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

Author(s):  
Gary J. Jedlovec ◽  
Shih-Hung Chou ◽  
Bradley T. Zavodsky ◽  
William Lapenta

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.


2020 ◽  
Vol 146 ◽  
pp. 1560-1577 ◽  
Author(s):  
Shahryar Khalique Ahmad ◽  
Faisal Hossain

2009 ◽  
Vol 29 ◽  
pp. 45 ◽  
Author(s):  
Renwick ◽  
Mullan ◽  
Thompson ◽  
Porteous

2007 ◽  
Author(s):  
Bradley T. Zavodsky ◽  
Shih-Hung Chou ◽  
Gary Jedlovec ◽  
William Lapenta

2018 ◽  
Vol 1 (1) ◽  
pp. 167-174
Author(s):  
Tadeusz Szelangiewicz ◽  
Katarzyna Żelazny

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.


Author(s):  
Montgomery L. Flora ◽  
Corey K. Potvin ◽  
Patrick S. Skinner ◽  
Shawn Handler ◽  
Amy McGovern

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.


2016 ◽  
Vol 177 ◽  
pp. 329-339 ◽  
Author(s):  
Yang Yang ◽  
Yuanlai Cui ◽  
Yufeng Luo ◽  
Xinwei Lyu ◽  
Seydou Traore ◽  
...  

2017 ◽  
Vol 64 (7) ◽  
pp. 903-915 ◽  
Author(s):  
Lei Zhang ◽  
Yuanlai Cui ◽  
Zhao Xiang ◽  
Shizong Zheng ◽  
Seydou Traore ◽  
...  

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