National Weather Service Research Articles

Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1)

Abstract. The National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) is on its way to deploying various operational prediction applications using the Unified Forecast System (https://ufscommunity.org/, last access: 18 June 2022), a community-based coupled, comprehensive Earth modeling system. An aerosol model component developed in collaboration between the Global Systems Laboratory, Chemical Science Laboratory, Air Resources Laboratory, and Environmental Modeling Center (GSL, CSL, ARL, EMC) was coupled online with the FV3 Global Forecast System (FV3GFS) using the National Unified Operational Prediction Capability (NUOPC)-based NOAA Environmental Modeling System (NEMS) software framework. This aerosol prediction system replaced the NEMS GFS Aerosol Component version 2 (NGACv2) system in the National Center for Environment Prediction (NCEP) production suite in September 2020 as one of the ensemble members of the Global Ensemble Forecast System (GEFS), dubbed GEFS-Aerosols v1. The aerosol component of atmospheric composition in the GEFS is based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). GEFS-Aerosols includes bulk modules from the Goddard Chemistry Aerosol Radiation and Transport model (GOCART). Additionally, the biomass burning plume rise module from High-Resolution Rapid Refresh (HRRR)-Smoke based on WRF-Chem was implemented. The GOCART dust scheme was replaced by the FENGSHA dust scheme (developed by ARL). The Blended Global Biomass Burning Emissions Product (GBBEPx version 3) provides biomass burning emission and fire radiative power (FRP) data. The global anthropogenic emission inventories are derived from the Community Emissions Data System (CEDS). All sub-grid-scale transport and deposition are handled inside the atmospheric physics routines, which required consistent implementation of positive definite tracer transport and wet scavenging in the physics parameterizations used by the NCEP's operational FV3GFS. This paper describes the details of GEFS-Aerosols model development and evaluation of real-time and retrospective runs using different observations from in situ measurement and satellite and aircraft data. GEFS-Aerosols predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational NGACv2 system with the fundamental updates (e.g., dust and fire emission) in the atmospheric and chemical transport model.

Hazard Assessment of NPP Krško for Republic of Croatia

While Croatia does not have nuclear power plant on its territory, NPP Krško in Slovenia is just 10 km from the Croatian border. It is important for Croatia to include NPP Krško in comprehensive hazard assessment. This article will present hazard assessment based on calculations using RODOS. Real-time weather prepared by Croatian National Weather Service and collected by the State Office for Radiological and Nuclear Safety over the years will be used. Scenario resulting in the large release from the NPP will be analysed. Results from hundreds of calculations will be statistically analysed and compared to the current protection zones in Croatia around the NPP Krško.

A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management

ABSTRACT Tweets posted by the general public during disaster events represent timely, up-to-date, and on-site data that may be useful for emergency responders. However, since Twitter data has been deemed to be unverifiable and untrustworthy, it is challenging to identify those reliable and relevant tweets that can inform emergency response operations. Although computational methods exist both to classify overwhelming amounts of tweets and to filter those relevant to emergency response, using contextual geographic information regarding the disaster event to filter tweets has been overlooked. We review the existing research on the quality of data contributed by the general public from a geographical perspective, and then propose an approach to prioritize tweets for emergency response based on their relevance and reliability. The novelty of the approach is twofold: a) the use of both authoritative data such as hazard-related information and on-the-ground reports provided by weather spotters and validated by the National Weather Service; and b) the fact that it leverages tweets content as well as their geographical context and location. Using Hurricane Harvey in 2017 as a case study, results show that by following the proposed approach 79% of tweets sent from post-identified flooded areas were classified as of high or medium relevance and reliability. This suggests that the proposed approach can provide an accurate prioritization of tweets to be used for real time emergency management.

The Impact of Scale-Aware Parameterization on the Next-Generation Global Prediction System in Taiwan for Front Predictions

In order to improve the precipitation forecast of the next-generation Global Prediction System with the Finite-Volume Cubed-Sphere Dynamical Core in Taiwan’s Central Weather Bureau, this study modified the convective processes in the New Simplified Arakawa-Schubert scheme based on the methodology of scale-aware parameterization developed in Kwon and Hong (2017) and investigated its impacts on a front event, which propagated across Taiwan and produced heavy rainfall in late May of 2020. Results show that the modified scale-aware parameterization has significantly improved the intensity and the spatial distribution of frontal precipitation forecasts due to the proper definition of convective updraft fraction. However, the synoptic-scale features perform a larger warm bias with the modified scale-aware parameterization. Therefore, further modification of the scale-aware capability of convective cloud water detrainment is proposed to reduce the heating from microphysical processes and result in a better overall performance for the medium-range weather forecasts.

Data-Driven Community Flood Resilience Prediction

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.

Heat stress in a temperate climate leads to adapted sensor-based behavioral patterns of dairy cows

Most research on heat stress has focused on (sub)tropical climates. The effects of higher ambient temperatures on the daily behavior of dairy cows in a maritime and temperate climate are less studied. With this retrospective observational study, we address that gap by associating the daily time budgets of dairy cows in the Netherlands with daily temperature and temperature-humidity index (THI) variables. During a period of 4 years, cows on 8 commercial dairy farms in the Netherlands were equipped with neck and leg sensors to collect data from 4,345 cow lactations regarding their daily time budget. The time spent eating, ruminating, lying, standing, and walking was recorded. Individual cow data were divided into 3 data sets: (1) lactating cows from 5 farms with a conventional milking system (CMS) and pasture access, (2) lactating cows from 3 farms with an automatic milking system (AMS) without pasture access, and (3) dry cows from all 8 farms. Hourly environment temperature and relative humidity data from the nearest weather station of the Dutch National Weather Service was used for THI calculation for each farm. Based on heat stress thresholds from previous studies, daily mean temperatures were grouped into 7 categories: 0 = (<0°C), 1 = (0-12°C, reference category), 2 = (12-16°C), 3 = (16-20°C), 4 = (20-24°C), 5 = (24-28°C), and 6 = (≥28°C). Temperature-humidity index values were grouped as follows: 0 = (THI <30), 1 = (THI 30-56, reference category), 2 = (THI 56-60), 3 = (THI 60-64), 4 = (THI 64-68), 5 = (THI 68-72) and 6 = (THI ≥72). To associate daily mean temperature and THI with sensor-based behavioral parameters of dry cows and of lactating cows from AMS and CMS farms, we used generalized linear mixed models. In addition, associations between sensor data and other climate variables, such as daily maximum and minimum temperature, and THI were analyzed. On the warmest days, eating time decreased in the CMS group by 92 min/d, in the AMS group by 87 min/d, and in the dry group by 75 min/d compared with the reference category. Lying time decreased in the CMS group by 36 min/d, in the AMS group by 56 min/d, and in the dry group by 33 min/d. Adaptation to daily temperature and THI was already noticeable from a mean temperature of 12°C or a mean THI of 56 or above, when dairy cows started spending less time lying and eating and spent more time standing. Further, rumination time decreased, although only in dry cows and cows on AMS farms. With higher values for daily mean THI and temperature, walking time decreased as well. These patterns were very similar for temperature and THI variables. These results show that dairy cows in temperate climates begin to adapt their behavior at a relatively low mean environmental temperature or THI. In the temperate maritime climate of the Netherlands, our results indicate that daily mean temperature suffices to study the effects of behavioral adaptation to heat stress in dairy cows.

Improving Tropical Cyclone Forecast Communication by Understanding NWS Partners’ Decision Timelines and Forecast Information Needs

Abstract As tropical cyclone threats evolve, broadcast meteorologists and emergency managers rely on timely forecast information to help them communicate risk with the public and protect public safety. This study aims to improve the usability and applicability of National Weather Service (NWS) forecast information in the context of these NWS core partners’ decisions during tropical cyclone threats. The research collected and analyzed data from in-depth interviews with broadcast meteorologists and emergency managers in three coastal U.S. states. These data were used to analyze broadcast meteorologists’ and emergency managers’ tropical cyclone decision and action timelines, their use of tropical cyclone information during different phases of threats, and gaps in forecast information for decision-making. Based on these findings, several opportunities for improving tropical cyclone risk communication were identified. Recommendations to address gaps in the NWS tropical cyclone product suite include designing improved ways to communicate storm-specific storm surge risk at greater than 48 h of lead time, expanding the use of concise highlights that help people quickly extract and understand key information, and improving product understandability and usability by more comprehensively integrating users’ perspectives into product research and development. Broader strategic recommendations include developing new approaches for informing broadcast meteorologists about major forecast updates, presenting forecast information in ways that enable locally relevant interpretation, and supporting human forecasters’ contributions to the effectiveness of NWS products and services. These findings and recommendations can help NOAA prioritize ways to modernize the current NWS tropical cyclone product suite as well as motivate research to enable longer-term high-priority improvements. Significance Statement Tropical cyclones pose significant risks to coastal and inland U.S. populations. This project aims to improve creation, communication, and use of tropical cyclone forecast and warning information by studying broadcast meteorologists’ and emergency managers’ information needs for decision-making during different phases of tropical cyclone threats. We identify several priority areas for improvement, including advancing longer-lead-time storm surge forecast communication, enhancing dissemination of forecast updates, and increasing use of concise text highlights. Additional findings include the importance of locally interpretable forecast information, the value of human forecasters in weather risk communication, and the need for iterative, user-informed forecast product development. These findings can help NOAA and the research community improve forecast communication and invest in research that facilitates continued improvements.

Utilizing the High-Resolution Ensemble Forecast System to Produce Calibrated Probabilistic Thunderstorm Guidance

Abstract The High-Resolution Ensemble Forecast system (HREF) calibrated thunder guidance is a suite of probabilistic forecast products designed to predict the likelihood of at least one cloud-to-ground (CG) lightning flash within 20 km (12 miles) of a point during a given 1-, 4-, and 24-h time interval. This guidance takes advantage of a combination of storm attribute and environmental fields produced by the convection-allowing HREF to objectively improve upon lightning forecasts generated by the non-convection-allowing Short-Range Ensemble Forecast system (SREF). This study provides an overview of how the HREF calibrated thunder guidance was developed and calibrated to be statistically reliable against observed CG lightning flashes recorded by the National Lightning Detection Network (NLDN). Performance metrics for the 1-, 4-, and 24-h guidance are provided and compared to the respective SREF calibrated probabilistic lightning forecasts. The HREF calibrated thunder guidance has been implemented operationally within the National Weather Service and is now available to the public. Significance Statement The NOAA Storm Prediction Center has created a suite of new calibrated probabilistic thunderstorm guidance products from a convection-allowing model ensemble, the HREF. The new guidance is a notable improvement over the long-running SREF calibrated thunder guidance and is now operational across the National Weather Service.

Utility of the Heat Index in defining the upper limits of thermal balance during light physical activity (PSU HEAT Project)

Extreme heat events and consequent detrimental heat-health outcomes have been increasing in recent decades and are expected to continue with future climate warming. While many indices have been created to quantify the combined atmospheric contributions to heat, few have been validated to determine how index-defined heat conditions impact human health. However, this subset of indices is likely not valid for all situations and populations nor easily understood and interpreted by health officials and the public. In this study, we compare the ability of thresholds determined from the National Weather Service’s (NWS) Heat Index (HI), the Wet Bulb Globe Temperature (WBGT), and the Universal Thermal Climate Index (UTCI) to predict the compensability of human heat stress (upper limits of heat balance) measured as part of the Pennsylvania State University’s Heat Environmental Age Thresholds (PSU HEAT) project. While the WBGT performed the best of the three indices for both minimal activities of daily living (MinAct; 83 W·m−2) and light ambulation (LightAmb; 133 W·m−2) in a cohort of young, healthy subjects, HI was likewise accurate in predicting heat stress compensability in MinAct conditions. HI was significantly correlated with subjects’ perception of temperature and humidity as well as their body core temperature, linking perception of the ambient environment with physiological responses in MinAct conditions. Given the familiarity the public has with HI, it may be better utilized in the expansion of safeguard policies and the issuance of heat warnings during extreme heat events, especially when access to engineered cooling strategies is unavailable.

Impact of WSR-88D Intra-Volume Low-Level Scans on Severe Weather Warning Performance

Abstract The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.

To Warn or Not to Warn: Factors Influencing National Weather Service Warning Meteorologists’ Tornado Warning Decisions

Abstract Weather warnings are critical risk communication messages because they have the potential to save lives and property during emergencies. However, making warning decisions is challenging. While there have been significant advances in technological weather forecasting, recent research suggests that social factors, including communication, influence warning meteorologists’ decisions to warn. We examine the roles of both scientific and social factors in predicting warning meteorologists’ decisions to warn on tornadoes. To do so, we conducted a cross-sectional survey of National Weather Service forecasters and members of management in the southern and the central regions of the United States, as well as conducted a retrospective data analysis of cross-sectional survey data from the central region Tornado Warning Improvement Project. Results reveal that dependency on radar velocity couplet and a variety of social factors predicted decisions to warn.

The Creation of a Research Television Studio to Test Probabilistic Hazard Information with Broadcast Meteorologists in NOAA’s Hazardous Weather Testbed

Abstract Broadcast meteorologists play an essential role in communicating severe weather information from the National Weather Service to the public. Because of their importance, researchers incorporated broadcast meteorologists in the development of probabilistic hazard information (PHI) in NOAA’s Hazardous Weather Testbed. As part of Forecasting a Continuum of Environmental Threats (FACETs), PHI is meant to bring additional context to severe weather warnings through the inclusion of probability information. Since this information represents a shift in the current paradigm of solely deterministic NWS warnings, understanding end user needs is paramount to create usable and accessible products that result in their intended outcome to serve the public. This paper outlines the establishment of “K-Probabilistic Hazard Information Television” (KPHI-TV), a research infrastructure under the Hazardous Weather Testbed created to study broadcast meteorologists and PHI. A description of the design of KPHI-TV and methods used by researchers are presented, including displaced real-time cases and semistructured interviews. Researchers completed an analysis of the 2018 experiment, using a quantitative analysis of television coverage decisions with PHI, and a thematic analysis of semistructured interviews. Results indicate that no clear probabilistic decision thresholds for PHI emerged among the participants. Other themes arose, including the relationship between PHI and the warning polygon, and communication challenges. Overall, broadcast participants preferred a system that includes PHI over the warning polygon alone, but raised other concerns, suggesting iterative research in the design and implementation of PHI should continue. Significance Statement Broadcast meteorologists are the primary source of severe weather warning information for the U.S. public. As a result, researchers at NOAA’s National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies developed a mock television studio to allow broadcast meteorologists to use and communicate experimental products “on air” as part of the research-and-development process. Feedback provided by broadcasters is incorporated into products through an iterative process. Since 2016, 18 broadcasters have tested probabilistic hazard information at the warning time scale (0–1 h) for severe wind and hail, tornadoes, and lightning.

A One‐Year‐Long Evaluation of a Wind‐Farm Parameterization in HARMONIE‐AROME

AbstractThe need to mitigate climate change will boost the demand for renewable energy and lead to more wind turbines both on‐ and offshore. In the near future, the effect these wind farms have on the atmosphere can no longer be neglected. In numerical weather prediction models wind‐farm parameterisations (WFP) can be used to model the effect of wind farms on the atmosphere. There are different modeling approaches, but the parameterization developed by Fitch et al. (2012) is mostly used in previous studies. It models the wind farm as a momentum sink and a source of power production and turbulent kinetic energy. In this paper, we have implemented the Fitch et al. (2012) WFP into HARMONIE‐AROME, the numerical weather prediction model that is currently used by at least 11 national weather services in Europe. We used HARMONIE‐AROME to make year‐long simulations for 2016 with and without the WFP. The results were extensively evaluated using lidar, tower and flight measurements at several locations near wind farms. Including the WFP reduces the model bias for wind speed near offshore wind farms. Wind farms not only affect wind, but also temperature and humidity, especially during stable atmospheric conditions: the enhanced mixing caused by the wind turbines reduces the stratification of temperature and humidity. Including the WFP in HARMONIE‐AROME results in a more realistic representation of the atmosphere near wind farms and makes it a more future‐proof model for weather forecasting.

A Climatology of Snow Squalls in Southern New England 1994–2018

Abstract Snow squalls are sudden snow events that last less than 1 h, are characterized by low visibility and gusty winds, and can result in notable societal impacts. This analysis develops a climatology of non-lake-effect snow squall events in southern New England for 1994–2018 and investigates the synoptic environment and mesoscale factors conducive to their formation. National Weather Service surface observations were used to identify events; sea level pressure maps, composite radar charts, and a cell-tracking algorithm were used to determine their organization and movement; and ERA5 hourly reanalysis data were used to analyze the associated synoptic and infer mesoscale features, as well as convective and symmetric instability. A total of 100 events were identified and categorized into four distinct types on the basis of the direction of movement of the associated radar echoes, which is closely linked to characteristic synoptic structures and mesoscale factors. The four types are Classic (squall movement from the northwest; 72 events), Atlantic (from the southwest; 15 events), Northern (from the north; 9 events), and Special (varying; 4 events). All types have a 500-hPa trough over the Northeast but differ in the structure of the trough and its relation to lower-level flow, which accounts for the differences in movement of the squalls. The snow events occur in shallow, convective squall lines, and the ingredients for convection were present in all cases. Both upright and symmetric instability are typically present, all cases had at least one lower-tropospheric layer with cyclonic differential vorticity advection, and many cases were also associated with frontogenesis.

Weather Conditions and Messaging Associated with Fatal Winter-Weather-Related Motor-Vehicle Crashes

Abstract Winter-weather conditions pose an extreme hazard to motorists, resulting in approximately 1000 fatalities annually on U.S. roadways. Minimizing adverse impacts of winter weather requires (i) the identification of hazardous weather conditions leading up to and at the time of fatal crashes, and (ii) effective, targeted messaging of those hazards to motorists. The first objective is addressed by matching motor-vehicle-related fatalities from 2008 to 2019 to nearby weather reports to determine how precipitation types and other observable weather conditions (i.e., precipitation intensity, obscurations, and visibility) change leading up to crashes. One-half of fatalities occur in snow, with 75% occurring in ongoing snowfall. Of fatalities during freezing precipitation, 41% occur near the onset of freezing precipitation. In addition, 42% of fatalities have deteriorating weather conditions prior to the crash, primarily visibility reductions of ≥25%. The second objective is addressed by examining language currently used in National Weather Service Winter Weather Warning, Watch, or Advisory (WSW) issuances for fatal crashes. Only one-third of fatalities have a WSW. These WSWs both identify a road hazard (e.g., “roads will become slick”) and provide an action item for motorists (e.g., “slow down and use caution while driving”) but do not clearly convey tiered road-hazard ratings. Examination of non-weather-related attributes of fatal crashes suggest that variable-message signs along highways may be useful to communicate road hazards, and that future messaging should urge motorists to leave additional space around their vehicles, slow down, prepare for rapidly deteriorating conditions, and teach strategies to regain control of their vehicle. Significance Statement We find that approximately 1000 fatalities occur each year on U.S. roadways during winter weather. To inform how to reduce fatalities in the future, we identify weather conditions leading up to and at the time of fatal crashes and determine whether road hazards were publicly messaged alongside weather warnings and advisories. Ongoing snowfall, the onset of freezing precipitation, and visibility reductions were prominent factors found in many fatal crashes, suggesting that these may be important factors to address in future safety studies. Winter-weather warnings and advisories often contain language cautioning road hazards, yet only one-third of fatalities occur during conditions with such official statements. However, these statements do not clearly indicate how hazardous roads will be.

Creating a Communication Framework for FACETs: How Probabilistic Hazard Information Affected Warning Operations in NOAA’s Hazardous Weather Testbed

Abstract Scientists at NOAA are testing a new tool that allows forecasters to communicate estimated probabilities of severe hazards (tornadoes, severe wind, and hail) as part of the Forecasting a Continuum of Environmental Threats (FACETs) framework. In this study, we employ the embedded systems theory (EST)—a communication framework that analyzes small group workplace practices as products of group, organizational, and local dynamics—to understand how probabilistic hazard information (PHI) is produced and negotiated among multiple NWS weather forecast offices in an experimental setting. Gathering feedback from NWS meteorologists who participated in the 2020 Hazard Services (HS)-PHI Interoffice Collaboration experiment, we explored implications of local and interoffice collaboration while using this experimental tool. By using a qualitative thematic analysis, it was found that differing probability thresholds, forecasting styles, social dynamics, and workload will be social factors that developers should consider as they bring PHI toward operational readiness. Warning operations in this new paradigm were also implemented into the EST model to create a communication ecosystem for future weather hazard communication research. Significance Statement Meteorologists are currently exploring how to use probabilities to communicate life-saving information. From tornadoes to hail, a new type of probabilistic hazard information could fundamentally change the way that NWS meteorologists collaborate with one another when issuing weather products, especially near and along the boundaries of County Warning Areas. To explore potential collaboration challenges and solutions, we applied a communication framework and explored perceptions that NWS meteorologists had while using this new tool in an experimental setting. NWS meteorologists expressed that differing ways of communicating hazard information between each office, along with forecasting styles and workload, would change the way they go about producing critical hazard information to the public.

Extreme Heat Exposure: Access and Barriers to Cooling Centers - Maricopa and Yuma Counties, Arizona, 2010-2020.

Extreme heat exposure increases the risk for heat-related illnesses (HRIs) and deaths, and comprehensive strategies to prevent HRIs are increasingly important in a warming climate (1). An estimated 702 HRI-associated deaths and 67,512 HRI-associated emergency department visits occur in the United States each year (2,3). In 2020, Phoenix and Yuma, Arizona, experienced a record 145 and 148 days, respectively, of temperatures >100°F (37.8°C), and a record 522 heat-related deaths occurred in the state. HRIs are preventable through individual and community-based strategies*,†; cooling centers,§ typically air-conditioned or cooled buildings designated as sites to provide respite and safety during extreme heat, have been established in Maricopa and Yuma counties to reduce HRIs among at-risk populations, such as older adults. This analysis examined trends in HRIs by age during 2010-2020 for Maricopa and Yuma counties and data from a survey of older adults related to cooling center availability and use in Yuma County during 2018-2019. Data from CDC's Social Vulnerability Index (SVI) were also used to overlay cooling center locations with SVI scores. During 2010-2020, heat days, defined as days with an excessive heat warning issued by the National Weather Service Phoenix Office,¶ for any part of Maricopa and Yuma counties (4), increased in both Maricopa County (1.18 days per year) and Yuma County (1.71 days per year) on average. Adults aged ≥65 years had higher rates of HRI hospitalization compared with those aged <65 years. In a survey of 39 adults aged ≥65 years in Yuma County, 44% reported recent HRI symptoms, and 18% reported electricity cost always or sometimes constrained their use of air conditioning. Barriers to cooling center access among older adults include awareness of location and transportation. Collaboration among diverse community sectors and health profession education programs is important to better prepare for rising heat exposure and HRIs. States and communities can implement adaptation and evaluation strategies to mitigate and assess heat risk, such as the use of cooling centers to protect communities disproportionately affected by HRI during periods of high temperatures.

Automatic hypocenter determination with the IPFx method for the 2018 Hualien earthquake sequence

The extended integrated particle filter (IPFx) method is an automatic source determination algorithm designed for the Japanese earthquake early warning (EEW) system. The method improved earthquake source determination during active seismicity by incorporating the smart phase association scheme. We applied this method to the 2018 Hualien earthquake sequence and evaluated its performance by comparing it to the manual catalog. We used 1-month continuous waveforms from February 2018 at 170 stations. Owing to the higher noise level, we improved the phase association algorithm to avoid noise contamination. Out of 127 earthquakes with a seismic intensity ≥ 4, 105 were successfully detected in one month, of which 103 had good accuracy with a location error of < 30 km. The detectability of earthquakes decreased immediately following large events. The IPFx method showed good performance in detecting earthquakes with seismic intensity ≥ 4 during the 2018 Hualien earthquake sequence. The method was also applied to the 1-day continuous data on April 18, 2021, and detected 14 earthquakes with a magnitude ~ 2 that were not on the manual catalog. Currently, the Central Weather Bureau in Taiwan uses the effective epicenter method to locate earthquakes for the EEW system. However, source determination for offshore events is difficult as most of the stations are on land. We expect the IPFx method to provide better location estimates for offshore earthquakes and during the period of active seismicity. It also provides an earlier warning as it sends the first message when three stations are triggered. This new method can potentially improve the speed and accuracy of the Taiwanese EEW system.

A Preliminary Examination of Warm Season Precipitation Displacement Errors in the Upper Midwest in the HRRRE and HREF Ensembles

Abstract Increased operational use of convection-allowing models and ensembles offers substantial improvements for some aspects of convective weather forecasting; however, errors in quantitative precipitation forecasts (QPFs) from these models, especially those related to incorrect placement of heavy rainfall systems, limit their usefulness as an input into hydrological models. To improve understanding of QPF location errors, this study quantifies the displacement errors for the centroids of both 0–18-h accumulated rainfall and rainfall in the first hour after initiation of precipitation systems in both the High-Resolution Rapid Refresh Ensemble (HRRRE) and the High-Resolution Ensemble Forecast (HREF) for 30 events in the 2018 warm season. Ensemble member QPFs are compared to quantitative precipitation estimates (QPEs) obtained from the North Central River Forecast Center (NCRFC). HRRRE is found to have less spread in centroid locations than HREF, and both HRRRE and HREF 0–18-h QPF accumulations have less spread than the 1-h QPF accumulation when the precipitation event initiates. Furthermore, QPF centroids are most often displaced to the west in HRRRE for both 0–18-h QPF accumulation and the 1-h QPF accumulation when the precipitation event initiates. The 0–18-h QPF accumulation displacement errors can be reduced when adjustments are made to the forecasted position based upon displacement errors present in the first hour of precipitation, but only when the adjustments are a function of the intercardinal quadrant in which the initial hour QPF centroid was displaced.

Distribution features of the weather conditions in Nha Trang Bay (The South China Sea)

Nha Trang Bay is a semi-envelop coastal basin. It is located on the west coast of the South China Sea as well as in the south of the central part of the Vietnamese coast. The climate of the bay belongs to the sub-equatorial zone or to the zone of equatorial-tropical monsoons. Determination of the meteorological characteristics of an ocean region, especially for nearshore bay waters, plays a very important role in the sustainable development of the social-economical system of Vietnam. This paper presents a spatial and temporal long-term analysis of several meteorological characteristics in Nha Trang Bay, such as wind, tropical cyclones, air temperature, air humidity, and rainfall. The meteorological data were collected from the Nha Trang meteorological station during 1977–2015 (wind data until 2020).&#x0D; The data for typhoons were extracted from the National Weather Service (USA) and the Japan Meteorological Agency during 1945–2015. Statistical methods for collecting and analyzing data to estimate the statistical parameters of meteorological characteristics were used in the study.&#x0D; A study of results shows that the meteorological characteristics in the Nha Trang Bay are a seasonal variation of the East Asian monsoon (northeastern-NE and southwestern-SW monsoons).&#x0D; The winds in Nha Trang Bay are the seasonal change, influenced by the NE monsoon from October to April and the SW monsoon from June to August. May and September are the transition periods.&#x0D; Nha Trang Bay has a temperate tropical climate because the temperature regime of the bay ranges from less than 25°C in the winter and more than 28°C in the summer.&#x0D; Extreme conditions, including typhoon activity and heavy rainfall (coinciding with high humidity), occurred in October, November, and December.