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Dylan J. Dodson
and
William A. Gallus Jr.

Abstract

Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) Model with 3- and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivities were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large-scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

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Scott D. Rudlosky
,
Joseph Patton
,
Eric Palagonia
,
John Y. N. Cho
, and
James M. Kurdzo

Abstract

Quantifying the costs of radar outages allows value to be attributed to the alternate datasets that help mitigate outages. When radars are offline, forecasters rely more heavily on nearby radars, surface reports, numerical weather prediction models, and satellite observations. Monetized radar benefit models allow value to be attributed to individual radars for mitigating the threat to life from tornadoes, flash floods, and severe winds. Eighteen radars exceed $20 million in annual benefits for mitigating the threat to life from these convective hazards. The Jackson, Mississippi, radar (KDGX) provides the most value ($41.4 million), with the vast majority related to tornado risk mitigation ($29.4 million). During 2020–23, the average radar is offline for 2.57% of minutes or 9.27 days per year and experiences an average of 58.9 outages per year lasting 4.32 h on average. Radar outage cost estimates vary by location and convective hazard. Outage cost estimates concentrate at the top, with 8, 2, 4, and 5 radars exceeding $1 million in outage costs during 2020, 2021, 2022, and 2023, respectively. The KDGX radar experiences outage frequencies of 4.92% and 5.50% during 2020 and 2023, resulting in outage cost estimates > $2 million in both years. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping mitigate radar outage impacts.

Significance Statement

This study combines information on radar status and monetized radar benefit models to attribute value to individual radars, estimate radar outage costs, and quantify the potential value of alternative datasets during outage-induced gaps in coverage. Eighteen radars exceed $20 million in annual benefits for mitigating the combined threat to life from tornadoes, flash floods, and severe winds. The first and third most valuable radars, both in Mississippi, experience outage frequencies twice the national average, accounting for a disproportionate share of the overall outage costs. Our findings suggest that characterizing and mitigating these outages might provide a near-term solution to better protect these communities from convective hazards. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping mitigate the impacts of radar outages.

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Charles M. Kuster
,
Keith D. Sherburn
,
Vivek N. Mahale
,
Terry J. Schuur
,
Olivia F. McCauley
, and
Jason S. Schaumann

Abstract

Recent operationally driven research has generated a framework, known as the three-ingredients method and mesovortex warning system, that can help forecasters anticipate mesovortex development and issue warnings within quasi-linear convective systems (QLCSs). However, dual-polarization radar data has not yet been incorporated into this framework. Therefore, several dual- and single-polarization radar signatures associated with QLCS mesovortices were analyzed to determine if they could provide additional information about mesovortex development and intensity. An analysis of 167 mesovortices showed that 1) KDP drops precede ~95% of mesovortices and provide an initial indication of where a mesovortex may develop, 2) midlevel KDP cores are a potentially useful precursor signature because they precede a majority of mesovortices and have higher magnitudes for mesovortices that produce wind damage or tornadoes, 3) low-level KDP cores and areas of enhanced spectrum width have higher magnitudes for mesovortices that produce wind damage or tornadoes, but tend to develop at about the same time as the mesovortex, which makes them more useful as diagnostic than as predictive signatures, and 4) as range from the radar increases, the radar signatures become less useful in anticipating mesovortex intensity but can still be used to anticipate mesovortex development or build confidence in mesovortex existence.

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Andrew Janiszeski
,
Robert M. Rauber
,
Brian F. Jewett
, and
Troy J. Zaremba

Abstract

This paper examines ice particle re-organization by three-dimensional horizontal kinematic flows within the comma head regions of two U.S. East Coast winter storms, and the effect of reorganization on particle concentrations within snowbands in each storm. In these simplified experiments, the kinematic flows are from the initialization of the HRRR model. Ice particles falling through the comma head were started from either 9, 8, or 7 km altitude, spaced every 200 m, and were transported north or northwest, arriving within the north or northwest half of the primary snowband in each storm. The greatest particle concentration enhancement within each band was a factor of 2.32–3.84 for the 16-17 Dec 2020 storm and 1.76–2.32 for the 29-30 January 2022 storm. Trajectory analyses for particles originating at 4 km on the southeast side of the comma head beneath the dry slot showed that this region supplied particles to the south side of the band with particle enhancements of factor of 1.36–2.08 for the 16-17 Dec 2020 storm and 1.04–2.16 for the 29-30 January 2022 storm. Snowfall within the bands had two source regions: 1) on the north/northwestern side, from ice particles falling from the comma head and, 2) on the southeastern side, from particles forming at or below 4 km altitude and transported northwestward by low-level flow off the Atlantic. While the findings give information on the source of particles in the bands, they do not definitively determine the cause of precipitation banding since other factors, such as large-scale ascent and embedded convection, also contribute to snow growth.

Open access
Kathryn Semmens
,
Rachel Hogan Carr
,
Burrell Montz
,
Keri Maxfield
,
Dana M. Tobin
,
Joshua Kastman
,
James A. Nelson
,
Kirstin Harnos
,
Margaret Beetstra
, and
Patrick Painter

Abstract

There is growing interest in impact-based decision support services to address complex decision-making, especially for winter storm forecasting. Understanding users’ needs for winter storm forecast information is necessary to make such impact-based winter forecasts relevant and useful to the diverse regions affected. A mixed-method social science research study investigated extending the Winter Storm Severity Index (WSSI) (operational for the contiguous United States (CONUS)) to Alaska, with consideration of the distinct needs of Alaskan stakeholders and the Alaskan climate. Data availability differences suggest the need for an Alaska specific WSSI, calling for user feedback to inform the direction of product modifications. Focus groups and surveys in six regions of Alaska provided information on how the WSSI components, definitions and categorization of impacts could align with stakeholder expectations and led to recommendations for the Weather Prediction Center to consider in developing the WSSI Alaska product. Overall, wind (strength and direction) and precipitation are key components to include. Air travel is a critical concern requiring wind and visibility information, while road travel is less emphasized (contrasting with CONUS needs). Special Weather Statements and Winter Storm Warnings are highly valued, and storm trajectory and transition (between precipitation types) information are important contexts for decision-makers. Alaska is accustomed to and prepared for winter impacts but being able to understand how components (wind, snow, ice) contribute to overall impact enhances the ability to respond and mitigate damage effectively. The WSSI adapted for Alaska can help address regional forecast needs, particularly valuable as the climate changes and typical winter conditions become more variable.

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Joshua B. Wadler
,
Joseph J. Cione
,
Samantha Michlowitz
,
Benjamin Jaimes de la Cruz
, and
Lynn K. Shay

Abstract

This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts.

Significance Statement

The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

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Dana M. Tobin
,
Joshua S. Kastman
,
James A. Nelson
, and
Heather D. Reeves

Abstract

Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture, and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation – and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day – are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.

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Dana M. Tobin
,
Joshua S. Kastman
,
James A. Nelson
, and
Heather D. Reeves

Abstract

In line with the continued focus of the National Weather Service (NWS) to provide Impact-based Decision Support Services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: The Hourly Winter Storm Severity Index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components – Snow Amount, Ice Accumulation, Snow Rate, Liquid Rate, and Blowing and Drifting Snow – each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces, and time-of-day factoring for active precipitation types, help to directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., a road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts, and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety.

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Clifford Mass
and
David Ovens

Abstract

On 8 August 2023, a wind-driven wildfire pushed across the city of Lahaina, located in West Maui, Hawaii, resulting in at least 100 deaths and an estimated economic loss of 4-6 billion dollars. The Lahaina wildfire was associated with strong, dry downslope winds gusting to 31-41 ms−1 (60-80 kt) that initiated the fire by damaging power infrastructure. The fire spread rapidly in invasive grasses growing in abandoned agricultural land upslope from Lahaina. This paper describes the synoptic and mesoscale meteorology associated with this event, as well as its predictability. Stronger than normal northeast trade winds, accompanied by a stable layer near the crest level of the West Maui Mountains, resulted in a high-amplitude mountain wave response and a strong downslope windstorm. Mesoscale model predictions were highly accurate regarding the location, strength, and timing of the strong winds. Hurricane Dora, which passed approximately 1300 km to the south of Maui, does not appear to have had a significant impact on the occurrence and intensity of the winds associated with the wildfire event. The Maui wildfire was preceded by a wetter-than-normal winter and near-normal summer conditions.

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Timothy B. Higgins
,
Aneesh C. Subramanian
,
Will E. Chapman
,
David A. Lavers
, and
Andrew C. Winters

Abstract

Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model,” of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction.

Significance Statement

The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes.

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