National Weather Service Research Articles

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 While this approach improved the forecasts over its predecessors, it is now quite outdated and is likely to result in suboptimal analysis.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.

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.

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.

Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model

As the successor of FORMOSAT-3/COSMIC (FS3/C1), FORMOSAT-7/COSMIC-2 (FS7/C2) was successfully launched on 25 June 2019. FS3 radio occultation (RO) data has contributed greatly to Taiwan’s meteorological progress, improving model representations of marine boundary layer heights, cyclogenesis, tropical cyclones/typhoons, and Mei-Yu front systems. The operational CWBWRF numerical weather prediction model with the 3DEnVar data assimilating system in the Taiwan Central Weather Bureau (CWB) was adopted to evaluate the impact of assimilating FS7 RO data. The following two experiments were conducted: one assimilated the in-situ observations as in the CWB operational task (nRO), and the other additionally assimilated FS7 RO refractivity profiles (wRO). Both experiments utilized 6-h assimilating window and full cycle data assimilation strategy and made 120-h forecasts after each assimilation. Within over 70 synoptic verification cases, the biases of geopotential height, temperature, and wind were reduced in the upper model levels in wRO results, and the typhoon track and intensity prediction error reductions were statistically significant. In addition, the wRO experiment improved the typhoon structure in the initial conditions and led to a better typhoon structure forecast. These results showed that the FS7 RO refractivity assimilation could improve model forecast performance, leading to its operational use in the CWB.

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.

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

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”

A Revised Meiyu-Season Onset Index for Taiwan Based on ERA5

Revisiting the defined Meiyu onset of Central Weather Bureau (CWB), this study applied a newer reanalysis dataset and added multiple timing and duration criteria to improve the Meiyu onset index. The previous Meiyu onset index was based on horizontal and vertical wind shears using older-generation reanalysis data. The horizontal shear captures the cyclonic vorticity while the vertical shear depicts overturning. However, this older index tends to predict the onset date too early from the actual maximum precipitation. After applying the modification that is described in this paper, the newer Meiyu onset index consistently leads the maximum precipitation in Taiwan only by a few days, except for two years over the 30-year analysis period. The implication of this modified and improved Meiyu onset index is that it can substitute model precipitation that tends to be problematic, as well as studying climate change impacts.

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.

Assessing heat index changes in the context of climate change: A case study of Hanoi (Vietnam)

Air temperature and humidity have a great impact on public health, leads to heat stress. The US National Weather Service uses temperature and relative humidity to build a heat index (HI) as a metric to identify the thresholds for heat stress as felt by the public. Under climate change conditions and especially in hot humid weather during summer, the number of hot days in Hanoi has increased in recent times. Subsequently, the heat index is rising in both number of occurrences and level of intensity leading to increasing temperature stress on people’s health. The daily heat index for the future was simulated using maximum daily temperature and minimum daily relative humidity based on climate change scenarios. Maximum daily temperature was provided by the climate change model, while minimum daily relative humidity was estimated from the following: maximum daily temperature, mean daily temperature and daily rainfall. Results show that in the future, the heat index will increase by 0.0777°C/year in the RCP 4.5 scenario and 0.08°C/year in the RCP 8.5 scenario. Number of weeks with heat at danger tends to increase to 5.5 weeks/5 year for scenario RCP 4.5, and it is 6 weeks/5 years under RCP 8.5 scenario. In particular, the number of days of heatstroke over a 30-year period (from 1991 to 2020) amounted to only 4, that is an average of 0.13 days of the year, which represents a very rare weather phenomenon in the past. In contrast, under an RCP 4.5 scenario in the future over a 30-year period, the average number of days per year will be 2, 57 days; while the average number of days per year under an RCP 8.5 scenario would be 3, 87 days. This phenomenon will be mainly concentrated in the months of June, July, and August. Projections of this type are a key tool for communities working out how they will adapt to heat stress in the context of climate change.

Communicating Uncertainty Information in a Dynamic Decision Environment

Abstract The likelihood of threatening events is often simplified for members of the public and presented as risk categories such as the “watches” and “warnings” currently issued by National Weather Service in the United States. However, research (e.g., Joslyn and LeClerc) suggests that explicit numeric uncertainty information—for example, 30%—improves people’s understanding as well as their decisions. Whether this benefit extends to dynamic situations in which users must process multiple forecast updates is as yet unknown. It may be that other likelihood expressions, such as color coding, are required under those circumstances. The experimental study reported here compared the effect of the categorical expressions “watches” and “warnings” with both color-coded and numeric percent chance expressions of the likelihood of a tornado in a situation with multiple updates. Participants decided whether and when to take shelter to protect themselves from a tornado on each of 40 trials, each with seven updated tornado forecasts. Understanding, decision quality, and trust were highest in conditions that provided percent chance information. Color-coded likelihood information inspired the least trust and led to the greatest overestimation of likelihood and confusion with severity information of all expressions.

Polarimetric Weather Radar: Overview of principles and applications

We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can distinguish among precipitation particles of different shapes, compositions, and orientations. A relatively simple electromagnetic idea resulted in the 2012 upgrade of the National Weather Service (NWS) network of 160 high-resolution Doppler weather radars in the United States, with dual-polarization technology. We describe the details of the Colorado State University (CSU)–CHILL research radar, featuring exceptional polarization purity and dual-frequency operation, and its setup and role in winter field experiments in Colorado.

Downscaling of Historical Wind Fields over Switzerland Using Generative Adversarial Networks

Abstract Near-surface wind is difficult to estimate using global numerical weather and climate models, because airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution digital elevation model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25-km grid to a 1.1-km grid. A 1.1-km-resolution wind dataset for 2016–20 from the operational numerical weather prediction model COSMO-1 of the national weather service MeteoSwiss is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction relative to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, that are not resolved in the original ERA5 fields. Significance Statement Statistical downscaling, which increases the resolution of atmospheric fields, is widely used to refine the outputs of global reanalysis and climate models, most commonly for temperature and precipitation. Near-surface winds are strongly modified by the underlying topography, generating local flow conditions that can be very difficult to estimate. This study develops a deep learning model that uses local topographic information to spatially downscale hourly near-surface winds from their original 25-km resolution to a 1.1-km grid over Switzerland. Our model produces realistic high-resolution gridded wind fields with expected orographic effects but performs better in flatter regions than in mountains. These downscaled fields are useful for impact assessment and decision-making in regions where global reanalysis data at coarse resolution may be the only products available.

Short-Term Weather Forecast Skill of Artificial Neural Networks

Abstract We evaluate the short-term weather forecast performance of three flavors of artificial neural networks (NNs): feed forward back propagation, radial basis function, and generalized regression. To prepare the application of the NNs to an operational setting, we tune NN hyperparameters using over two years of historical data. Five objective guidance products serve as predictors to the NNs: North American Mesoscale and Global Forecast System model output statistics (MOS) forecasts, the High-Resolution Rapid Refresh (HRRR) model, National Weather Service forecasts, and the National Blend of Models product. We independently test NN performance using 96 real-time forecasts of temperature, wind, and precipitation across 11 U.S. cities made during the WxChallenge, a weather forecasting competition. We demonstrate that all NNs significantly improve short-range weather forecasts relative to the traditional objective guidance aids used to train the networks. For example, 1-day maximum and minimum temperature forecast error is 20%–30% lower than MOS. However, NN improvement over multiple linear regression for short-term forecasts is not significant. We suggest this may be attributed to the small number of training samples, the operational nature of the experiment, and the short forecast lead times. Regardless, our results are consistent with previous work suggesting that applying NNs to model forecasts can have a positive impact on operational forecast skill and will become valuable tools when integrated into the forecast enterprise. Significance Statement We used approximately two years of historical weather data and objective forecasts for a number of cities to tune a series of artificial neural networks (NNs) to forecast 1-day values of maximum and minimum temperature, maximum sustained wind speed, and quantitative precipitation. We compare forecast error against common objective guidance and multiple linear regression. We found that the NNs exhibit about 25% lower error than common objective guidance for temperature forecasting and 50% lower error for wind speed. Our results suggest that NNs will be a valuable contributor to improving weather forecast skill when adopted into the existing forecast enterprise.

Backyard Hydroclimatology: Citizen Scientists Contribute to Drought Detection and Monitoring

Abstract The Community Collaborative Rain, Hail and Snow (CoCoRaHS) network is a well-regarded, trusted source of precipitation data. The network’s volunteers also provide weather and climate observations through daily comments, significant weather reports, and condition monitoring reports. Designed to meet a need for local information about drought events and their impacts, “condition monitoring” was initiated as a pilot project in North Carolina and South Carolina in 2013 and launched nationally in October 2016. Volunteers regularly report on how precipitation, or a lack thereof, affects their local environment and community by ranking current conditions on a seven-point scale ranging from severely dry to severely wet and sharing observations through written narratives. This study assesses the usefulness of these reports for drought monitoring and decision-making, drawing from the >7,100 reports submitted in the Carolinas between October 2016 and June 2020. This period encompasses the Carolinas’ climate patterns and extreme events such as droughts, wildfires, and hurricanes (“drought busters”). Three aspects of usefulness were evaluated in the reports: the extent to which volunteers’ assessments of dry-to-wet conditions correspond to objective drought indicators (EDDI, SPI, SPEI) typically employed for monitoring drought; how volunteers’ qualitative observations depict changing conditions, focusing on two flash droughts in 2019; and actual use of the reports by National Weather Service offices, State Climate Offices, U.S. Drought Monitor authors, and drought response committees. Although ­report content can vary widely, findings show that volunteers’ assessments reflect meteorological conditions and provide on-the-ground details that are being incorporated into existing drought monitoring processes.

Heat Stress During Arba’een Foot‐Pilgrimage (World’s Largest Gathering) Projected to Reach “Dangerous” Levels Due To Climate Change

AbstractArba'een, a Muslim pilgrimage, is one of the largest annual mass gatherings in the world, with a date fixed according to the lunar calendar. Most pilgrims start their long walking journey from Basra/Najaf toward Karbala (about 70–500 km) and are significantly affected by outdoor weather conditions during this period. Here, based on simulations performed using carefully‐selected climate models, we project that heat stress during the pilgrimage is likely to reach a “dangerous” level, defined according to the US National Weather Service criteria, by the end of this century. Moreover, a significant increase in consecutive occurrence of hot days and hot nights is expected within the coming decades, which may cause a high incidence of heat‐related disorders as the human body may not recover from the daytime heat loads. Our study suggests that sound adaptation measures and stringent mitigation actions must be established to ensure a safe pilgrimage in the future.

Comparison of various heat waves definitions and the burden of heat-related mortality in France: Implications for existing early warning systems

IntroductionIn France, a heat warning system (HWS) has been implemented almost two decades ago and rely on some official heat wave (HW) definitions. However, no study has compared the burden associated with a large set of alternative HW definitions to the official definitions. Such comparison could be particularly helpful to identify HW conditions for which effective HWS would minimize the health burden across various geographical contexts and possibly update thresholds to trigger HWS. The aim of this study is to identify (and rank) definitions that drive the highest health burden in terms of mortality to inform future HWS across multiple cities in France. MethodsBased on weather data for 16 French cities, we compared the two official definitions used in France to: i) the Excess Heat Factor (EHF) used in Australia, and ii) 18 alternative hypothetical HW definitions based on various combinations of temperature metrics, intensity, and duration. Propensity score matching and Poisson regressions were used to estimate the effect of each HW exposure on non-accidental mortality for the May–September period from 2000 to 2015. ResultsThe associations between HW and mortality differed greatly depending on the definition. The greatest burden of heat was 1,055 (95% confidence interval “CI”: [856; 1,302]) deaths per summer and was obtained with the EHF. The EHF identified HW with 2.46 (95% CI: [1.92; 3.58]) or 8.18 (95% CI: [6.63; 10.61]) times the global burden at the national level obtained with the climatological indicator of the French national weather service and the HW indicator of the French national HWS, respectively and was the most impactful definition pattern for both temperate oceanic and Mediterranean climate types. ConclusionIdentifying the set of extreme heat conditions that drive the highest health burden in a given geographical context is particularly helpful when designing or updating heat early warning systems.