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Pierre-Emmanuel Kirstetter
,
Y. Hong
,
J. J. Gourley
,
S. Chen
,
Z. Flamig
,
J. Zhang
,
M. Schwaller
,
W. Petersen
, and
E. Amitai

Abstract

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar–based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall-rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

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Yixin Wen
,
Qing Cao
,
Pierre-Emmanuel Kirstetter
,
Yang Hong
,
Jonathan J. Gourley
,
Jian Zhang
,
Guifu Zhang
, and
Bin Yong

Abstract

This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.

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Noah S. Brauer
,
Jeffrey B. Basara
,
Pierre E. Kirstetter
,
Ryann A. Wakefield
,
Cameron R. Homeyer
,
Jinwoong Yoo
,
Marshall Shepherd
, and
Joseph. A. Santanello Jr.

Abstract

Tropical Storm Bill produced over 400 mm of rainfall in portions of southern Oklahoma from 16 to 20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes, which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as Z H , Z DR, K DP, and ρ hv were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision–coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision–coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky.

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Jonathan J. Gourley
,
Zachary L. Flamig
,
Humberto Vergara
,
Pierre-Emmanuel Kirstetter
,
Robert A. Clark III
,
Elizabeth Argyle
,
Ami Arthur
,
Steven Martinaitis
,
Galateia Terti
,
Jessica M. Erlingis
,
Yang Hong
, and
Kenneth W. Howard

Abstract

This study introduces the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH is the first system to generate a suite of hydrometeorological products at flash flood scale in real-time across the conterminous United States, including rainfall average recurrence intervals, ratios of rainfall to flash flood guidance, and distributed hydrologic model–based discharge forecasts. The key aspects of the system are 1) precipitation forcing from the National Severe Storms Laboratory (NSSL)’s Multi-Radar Multi-Sensor (MRMS) system, 2) a computationally efficient distributed hydrologic modeling framework with sufficient representation of physical processes for flood prediction, 3) capability to provide forecasts at all grid points covered by radars without the requirement of model calibration, and 4) an open-access development platform, product display, and verification system for testing new ideas in a real-time demonstration environment and for fostering collaborations.

This study assesses the FLASH system’s ability to accurately simulate unit peak discharges over a 7-yr period in 1,643 unregulated gauged basins. The evaluation indicates that FLASH’s unit peak discharges had a linear and rank correlation of 0.64 and 0.79, respectively, and that the timing of the peak discharges has errors less than 2 h. The critical success index with FLASH was 0.38 for flood events that exceeded action stage. FLASH performance is demonstrated and evaluated for case studies, including the 2013 deadly flash flood case in Oklahoma City, Oklahoma, and the 2015 event in Houston, Texas—both of which occurred on Memorial Day weekends.

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Lisa Milani
,
Mark S. Kulie
,
Daniele Casella
,
Pierre E. Kirstetter
,
Giulia Panegrossi
,
Veljko Petkovic
,
Sarah E. Ringerud
,
Jean-François Rysman
,
Paolo Sanò
,
Nai-Yu Wang
,
Yalei You
, and
Gail Skofronick-Jackson

Abstract

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

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Daniel C. Watters
,
Patrick N. Gatlin
,
David T. Bolvin
,
George J. Huffman
,
Robert Joyce
,
Pierre Kirstetter
,
Eric J. Nelkin
,
Sarah Ringerud
,
Jackson Tan
,
Jianxin Wang
, and
David Wolff

Abstract

NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.

Significance Statement

Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Open access
Guy Delrieu
,
John Nicol
,
Eddy Yates
,
Pierre-Emmanuel Kirstetter
,
Jean-Dominique Creutin
,
Sandrine Anquetin
,
Charles Obled
,
Georges-Marie Saulnier
,
Véronique Ducrocq
,
Eric Gaume
,
Olivier Payrastre
,
Hervé Andrieu
,
Pierre-Alain Ayral
,
Christophe Bouvier
,
Luc Neppel
,
Marc Livet
,
Michel Lang
,
Jacques Parent du-Châtelet
,
Andrea Walpersdorf
, and
Wolfram Wobrock

Abstract

The Cévennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented.

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Gail Skofronick-Jackson
,
Walter A. Petersen
,
Wesley Berg
,
Chris Kidd
,
Erich F. Stocker
,
Dalia B. Kirschbaum
,
Ramesh Kakar
,
Scott A. Braun
,
George J. Huffman
,
Toshio Iguchi
,
Pierre E. Kirstetter
,
Christian Kummerow
,
Robert Meneghini
,
Riko Oki
,
William S. Olson
,
Yukari N. Takayabu
,
Kinji Furukawa
, and
Thomas Wilheit

Abstract

Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

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