estimates in complex terrain and warm season extreme flood events.

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SeungUk Kim

SeungUk is a PhD student in the Department of Atmospheric Sciences at the University of Illinois, Urbana Champaign, where he is a member of the Hydrometeorology Group led by Professor Francina Dominguez.

His research interests include the development of extreme floods and droughts in the US Midwest and the role of land-atmosphere interaction during such events. SeungUk recently completed an analysis of extreme floods, investigating sources of moisture and physical mechanisms, and is currently executing a similar analysis of extreme droughts.

SeungUk earned his Bachelors of Science degree in Atmospheric Sciences and Computational Sciences from Seoul National University in South Korea in 2016. He then served as a weather officer and chief forecaster for the Republic of Korea Air Force at the Seoul Air Base until May 2019.

Warm Season Extreme Flood Events in the Midwestern US – Sources of Moisture and Physical Mechanisms 

A comprehensive picture of the development of warm season extreme floods in the Midwestern US is presented. We first identify the climatological moisture sources for precipitation in the Midwest using the two-layer dynamic recycling model (2L-DRM) with ECMWF Reanalysis v5 (ERA5) data. Terrestrial sources supply most of the moisture for Midwestern US precipitation during the warm season, while oceanic sources dominate during the cold season. EOF analysis is used to select extreme flood events characterized by both positive soil moisture and precipitation anomalies. During the warm season flood events, moisture coming from oceanic sources increases by more than 45% compared to the climatology. In addition, our results show that moisture can come from remote regions due to sustained anomalous circulation. Low-level circulation anomalies associated with wave trains that traverse the continent enhance moisture contributions from terrestrial sources along narrow paths. However, moisture budget analysis reveals that the primary flood-producing mechanism is the convergence of moisture due to intense circulation anomalies. Moisture advection and thermodynamic terms are responsible for flood termination. Our results suggest that knowledge of antecedent wet soil moisture conditions is unlikely to improve the predictability of flood-producing storms.

Mochi Liao

Ph.D. candidate Mochi Liao was born in Lixian, Hunan, China, spent 4 years in Wuhan for bachelor degree, and has lived in United States after graduation. He is now pursuing his doctoral degree at Duke University and his final dissertation is scheduled in the summer 2023. His graduate studies were supported by NASA Earth and Space Science Fellowship and National Park Services. 

Mr. Liao is currently a visiting scholar at University of Illinois Urbana-Champaign. His primary research interests are in Physical Hydrology and Hydrometeorology with a focus on understanding and characterizing the spatiotemporal error structure of radar-based Quantitative Precipitation Estimation (QPE) in the mountains, particularly flood-producing precipitation events. 

ABSTRACT: Physics-guided AI framework for estimating precipitation uncertainty in complex terrain 

His research features a multidisciplinary background, such as building a physics-guided QPE error prediction system (Liao and Barros, 2022). The big data analytics he used such as machine learning and deep learning are guided by fundamental physics rather than statistical assumptions, and these techniques are quickly gaining popularity in earth sciences. The resulting data products are accessed by governmental agencies, academic institutions and industry sectors, providing tangible societal benefits.

Julia Shates

Julia Shates is a PhD candidate in the Atmospheric and Oceanic Sciences Department at the University of Wisconsin-Madison.  A primary focus of her research is understanding variability and phase transitions within the satellite radar blind zone that have important implications for the detection and quantification of snow and rain. She leverages observations from ground-based multi-instrument suites to examine micro- and macro-physical characteristics of clouds and precipitation.

ABSTRACT: Multi-year Analysis of Rain-Snow Levels at Marquette, Michigan

 As the climate warms, precipitation is changing and rainfall is replacing snowfall in many regions. Precipitation phase and intensity impact snow cover and accumulation, glacier mass balance, flood forecasting, and safety and transportation across the mid- and high- latitude regions. The height above the surface where ice fully melts to rain, here referred to as the rain-snow level (RSL), influences the precipiation reaching the surface. Ground-based and space-based radars can detect melting; however, space-based radars are limited in their ability to capture precipitation near the surface (< 2 km) due to ground-clutter. Also, rain-snow levels are lower in the mid and high latitudes and during the cold season, which makes detecting melting in the satellite radar blind zone difficult. This study uses ground-based data from an instrument suite at the National Weather Service station in Marquette, MI (MQT) to investigate RSLs in stratiform rainfall in the Upper Great Lakes Region. Rain events from January 2014 to April 2020 are examined using a ground-based vertically profiling radar (Micro Rain Radar; MRR), a custom NASA-developed video disdrometer (Precipitation Imaging Package; PIP), and data products from the ECMWF ERA5 and NASA MERRA-2 reanalyses. Results show distinct macro- and micro-physical characteristics between events with shallow RSLs (< 1.8 km above ground level [AGL]) and intermediate RSLs (> 1.8 km AGL). PIP observations reveal differences in the drop size and fall speed distributions and rain rates as a function of RSL. Seasonal differences, such as shallow RSLs in winter, fall, and spring, have implications for successful detection of melting in the satellite radar blind zone and subsequent identification of surface precipitation phase.


AGU Fall Meeting ECSPrecip Event 2022

Three speakers in different career stages of the precipitation field  shared their experiences throughout their career.

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Dr. Christopher Kidd

Dr. Kidd is currently a Research Scientist at the Earth System Science Interdisciplinary Center of the University of Maryland, working at NASA’s Goddard Space Flight Center. He is a Geographer having studied Geography, Physics and Maths at high school, and going on to study Geography at the University of Nottingham (UK). His PhD, on precipitation retrievals over land, is from the University of Bristol (UK). After his PhD he took up a tenured position at the University of Birmingham (UK), teaching remote sensing, applied meteorology and climatology, as well as research focusing on the retrieval of precipitation from multi-spectral, multi-sensor satellite observations, and representation of precipitation from surface observations (both radar and gauges). His current work involves improving precipitation measurements from both operational and research satellite sensors. He is a previous co-chair of the International Precipitation Working Group and co-leads the IPWG smallsat technology group. He is currently co-lead of the Precipitation-Virtual Constellation working group of CEOS, and a member of the National Academies Committee on Radio Frequencies. He has also served on several advisory panels and committees for new satellite missions. He has a total of over 70 peer-reviewed journal articles and book chapters, together with over 200 conference presentations.

Dr. Rachel McCrary

Dr. Rachel McCrary is a Project Scientist II at the National Center for Atmospheric Research in the Research Applications Laboratory’s Regional Integrated Science Collective.  Her research is primarily focused on understanding future changes in North American precipitation, snowfall and surface snowpacks.  She works with global, regional, and statistically downscaled climate information to help stakeholders and decision makers understand the potential impacts of climate change.  She is currently leading a NOAA-MAPP funded project to understand storm-scale changes in precipitation and snowfall associated with Extratropical Cyclones over the U.S.

Dr. Yixin 'Berry' Wen

Dr. Yixin ‘Berry’ Wen earned her M.S. degree of Geoinformatics and PhD degree of Meteorology and her from University of Oklahoma. She did her postdoc in Atmospheric Physics And Weather group at NASA/JPL, then she was a research scientist at the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) at University of Oklahoma and National Severe Storms Laboratory at NOAA. She is currently an Assistant Professor in the Department of Geography, University of Florida. She is also a member of the User Working Group in NASA/ Goddard Earth Science Data and Information Service Center. Her research interests include radar and satellite precipitation retrievals, ground validation of remote sensing products, applications of the advanced machine learning technologies in earth science, and climate injustice. 


ECSPrecip Seminar Series 2022: #2

Introduction to various precipitation products:
Satellites/Field Experiments/Models

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Download slides:
Satellite Products (Dr. Jackson Tan)
Field Experiments (Dr. Brenda Dolan)
Models (Dr. Elinor Martin)

Dr. Jackson Tan

Jackson Tan is an Associate Research Scientist with the University of Maryland, Baltimore County, working on the GPM Mission and the IMERG precipitation algorithm at NASA Goddard Space Flight Center. His research focus also includes MODIS and ISCCP cloud regimes, the relationship between clouds and precipitation, and the ground validation of satellite precipitation. He received his Ph.D. in Atmospheric Sciences from Monash University in Australia in 2014.

ABSTRACT: An Overview of Satellite Precipitation Products

Satellite precipitation products are currently the only observation-based source of precipitation with global coverage and, with a record that now extends to decades, are often used in a wide range of scientific and societal applications. In this seminar, I will provide an overview of these satellite precipitation products. I will explain how precipitation is estimated from different types of satellite measurements and how different techniques combine the various streams of observations. From this, I will then summarize the different precipitation products that are available, their advantages and limitations, and—in particular—aspects users should watch out for from the perspective of an algorithm developer.

Dr. Brenda Dolan

Dr. Brenda Dolan’s interests include radar analysis and algorithms, observational integration and validation of cloud-resolving models, precipitation processes, and cloud physics. She is an expert in polarimetric radar, having developed one of the first hydrometeor identification algorithms for short-wavelength radars (X- and C-band). Additionally, she has worked with a wide variety of radars around the world including ground, ship, and satellite-based radars from Ka- to S-band. Brenda has participated in over 10 field projects as a radar scientist or mission scientist. She has worked with large datasets including from radar and disdrometer. Finally she works at the interface of cloud resolving models and observations, using them synergistically to improve observational retrievals and better model representation of physical processes. Recently she has collaborated to develop a polarimetric radar forward operator which can be used with cloud resolving models such as WRF and RAMS.

ABSTRACT: Precipitation-focused Field Campaign Data from Precipitation Radars and Disdrometers 

Field observations of precipitation processes and surface rainfall can fill critical gaps in our understanding of the global water cycle and climate. Assets such as mobile radars, disdrometers, and particle probes can be placed in challenging or data sparse locations such as over the oceans or in complex topography to provide high resolution and detailed observations to better our understanding of precipitation processes and variability, as well as constrain model parameterizations. However, such data is often unique and presents its own challenges for usage, including scanning strategies, data formats, quality control, coverage, and continuity. This talk will focus on radar and disdrometer data collected in recent field campaigns over the oceans and in complex terrain, including a broad overview of available datasets. Specifically, this talk will overview the applications, challenges and advantages of data collected by precipitation radars and disdrometers during field experiments which can be leveraged for precipitation research.

Dr. Elinor Martin

Dr. Elinor Martin is an Associate Professor in the School of Meteorology at the University of Oklahoma (OU), and an OU co-PI of the South Central Climate Adaptation Science Center. The research team she leads aims to understand climate variability and change, with a specific focus on precipitation variability across the Americas and tropics.

ABSTRACT: Precipitation from climate models

This presentation will discuss precipitation from climate models, with a focus on models used as part of the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6. We will discuss what model precipitation data looks like as well as ways to access CMIP5 and 6 model output of precipitation and other fields. We will also address some of the limitations of precipitation in climate model systems and briefly mention climate downscaling for precipitation.


ECSPrecip Seminar Series 2022: #1

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Alyssa Stansfield

Alyssa Stansfield graduated in May 2022 with her PhD in Atmospheric Science from Stony Brook University. Her dissertation research utilized both observations and climate models to study how tropical cyclones and their precipitation are impacted by climate change. This September she will start an NSF AGS postdoctoral fellowship at Colorado State University working with Dr. Kristen Rasmussen.


The estimated rate at which climate change increases precipitation within tropical cyclones (TCs) has a wide range of uncertainty. Previous studies using various methods have found rates both below and greatly above the theoretical Clausius-Clapeyron rate of ~6-7% per °C of warming. Modeling studies simulate increasing TC intensities with warming sea surface temperatures (SSTs), which may push the precipitation increase above the Clausius-Clapeyron rate. This work explores and compares the relationship between TC precipitation, SST, and storm intensity in idealized aquaplanet climate model simulations, Earth-like high-resolution climate model simulations, and observations. The methodology involves extracting TC precipitation using an automated algorithm, binning TCs by relevant characteristics (i.e., their local-environment SSTs, intensities, and outer sizes), extracting various precipitation metrics from their precipitation fields, and calculating relationships between the precipitation metrics, TC characteristics, and SSTs.

Fraser King

I am a PhD Candidate at the University of Waterloo studying the intersection of remote sensing and machine learning of precipitation. My background is in computer science and statistical modeling with a master’s focus on remote sensing of snowfall.


Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval from a deep convolutional neural network (DeepPrecip) to estimate 20-minute average surface precipitation accumulation using near-surface radar data. DeepPrecip displays high retrieval skill and can accurately model precipitation accumulation, with a mean square error 99\% lower, on average, than current methods. DeepPrecip also outperforms less complex machine learning retrieval algorithms, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses indicate a combination of both near-surface (below 1 km) and higher-altitude (1.5 - 2 km) radar measurements are the primary features contributing to retrieval accuracy. This research reveals the important role for deep learning in extracting relevant information about precipitation from radar retrievals.

Yoonjin Lee

I got my master’s and PhD degree in atmospheric science at Colorado State University. I am currently a postdoc at Cooperative Institute for Research in the Atmosphere (CIRA). 


The High-Resolution Rapid Refresh (HRRR), current operational forecast model in the United States uses latent heating (LH) derived from ground-based radars to initiate convection during short-term forecast. Although radar reflectivity is a good indicator for LH intensity, and it is efficient in initiating convection, ground-based radar data are not available over the ocean or mountainous regions. Data that are available continuously in such regions and that can be readily used in short-term forecasts are from a geostationary satellite. Despite its inherent limitation of seeing only the cloud top, it can still be useful if taking advantage of its high spatial and temporal resolution data. This study presents methods to detect convection and retrieve LH of the convective clouds from GOES-16, and evaluates impacts of using the retrieved LH in convective initialization for precipitation forecast. The results show that LH from GOES-16 significantly improves precipitation forecast, and it has similar impacts in the forecast if the detection is correctly made.


AGU Fall Meeting ECSPrecip Event 2021

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AGU Fall Meeting ECSPrecip Event 2020

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