Tag: Precipitation

Figure: S-NPP SFR during the first accumulating snow of the 2021-2022 winter season in the central Appalachian counties.

Snowfall Rate Page for Local NWS Office

December 7, 2021

The ESSIC/CISESS snowfall rate (SFR) team, Huan Meng, Jun Dong, and Yongzhen Fan, set up a webpage for the NWS Sterling, VA Weather Forecast Office (Office Call Sign: LWX) at the request of Luis Rosa, a senior forecaster from the office. The page is set for the LWX county warning area (CWA). Currently, the page has the operational SFR images from five satellites but will be expanded to include the experimental SFR from four other satellites. The SFR product is produced at CISESS from direct broadcast data retrieved from the University of Wisconsin. The product latency ranges from 12-25 min depending on the satellite.

Figure 1. MiRS NOAA-20/ATMS retrieved TPW (descending and ascending orbits) on 24 October 2021 over the eastern Pacific and West Coast region. The atmospheric river is clearly seen. Locations of the four vertical cross-sections shown in Figure 2 are indicated by the dashed lines.

NOAA’s MiRs Captures Category 5 Rainfall Event in California

November 3, 2021

On October 24, a powerful Category 5 (the maximum possible) atmospheric river (AR) occurred over the northern and central parts of California. The storm system featured record breaking precipitation, leading to flooding and mudslides in some locations, along with dangerous winds exceeding 70 miles per hour at higher elevations. San Francisco recorded its fourth highest single-day rainfall amount of over 4 inches. Satellite passive microwave measurements are one of the observational tools that allow depiction of these extreme events, since microwaves are less affected by clouds and precipitation.

Figure 1. Global and regional validation of the logistic regression (blue), deep neural network (orange), random forest(green) and XGboost (red) snowfall detection (SD) model for S-NPP.

Fan and Meng Develop New Machine Learning Snowfall Detection Algorithm

October 18, 2021

ESSIC/CISESS scientists Huan Meng and Yongzhen Fan have recently developed a new machine learning snowfall detection (SD) algorithm, based on eXtreme Gradient Boosting (XGB). The algorithm was developed for the Advanced Technology Microwave Sounder (ATMS) onboard NPP and NOAA-20 as well as the MHS/AMSU-A onboard Metop-A, Metop-B, Metop-C and NOAA-19.

A NOAA visualization of Tropical Storm Fred's path up the East Coast, which spreads from Alabama and Mississippi to northern New York and western Massachussets

Performance of NOAA Satellite-based Precipitation Products During Tropical Storm Fred

September 7, 2021

On August 18, 2021, the western part of North Carolina suffered a catastrophic flash flood caused by Tropical Storm Fred. As part of a NOAA/STAR precipitation validation project, CISESS science team Malar Arulraj, Veljko Petković, Ralph Ferraro, and Huan Meng evaluated the performance of different satellite-based precipitation products during this event using Multi-Radar/Multi-Sensor (MRMS) observations.

Figure: This is a precipitation event on 29 August 2020 over the Pacific Ocean near the lower California peninsula. (a) the cross-track radiometer precipitation data, (b) the conical scanning radiometer precipitation data, (c) the reference data for the event, and (d) the “morphed” radiometer precipitation data. The box shows the area of improvement due to morphing.

Improving Satellite Precipitation Retrieval

August 24, 2021

ESSIC/CISESS Scientists Yalei You, John Xun Yang, and Jun Dong have a new article on using “morphing” to improve rain data from cross-track scanning radiometers. The paper, titled “Improving Cross-track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing”, is in press at the Journal of Hydrometeorology.

Acid Rain Scenes from Great Smoky Mountains National Park - Clingmans Dome

A Study of Two Impactful Heavy Rainfall Events

July 9, 2021

Several ESSIC/CISESS scientists including Malarvizhi Arulraj, Jifu Yin, Christopher Grassotti, Veljko Petkovic collaborated on a multi-author, two-part study led by Douglas Miller, Professor of Atmospheric Sciences at the University of North Carolina, Asheville.

Petković is Primary Convener for AGU Fall Meeting Session

July 2, 2021

Veljko Petkovic, ESSIC/CISESS Assistant Research Scientist, is a primary convener for the AGU Fall Meeting 2021 session on Machine Learning applications on precipitation processes. This session, titled “Precipitation Through the Eyes of Machine Learning and Advanced Statistics: Remote Sensing, Uncertainties and Variability” will highlight contributions on precipitation-related AI/ML studies seeking physical implications, model interpretations, visualizations, remote sensing applications and uncertainty analyses that can identify novel elements in understanding of precipitation processes.

Figure 1. Maps of (a) elevation across the CONUS, (b) spatial distributions of GAGES-II sites matching with selecting criteria, (c) the Köppen’s climate type and (d) MRVBF class of final selected 1,548 river basins.

Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States

June 23, 2021

ESSIC Visiting Assistant Research Scientist Huan Wu has a new paper in Journal of Hydrometeorology titled “Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States” alongside Naijun Zhou from UMD’s Department of Geographical Sciences.

Vast stretches of greenery mark the banks of Kunene River

Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia

May 26, 2021

ESSIC Associate Research Scientist Toshihisa Matsui is a co-author on a new international paper in Climate titled “Convection Parametrization and Multi-Nesting Dependence of a Heavy Rainfall Event over Namibia with Weather Research and Forecasting (WRF) Model”. In the study, Matsui and international co-authors investigate the effects of using multi-nests and a convection scheme, numerical weather prediction models, on the simulation of a heavy rainfall event over the north-western region of Kunene, Namibia.

Sarah Ringerud smiles in front of a dark backdrop, wearing a striped t-shirt

Ringerud, You Win IEEE Paper Award with New Study on An Active–Passive Microwave Land Surface Database

May 6, 2021

A paper co-authored by ESSIC/CISESS Assistant Research Scientists Sarah Ringerud and Yalei You was selected as the winner of the IEEE Geoscience and Remote Sensing Society 2020 Transactions Prize Paper Award. The paper, titled “An Active–Passive Microwave Land Surface Database From GPM”, is also by coauthors S. Joseph Munchak and Ludovic Brucker from NASA Goddard Space Flight Center and international researchers Iris de Gelis and Catherine Prigent.