scholarly journals Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts

2009 ◽  
Vol 114 (D18) ◽  
Author(s):  
Will McCarty ◽  
Gary Jedlovec ◽  
Timothy L. Miller
Keyword(s):  
Short Term ◽  
Atmospheric Infrared Sounder ◽  
Weather Forecasts

The impact of atmospheric infrared sounder (AIRS) profiles on short-term weather forecasts

2007 ◽  
Author(s):  
Bradley T. Zavodsky ◽  
Shih-Hung Chou ◽  
Gary Jedlovec ◽  
William Lapenta
Keyword(s):  
Short Term ◽  
Atmospheric Infrared Sounder ◽  
Weather Forecasts ◽  
The Impact

scholarly journals Simulation of Temperature Regime of Pavement Layers in Transition Periods of Road Climatic Zone IV

2019 ◽  
Vol 9 (1) ◽  
pp. 6142-6148
Keyword(s):  
Temperature Regime ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Climatic Zone ◽  
Short Term ◽  
Zero Crossing ◽  
Krasnodar Krai ◽  
Weather Forecasts ◽  
Road Pavement ◽  
Pavement Layers

Pavement icing during short-term night temperature drops leads to deterioration of highway performances and increase in road traffic accidents (RTA) in Krasnodar Krai. Peculiar features of temperature regime in road climatic zone (RCZ) IV are analyzed. The considered climatic zone is characterized by frequent zero crossing temperatures of air and road pavement, sharp short-term temperature drops in nighttime, frequent icing of road pavement. Main factors are highlighted which effect temperature regime of road structures. Mathematical model is presented for prediction of road pavement temperature based on weather forecasts. Possibility to decrease the volume of pavement icing by means of thermophysical properties of pavement layers is analyzed.

Impact of atmospheric infrared sounder observations on weather forecasts

Eos ◽  
2005 ◽  
Vol 86 (11) ◽  
pp. 109-116 ◽  
Author(s):  
J. Le Marshall ◽  
J. Jung ◽  
J. Derber ◽  
R. Treadon ◽  
S. J. Lord ◽  
...  
Keyword(s):  
Atmospheric Infrared Sounder ◽  
Weather Forecasts

The use of error estimates with AIRS profiles to improve short-term weather forecasts

2006 ◽  
Author(s):  
Gary J. Jedlovec ◽  
Shih-Hung Chou ◽  
Bradley T. Zavodsky ◽  
William Lapenta
Keyword(s):  
Error Estimates ◽  
Short Term ◽  
Weather Forecasts

Maximizing energy production from hydropower dams using short-term weather forecasts

2020 ◽  
Vol 146 ◽  
pp. 1560-1577 ◽  
Author(s):  
Shahryar Khalique Ahmad ◽  
Faisal Hossain
Keyword(s):  
Energy Production ◽  
Short Term ◽  
Weather Forecasts ◽  
Hydropower Dams

Downscaling 15-day Ensemble Weather Forecasts and Extension to Short-term Climate Outlooks

2009 ◽  
Vol 29 ◽  
pp. 45 ◽  
Author(s):  
Renwick ◽  
Mullan ◽  
Thompson ◽  
Porteous
Keyword(s):  
Short Term ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

scholarly journals Intensity of Dangerous Phones from Waves on Selected Oceanic Routes

2018 ◽  
Vol 1 (1) ◽  
pp. 167-174
Author(s):  
Tadeusz Szelangiewicz ◽  
Katarzyna Żelazny
Keyword(s):  
Short Term ◽  
Weather Forecasts ◽  
Economic Criteria

Abstract Ocean routes are recommended for ships based on economic criteria. Under the influence of waves. during sailing of the ship. various dangerous phenomena arise which can lead to marine crashes. Although weather sites are known to provide short-term weather forecasts (wave and wind parameters). this information is not used to calculate corrections of ocean-route guidelines. A captain based on his knowledge and experience can make such adjustments. The article presents the results of calculations of the values of dangerous parameters of phenomena which may occur during a cruise on example ocean routes. For the calculation of dangerous phenomena. the average statistical long-term (seasonal) parameters contained in weather atlas were used.

scholarly journals Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

2021 ◽  
Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Shawn Handler ◽  
Amy McGovern
Keyword(s):  
Machine Learning ◽  
Storm Track ◽  
Severe Weather ◽  
Storm Tracks ◽  
State Variables ◽  
Post Processing ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Environment Variables

AbstractA primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Post-processing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for post-processing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output.Our dataset includes WoFS ensemble forecasts available every 5 minutes out to 150 min of lead time from the 2017-2019 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm track identification method, we extracted three sets of predictors from the WoFS forecasts: intra-storm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based post-processing of dynamical ensemble output can improve short term, storm-scale severe weather probabilistic guidance.

Sign in / Sign up

Export Citation Format

Share Document