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  • Author or Editor: William E. Johns x
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William L. Chapman
and
John E. Walsh

Abstract

Gridded fields of sea ice concentration are used to evaluate weekly and monthly anomalies of sea ice coverage in 22 Arctic subregions. The primary period of study is 1972–1988, although statistical comparisons are made with data of lesser quality from 1953–1971. The various time series of regional ice coverage permit the evaluation of ice anomaly persistence as a function of region, season, and lag (forecast range). The fractions of variance explained by anomaly persistence in most regions are considerably larger than corresponding atmospheric values. The fractions typically decrease from 50% to 10% as the forecast range increases from several weeks to several months. Anomaly persistence from the winter months is generally largest, although the regions of greatest persistence-derived forecast skill tend to migrate seasonally with the marginal ice zone. Biases in the regional analyses of the 1950s and 1960s inflate the apparent persistences in the North Atlantic during 1953–1971, but the persistences in most other regions are generally similar in the pre-1972 and post-1972 data. The inclusion of lagged regional cross-correlations provides little increment of forecast skill over persistence at the 1-month range, but this strategy appears to have the potential to enhance the usefulness of ice forecasts at ranges of several months. Analog-based forecasts show statistically significant skill but are generally unable to outperform persistence at the 1-month range.

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Pamela L. Heinselman
,
Patrick C. Burke
,
Louis J. Wicker
,
Adam J. Clark
,
John S. Kain
,
Jidong Gao
,
Nusrat Yussouf
,
Thomas A. Jones
,
Patrick S. Skinner
,
Corey K. Potvin
,
Katie A. Wilson
,
Burkely T. Gallo
,
Montgomery L. Flora
,
Joshua Martin
,
Gerry Creager
,
Kent H. Knopfmeier
,
Yunheng Wang
,
Brian C. Matilla
,
David C. Dowell
,
Edward R. Mansell
,
Brett Roberts
,
Kimberly A. Hoogewind
,
Derek R. Stratman
,
Jorge Guerra
,
Anthony E. Reinhart
,
Christopher A. Kerr
, and
William Miller

Abstract

In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction.

Significance Statement

The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

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