A Coastal Flood Event Database for the Southeastern Georgia and Southeastern South Carolina Coast and the Operational Implementation of a Tide Forecast Tool

Coastal flooding occurs when saltwater inundates normally dry land and the resulting impacts can range from minor flooding of low-lying areas along the coast, to significant damage to property and structures. Previous research consistently suggests that if sea-level rise continues to increase along the East Coast of the United States, coastal flooding will occur more frequently. In order to document the history of coastal flooding along the southeastern Georgia and southeastern South Carolina coast, a coastal flood event database was created for National Ocean Service tide gauges located in Charleston Harbor, South Carolina and Fort Pulaski, Georgia. Trends from the data show that coastal flooding is occurring more frequently with time at both tide gauges, particularly over the last five to ten years. Because of the increased frequency and worsening impacts of tidal flooding, a tide forecast tool is implemented operationally in an effort to improve deterministic tide forecasts. This study extends the dataset used in the Charleston Harbor forecast tool, expands the tool to Fort Pulaski, and compares the synoptic category forecast equations to an all-inclusive equation that does not differentiate by synoptic category. Results show that there is virtually no difference in the forecast accuracy between the all-inclusive forecast equation and the specific forecast equations based on synoptic category. Furthermore, the all-inclusive forecast equation can be implemented operationally, will help improve deterministic tide forecasts, and will likely aid in the decision-making process for Coastal Flood Watches, Warnings, and Advisories issued by the National Weather Service office in Charleston, South Carolina.

Communicating Probability Information in Hurricane Forecasts: Assessing Statements that Forecasters Use on Social Media and Implications for Public Assessments of Reliability

Forecasters are responsible for predicting the weather and communicating risk with stakeholders and members of the public. This study investigates the statements that forecasters use to communicate probability information in hurricane forecasts and the impact these statements may have on how members of the public evaluate forecast reliability. We use messages on Twitter to descriptively analyze probability statements in forecasts leading up to Hurricanes Harvey, Irma, Maria, and Florence from forecasters in three different groups: the National Hurricane Center, local Weather Forecast Offices, and in the television broadcast community. We then use data from a representative survey of United States adults to assess how members of the public wish to receive probability information and the impact of information format on assessments of forecast reliability. Results from the descriptive analysis indicate forecasters overwhelmingly use words and phrases in place of numbers to communicate probability information. In addition, the words and phrases forecasters use are generally vague in nature -- they seldom include rank adjectives (e.g., “low” or “high”) to qualify blanket expressions of uncertainty (e.g., “there is a chance of flooding”). Results from the survey show members of the public generally prefer both words/phrases and numbers when receiving forecast information. They also show information format affects public judgments of forecast reliability; on average, people believe forecasts are more reliable when they include numeric probability information.

Survey of General Aviation Pilot Reports (PIREPs) Conformity, Consistency, and Quality

Understanding barriers to submitting pilot weather reports (PIREPs) has been the focus of recent attention in the general aviation community. The goal is to help increase the submission frequency of these reports, which are valuable for aviation operations and situational awareness. Additionally, the perception of the quality of these reports by pilots can impact the level of trust users have in the data. This study aims to evaluate aspects of the reporting frequency and quality of PIREPs particularly from the general aviation perspective. PIREPs were subjected to a range of logical, qualitative, and quantitative tests. Commercial applications are shown to improve the data quantity transmitted in the reports, particularly the non-mandatory sections such as sky and weather conditions, as well as to help alleviate some of the transcription errors. Reported times of the PIREPs indicate impacts from rounding that may limit the utility of the data in some instances. Analysis of individual geophysical measurements show varying quality with potential gaps noted in the icing type assessment and a bias towards higher turbulence intensity reporting, though air temperature compares well to independent data.

Evaluating Precursor Signals for QLCS Tornado and Higher Impact Straight-Line Wind Events

Tornadoes produced by quasi-linear convective systems (QLCS) present a significant challenge to National Weather Service warning operations. Given the speed and scale at which they develop, different methods for tornado warning decision making are required than what traditionally are used for supercell storms. This study evaluates the skill of one of those techniques—the so-called three-ingredients method—and produces new approaches. The three-ingredients method is found to be reasonably skillful at short lead times, particularly for systems that are clearly linear. From the concepts and science of the three-ingredients method, several new combinations of environmental and radar parameters emerge that appear slightly more skillful, and may prove easier to execute in real time. Similar skill between the emerging methods provides the forecaster with options for what might work best in any given scenario. A moderate positive correlation with overall wind speed with some radar and environmental variables also is identified. Additionally, mesoscale convective vortices and supercell-like features in QLCS are found to produce tornadoes at a much higher rate than purely linear systems.

Assessing the Potential for Pyroconvection and Wildfire Blow Ups

Fire meteorologists have few tools for assessing atmospheric stability in the context of wildfires. Most tools at our disposal were developed for assessing thunderstorms and general convection, and so they ignore heat and moisture supplied by the wildfire. We propose a simple parcel-based model that can be used to assess how the atmosphere will affect a growing wildfire plume by also taking into account the heat and moisture released from the fire. From this model, we can infer trends in day to day atmospheric stability as it relates to fire plumes. We can also infer how significant the appearance of a pyrocumulus cloud on the top of a fire column is. In some cases, the appearance of a pyrocumulus indicates that the fire is near if not already blowing up, whereas in other cases environmental conditions remain too stable to have a significant effect. A qualitative application of the model is demonstrated through application to a 2017 wildfire case in Western Montana.

The Impact of the Storm Prediction Center's Convective Outlooks and Watches on Emergency Management Operational Planning

For many emergency managers (EMs) and National Weather Service (NWS) forecasters, Convective Outlooks issued by the Storm Prediction Center (SPC) influence the preparation for near-term severe weather events. However, research into how and when EMs utilize that information, and how it influences their emergency operations plan, is limited. Therefore, to better understand how SPC Convective Outlooks are used for severe weather planning, a survey was conducted of NWS core partners in the emergency management sector. The results show EMs prefer to wait until an Enhanced Risk for severe thunderstorms is issued to prepare for severe weather. In addition, the Day 2 Convective Outlook serves as the threshold for higher, value-based decision making. The survey was also used to analyze how the issuance of different risk levels in SPC Convective Outlooks impact emergency management preparedness compared to preparations conducted when a Convective Watch is issued.

Improving Tornado Warning Communication for Deaf and Hard of Hearing Audiences

Although specialized personal and residential Deaf warning technologies exist, receipt and comprehension of tornado warning information from local television is often delayed or misunderstood because of closed-captioning deficiencies. In order to suggest improvements for the communication of tornado warnings to Deaf and Hard of Hearing (D/HoH) audiences, interviews and a focus group were conducted within the active tornado counties of Alabama. D/HoH individuals generally use more information sources than the hearing population to better understand their risk. Protective action decision-making by our sample was characterized by more hesitation, uncertainty, and indecision than in the hearing population. The most common suggestion for improving tornado-warning communication was to have an American Sign Language (ASL) interpreter shown on screen with a local television meteorologist during a tornado warning. A split-screen television product with an ASL interpreter in a remote studio was prototyped showing that this type of live broadcast is possible for local tornado-warning coverage. Several screen formats were evaluated by a focus group with the conclusion that the ASL interpreter should be on the left side of the screen without obscuring any part of the weather broadcast. The split-screen product with an ASL interpreter resulted in full access to all broadcast information, the ability to make immediate safety decisions, and was welcomed with excitement by the focus-group participants. This modification, along with the education and preparedness efforts of the National Weather Service, help remedy the information gaps and comprehension delays of this underserved population.

A Rare Ice Storm in the Colorado Rockies

During the early morning hours of 9 January 2017, freezing rain developed across several valley locations in western Colorado. The resultant ice accumulation led to extremely treacherous travel conditions with hundreds of vehicle accidents reported in the vicinity of Grand Junction, Colorado and near Durango, Colorado. Additionally, widespread power outages were reported in Durango and near Steamboat Springs, Colorado. First responders were overwhelmed by the volume increase of emergency calls, and secondary services were requested from nearby municipalities to help with the increased workload. The emergency operations center in Mesa County, Colorado (Grand Junction) was activated as a result of the numerous accidents and injuries across the region. An ice storm of this magnitude has not been experienced in Grand Junction’s period of record, which dates back to 1893. A detailed investigation explores the physical processes responsible for this ice storm over the complex terrain of the Intermountain West.

SIFTing through satellite imagery with the Satellite Information Familiarization Tool

The Satellite Information Familiarization Tool (SIFT) is an open-source, multi-platform graphical user interface designed to easily display spectral and temporal sequences of geostationary satellite imagery. The Advanced Baseline Imager (ABI) and Advanced Himawari Imager (AHI) on the “new generation” of geostationary satellites collect imagery with a spatial resolution four times greater than previously available. Combined with the increased number of spectral bands and more frequent imaging, the new series imagers collect approximately 60 times more data. Given the resulting large file sizes, the development of SIFT is a multiyear effort to make those satellite imagery data files accessible to the broad community of students, scientists, and operational meteorologists. To achieve the objective of releasing software that provides an intuitive user experience to complement optimum performance on consumer-grade computers, SIFT was built to leverage modern graphics processing units (GPUs) through existing open-source Python packages, and runs on the three major operating systems: Windows, Mac, and Linux. The United States National Weather Service funded the development of SIFT to help enhance the satellite meteorology acumen of their operational meteorologists. SIFT has basic image visualization capabilities and enables the fluid animation and interrogation of satellite images, creation of Red-Green-Blue (RGB) composites and algebraic combinations of multiple spectral bands, and comparison of imagery with numerical weather prediction output. Open for community development, SIFT users and features continue to grow. SIFT is freely available with short tutorials and a user guide online. The mandate for the software, its development, realized applications, and envisioned role in science and training are explained.

Initial Assessment of Unmanned Aircraft System Characteristics Required to Fill Data Gaps for Short-term Forecasts: Results from Focus Groups and Interviews

The integration of unmanned aircraft systems (UAS) into the weather surveillance network must be guided by the data needs of the principal stakeholders. This work aims to assess data needs/gaps for short-term forecasts (<1-day lead time) issued by the National Weather Service (NWS) and then identify UAS characteristics required to fill these gaps. Results from focus groups and interviews of forecasters in the central United States are presented. Participant verbal responses were coded and then categorized into a set of 25 unique features. Each feature was classified according to four characteristics: 1) environmental properties that need to be measured to represent a given feature, 2) flight type (vertical profile, horizontal transect, and/or survey) 3) flight height required to measure the environmental properties, and 4) relevance of feature to the forecasting of deep convection. Findings indicate the majority of identified features require measurement of typical state variables (temperature, moisture, and wind), but more than a third require visual imagery. Almost all of the features require either survey flight operations or vertical profiles. Additionally, 96% of the features require observations collected below 1000 m. Nearly two-thirds of the features are associated with deep convection. This work represents the first step towards establishing how UAS could be used to fill data gaps that exist for short-term forecasts issued by the NWS. The results stand alone in demonstrating the potential applications of UAS from the perspective of operational forecasters and have also informed ongoing efforts to develop a nationwide survey of forecasters.