National Weather Service Research Articles

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.

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© 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.

Weather Information and Decision-Support Tool Needs Assessment

University of Maine researchers developed three commodity-specific grower focus groups (lowbush blueberry, apple, mixed vegetable) to discuss and survey 1) current access to weather information, and 2) interest in weather-based decision support tools. NOAA National Weather Service (30%) (n=47) was most commonly cited as a weather information source. Growers ranked greater forecast accuracy (31%) and localization (19%) (n=48) as highest priorities. Only 34% of growers reported current use of weather-based decision support tools, but 86% expressed interest in future use (n=134). The project team is using grower input to improve weather data access and decision-support tools for Maine farmers.

Heat Policies and AC Availability in US Prison Systems

BACKGROUND AND AIM Climate change will increase the number of hot days and heatwaves leaving incarcerated populations particularly vulnerable to extreme heat due to their lack of agency and control over their heat exposure. Previous prison heat policy analysis has not utilized Heat Index to provide context for the implementation of policies or had access to air-conditioning data. We aimed to examine the coverage and gaps of US prison system heat policies and air-conditioning access. METHODS Prison heat policies were identified using 52 prison system (50 states, DC, and Federal Bureau of Prisons) websites and complemented by state legislation. Heat policies were analyzed using the National Weather Service’s (NWS) Heat Index Calculator on maximum reported temperature values. Facility-level air-conditioning data for 18 prison systems were public or received through public records requests by UCLA’s COVID-19 Behind Bars, with facility function, capacity, and heat risk collected from federal, state, and the Intercept’s Climate and Punishment datasets. RESULTS Half of the prison systems had publicly-available heat policies (n=52), of which 81% included temperature values (n=21). 64% of heat indices using maximum indoor temperature (n=14) would be classified as "caution” and 63% of heat indices using temperatures when respite resources would be provided (e.g. extra water) (n=8) were classified as "extreme caution” by the NWS assuming 50% relative humidity. 18% of facilities (n=374) lacked AC access completely. Among these, 55% of non-air-conditioned facilities (41,000 operational capacity) are at "severe or extreme heat risk”. Overall, 23% of facilities whose function includes serving heat-sensitive populations (n=202), lack any air-conditioning despite their heat risk. CONCLUSIONS This study emphasizes the need for national correctional temperature policies and air-conditioning requirements to protect the thermal health of those who live and work in carceral facilities. KEYWORDS environmental justice built environment prison

Impact of meteorological factors and psychiatric disorders : a French exploratory study.

Introduction : The objective of our work was to study the link between meteorological factors (temperature, sunshine duration, rain, wind, humidity) and psychiatric disorders. For this purpose, we conducted two exploratory studies in France : the first one, studying the link between the weather and suicidal behaviors (suicides and suicide attempts); the other one between alcohol use disorders (AUD) and the weather. Methods : We analyzed the number of deaths by suicide and suicide attempts in metropolitan France (2009-2018) from French national hospitalization databases; patients who were admitted to an emergency department for an alcohol related condition were included using the Oscour database, and we used meteorological database from national weather services. Based on this model, we determined the association of suicides and suicide attempts with weather factors, and then between AUD and meteorological factors. Results: The number of suicides and suicide attempts decreased over the last ten years in France (mean decrease of 14.49% and 11.69% per year). We observed a significant and recurrent seasonal pattern of suicides and suicide attempts in France, with a peak in spring. The suicide and suicide attempt rates were higher in the northern departments of France. We found significant positive correlations between the number of alcohol-related ED visits and the mean temperature (r = 0.55; p = 1.87e) and the duration of sunlight (r = 0.42; p = .0015). Negative correlations were also found significant with rain (r = −0.40; p = .0014), humidity (r = –0.41; p = .0023) and wind speed (r = −0.40; p = .0031).  Conclusion: The number of suicides and suicide attempts decreased in France over the last 10 years, with large and recurrent seasonal variations. Emergency visits for AUD seem to increase with the temperature and duration of sunlight, and decrease with rain, humidity and wind speed.

Ambient temperature and preterm birth risk in New York City: A time-series analysis

BACKGROUND AND AIM Recent studies suggest an association between daily variation in ambient temperature during pregnancy and preterm birth (PTB). Our objective is to examine the short-term effects of heat and cold on PTB risk from 2008 to 2016 in New York City (NYC). METHODS We applied quasi-Poisson regression with distributed lag nonlinear models to estimate the cumulative relative risk (CRR) of daily counts of spontaneous PTB (<37 weeks gestation) and outdoor temperature, and to estimate the fraction of PTBs attributable to temperature, by season, using NYC Vital Statistics birth records and National Weather Service daily temperature data at LaGuardia airport. Models were adjusted for within-season trends and daily number of pregnant people at risk for preterm delivery. Heat and cold were defined as the 95th versus 50th percentile of maximum temperature during the warm season and 5th versus 50th percentile of minimum temperature during the cold season, respectively. Maternal race/ethnicity was assessed for effect modification. RESULTS Our analysis consisted of 38,593 spontaneous PTBs. Heat was not associated with PTB during the warm season [CRR: 0.98 (95% CI: 0.92, 1.05)]. There was a nonlinear association between cold and PTB. During the cold season, the CRR was 1.08 (0.99, 1.17) for cold up to three days before delivery (lag 0-3). The estimated fraction of PTBs attributable to cold was 9.3% (-0.6%, 17.4%), which corresponded to 81 (-2, 154) cold-attributable PTBs over the 9-year study period. Among non-Hispanic Blacks, the CRR was 1.20 (1.04, 1.39) for cold over lag 0-3 days, and an estimated 23.2% (10.3%, 34.3%) of non-Hispanic Black PTBs, equivalent to 69 (29, 105) PTBs, were attributable to cold. CONCLUSIONS Heat had no measurable impact. Cold potentially increased risk of PTB. Our finding for the impact of cold on non-Hispanic Black PTBs warrants additional research. KEYWORDS Preterm birth Temperature

The Association of Visibility and Adenocarcinoma Lung Cancer in Southern Taiwan

Background/Aim Studies on association between lung cancer and long-term exposure to air pollution are limited due to the unavailability of air pollution data. The study aims to assess association of visibility, a proxy indicator of long-term air pollution exposure, with adenocarcinoma lung cancer. Methods Our study area is in southern Taiwan, an industrial area with 2.7 million people in 5.7 thousand km². Annual lung cancer incidence data with the ICD-O-3 codes from C34.0-C34.9 were obtained from Taiwan Cancer Registry (TCR) from 1997 to 2018. Age-adjusted incidence of lung cancers were standardized by the WHO 2000 world standard population. Hourly atmospheric visibility data from 1961 to 2022 were obtained from Taiwan Central Weather Bureau. 30-years moving averages were then calculated from 1967 onward to represent long-term visibility in the study area. Linear regression models were employed to estimate associations of 30-years averaging visibility annual age-adjusted lung cancers incidence for both adenocarcinoma and non-adenocarcinoma from 1997 to 2018. Results From 1997 to 2018, southern Taiwan's adenocarcinoma lung cancer incidence increased from 8.93 to 25.81 per 100,000 population, while non-adenocarcinoma incidence decreased from 14.61 to 10.41 per 100,000 population. The 30-years averaged visibility in the study area declined from 14.90 km to 7.65 km in the same period. Reducing visibility is significantly associated adenocarcinoma lung cancer, with a 2.43% (95% confidence interval [CI]: 2.77-2.11, adjusted R2 value: 0.92) increase in incidence by 1 km decrease in visibility during this period. Such associations exist for both females and males with increasing incidence of 2.57% (95% CI: 2.92-2.22, adj R2: 0.92) and 2.32% (95% CI: 2.69-1.95, adj R2: 0.89) by 1 km visibility reduction, respectively. By contrast, visibility reduction was not associated with increased in non-adenocarcinoma incidence. Conclusions Long-term visibility reduction is associated with increase in adenocarcinoma lung cancer incidence but not with non-adnocarcinoma.