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Karsten A. Shein
,
Dennis P. Todey
,
F. Adnan Akyuz
,
James R. Angel
,
Timothy M. Kearns
, and
James L. Zdrojewski

New all-time extreme climate records have been set in several states over the past few years. These records highlighted a need to review the existing statewide climate extremes tables maintained by the NOAA National Climatic Data Center (NCDC). Also, since these tables were last up-dated, NCDC has greatly extended its digital data record into the past for many locations and has applied improved quality assurance processes to its archived data, revealing several potential new record values. To ensure the records maintained in the statewide climate extremes tables accurately reflect the most current and valid data available, the records were reevaluated. The all-time maximum and minimum temperature, all-time greatest 24-h precipitation and snowfall, and all-time greatest snow depth for each of the 50 states, Puerto Rico, and the U.S. Virgin Islands were manually examined to determine their validity, accuracy, accessibility, and provenance. NCDC's data holdings were scoured for values that might exceed established records, and the validity of each potentially record-breaking observation was evaluated. The revised extremes tables were vetted by the National Weather Service, regional climate centers, and state climatologists to ensure agreement. In conjunction with this revision, a new state climate extremes evaluation process has been established to formally consider any potential challenges to the existing records and update the records tables as necessary.

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Kenneth E. Kunkel
,
Stanley A. Changnon
,
Steven E. Hollinger
,
Beth C. Reinke
,
Wayne M. Wendland
, and
James R. Angel

Effective responses by government agencies, businesses, and private industry to climate disasters such as the disastrous Mississippi River flood of 1993 hinge on the regional availability of diverse up-to-date weather, climate, and water information. In addition to the obvious need for accurate forecasts and warnings of severe weather and floods, other types of meteorologically based information can contribute to effective responses. Some examples of information requested during and after the 1993 flood include 1) hydroclimatic assessments of the magnitude of the event, 2) agricultural assessments of the impacts of heavy rains and flooding on corn and soybean production, and 3) probabilistic outlooks of the recurrence of flooding based on soil moisture conditions. Quick responses to these climate information needs necessitate 1) a real-time climate monitoring system, 2) physical models to assess effects and impacts, and 3) scientific expertise to address complex issues.

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Kenneth E. Kunkel
,
Karen Andsager
,
Glen Conner
,
Wayne L. Decker
,
Harry J. Hillaker Jr.
,
Pam Naber Knox
,
Fred V. Nurnberger
,
Jeffrey C. Rogers
,
Kenneth Scheeringa
,
Wayne M. Wendland
,
James Zandlo
, and
James R. Angel

Daily observations of precipitation and maximum and minimum temperature from the National Weather Service's cooperative observer network collected prior to 1948 were keyed into a digital database. This database includes stations in the nine midwestern states of Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. The primary source used in this project was the publication Climatological Data, which began in 1896. This database provides a substantial enhancement to the National Climatic Data Center's TD-3200 Summary of the Day database, which includes little data prior to 1948. Approximately 2 × 107 data values were keyed, increasing the amount of pre- 1948 digital data by about a factor of 3 and substantially improving its spatial uniformity. The data were subjected to an extensive set of quality control procedures. It is expected that these data will find their greatest value in applications requiring very long historical records, such as assessments of the risks of extreme events.

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Christopher J. White
,
Daniela I. V. Domeisen
,
Nachiketa Acharya
,
Elijah A. Adefisan
,
Michael L. Anderson
,
Stella Aura
,
Ahmed A. Balogun
,
Douglas Bertram
,
Sonia Bluhm
,
David J. Brayshaw
,
Jethro Browell
,
Dominik Büeler
,
Andrew Charlton-Perez
,
Xandre Chourio
,
Isadora Christel
,
Caio A. S. Coelho
,
Michael J. DeFlorio
,
Luca Delle Monache
,
Francesca Di Giuseppe
,
Ana María García-Solórzano
,
Peter B. Gibson
,
Lisa Goddard
,
Carmen González Romero
,
Richard J. Graham
,
Robert M. Graham
,
Christian M. Grams
,
Alan Halford
,
W. T. Katty Huang
,
Kjeld Jensen
,
Mary Kilavi
,
Kamoru A. Lawal
,
Robert W. Lee
,
David MacLeod
,
Andrea Manrique-Suñén
,
Eduardo S. P. R. Martins
,
Carolyn J. Maxwell
,
William J. Merryfield
,
Ángel G. Muñoz
,
Eniola Olaniyan
,
George Otieno
,
John A. Oyedepo
,
Lluís Palma
,
Ilias G. Pechlivanidis
,
Diego Pons
,
F. Martin Ralph
,
Dirceu S. Reis Jr.
,
Tomas A. Remenyi
,
James S. Risbey
,
Donald J. C. Robertson
,
Andrew W. Robertson
,
Stefan Smith
,
Albert Soret
,
Ting Sun
,
Martin C. Todd
,
Carly R. Tozer
,
Francisco C. Vasconcelos Jr.
,
Ilaria Vigo
,
Duane E. Waliser
,
Fredrik Wetterhall
, and
Robert G. Wilson

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.

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