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Bhupendra A. Raut
,
Robert Jackson
,
Mark Picel
,
Scott M. Collis
,
Martin Bergemann
, and
Christian Jakob

Abstract

A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm’s ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features.

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Valentin Louf
,
Alain Protat
,
Robert C. Jackson
,
Scott M. Collis
, and
Jonathan Helmus

Abstract

Unfold Radar Velocity (UNRAVEL) is an open-source modular Doppler velocity dealiasing algorithm for weather radars. UNRAVEL is an algorithm that does not need external reference velocity data, making it easily applicable. The proposed algorithm includes 11 core modules and 2 dealiasing strategies. UNRAVEL is an iterative algorithm. The goal is to build the dealiasing results starting with the strictest possible continuity tests in azimuth and range and, after each step, relaxing the parameters to include more results from a progressively growing number of reference points. UNRAVEL also has modules that perform 3D continuity checks. Thanks to this modular design, the number of dealiasing strategies can be expanded in order to optimize the dealiasing results. While the first driver dealiases Doppler velocity from each tilt independently from one another, the second driver also performs a three-dimensional continuity check of the velocity using successive elevations. The proposed dealiasing algorithm is tested using severe weather data from an S-band Doppler radar that have been aliased to mimic aliased radial velocity patterns that would be observed by a C-band Doppler radar. Artificially aliasing S-band data permits creation of a reference to which the performance of various dealiasing techniques can be compared. Comparisons show that UNRAVEL consistently outperforms other established dealiasing algorithms for the test period selected in this work.

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Christopher J. Smith
,
Julia A. Crook
,
Rolf Crook
,
Lawrence S. Jackson
,
Scott M. Osprey
, and
Piers M. Forster

Abstract

In recent years, the idea of geoengineering, artificially modifying the climate to reduce global temperatures, has received increasing attention because of the lack of progress in reducing global greenhouse gas emissions. Stratospheric sulfate injection (SSI) is a geoengineering method proposed to reduce planetary warming by reflecting a proportion of solar radiation back into space that would otherwise warm the surface and lower atmosphere. The authors analyze results from the Met Office Hadley Centre Global Environment Model, version 2, Carbon Cycle Stratosphere (HadGEM2-CCS) climate model with stratospheric emissions of 10 Tg yr−1 of SO2, designed to offset global temperature rise by around 1°C. A reduction in concentrating solar power output of 5.9% on average over land is shown under SSI relative to a baseline future climate change scenario (RCP4.5) caused by a decrease in direct radiation. Solar photovoltaic energy is generally less affected as it can use diffuse radiation, which increases under SSI, at the expense of direct radiation. The results from HadGEM2-CCS are compared with the Goddard Earth Observing System Chemistry–Climate Model (GEOSCCM) from the Geoengineering Model Intercomparison Project (GeoMIP), with 5 Tg yr−1 emission of SO2. In many regions, the differences predicted in solar energy output between the SSI and RCP4.5 simulations are robust, as the sign of the changes for both HadGEM2-CCS and GEOSCCM agree. Furthermore, the sign of the total and direct annual mean radiation changes evaluated by HadGEM2-CCS agrees with the sign of the multimodel mean changes of an ensemble of GeoMIP models over the majority of the world.

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Yalei You
,
Christa Peters-Lidard
,
S. Joseph Munchak
,
Jackson Tan
,
Scott Braun
,
Sarah Ringerud
,
William Blackwell
,
John Xun Yang
,
Eric Nelkin
, and
Jun Dong

Abstract

Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. The cross-track scanning radiometers include the Advanced Technology Microwave Sounder (ATMS) on board the SNPP satellite and four Microwave Humidity Sounders (MHSs). The conical scanning radiometers include the Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs), while the precipitation retrievals from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) are taken as the reference. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.

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Brian Eder
,
Daiwen Kang
,
S. Trivikrama Rao
,
Rohit Mathur
,
Shaocai Yu
,
Tanya Otte
,
Ken Schere
,
Richard Wayland
,
Scott Jackson
,
Paula Davidson
,
Jeff McQueen
, and
George Bridgers

The National Air Quality Forecast Capability (NAQFC) currently provides next-day forecasts of ozone concentrations over the contiguous United States. It was developed collaboratively by NOAA and Environmental Protection Agency (EPA) in order to provide state and local agencies, as well as the general public, air quality forecast guidance. As part of the development process, the NAQFC has been evaluated utilizing strict monitor-to-gridcell matching criteria, and discrete-type statistics of forecast concentrations. While such an evaluation is important to the developers, it is equally, if not more important, to evaluate the performance using the same protocol as the model's intended application. Accordingly, the purpose of this article is to demonstrate the efficacy of the NAQFC from the perspective of a local forecaster, thereby promoting its use. Such an approach has required the development of a new evaluation protocol: one that examines the ability of the NAQFC to forecast values of the EPA's Air Quality Index (AQI) rather than ambient air concentrations; focuses on the use of categorical-type statistics related to exceedances and nonexceedances; and, most challenging, examines performance, not based on matched grid cells and monitors, but rather over a “local forecast region,” such as an air shed or metropolitan statistical area (MSA). Results from this approach, which is demonstrated for the Charlotte, North Carolina, MSA and subsequently applied to four additional MSAs during the summer of 2007, reveal that the quality of the NAQFC forecasts is generally comparable to forecasts from local agencies. Such findings will hopefully persuade forecasters, whether they are experienced with numerous tools at their disposal or inexperienced with limited resources, to utilize the NAQFC as forecast guidance.

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Dalia B. Kirschbaum
,
George J. Huffman
,
Robert F. Adler
,
Scott Braun
,
Kevin Garrett
,
Erin Jones
,
Amy McNally
,
Gail Skofronick-Jackson
,
Erich Stocker
,
Huan Wu
, and
Benjamin F. Zaitchik

Abstract

Precipitation is the fundamental source of freshwater in the water cycle. It is critical for everyone, from subsistence farmers in Africa to weather forecasters around the world, to know when, where, and how much rain and snow is falling. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched in February 2014, has the most advanced instruments to measure precipitation from space and, together with other satellite information, provides high-quality merged data on rain and snow worldwide every 30 min. Data from GPM and the predecessor Tropical Rainfall Measuring Mission (TRMM) have been fundamental to a broad range of applications and end-user groups and are among the most widely downloaded Earth science data products across NASA. End-user applications have rapidly become an integral component in translating satellite data into actionable information and knowledge used to inform policy and enhance decision-making at local to global scales. In this article, we present NASA precipitation data, capabilities, and opportunities from the perspective of end users. We outline some key examples of how TRMM and GPM data are being applied across a broad range of sectors, including numerical weather prediction, disaster modeling, agricultural monitoring, and public health research. This work provides a discussion of some of the current needs of the community as well as future plans to better support end-user communities across the globe to utilize this data for their own applications.

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Lynn A. McMurdie
,
Gerald M. Heymsfield
,
John E. Yorks
,
Scott A. Braun
,
Gail Skofronick-Jackson
,
Robert M. Rauber
,
Sandra Yuter
,
Brian Colle
,
Greg M. McFarquhar
,
Michael Poellot
,
David R. Novak
,
Timothy J. Lang
,
Rachael Kroodsma
,
Matthew McLinden
,
Mariko Oue
,
Pavlos Kollias
,
Matthew R. Kumjian
,
Steven J. Greybush
,
Andrew J. Heymsfield
,
Joseph A. Finlon
,
Victoria L. McDonald
, and
Stephen Nicholls

Abstract

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests.

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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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B. Soden
,
S. Tjemkes
,
J. Schmetz
,
R. Saunders
,
J. Bates
,
B. Ellingson
,
R. Engelen
,
L. Garand
,
D. Jackson
,
G. Jedlovec
,
T. Kleespies
,
D. Randel
,
P. Rayer
,
E. Salathe
,
D. Schwarzkopf
,
N. Scott
,
B. Sohn
,
S. de Souza-Machado
,
L. Strow
,
D. Tobin
,
D. Turner
,
P. van Delst
, and
T. Wehr

An intercomparison of radiation codes used in retrieving upper-tropospheric humidity (UTH) from observations in the ν2 (6.3 μm) water vapor absorption band was performed. This intercomparison is one part of a coordinated effort within the Global Energy and Water Cycle Experiment Water Vapor Project to assess our ability to monitor the distribution and variations of upper-tropospheric moisture from spaceborne sensors. A total of 23 different codes, ranging from detailed line-by-line (LBL) models, to coarser-resolution narrowband (NB) models, to highly parameterized single-band (SB) models participated in the study. Forward calculations were performed using a carefully selected set of temperature and moisture profiles chosen to be representative of a wide range of atmospheric conditions. The LBL model calculations exhibited the greatest consistency with each other, typically agreeing to within 0.5 K in terms of the equivalent blackbody brightness temperature (Tb ). The majority of NB and SB models agreed to within ±1 K of the LBL models, although a few older models exhibited systematic Tb biases in excess of 2 K. A discussion of the discrepancies between various models, their association with differences in model physics (e.g., continuum absorption), and their implications for UTH retrieval and radiance assimilation is presented.

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Randall M. Dole
,
J. Ryan Spackman
,
Matthew Newman
,
Gilbert P. Compo
,
Catherine A. Smith
,
Leslie M. Hartten
,
Joseph J. Barsugli
,
Robert S. Webb
,
Martin P. Hoerling
,
Robert Cifelli
,
Klaus Wolter
,
Christopher D. Barnet
,
Maria Gehne
,
Ronald Gelaro
,
George N. Kiladis
,
Scott Abbott
,
Elena Akish
,
John Albers
,
John M. Brown
,
Christopher J. Cox
,
Lisa Darby
,
Gijs de Boer
,
Barbara DeLuisi
,
Juliana Dias
,
Jason Dunion
,
Jon Eischeid
,
Christopher Fairall
,
Antonia Gambacorta
,
Brian K. Gorton
,
Andrew Hoell
,
Janet Intrieri
,
Darren Jackson
,
Paul E. Johnston
,
Richard Lataitis
,
Kelly M. Mahoney
,
Katherine McCaffrey
,
H. Alex McColl
,
Michael J. Mueller
,
Donald Murray
,
Paul J. Neiman
,
William Otto
,
Ola Persson
,
Xiao-Wei Quan
,
Imtiaz Rangwala
,
Andrea J. Ray
,
David Reynolds
,
Emily Riley Dellaripa
,
Karen Rosenlof
,
Naoko Sakaeda
,
Prashant D. Sardeshmukh
,
Laura C. Slivinski
,
Lesley Smith
,
Amy Solomon
,
Dustin Swales
,
Stefan Tulich
,
Allen White
,
Gary Wick
,
Matthew G. Winterkorn
,
Daniel E. Wolfe
, and
Robert Zamora

Abstract

Forecasts by mid-2015 for a strong El Niño during winter 2015/16 presented an exceptional scientific opportunity to accelerate advances in understanding and predictions of an extreme climate event and its impacts while the event was ongoing. Seizing this opportunity, the National Oceanic and Atmospheric Administration (NOAA) initiated an El Niño Rapid Response (ENRR), conducting the first field campaign to obtain intensive atmospheric observations over the tropical Pacific during El Niño.

The overarching ENRR goal was to determine the atmospheric response to El Niño and the implications for predicting extratropical storms and U.S. West Coast rainfall. The field campaign observations extended from the central tropical Pacific to the West Coast, with a primary focus on the initial tropical atmospheric response that links El Niño to its global impacts. NOAA deployed its Gulfstream-IV (G-IV) aircraft to obtain observations around organized tropical convection and poleward convective outflow near the heart of El Niño. Additional tropical Pacific observations were obtained by radiosondes launched from Kiritimati , Kiribati, and the NOAA ship Ronald H. Brown, and in the eastern North Pacific by the National Aeronautics and Space Administration (NASA) Global Hawk unmanned aerial system. These observations were all transmitted in real time for use in operational prediction models. An X-band radar installed in Santa Clara, California, helped characterize precipitation distributions. This suite supported an end-to-end capability extending from tropical Pacific processes to West Coast impacts. The ENRR observations were used during the event in operational predictions. They now provide an unprecedented dataset for further research to improve understanding and predictions of El Niño and its impacts.

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