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Noah S. Brauer, Jeffrey B. Basara, Cameron R. Homeyer, Greg M. McFarquhar, and Pierre E. Kirstetter

Abstract

Hurricane Harvey produced unprecedented widespread rainfall amounts over 1000 mm in portions of southeast Texas, including Houston, from 26 to 31 August 2017. The highly efficient and prolonged warm rain processes associated with Harvey played a key role in the catastrophic flooding that occurred throughout the region. Precipitation efficiency (PE) is widely referred to in the scientific literature when discussing excessive precipitation events that lead to catastrophic flash flooding, but has yet to be explored or quantified in tropical cyclones coincident with polarimetric radar observations. With the introduction of dual-polarization radar to the NEXRAD WSR-88D network, polarimetric radar variables such as Z H, Z DR, and K DP can be used to gain insight into the precipitation processes that contribute to enhanced PE. It was found that 6-h mean values of Z H between 35 and 45 dBZ, Z DR between 1 and 1.5 dB, and K DP greater than 1° km−1 were collocated with the regions of PE greater than 100% between 27 and 29 August. Additionally, supercell thunderstorms embedded in the outer bands of Harvey were identified via 3–6 km Multi-Radar Multi-Senor (MRMS) rotation tracks and were collocated with swaths of enhanced positive Z H, Z DR, and K DP. A polarimetric rainfall relationship estimates that 1-h mean rainfall rates in these supercells were as high as 85 mm h−1 and made a significant contribution to the excessive precipitation event that occurred over the region.

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

Abstract

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has provided the global community a widely used multisatellite (and multisensor type) estimate of quasi-global precipitation. One of the TMPA level-3 products, 3B42RT/TMPA-RT (where RT indicates real time), is a merged product of microwave (MW) and infrared (IR) precipitation estimates, which attempts to exploit the most desirable aspects of both types of sensors, namely, quality rainfall estimation and spatiotemporal resolution. This study extensively and systematically evaluates multisatellite precipitation errors by tracking the sensor-specific error sources and quantifying the biases originating from multiple sensors. High-resolution, ground-based radar precipitation estimates from the Multi-Radar Multi-Sensor (MRMS) system, developed by the National Severe Storms Laboratory (NSSL), are utilized as reference data. The analysis procedure involves segregating the grid precipitation estimate as a function of sensor source, decomposing the bias, and then quantifying the error contribution per grid. The results of this study reveal that while all three aspects of detection (i.e., hit, missed-rain, and false-rain biases) contribute to the total bias associated with IR precipitation estimates, overestimation bias (positive hit bias) and missed precipitation are the dominant error sources for MW precipitation estimates. Considering only MW sensors, the TRMM Microwave Imager (TMI) shows the largest missed-rain and overestimation biases (nearly double that of the other MW estimates) per grid box during the summer and winter seasons. The Special Sensor Microwave Imagers/Sounders (SSMIS on board F17 and F16) also show major error during winter and spring, respectively.

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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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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 mmof rainfall to portions of southern Oklahoma from 16-20 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 ZH, ZDR, KDP, and ρhv 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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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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Jonathan J. Gourley, Yang Hong, Zachary L. Flamig, Ami Arthur, Robert Clark, Martin Calianno, Isabelle Ruin, Terry Ortel, Michael E. Wieczorek, Pierre-Emmanuel Kirstetter, Edward Clark, and Witold F. Krajewski

Despite flash flooding being one of the most deadly and costly weather-related natural hazards worldwide, individual datasets to characterize them in the United States are hampered by limited documentation and can be difficult to access. This study is the first of its kind to assemble, reprocess, describe, and disseminate a georeferenced U.S. database providing a long-term, detailed characterization of flash flooding in terms of spatiotemporal behavior and specificity of impacts. The database is composed of three primary sources: 1) the entire archive of automated discharge observations from the U.S. Geological Survey that has been reprocessed to describe individual flooding events, 2) flash-flooding reports collected by the National Weather Service from 2006 to the present, and 3) witness reports obtained directly from the public in the Severe Hazards Analysis and Verification Experiment during the summers 2008–10. Each observational data source has limitations; a major asset of the unified flash flood database is its collation of relevant information from a variety of sources that is now readily available to the community in common formats. It is anticipated that this database will be used for many diverse purposes, such as evaluating tools to predict flash flooding, characterizing seasonal and regional trends, and improving understanding of dominant flood-producing processes. We envision the initiation of this community database effort will attract and encompass future datasets.

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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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