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.

scholarly journals The Performance of MOS in the Digital Age

2009 ◽  
Vol 24 (2) ◽  
pp. 504-519 ◽  
Author(s):  
David P. Ruth ◽  
Bob Glahn ◽  
Valery Dagostaro ◽  
Kathryn Gilbert
Keyword(s):  
United States ◽  
Long Range ◽  
Complex Terrain ◽  
Short Range ◽  
Digital Age ◽  
Model Output ◽  
National Weather Service ◽  
Model Output Statistics ◽  
Weather Service

Abstract Model output statistics (MOS) guidance forecasts have been produced for over three decades. Until recently, MOS guidance was prepared for observing stations and formatted in text bulletins while official National Weather Service (NWS) forecasts for stations and zones were prepared by forecasters typing text. The flagship product of today’s NWS is the National Digital Forecast Database (NDFD). In support of NDFD, MOS is now also produced on grids. This paper compares MOS and gridded MOS (GMOS) to the forecaster-produced NDFD at approximately 1200 station locations in the conterminous United States. Results indicate that GMOS should provide good guidance for preparing the NDFD. In those areas of the country where station observations well represent the grid, GMOS features accuracy comparable to that of NDFD. In areas of complex terrain not well represented by station observations, GMOS appears similar to NDFD in its depiction. A new score is introduced to measure convergence from a long-range forecast to the final short-range forecast. This shows good GMOS forecast continuity when compared to station MOS and NDFD.

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 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.

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 A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment

2005 ◽  
Vol 20 (6) ◽  
pp. 1006-1020 ◽  
Author(s):  
Andrew A. Taylor ◽  
Lance M. Leslie
Keyword(s):  
United States ◽  
The United States ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Forecast Errors ◽  
Model Output ◽  
Error Characteristics ◽  
Temperature Forecast ◽  
Model Output Statistics

Abstract Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented. The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures. Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.

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