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Daniel D. Tripp
,
Joseph E. Trujillo-Falcón
,
Kim E. Klockow-McClain
,
Heather D. Reeves
,
Kodi L. Berry
,
Jeff S. Waldstreicher
, and
James A. Nelson

Abstract

This study explores forecaster perceptions of emerging needs for probabilistic forecasting of winter weather hazards through a nationwide survey disseminated to National Weather Service (NWS) forecasters. Questions addressed four relevant thematic areas: 1) messaging timelines for specific hazards, 2) modeling needs, 3) current preparedness to interpret and communicate probabilistic winter information, and 4) winter forecasting tools. The results suggest that winter hazards are messaged on varying time scales that sometimes do not match the needs of stakeholders. Most participants responded favorably to the idea of incorporating new hazard-specific regional ensemble guidance to fill gaps in the winter forecasting process. Forecasters provided recommendations for ensemble run length and output frequencies that would be needed to capture individual winter hazards. Qualitatively, forecasters expressed more difficulties communicating, rather than interpreting, probabilistic winter hazard information. Differences in training and the need for social-science-driven practices were identified as a few of the drivers limiting forecasters’ ability to provide strategic winter messaging. In the future, forecasters are looking for new winter tools to address forecasting difficulties, enhance stakeholder partnerships, and also be useful to the local community. On the regional scale, an ensemble system could potentially accommodate these needs and provide specialized guidance on timing and sensitive/high-impact winter events.

Significance Statement

Probabilistic information gives forecasters the ability to see a range of potential outcomes so that they can know how much confidence to place in the forecast. In this study, we surveyed forecasters so that we can understand how the research community can support probabilistic forecasting in winter. We found that forecasters want new technologies that help them understand hard forecast situations, improve their communication skills, and that are useful to their local communities. Most forecasters feel comfortable interpreting probabilistic information, but sometimes are not sure how to communicate it to the public. We asked forecasters to share their recommendations for new weather models and tools and we provide an overview of how the research community can support probabilistic winter forecasting efforts.

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William A. Gallus Jr.
and
Anna C. Duhachek

Abstract

Because bow echoes are often associated with damaging wind, accurate prediction of their severity is important. Recent work by Mauri and Gallus showed that despite increased challenges in forecasting nocturnal bows due to an incomplete understanding of how elevated convection interacts with the nocturnal stable boundary layer, several near-storm environmental parameters worked well to distinguish between bow echoes not producing severe winds (NS), those only producing low-intensity severe winds [LS; 50–55 kt (1 kt ≈ 0.51 m s−1)], and those associated with high-intensity (HS; >70 kt) severe winds. The present study performs a similar comparison for daytime warm-season bow echoes examining the same 43 SPC mesoanalysis parameters for 158 events occurring from 2010 to 2018. Although low-level shear and the meridional component of the wind discriminate well for nocturnal bow severity, they do not significantly differ in daytime bows. CAPE parameters discriminate well between daytime NS events and severe ones, but not between LS and HS, differing from nocturnal events where they discriminate between HS and the other types. The 500–850-hPa layer lapse rate works better to differentiate daytime bow severity, whereas the 500–700-hPa layer works better at night. Composite parameters work well to differentiate between all three severity types for daytime bow echoes, just as they do for nighttime ones, with the derecho composite parameter performing especially well. Heidke skill scores indicate that both individual and pairs of parameters generally are not as skillful at predicting daytime bow echo wind severity as they are at predicting nocturnal bow wind severity.

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Hai Zhang
,
Zigang Wei
,
Barron H. Henderson
,
Susan C. Anenberg
,
Katelyn O’Dell
, and
Shobha Kondragunta

Abstract

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States were derived from NOAA’s operational geostationary satellites’ Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of −21.4% and −15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg m−3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the United States, but with pronounced differences in the western United States due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (>35 μg m−3), the hourly estimates of PM2.5 used in nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality 10 times more than standard ground observations (1.8 versus 0.17 million people per hour).

Significance Statement

Fine particulate matter (PM2.5; diameters less than 2.5 μm) are generated from smoke, dust, and emissions from industrial, transportation, and other sectors. They are harmful to human health and even lead to premature mortality. Data from geostationary satellites can help estimate surface PM2.5 exposure by filling in gaps that are not covered by ground monitors. With this information, people can plan their outdoor activities accordingly. This study shows that availability of hourly PM2.5 observations covering the entire continental United States is more informative to the public about harmful exposure to pollution. On average, 1.8 million people per hour can be informed using satellite data compared to 0.17 million people per hour based on ground observations alone.

Open access
Morris L. Weisman
,
Kevin W. Manning
,
Ryan A. Sobash
, and
Craig S. Schwartz

Abstract

Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1 and 3 km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1 km and 3 km simulations were also well replicated for the differing environments, with the 1 km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3 km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1 km grid spacing.

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John Eylander
,
Sujay Kumar
,
Christa Peters-Lidard
,
Ted Lewiston
,
Christopher Franks
, and
Jerry Wegiel

Abstract

The USAF Weather (AFW) supports a number of military and U.S. government agencies by providing authoritative weather analysis and forecast products for any location globally, including soil moisture analyses. The long history of supporting soil moisture products and partnering with other U.S. government agencies led to the partnering between the U.S. Air Force (USAF) and NASA Goddard Space Flight Center, resulting in a merger of those organizations’ modeling systems, collaborative development of the Land Information System (LIS), and operational fielding of the system within the USAF 557th Weather Wing [557 WW; formerly, Headquarters Air Force Weather Agency (HQ AFWA)]. In 2009, the USAF implemented the NASA LIS and later made it the primary software system to generate global soil hydrology and energy budget products. The implementation of LIS delivered a significant upgrade over the existing Land Data Assimilation System (LDAS) the USAF operated, the Agriculture Meteorology (AGRMET) system. Implementation enabled the rapid integration of new LDAS technology into USAF operations, and led to a long-term NASA–USAF partnership resulting in continued development, integration, and implementation of new LIS capabilities. This paper documents both the history of the USAF Weather organization capabilities enabling the generation of soil moisture and other land surface analysis products, and describes the USAF–NASA partnership leading to the development of the merged LIS-AGRMET system. The article also presents a successful example of a mutually beneficial partnership that has enabled cutting-edge land analysis capabilities at the USAF, while transitioning NASA software and satellite data into USAF operations.

Open access
Joseph James
,
Chen Ling
,
Alyssa Bates
,
Gregory J. Stumpf
,
Kim Klockow-McClain
,
Pat Hyland
,
Jim LaDue
,
Kodi L. Berry
, and
Kevin Manross

Abstract

This project tested software capabilities and operational implications related to interoffice collaboration during NWS severe weather warning operations within a proposed paradigm, Forecasting A Continuum of Environmental Threats (FACETs). Current NWS policy of each forecast office issuing warnings for an exclusive area of responsibility may result in inconsistent messaging. In contrast, the FACETs paradigm, with object-based, moving probabilistic and deterministic hazard information, could provide seamless information across NWS County Warning Areas (CWAs). An experiment was conducted that allowed NWS forecasters to test new software that incorporates FACETs-based hazard information and potential concepts of operation to improve messaging consistency between adjacent WFOs. Experiment scenarios consisted of a variety of storm and office border interactions, fictional events requiring nowcasts, and directives that mimicked differing inter-WFO warning philosophies. Surveys and semi-structured interviews were conducted to gauge forecasters’ confidence and workload levels, and to discuss potential solutions for interoffice collaboration and software issues. We found that forecasters were able to adapt quickly to the new software and concepts and were comfortable with collaborating with their neighboring WFO in warning operations. Although forecasters felt the software’s collaboration tools enabled them to communicate in a timely manner, adding this collaboration increased their workload when compared to their workload during current warning operations.

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