A Probability Model for Verifying Deterministic Forecasts of Extreme Events

2007 ◽  
Vol 22 (5) ◽  
pp. 1089-1100 ◽  
Author(s):  
Christopher A. T. Ferro
Keyword(s):  
Extreme Events ◽  
Prediction Models ◽  
Daily Precipitation ◽  
Probability Model ◽  
Weather Prediction ◽  
Confidence Regions ◽  
Value Theory ◽  
High Threshold ◽  
Numerical Weather Prediction Models

Abstract This article proposes a method for verifying deterministic forecasts of rare, extreme events defined by exceedance above a high threshold. A probability model for the joint distribution of forecasts and observations, and based on extreme-value theory, characterizes the quality of forecasting systems with two key parameters. This enables verification measures to be estimated for any event rarity and helps to reduce the uncertainty associated with direct estimation. Confidence regions are obtained and the method is used to compare daily precipitation forecasts from two operational numerical weather prediction models.

scholarly journals A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts

2019 ◽  
Vol 148 (1) ◽  
pp. 241-257 ◽  
Author(s):  
Wentao Li ◽  
Quan J. Wang ◽  
Qingyun Duan
Keyword(s):  
Correlation Coefficient ◽  
Extreme Events ◽  
Prediction Models ◽  
Probability Model ◽  
Weather Prediction ◽  
Joint Probability ◽  
Correlation Model ◽  
Asymmetric Dependence ◽  
Huai River ◽  
Joint Probability Model

Abstract Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.

Comparison of Single-Valued Forecasts in a User-Oriented Framework

2020 ◽  
Vol 35 (3) ◽  
pp. 1067-1080
Author(s):  
Michael Foley ◽  
Nicholas Loveday
Keyword(s):  
Numerical Weather Prediction ◽  
Extreme Events ◽  
Prediction Models ◽  
Weather Prediction ◽  
Economic Value ◽  
Extreme Conditions ◽  
Single Model ◽  
Consensus Forecasts ◽  
Theoretical Results ◽  
Numerical Weather Prediction Models

Abstract We compare single-valued forecasts from a consensus of numerical weather prediction models to forecasts from a single model across a range of user decision thresholds and sensitivities, using the relative economic value framework, and present this comparison in a new graphical format. With the help of a simple linear error model, we obtain theoretical results and perform synthetic calculations to gain insights into how the results relate to the characteristics of the different forecast systems. We find that multimodel consensus forecasts are more beneficial for users interested in decisions near the climatological mean, due to their reduced spread of errors compared to the constituent models. Single model forecasts may present greater benefit for users sensitive to extreme events if the forecasts have smaller conditional biases than the consensus forecasts and hence better resolution of such events. The results support use of consensus averaging approaches for single-valued forecast services in typical conditions. However, it is hard to cater for all user sensitivities in more extreme conditions. This underscores the importance of providing probability-based services for unusual conditions.

scholarly journals Overview of the European project FUMAPEX

2005 ◽  
Vol 5 (6) ◽  
pp. 11679-11702 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project ''Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

scholarly journals Overview of the European project FUMAPEX

2006 ◽  
Vol 6 (7) ◽  
pp. 2005-2015 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 89
Author(s):  
Harel. B. Muskatel ◽  
Ulrich Blahak ◽  
Pavel Khain ◽  
Yoav Levi ◽  
Qiang Fu
Keyword(s):  
Optical Properties ◽  
Numerical Weather Prediction ◽  
Radiation Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Effective Size ◽  
Asymmetry Factor ◽  
Numerical Weather ◽  
Numerical Weather Prediction Models ◽  
Spectral Averaging

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (bext, v, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

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