Flood Forecasting in the United States NOAA/National Weather Service

2016 ◽  
pp. 249-310 ◽  
Author(s):  
T.E. Adams
2019 ◽  
Vol 36 (1) ◽  
pp. 129-137 ◽  
Author(s):  
Micheal Hicks ◽  
Belay Demoz ◽  
Kevin Vermeesch ◽  
Dennis Atkinson

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.


Author(s):  
Pedro J. Restrepo

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.


2018 ◽  
Vol 10 (4) ◽  
pp. 673-691 ◽  
Author(s):  
Michelle E. Saunders ◽  
Kevin D. Ash ◽  
Jennifer M. Collins

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.


2005 ◽  
Vol 20 (6) ◽  
pp. 1034-1047 ◽  
Author(s):  
Jeffrey A. Baars ◽  
Clifford F. Mass

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.


Author(s):  
Frederick L. Crosby

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.


Author(s):  
Evan S. Bentley ◽  
Richard L. Thompson ◽  
Barry R. Bowers ◽  
Justin G. Gibbs ◽  
Steven E. Nelson

AbstractPrevious work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–2018) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance.Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1nm) circulations in a poor (STP=0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.


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