scholarly journals Trends in the predictive performance of raw ensemble weather forecasts

2014 ◽  
Vol 41 (24) ◽  
pp. 9197-9205 ◽  
Author(s):  
S. Hemri ◽  
M. Scheuerer ◽  
F. Pappenberger ◽  
K. Bogner ◽  
T. Haiden
Keyword(s):  
Predictive Performance ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

scholarly journals Visibility Prediction over South Korea Based on Random Forest

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 552
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang ◽  
Chulkyu Lee
Keyword(s):  
Random Forest ◽  
South Korea ◽  
Correlation Coefficients ◽  
Predictive Performance ◽  
Air Pollutant ◽  
Economic Losses ◽  
Observation Data ◽  
Local Data ◽  
Weather Forecasts ◽  
Monitoring Service

In this study, the visibility of South Korea was predicted (VISRF) using a random forest (RF) model based on ground observation data from the Automated Synoptic Observing System (ASOS) and air pollutant data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) model. Visibility was predicted and evaluated using a training set for the period 2017–2018 and a test set for 2019. VISRF results were compared and analyzed using visibility data from the ASOS (VISASOS) and the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) (VISLDAPS) operated by the Korea Meteorological Administration (KMA). Bias, root mean square error (RMSE), and correlation coefficients (R) for the VISASOS and VISLDAPS datasets were 3.67 km, 6.12 km, and 0.36, respectively, compared to 0.14 km, 2.84 km, and 0.81, respectively, for the VISASOS and VISRF datasets. Based on these comparisons, the applied RF model offers significantly better predictive performance and more accurate visibility data (VISRF) than the currently available VISLDAPS outputs. This modeling approach can be implemented by authorities to accurately estimate visibility and thereby reduce accidents, risks to public health, and economic losses, as well as inform on urban development policies and environmental regulations.

scholarly journals Generation and evaluation of an ensemble of wildland fire simulations

2020 ◽  
Vol 29 (2) ◽  
pp. 160 ◽  
Author(s):  
Frédéric Allaire ◽  
Jean-Baptiste Filippi ◽  
Vivien Mallet
Keyword(s):  
Wildland Fire ◽  
Probability Distributions ◽  
Predictive Performance ◽  
Weather Forecasts ◽  
Probability Map ◽  
Burn Probability ◽  
Wildfire Spread ◽  
Fire Characteristics ◽  
Computational Resources ◽  
Ensemble Generation

Numerical simulations of wildfire spread can provide support in deciding firefighting actions but their predictive performance is challenged by the uncertainty of model inputs stemming from weather forecasts, fuel parameterisation and other fire characteristics. In this study, we assign probability distributions to the inputs and propagate the uncertainty by running hundreds of Monte Carlo simulations. The ensemble of simulations is summarised via a burn probability map whose evaluation based on the corresponding observed burned surface is not obvious. We define several properties and introduce probabilistic scores that are common in meteorological applications. Based on these elements, we evaluate the predictive performance of our ensembles for seven fires that occurred in Corsica from mid-2017 to early 2018. We obtain fair performance in some of the cases but accuracy and reliability of the forecasts can be improved. The ensemble generation can be accomplished in a reasonable amount of time and could be used in an operational context provided that sufficient computational resources are available. The proposed probabilistic scores are also appropriate in a calibration process to improve the ensembles.

scholarly journals Comparative Evaluation of Three Schaake Shuffle Schemes in Postprocessing GEFS Precipitation Ensemble Forecasts

2018 ◽  
Vol 19 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Limin Wu ◽  
Yu Zhang ◽  
Thomas Adams ◽  
Haksu Lee ◽  
Yuqiong Liu ◽  
...  
Keyword(s):  
The United States ◽  
Spatiotemporal Variability ◽  
Precipitation Event ◽  
Precipitation Pattern ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Temporal Correlations ◽  
Extreme Precipitation Events ◽  
Proper Size ◽  
Ensemble Weather Forecasts

Abstract Natural weather systems possess certain spatiotemporal variability and correlations. Preserving these spatiotemporal properties is a significant challenge in postprocessing ensemble weather forecasts. To address this challenge, several rank-based methods, the Schaake Shuffle and its variants, have been developed in recent years. This paper presents an extensive assessment of the Schaake Shuffle and its two variants. These schemes differ in how the reference multivariate rank structure is established. The first scheme (SS-CLM), an implementation of the original Schaake Shuffle method, relies on climatological observations to construct rank structures. The second scheme (SS-ANA) utilizes precipitation event analogs obtained from a historical archive of observations. The third scheme (SS-ENS) employs ensemble members from the Global Ensemble Forecast System (GEFS). Each of the three schemes is applied to postprocess precipitation ensemble forecasts from the GEFS for its first three forecast days over the mid-Atlantic region of the United States. In general, the effectiveness of these schemes depends on several factors, including the season (or precipitation pattern) and the level of gridcell aggregation. It is found that 1) the SS-CLM and SS-ANA behave similarly in spatial and temporal correlations; 2) by a measure for capturing spatial variability, the SS-ENS outperforms the SS-ANA, which in turn outperforms the SS-CLM; and 3), overall, the SS-ANA performs better than the SS-CLM. The study also reveals that it is important to choose a proper size for the postprocessed ensembles in order to capture extreme precipitation events.

A comparative study of deterministic and ensemble weather forecasts for weather routing

2014 ◽  
Vol 20 (3) ◽  
pp. 429-441 ◽  
Author(s):  
Lukas Skoglund ◽  
Jakob Kuttenkeuler ◽  
Anders Rosén ◽  
Erik Ovegård
Keyword(s):  
Comparative Study ◽  
Weather Forecasts ◽  
Weather Routing ◽  
Ensemble Weather Forecasts

scholarly journals Remember the past: A comparison of time-adaptive training schemes for non-homogeneous regression

2020 ◽  
Author(s):  
Moritz N. Lang ◽  
Sebastian Lerch ◽  
Georg J. Mayr ◽  
Thorsten Simon ◽  
Reto Stauffer ◽  
...  
Keyword(s):  
Sliding Window ◽  
Predictive Performance ◽  
Training Data ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Adaptive Training ◽  
Weather Forecasts ◽  
The Past ◽  
Training Schemes ◽  
Window Approach

<p>Non-homogeneous regression is a frequently-used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across Central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing.</p><p>The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that "remembering the past" from multiple years of training data is typically also superior to the classical sliding-window when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.</p>

scholarly journals A New View of Heat Wave Dynamics and Predictability over the Eastern Mediterranean

2020 ◽  
Author(s):  
Assaf Hochman ◽  
Sebastian Scher ◽  
Julian Quinting ◽  
Joaquim G. Pinto ◽  
Gabriele Messori
Keyword(s):  
Dynamical Systems ◽  
Heat Wave ◽  
Heat Waves ◽  
Eastern Mediterranean ◽  
Weather Prediction ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Stress Index ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

Abstract. Skillful forecasts of extreme weather events have a major socio-economic relevance. Here, we compare two complementary approaches to diagnose the predictability of extreme weather: recent developments in dynamical systems theory and numerical ensemble weather forecasts. The former allows us to define atmospheric configurations in terms of their persistence and local dimension, which inform on how the atmosphere evolves to and from a given state of interest. These metrics may be used as proxies for the intrinsic predictability of the atmosphere, which depends exclusively on the atmosphere’s properties. Ensemble weather forecasts inform on the practical predictability of the atmosphere, which primarily depends on the performance of the numerical model used. We focus on heat waves affecting the Eastern Mediterranean. These are identified using the Climatic Stress Index (CSI), which was explicitly developed for the summer weather conditions in this region and differentiates between heat waves (upper decile) and cool days (lower decile). Significant differences are found between the two groups from both the dynamical systems and the numerical weather prediction perspectives. Specifically, heat waves show relatively stable flow characteristics (high intrinsic predictability), but comparatively low practical predictability (large model spread/error). For 500 hPa geopotential height fields, the intrinsic predictability of heat waves is lowest at the event’s onset and decay. We relate these results to the physical processes governing Eastern Mediterranean summer heat waves: adiabatic descent of the air parcels over the region and the geographical origin of the air parcels over land prior to the onset of a heat wave. A detailed analysis of the mid-August 2010 record-breaking heat wave provides further insights into the range of different regional atmospheric configurations conducive to heat waves. We conclude that the dynamical systems approach can be a useful complement to conventional numerical forecasts for understanding the dynamics of Eastern Mediterranean heat waves.

Combining Postprocessed Ensemble Weather Forecasts and Multiple Hydrological Models for Ensemble Streamflow Predictions

2020 ◽  
Vol 25 (1) ◽  
pp. 04019060 ◽  
Author(s):  
Jianke Zhang ◽  
Jie Chen ◽  
Xiangquan Li ◽  
Hua Chen ◽  
Ping Xie ◽  
...  
Keyword(s):  
Hydrological Models ◽  
Weather Forecasts ◽  
Ensemble Weather Forecasts

scholarly journals Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

2020 ◽  
Vol 27 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Moritz N. Lang ◽  
Sebastian Lerch ◽  
Georg J. Mayr ◽  
Thorsten Simon ◽  
Reto Stauffer ◽  
...  
Keyword(s):  
Sliding Window ◽  
Predictive Performance ◽  
Training Data ◽  
Ensemble Prediction ◽  
Post Processing ◽  
Adaptive Training ◽  
Weather Forecasts ◽  
The Past ◽  
Training Schemes ◽  
Window Approach

Abstract. Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different time-adaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only.

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
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