scholarly journals An Assessment of National Weather Service Warning Procedures for Ice Storms

2009 ◽  
Vol 24 (1) ◽  
pp. 104-120 ◽  
Author(s):  
David A. Call
Keyword(s):  
Winter Season ◽  
The United States ◽  
Ice Storm ◽  
National Weather Service ◽  
Intended Audience ◽  
Ice Storms ◽  
High Level ◽  
Communication Policies ◽  
Proactive Communication ◽  
Weather Service

Abstract Ice storms cause substantial damage to the United States every winter season, and the costs have increased in recent years. Accurate prediction and timely dissemination of warnings are effective ways to reduce the effects, because institutions and individuals can take actions to reduce the impacts. The National Weather Service (NWS) is the U.S. government agency charged with issuing warnings of impending ice storms. A survey of NWS warning coordination meteorologists was conducted to assess their awareness of the ice storm hazard, procedures followed to warn for ice storms, and level of contact with members of the community. Several warnings issued in advance of a recent ice storm were also examined. The findings of this research are twofold. First, most meteorologists with the NWS perceive the ice storm hazard with a level of seriousness consistent with climatology. Most follow established procedure and actively engage in warning specific groups before a storm. The second finding was that individual offices maintain a high level of autonomy. While this offers valuable flexibility and the opportunity to try new approaches, there is significant variation in the length and tone of ice storm warnings themselves. Additionally, several offices do not contact outsiders or offer general educational products, which may underserve constituents in their forecast areas. To solve these problems, it is suggested that NWS management encourage and support proactive communication policies. The NWS should also analyze the audience of their warning products and consider guidelines regarding intended audience, tone, and length.

scholarly journals Quantitative Ice Accretion Information from the Automated Surface Observing System

2007 ◽  
Vol 46 (9) ◽  
pp. 1423-1437 ◽  
Author(s):  
Charles C. Ryerson ◽  
Allan C. Ramsay
Keyword(s):  
United States ◽  
The United States ◽  
Ice Thickness ◽  
Ice Storm ◽  
Design Standards ◽  
Ice Storms ◽  
Ice Accumulation ◽  
Direct Measurements ◽  
Improved Design ◽  
Precipitation Events

Abstract Freezing precipitation is a persistent winter weather problem that costs the United States millions of dollars annually. Costs and infrastructure disruption may be greatly reduced by ice-storm warnings issued by the National Weather Service (NWS), and by the development of climatologies that allow improved design of infrastructure elements. However, neither the NWS nor developers of climatologies have had direct measurements of ice-storm accumulations as a basis for issuing warnings and developing storm design standards. This paper describes the development of an aviation routine/special weather report (METAR/SPECI) remark that will report quantitative ice thickness at over 650 locations during ice storms using new algorithms developed for the Automated Surface Observing System (ASOS). Characteristics of the ASOS icing sensor, a field program to develop the algorithms, tests of accuracy, application of the algorithms, and sources of error are described, as is the implementation of an ice-thickness METAR/SPECI remark. The algorithms will potentially allow freezing precipitation events to be tracked with regard to ice accumulation in near–real time as they progress across the United States.

scholarly journals Intercomparison of Mixing Layer Heights from the National Weather Service Ceilometer Test Sites and Collocated Radiosondes

2019 ◽  
Vol 36 (1) ◽  
pp. 129-137 ◽  
Author(s):  
Micheal Hicks ◽  
Belay Demoz ◽  
Kevin Vermeesch ◽  
Dennis Atkinson
Keyword(s):  
United States ◽  
Mixing Layer ◽  
The United States ◽  
Arid Climate ◽  
National Weather Service ◽  
Layer Height ◽  
Hot Arid Climate ◽  
Mixing Layer Height ◽  
Sky Conditions ◽  
Weather Service

AbstractA network of automated weather stations (AWS) with ceilometers can be used to detect sky conditions, aerosol dispersion, and mixing layer heights, in addition to the routine surface meteorological parameters (temperature, pressure, humidity, etc.). Currently, a dense network of AWSs that observe all of these parameters does not exist in the United States even though networks of them with ceilometers exist. These networks normally use ceilometers for determining only sky conditions. Updating AWS networks to obtain those nonstandard observations with ceilometers, especially mixing layer height, across the United States would provide valuable information for validating and improving weather/climate forecast models. In this respect, an aerosol-based mixing layer height detection method, called the combined-hybrid method, is developed and evaluated for its uncertainty characteristics for application in the United States. Four years of ceilometer data from the National Weather Service Ceilometer Proof of Concept Project taken in temperate, maritime polar, and hot/arid climate regimes are utilized in this evaluation. Overall, the method proved to be a strong candidate for estimating mixing layer heights with ceilometer data, with averaged uncertainties of 237 ± 398 m in all tested climate regimes and 69 ± 250 m when excluding the hot/arid climate regime.

scholarly journals Estimating the Uncertainty in Spatial Estimates of Areal Snow Water Equivalent

1996 ◽  
Vol 27 (5) ◽  
pp. 295-312
Author(s):  
Steven S. Carroll
Keyword(s):  
River Basin ◽  
Snow Water Equivalent ◽  
Simulation Models ◽  
The United States ◽  
Hydrologic Simulation ◽  
National Weather Service ◽  
The West ◽  
Snow Water ◽  
Snow Model ◽  
Weather Service

With the increased demand for water in the United States, particularly in the West, it is essential that water resources be accurately monitored. Consequently, the National Weather Service (NWS) maintains a set of conceptual, continuous, hydrologic simulation models used to generate extended streamflow predictions, water supply outlooks, and flood forecasts. A vital component of the hydrologic simulation models is a snow accumulation and ablation model that uses observed temperature and precipitation date to simulate snow cover conditions. The simulated model states are updated throughout the snow season using snow water equivalent estimates (estimates of the water content of snowpack) obtained from airborne and ground-based snow water equivalent data. The National Weather Service has developed a spatial geostatistical model to estimate the areal snow water equivalent in a river basin. The estimates, which are obtained for river basins throughout the West, are used to update the snow model. To facilitate accurate updating of the simulated snow water equivalent estimates generated by the snow model, it is necessary to incorporate measures of uncertainty of the areal snow water equivalent estimates. In this research, we derive the expression for the mean-squared prediction error of the areal snow water equivalent estimate and illustrate the methodology with an example from the Upper Colorado River basin.

The United States National Weather Service Real-Time Flood Forecasting

2017 ◽  
Author(s):  
Pedro J. Restrepo
Keyword(s):  
Real Time ◽  
Snow Water Equivalent ◽  
Weather Forecast ◽  
The United States ◽  
Flood Forecasting ◽  
Federal State ◽  
National Weather Service ◽  
Precipitation Temperature ◽  
Snow Water ◽  
Weather Service

The U.S. National Weather Service (NWS) is the agency responsible for flood forecasting. Operational flow forecasting at the NWS is carried out at the 13 river forecasting centers for main river flows. Flash floods, which occur in small localized areas, are forecast at the 122 weather forecast offices. Real-time flood forecasting is a complex process that requires the acquisition and quality control of remotely sensed and ground-based observations, weather and climate forecasts, and operation of reservoirs, water diversions, and returns. Currently used remote-sense observations for operational hydrologic forecasts include satellite observations of precipitation, temperature, snow cover, radar observations of precipitation, and airborne observations of snow water equivalent. Ground-based observations include point precipitation, temperature, snow water equivalent, soil moisture and temperature, river stages, and discharge. Observations are collected by a number of federal, state, municipal, tribal and private entities, and transmitted to the NWS on a daily basis. Once the observations have been checked for quality, a hydrologic forecaster uses the Community Hydrologic Prediction System (CHPS), which takes care of managing the sequence of models and their corresponding data needs along river reaches. Current operational forecasting requires an interaction between the forecaster and the models, in order to adjust differences between the model predictions and the observations, thus improving the forecasts. The final step in the forecast process is the publication of forecasts.

scholarly journals Usefulness of the United States National Weather Service Radar Display as Rated by Website Users

2018 ◽  
Vol 10 (4) ◽  
pp. 673-691 ◽  
Author(s):  
Michelle E. Saunders ◽  
Kevin D. Ash ◽  
Jennifer M. Collins
Keyword(s):  
United States ◽  
Spatial Clustering ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
The United States ◽  
Perceived Usefulness ◽  
National Weather Service ◽  
Weather Information ◽  
Weather Service

Abstract Weather radar is now widely viewed by the general public in the United States via television, computers/tablets, and smartphones. Anyone can consult near-real-time maps and animations of weather radar data when weather conditions are a factor. However, the usefulness of weather radar data for each user depends on a complex interaction of factors. There have been few studies providing conceptual arguments and empirical data to better understand what the most important factors are and to comprehend patterns of public weather radar use across the United States. The first part of this research provides a basic conceptual framework for research investigating the usefulness of weather radar displays as a source of weather information and as a decision aid. The second part aims to uncover several factors that influence the perceived usefulness rating of the National Weather Service (NWS) website’s weather radar display at both national and regional levels using variables gathered from the 2014 NWS customer satisfaction survey alongside relevant geographic and climatological variables. Data analyses include spatial clustering and ordinal regression utilized within a generalized linear model methodology. Overall, respondents who are more familiar with the NWS and their products, as well as those who indicate they are more likely to take action based on information provided by the NWS, are more likely to find the NWS radar display useful. Geographically, the NWS radar display is most useful to persons residing in the southern United States. Lightning is the most important hazard associated with higher radar usefulness ratings.

Evolving the National Weather Service to Build a Weather-Ready Nation: Connecting Observations, Forecasts, and Warnings to Decision-Makers through Impact-Based Decision Support Services

2019 ◽  
Vol 100 (10) ◽  
pp. 1923-1942 ◽  
Author(s):  
Louis W. Uccellini ◽  
John E. Ten Hoeve
Keyword(s):  
Decision Support ◽  
Support Services ◽  
The United States ◽  
Extreme Weather ◽  
Lessons Learned ◽  
Federal State ◽  
Supporting Evidence ◽  
National Weather Service ◽  
Climate Events ◽  
Weather Service

AbstractAs the cost and societal impacts of extreme weather, water, and climate events continue to rise across the United States, the National Weather Service (NWS) has adopted a strategic vision of a Weather-Ready Nation that aims to help all citizens be ready, responsive, and resilient to extreme weather, water, and climate events. To achieve this vision and to meet the NWS mission of saving lives and property and enhancing the national economy, the NWS must improve the accuracy and timeliness of forecasts and warnings, and must directly connect these forecasts and warnings to critical life- and property-saving decisions through the provision of impact-based decision support services (IDSS). While the NWS has been moving in this direction for years, the shift to delivering IDSS holistically requires an agency-wide transformation. This article discusses the elements driving the need for change at the NWS to build a Weather-Ready Nation; the foundational basis for IDSS; ongoing challenges to provide IDSS across federal, state, local, tribal, and territorial levels of government; the path toward evolving the NWS to deliver more effective IDSS; the importance of partnerships within the weather, water, and climate enterprise and with those responsible for public safety to achieve the Weather-Ready Nation vision; and initial supporting evidence and lessons learned from early efforts.

Performance of National Weather Service Forecasts Compared to Operational, Consensus, and Weighted Model Output Statistics

2005 ◽  
Vol 20 (6) ◽  
pp. 1034-1047 ◽  
Author(s):  
Jeffrey A. Baars ◽  
Clifford F. Mass
Keyword(s):  
United States ◽  
The United States ◽  
Minimum Variance ◽  
Model Output ◽  
National Weather Service ◽  
Past Performance ◽  
Temperature Changes ◽  
Temperature And Precipitation ◽  
Model Output Statistics ◽  
Weather Service

Abstract Model output statistics (MOS) guidance has been the central model postprocessing approach used by the National Weather Service since the 1970s. A recent advancement in the use of MOS is the application of “consensus” MOS (CMOS), an average of MOS from two or more models. CMOS has shown additional skill over individual MOS forecasts and has performed well compared to humans in forecasting contests. This study compares MOS, CMOS, and WMOS (weighting component MOS predictions by their past performance) forecasts of temperature and precipitation to those of the National Weather Service (NWS) subjective forecasts. Data from 29 locations throughout the United States from 1 August 2003 through 1 August 2004 are used. MOS forecasts from the Global Forecast System (GMOS), Eta (EMOS), and Nested Grid Model (NMOS) models are included, with CMOS being a simple average of these three forecasts. WMOS is calculated using weights determined from a minimum variance method, with varying training periods for each station and variable. Performance is analyzed at various forecast periods, by region of the United States, and by time/season, as well as for periods of large daily temperature changes or large departures from climatology. The results show that CMOS is competitive or superior to human forecasts at nearly all locations and that WMOS is superior to CMOS. Human forecasts are most skillful compared to MOS during the first forecast day and for periods when temperatures differ greatly from climatology. The implications of these results regarding the future role of human forecasters are examined in the conclusions.

Weather Analysis and Forecasting

1979 ◽  
Author(s):  
Frederick L. Crosby
Keyword(s):  
United States ◽  
The United States ◽  
National Weather Service ◽  
Weather Service

I appreciate the opportunity to talk to the 25th Conference for a few minutes today on the procedures and programs used by the National Weather Service to provide a meteorological service to the United States. Paper published with permission.

scholarly journals Exploratory study of the global intent to accept COVID-19 vaccinations

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexandre de Figueiredo ◽  
Heidi J. Larson
Keyword(s):  
Exploratory Study ◽  
Large Scale ◽  
Population Level ◽  
The United States ◽  
Vaccine Acceptance ◽  
Sociodemographic Determinants ◽  
Nationally Representative ◽  
The Government ◽  
High Level ◽  
Communication Policies

Abstract Background As the world begins the rollout of multiple COVID-19 vaccines, pandemic exit strategies hinge on widespread acceptance of these vaccines. In this study, we perform a large-scale global exploratory study to examine the levels of COVID-19 vaccine acceptance and explore sociodemographic determinants of acceptance. Methods Between October 31, 2020 and December 15, 2020, 26,759 individuals were surveyed across 32 countries via nationally representative survey designs. Bayesian methods are used to estimate COVID-19 vaccination acceptance and explore the sociodemographic determinants of uptake, as well as the link between self-reported health and faith in the government’s handling of the pandemic and acceptance. Results Here we show that intent to accept a COVID-19 vaccine is low in Lebanon, France, Croatia, and Serbia and there is population-level polarisation in acceptance in Poland and Pakistan. Averaged across all countries, being male, over 65, having a high level of education, and believing that the government is handling the pandemic well are associated with increased stated acceptance, but there are country-specific deviations. A belief that the government is handling the pandemic well in Brazil and the United States is associated with lower vaccination intent. In the United Kingdom, we find that approval of the first COVID-19 vaccine in December 2020 did not appear to have an impact on the UK’s vaccine acceptance, though as rollout has continued into 2021, the UK’s uptake exceeds stated intent in large-scale surveys conducted before rollout. Conclusions Identifying factors that may modulate uptake of novel COVID-19 vaccines can inform effective immunisation programmes and policies. Differential stated intent to accept vaccines between socio-demographic groups may yield insights into the specific causes of low confidence and may suggest and inform targeted communication policies to boost confidence.

Sign in / Sign up

Export Citation Format

Share Document