Weather Service Research Articles

Foundational Needs of Forecasters for Probabilistic Winter Forecasting

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.

WRF Physics Ensemble Performance Evaluation over Continental and Coastal Regions in Germany

WRF is used to simulate eight extreme precipitation events that occurred over the regions of Schleswig–Holstein and Baden–Wurttemberg in Germany. The events were chosen from the German Weather Service (DWD) catalog and exceeded the DWD’s warning level 3 (i.e., rainfall > 40 mm/h). A two-way nesting approach is used with 9 and 3 km spatial resolutions. Initial and boundary conditions are obtained from the ERA5 dataset at 0.25° × 0.25°. To model each event, thirty different parameterization configurations were used, accounting for all possible combinations of five microphysics (MP), three cumulus (CU), and two planetary boundary layer (PBL) parameterization methods, yielding a total of 240 simulations. TOPSIS multicriteria analysis technique is employed to determine the performance skill of each setup and rank them, using six categorical and five statistical metrics. Resolution increase from 9 to 3 km did not improve forecasting accuracy temporally or in intensity. According to TOPSIS ranking, when treating each event individually, the ideal parameterizations combination is spatiotemporally dependent, with certain members ranking higher. When all events are considered, the Morrison double-moment MP–Grell–Freitas CU–YSU PBL combination works best with a frequency of occurrence in the top five performing scenarios of 30%, 47.5%, and 57.5% respectively.

Perceived Age in Patients Exposed to Distinct UV Indexes: A Systematic Review.

Photodamage is caused by chronic sun exposure and ultraviolet radiation and presents as wrinkles, sagging, and pigmented spots. An increase in the ultraviolet index can increase a person's perceived age by worsening skin photodamage. However, since the ultraviolet index varies considerably between geographical regions, perceived age might vary substantially among them. This review aims to describe the differences in chronological and perceived age in regions of the world with different ultraviolet indexes. A literature search of three databases was conducted for studies analyzing perceived age and its relationship to sun exposure. Ultraviolet indexes from the included studies were retrieved from the National Weather Service and the Tropospheric Emission Monitoring Internet Service. Out of 104 studies, seven fulfilled the inclusion criteria. Overall, 3,352 patients were evaluated for perceived age. All studies found that patients with the highest daily sun exposures had the highest perceived ages for their chronological age ( p < 0.05). People with high sun exposure behaviors living in regions with high ultraviolet indexes will look significantly older than same-aged peers living in lower ultraviolet index regions.

Anticipating ‘The Big One’: Developing decision support with emergency managers to mitigate flood impacts

Flooding from excessive rainfall remains a major weather hazard with respect to lives and property. The US National Weather Service Weather Prediction Center and National Water Center work continuously and collaboratively to produce accurate and timely forecasts of potential flooding disasters for the public. This paper explores their ongoing efforts to mitigate flood impacts with enhanced impact-based decision support services, via the Excessive Rainfall Outlook (ERO) and Flood Inundation Mapping (FIM) services. Future directions for precipitation and flood inundation forecasting are also discussed. Through the ERO and FIM services, the National Weather Service hopes to strengthen collaboration with partners in the weather industry, emergency management and water resources management to develop weather, water and climate readiness in communities around the nation.

Risk Factors and Occurrence of Bovine Brucellosis in the Limpopo Province, South Africa

Background: Bovine brucellosis causes substantial loses in livestock production systems through abortions and infertility. The aim of this study was to determine whether the occurrence of bovine brucellosis is influenced by climatic variabilities. Methods: Data on confirmed cases of bovine brucellosis from the year 2013 to 2018 was retrieved from the Limpopo Department of Agriculture Land Reform and Rural Development. Temperature and rainfall data was retrieved from the South African Weather Services. A semi structured questionnaire was used to gather information on management related risk factors from 60 purposively selected farmers. Data were analysed using microsoft excel, simple linear regression, Pearson’s correlation coefficient and Mann-Kendall test. Result: The average occurrence rate of bovine brucellosis was 3.96%. Simple linear regression coefficient analysis indicated a non-significant (p greater than 0.05) effect of temperature and rainfall variabilities on occurrence of bovine brucellosis. Seventy seven per cent of cattle farmers were aware of bovine brucellosis and 83% experienced cases of abortion in their herds.

Atmospheric conditions favouring extreme precipitation and flash floods in temperate regions of Europe

Abstract. In recent years, flash floods have repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past (especially in the 1990s), floods have been characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes. Here, we explore potential links and causes between the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions. We hypothesize that a change in atmospheric conditions has led to more frequent extreme precipitation events that have subsequently triggered flash flood events in central western Europe. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h−1 from radar data (the RADOLAN “Radar-Online-Adjustment” dataset from the German Weather Service). Moreover, we identified proxy parameters representative of extreme precipitation favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity (q) in the lower troposphere (q≥0.004 kg kg−1), sufficient latent instability (convective available potential energy (CAPE) ≥ 327 J kg−1), and weak wind speeds between 10 m a.g.l. and 500 hPa (WS10m-500hPa≤6 m s−1) proved to be characteristic of intense rainfall that can potentially trigger flash floods. We relied on linear models to analyse 40 years worth (1981–2020) of atmospheric parameters as well as related precipitation events. We found significant increases in the atmospheric moisture content and increases in atmospheric instability. Parameters representing the motion and organization of convective systems remained largely unchanged in the considered period (1981–2020); however, the number of precipitation events, their maximum 5 min intensities, and their hourly sums were characterized by large interannual variations, and no trends could be identified between 2002 and 2020. Our study shows that there is no single mechanistic path leading from atmospheric conditions to extreme precipitation and subsequently to flash floods. The interactions between the processes involved are so intricate that more analyses which consider other potentially relevant factors, such as intra-annual precipitation patterns or catchment-specific parameters, are required.

Meteorological Drought Variability and Its Impact on Wheat Yields across South Africa

Drought is one of the natural hazards that have negatively affected the agricultural sector worldwide. The aims of this study were to track drought characteristics (duration (DD), severity (DS), and frequency (DF)) in South Africa between 2002 and 2021 and to evaluate its impact on wheat production. Climate data were collected from the South African Weather Service (SAWS) along with wheat yield data from the Department of Agriculture, Forestry and Fisheries (2002-2021). The standard precipitation index (SPI) was calculated on 3-, 6-, 9-, and 12-month time scales, and the trend was then tracked using the Mann-Kendall (MK) test. To signify the climatic effects on crop yield, the standardized yield residual series (SYRS) was computed along with the crop-drought resilience factor (CR) on a provincial scale (2002-2021). The output of the SPI analysis for 32 stations covering all of South Africa indicates a drought tendency across the country. On a regional scale, western coastal provinces (WES-C and NR-C) have been more vulnerable to meteorological droughts over the past 20 years. Positive correlation results between SYRS and wheat yield indicate that the WES-C province was highly influenced by drought during all stages of wheat growth (Apr-Nov). Historical drought spells in 2003, 2009, and 2010 with low CR = 0.64 caused the province to be highly impacted by the negative impacts of droughts on yield loss. Overall, drought events have historically impacted the western part of the country and dominated in the coastal area. Thus, mitigation plans should be commenced, and priority should be given to this region. These findings can assist policymakers in budgeting for irrigation demand in rainfed agricultural regions.

Klerksdorp Tornado on March 4, 2007, in South Africa: A Synoptic Overview

This research delves into the factors contributing to tornado formation in South Africa, with a specific focus on the Klerksdorp tornado that occurred on March 4, 2007, in Northwest Province. Despite their recurrent occurrence and significant potential for damage, tornadoes have received relatively little attention. Between 1990 and 2014, these weather phenomena incurred estimated costs exceeding half a billion American dollars in addition to other weather-related disasters. In this study, data from multiple observation systems, including the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP), the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) in Germany, and the South African Weather Service (SAWS), were thoroughly analyzed. The findings reveal that the Klerksdorp tornado was linked to a cold front and a cut-off low, which were the dominant weather systems on the day of the tornado. This case study enhances our comprehension of tornado dynamics in South Africa, aiding in improving short-term forecasts and potentially early warning systems. Future research should concentrate on the recurring nature of tornadoes in association with tropical weather systems and locally-driven factors.

An Optimal Interpolation–Based Snow Data Assimilation for NOAA’s Unified Forecast System (UFS)

Abstract Within the National Weather Service’s Unified Forecast System (UFS), snow depth and snow cover observations are assimilated once daily using a rule-based method designed to correct for gross errors. While this approach improved the forecasts over its predecessors, it is now quite outdated and is likely to result in suboptimal analysis. We have then implemented and evaluated a snow data assimilation using the 2D optimal interpolation (OI) method, which accounts for model and observation errors and their spatial correlations as a function of distances between the observations and model grid cells. The performance of the OI was evaluated by assimilating daily snow depth observations from the Global Historical Climatology Network (GHCN) and the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover data into the UFS, from October 2019 to March 2020. Compared to the control analysis, which is very similar to the method currently in operational use, the OI improves the forecast snow depth and snow cover. For instance, the unbiased snow depth root-mean-squared error (ubRMSE) was reduced by 45 mm and the snow cover hit rate increased by 4%. This leads to modest improvements to globally averaged near-surface temperature (an average reduction of 0.23 K in temperature bias), with significant local improvements in some regions (much of Asia, the central United States). The reduction in near-surface temperature error was primarily caused by improved snow cover fraction from the data assimilation. Based on these results, the OI DA is currently being transitioned into operational use for the UFS. Significance Statement Weather and climate forecasting systems rely on accurate modeling of the evolution of atmospheric, oceanic, and land processes. In addition, model forecasts are substantially improved by continuous incorporation of observations to models, through a process called data assimilation. In this work, we upgraded the snow data assimilation used in the U.S. National Weather Service (NWS) global weather prediction system. Compared to the method currently in operational use, the new snow data assimilation improves both the forecasted snow quantity and near-surface air temperatures over snowy regions. Based on the positive results obtained in the experiments presented here, the new snow data assimilation method is being implemented in the NWS operational forecast system.

Climate services for the railway sector: A synthesis of adaptation information needs in Europe

Given that climate variability and change present unprecedented challenges to the rail sector, efforts to produce relevant climate data/information for climate risk management and adaptive decision making in the rail sector are gaining traction. However, inadequate understanding of climate change impact and information needs raises several concerns for the sector. This paper addressed the question: What climate risk information services are needed to support the adaptation needs of the rail sector? Data from interviews, literature reviews, and workshops were used. The results show that changes in precipitation, temperature, sea-level rise, and thunderstorms are the top drivers of climate risk in the sector. Additionally, the need for tailor-made climate information to manage these changes is in high demand. Although insufficient, rail organizations use special protocols to manage climate risk. Understudied countries have operational and design standards formulated in metrics and codes related to specific critical weather conditions as part of their Natural Hazard Management process. However, desirable adjustments in the standards are currently based on past events rather than future climate conditions. Future climate change information is relevant for medium- to longer-term decisions, strategy, and policymaking. For operational and design standards, weather and climate information provided by national weather service agencies are used but they also refer to the European standards and databases. National level data/information is preferred for developing thresholds for standards yet pan-European level information is also relevant in filling in missing data gaps. Therefore, rail organizations operate on flexibility and a “use of best available data” policy. Understanding how climate information is used to support decision-making in the rail sector is by no means an easy task given the variety of decisions to be taken at different spatial and temporal scales. However, stakeholder engagement proved to be an important step to better inform tailor-made information that is user relevant.

Ensemble Cloud Model Application in Simulating the Catastrophic Heavy Rainfall Event

An attempt has been made in the present research to simulate a deadly flash-flood event over the City of Skopje, Macedonia on 6 August 2016. A cloud model ensemble forecast method is developed to simulate a super-cell storm's initiation and evolutionary features. Sounding data are generated using an ensemble approach, that utilizes a triple-nested WRF model. A three-dimensional (3-D) convective cloud model (CCM) with a very fine horizontal grid resolution of 250-m is initialized, using the initial representative sounding data, derived from the WRF 1-km forecast outputs. CCM is configured and run with an open lateral boundary conditions LBC, allowing explicit simulation of convective scale processes. This preliminary study showed that the ensemble approach has some advantages in the generation of the initial data and the model initialization. The applied method minimizes the uncertainties and provides a more qualitative-quantitative assessment of super-cell storm initiation, cell structure, evolutionary properties, and intensity. A high-resolution 3-D run is capable to resolve detailed aspects of convection, including high-intensity convective precipitation. The results are significant not only from the aspect of the cloud model's ability to provide a qualitative-quantitative assessment of intense precipitation but also for a deeper understanding of the essence of storm development, its vortex dynamics, and the meaning of micro-physical processes for the production and release of large amounts of precipitation that were the cause of the catastrophic flood in an urban area. After a series of experiments and verification, such a system could be a reliable tool in weather services for very short-range forecasting (nowcasting) and early warning of weather disasters.

Near-term regional climate change in East Africa

In the coming few decades, projected increases in global temperature and humidity are generally expected to exacerbate human exposure to climate extremes (e.g., humid-heat and rainfall extremes). Despite the growing risk of humid-heat stress (measured by wet-bulb temperature), it has received less attention in East Africa, where arid and semi-arid climatic conditions prevail. Moreover, no consensus has yet been reached across models regarding future changes in rainfall over this region. Here, we screen Global Climate Models (GCMs) from CMIP5 and CMIP6 and use, for boundary conditions, simulations from only those GCMs that simulate successfully recent climatic trends. Based on these GCMs and Regional Climate Model (RCM) simulations, we project that annual mean temperature is likely to rise by 2 ℃ toward midcentury (2021–2050) ​at a faster rate than the global average (about 1.5 ℃), under the RCP8.5 and SSP5-8.5 scenarios, associated with more frequent and severe climate extremes. In particular, low-lying regions in East Africa will be vulnerable to severe heat stress, with an extreme wet-bulb temperature approaching or exceeding the US National Weather Service’s extreme danger threshold of 31 ℃. On the other hand, population centers in the highlands of Ethiopia will receive significantly more precipitation during the autumn season and will see more extreme rainfall events, with implications for flooding and agriculture. The robustness of these results across all GCM and RCM simulations, and for both of CMIP5 and CMIP6 frameworks (CMIP: Coupled Model Inter-comparison Project) supports the reliability of these future projections. Our simulations of near-term climate change impacts are designed to inform the development of sound adaptation strategies for the region.

Impact of ocean data assimilation on climate predictions with ICON-ESM

We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.

Show Me and What Will I Remember? Exploring Recall in Response to NWS Tornado Warning Graphics

It is critical that organizations deliver timely, effective communication about potential risks and life-saving information. The National Weather Service (NWS) developed a suite of messages known as “experimental graphic products” to be automatically distributed through their local official Twitter accounts at the onset of fast-moving, severe weather events such as tornadoes. However, recent research has suggested messages need to be carefully constructed for audiences to place attention to the content, remember the content, and later act in response to the content. The purpose of this study is to explore what people recall of the NWS Twitter message. We used an online survey instrument, distributed via Qualtrics, to investigate participant responses to three open-ended questions about the message. We performed a quantitative analysis to summarize the frequency of message features recorded by participants, and a qualitative analysis to identify themes that provide a deeper description of what was recalled. We found that participants encoded the hazard type, the time the message was sent and would expire, and the types of impacts that might occur. Graphic design cues elicited attention as they “stood out” to the participants. When asked about importance and what they would tell others, respondents described protective action, indicating participants may have activated prior knowledge of the threat, as it was not included in the message. Risk, disaster, and science communicators can draw guidance about communicating during a disaster. It provides a lens for researching message construction, and the importance of communicating protective action guidance during severe weather events.

Reply to “Comments on ‘The Myth of Cleveland Abbe: A Review of a Manufactured History of the Creation of the National Weather Service’”

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). *Emeritus Corresponding Author: Gregory F. Romano, gregory.romano@noaa.gov

Comments on “The Myth of Cleveland Abbe: A Review of a Manufactured History of the Creation of the National Weather Service”

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). *Schultz is the Chief Editor of Monthly Weather Review. Corresponding author: David M. Schultz, david.schultz@manchester.ac.uk

Validation and Bias Correction of Forecast Reference Evapotranspiration for Agricultural Applications in Nevada

Accurate estimates of reference evapotranspiration (ET0) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET0 and its driving variables from the National Weather Service’s National Digital Forecast Database (NDFD). Daily NDFD ET0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET0. Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET0, which reduced the root-mean squared error by 5%–30% for most locations. Bias-corrected ET0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.

Comments on “The Myth of Cleveland Abbe: A Review of a Manufactured History of the Creation of the National Weather Service”

Comments on “The Myth of Cleveland Abbe: A Review of a Manufactured History of the Creation of the National Weather Service”

Pre-industrial temperature variability on the Swiss Plateau derived from the instrumental daily series of Bern and Zurich

Abstract. We describe the compilation of two early instrumental daily temperature series from Bern and Zurich, Switzerland, starting from 1760 and 1756, respectively. The series are a combination of numerous small segments from different observers at different locations within and outside the two cities that are converted to modern units and homogenized. In addition, we introduce a methodology to estimate the errors affecting daily and monthly mean values derived from early instrumental observations. Given the frequent small data gaps, we merge the two daily series into a more complete series representing the central Swiss Plateau. We finally compare the homogenized monthly series with other temperature reconstructions for Switzerland. We find significant differences before 1860, pointing to biases that might affect some of the most widely used instrumental data sets. In general, the homogenization of temperature measurements at the transition between the early instrumental and national weather service eras remains a problematic issue in historical climatology and has significant implications for other fields of climate research.

Why K-12 education is important to achieving a Weather-Ready Nation

We propose the formal inclusion of the K-12 education system as a critical step to achieving the National Weather Service’s (NWS) vision of a Weather Ready Nation (WRN). The WRN is a NWS initiative to build resilience within the U.S. population to extreme weather, water, and climate events, and includes considerable research investments in science and technology and social, behavioral, and economic science (SBES). The SBES research has been focused on weather communications/messaging and development of impacts-based products, with the goal of better-informed decisions by the public during hazardous weather. While there is a long history of educators working with meteorological professionals on educational initiatives within the K-12 system, the majority of these efforts have involved individual educator initiatives. We believe that including K-12 educators as formal stakeholders in the NWS WRN strategy is one of the most effective ways to reach the greatest segment of the population, and our limited literature review reveals evidence of educational efforts leading to better preparedness. We suggest that formally integrating K-12 educators as partners can best be accomplished by creating a committee of NWS meteorologists, emergency managers, school administrators, and faculty in order to ensure that each partner’s concerns are addressed. The curriculum itself should be designed to “connect the dots” from meteorological theory to hazardous weather, hazard products and understanding them to make safe, informed decisions during dangerous weather. We recommend testing this curriculum through a pilot study in a single district or school, with a longitudinal component to monitor its effectiveness.