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Paquita Zuidema
,
Ping Chang
,
Brian Medeiros
,
Ben P. Kirtman
,
Roberto Mechoso
,
Edwin K. Schneider
,
Thomas Toniazzo
,
Ingo Richter
,
R. Justin Small
,
Katinka Bellomo
,
Peter Brandt
,
Simon de Szoeke
,
J. Thomas Farrar
,
Eunsil Jung
,
Seiji Kato
,
Mingkui Li
,
Christina Patricola
,
Zaiyu Wang
,
Robert Wood
, and
Zhao Xu

Abstract

Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.

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Thomas Jung
,
Neil D. Gordon
,
Peter Bauer
,
David H. Bromwich
,
Matthieu Chevallier
,
Jonathan J. Day
,
Jackie Dawson
,
Francisco Doblas-Reyes
,
Christopher Fairall
,
Helge F. Goessling
,
Marika Holland
,
Jun Inoue
,
Trond Iversen
,
Stefanie Klebe
,
Peter Lemke
,
Martin Losch
,
Alexander Makshtas
,
Brian Mills
,
Pertti Nurmi
,
Donald Perovich
,
Philip Reid
,
Ian A. Renfrew
,
Gregory Smith
,
Gunilla Svensson
,
Mikhail Tolstykh
, and
Qinghua Yang

Abstract

The polar regions have been attracting more and more attention in recent years, fueled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with fewer in situ observations, and with numerous local physical processes that are less well represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere–sea ice–ocean models, even for short-term prediction; and insight into polar–lower-latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting communites will work together with stakeholders in a period of intensive observing, modeling, prediction, verification, user engagement, and educational activities.

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Mark Weber
,
Kurt Hondl
,
Nusrat Yussouf
,
Youngsun Jung
,
Derek Stratman
,
Bryan Putnam
,
Xuguang Wang
,
Terry Schuur
,
Charles Kuster
,
Yixin Wen
,
Juanzhen Sun
,
Jeff Keeler
,
Zhuming Ying
,
John Cho
,
James Kurdzo
,
Sebastian Torres
,
Chris Curtis
,
David Schvartzman
,
Jami Boettcher
,
Feng Nai
,
Henry Thomas
,
Dusan Zrnić
,
Igor Ivić
,
Djordje Mirković
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Caleb Fulton
,
Jorge Salazar
,
Guifu Zhang
,
Robert Palmer
,
Mark Yeary
,
Kevin Cooley
,
Michael Istok
, and
Mark Vincent

Abstract

This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA’s future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented.

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Jhoon Kim
,
Ukkyo Jeong
,
Myoung-Hwan Ahn
,
Jae H. Kim
,
Rokjin J. Park
,
Hanlim Lee
,
Chul Han Song
,
Yong-Sang Choi
,
Kwon-Ho Lee
,
Jung-Moon Yoo
,
Myeong-Jae Jeong
,
Seon Ki Park
,
Kwang-Mog Lee
,
Chang-Keun Song
,
Sang-Woo Kim
,
Young Joon Kim
,
Si-Wan Kim
,
Mijin Kim
,
Sujung Go
,
Xiong Liu
,
Kelly Chance
,
Christopher Chan Miller
,
Jay Al-Saadi
,
Ben Veihelmann
,
Pawan K. Bhartia
,
Omar Torres
,
Gonzalo González Abad
,
David P. Haffner
,
Dai Ho Ko
,
Seung Hoon Lee
,
Jung-Hun Woo
,
Heesung Chong
,
Sang Seo Park
,
Dennis Nicks
,
Won Jun Choi
,
Kyung-Jung Moon
,
Ara Cho
,
Jongmin Yoon
,
Sang-kyun Kim
,
Hyunkee Hong
,
Kyunghwa Lee
,
Hana Lee
,
Seoyoung Lee
,
Myungje Choi
,
Pepijn Veefkind
,
Pieternel F. Levelt
,
David P. Edwards
,
Mina Kang
,
Mijin Eo
,
Juseon Bak
,
Kanghyun Baek
,
Hyeong-Ahn Kwon
,
Jiwon Yang
,
Junsung Park
,
Kyung Man Han
,
Bo-Ram Kim
,
Hee-Woo Shin
,
Haklim Choi
,
Ebony Lee
,
Jihyo Chong
,
Yesol Cha
,
Ja-Ho Koo
,
Hitoshi Irie
,
Sachiko Hayashida
,
Yasko Kasai
,
Yugo Kanaya
,
Cheng Liu
,
Jintai Lin
,
James H. Crawford
,
Gregory R. Carmichael
,
Michael J. Newchurch
,
Barry L. Lefer
,
Jay R. Herman
,
Robert J. Swap
,
Alexis K. H. Lau
,
Thomas P. Kurosu
,
Glen Jaross
,
Berit Ahlers
,
Marcel Dobber
,
C. Thomas McElroy
, and
Yunsoo Choi

Abstract

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA’s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).

Free access
John H. Seinfeld
,
Gregory R. Carmichael
,
Richard Arimoto
,
William C. Conant
,
Frederick J. Brechtel
,
Timothy S. Bates
,
Thomas A. Cahill
,
Antony D. Clarke
,
Sarah J. Doherty
,
Piotr J. Flatau
,
Barry J. Huebert
,
Jiyoung Kim
,
Krzysztof M. Markowicz
,
Patricia K. Quinn
,
Lynn M. Russell
,
Philip B. Russell
,
Atsushi Shimizu
,
Yohei Shinozuka
,
Chul H. Song
,
Youhua Tang
,
Itsushi Uno
,
Andrew M. Vogelmann
,
Rodney J. Weber
,
Jung-Hun Woo
, and
Xiao Y. Zhang

Although continental-scale plumes of Asian dust and pollution reduce the amount of solar radiation reaching the earth's surface and perturb the chemistry of the atmosphere, our ability to quantify these effects has been limited by a lack of critical observations, particularly of layers above the surface. Comprehensive surface, airborne, shipboard, and satellite measurements of Asian aerosol chemical composition, size, optical properties, and radiative impacts were performed during the Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) study. Measurements within a massive Chinese dust storm at numerous widely spaced sampling locations revealed the highly complex structure of the atmosphere, in which layers of dust, urban pollution, and biomass- burning smoke may be transported long distances as distinct entities or mixed together. The data allow a first-time assessment of the regional climatic and atmospheric chemical effects of a continental-scale mixture of dust and pollution. Our results show that radiative flux reductions during such episodes are sufficient to cause regional climate change.

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Adam J. Clark
,
Israel L. Jirak
,
Scott R. Dembek
,
Gerry J. Creager
,
Fanyou Kong
,
Kevin W. Thomas
,
Kent H. Knopfmeier
,
Burkely T. Gallo
,
Christopher J. Melick
,
Ming Xue
,
Keith A. Brewster
,
Youngsun Jung
,
Aaron Kennedy
,
Xiquan Dong
,
Joshua Markel
,
Matthew Gilmore
,
Glen S. Romine
,
Kathryn R. Fossell
,
Ryan A. Sobash
,
Jacob R. Carley
,
Brad S. Ferrier
,
Matthew Pyle
,
Curtis R. Alexander
,
Steven J. Weiss
,
John S. Kain
,
Louis J. Wicker
,
Gregory Thompson
,
Rebecca D. Adams-Selin
, and
David A. Imy

Abstract

One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.

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