scholarly journals Skill prediction of local weather forecasts based on the ECMWF ensemble

2001 ◽  
Vol 8 (6) ◽  
pp. 419-428 ◽  
Author(s):  
C. Ziehmann
Keyword(s):  
Weather Forecasting ◽  
A Priori ◽  
A Priori Estimate ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Data Set ◽  
Weather Forecasts ◽  
Numerical Weather Forecasting ◽  
Similar Good ◽  
Local Weather

Abstract. Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF) temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

scholarly journals Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

2015 ◽  
Vol 8 (7) ◽  
pp. 2355-2377 ◽  
Author(s):  
M. Rautenhaus ◽  
C. M. Grams ◽  
A. Schäfler ◽  
R. Westermann
Keyword(s):  
System Integration ◽  
Visual Analysis ◽  
Weather Forecasting ◽  
Three Dimensional ◽  
Conveyor Belt ◽  
Ensemble Prediction ◽  
Wind Fields ◽  
Uncertainty Visualization ◽  
Ensemble Prediction System ◽  
Weather Forecasts

Abstract. We present the application of interactive three-dimensional (3-D) visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment) campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs) has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF) ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off).

Predictions of 2010’s Tropical Cyclones Using the GFS and Ensemble-Based Data Assimilation Methods

2011 ◽  
Vol 139 (10) ◽  
pp. 3243-3247 ◽  
Author(s):  
Thomas M. Hamill ◽  
Jeffrey S. Whitaker ◽  
Daryl T. Kleist ◽  
Michael Fiorino ◽  
Stanley G. Benjamin
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Successful Testing ◽  
Gfs Model ◽  
Environmental Prediction ◽  
Statistical Interpolation

Abstract Experimental ensemble predictions of tropical cyclone (TC) tracks from the ensemble Kalman filter (EnKF) using the Global Forecast System (GFS) model were recently validated for the 2009 Northern Hemisphere hurricane season by Hamill et al. A similar suite of tests is described here for the 2010 season. Two major changes were made this season: 1) a reduction in the resolution of the GFS model, from 2009’s T384L64 (~31 km at 25°N) to 2010’s T254L64 (~47 km at 25°N), and some changes in model physics; and 2) the addition of a limited test of deterministic forecasts initialized from a hybrid three-dimensional variational data assimilation (3D-Var)/EnKF method. The GFS/EnKF ensembles continued to produce reduced track errors relative to operational ensemble forecasts created by the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC). The GFS/EnKF was not uniformly as skillful as the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. GFS/EnKF track forecasts had slightly higher error than ECMWF at longer leads, especially in the western North Pacific, and exhibited poorer calibration between spread and error than in 2009, perhaps in part because of lower model resolution. Deterministic forecasts from the hybrid were competitive with deterministic EnKF ensemble-mean forecasts and superior in track error to those initialized from the operational variational algorithm, the Gridpoint Statistical Interpolation (GSI). Pending further successful testing, the National Oceanic and Atmospheric Administration (NOAA) intends to implement the global hybrid system operationally for data assimilation.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 8 (2) ◽  
pp. 2739-2782 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Criterion Score ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction System (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of members) of a HEPS configured with 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Center for Medium-range Weather Forecasts (ECMWF). Here, the selection of the most relevant members is proposed using a Backward greedy technique with k-fold cross-validation, allowing an optimal use of the information. The methodology draws from a multi-criterion score that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

scholarly journals Postprocessing ensemble forecasts of vertical temperature profiles

2020 ◽  
Vol 6 (1) ◽  
pp. 45-60
Author(s):  
David Schoenach ◽  
Thorsten Simon ◽  
Georg Johann Mayr
Keyword(s):  
Rank Order ◽  
Probability Distributions ◽  
Ensemble Prediction ◽  
Order Structure ◽  
Temperature Profiles ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Atmospheric Observations ◽  
Prediction Systems

Abstract. Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric variables. This paper extends the correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the probability distributions separately at each vertical level. In the second step copula coupling re-installs the dependence among neighboring levels by using the rank order structure of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model levels interpolated to four locations in Germany, from which radiosondes are released to measure profiles of temperature and other variables four times a day. A winter case study and a summer case study, respectively, exemplify that univariate postprocessing fails to preserve stable layers, which are crucial for many atmospheric processes. Quantile resampling and a resampling that preserves the relative distance between individual EPS members improve the calibration of the raw forecasts of the temperature profiles as shown by rank histograms. They also improve the multivariate metrics of energy score and variogram score and retain the stable layers. Improvements take place over all times of the day and all seasons. They are largest within the atmospheric boundary layer and for shorter lead times.

Weather Forecasts

2020 ◽  
pp. 55-74
Author(s):  
Chris Bleakley
Keyword(s):  
Particle Physics ◽  
Weather Forecasting ◽  
Weather Forecast ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Von Neumann ◽  
Weather Forecasts ◽  
Numerical Weather Forecasting ◽  
Stanislaw Ulam ◽  
Lewis Fry Richardson

Chapter 4 tells the story of numerical weather forecasting from its inception to today’s supercomputing algorithms. In 1922, Lewis Fry Richardson proposed that, since the atmosphere is subject to the laws of physics, future weather can be predicted by means of algorithmic calculations. His attempt at forecasting a single day’s weather by means of manual calculations took several months. In the late 1940s, John von Neumann resurrected Richardson’s idea and launched a project to conduct the first weather forecast by computer. The world’s first operational electronic computer – ENIAC - completed a 24-hour forecast in just one day. It appeared that accurate forecasting simply required faster computers. In 1969, Edward Lorenz discovered that tiny errors in weather measurements can accumulate during numerical forecasting to produce large errors. The so-called Butterfly Effect was alleviated by the Monte Carlo simulation method invented by Stanislaw Ulam for particle physics.

scholarly journals A comparison between ensemble and deterministic hydrological forecasts in an operational context

2011 ◽  
Vol 29 ◽  
pp. 85-94 ◽  
Author(s):  
M.-A. Boucher ◽  
F. Anctil ◽  
L. Perreault ◽  
D. Tremblay
Keyword(s):  
Weather Forecasting ◽  
Ensemble Forecasts ◽  
Meteorological Model ◽  
Resources Management ◽  
Weather Forecasts ◽  
Hydropower Production ◽  
Forecasting System ◽  
The Cost ◽  
Ensemble Weather Forecasts

Abstract. Ensemble forecasts can greatly benefit water resources management as they provide useful information regarding the uncertainty of the situation at hand. However, weather forecasting systems are evolving and the cost for reanalysis and reforecasts is prohibitive. Consequently, series of ensemble weather forecasts from a particular version of the forecasting system are often short. In this case study, we consider a hydrological event that took place in 2003 on the Gatineau watershed in Canada and caused management difficulties in a hydropower production context. The weather ensemble forecasting system in place at that time is now obsolete, but we show that with minimal post-processing of the forecasts, it is still beneficial to exploit ensemble rather than deterministic forecasts, even if the latter emerge from a more advanced meteorological model and possess superior spatial resolution.

scholarly journals Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0

2020 ◽  
Author(s):  
Pirkka Ollinaho ◽  
Glenn D. Carver ◽  
Simon T. K. Lang ◽  
Lauri Tuppi ◽  
Madeleine Ekblom ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Complex Data ◽  
Ensemble Forecasts ◽  
Creative Commons ◽  
Initial States ◽  
Indispensable Tool ◽  
One Year

Abstract. Ensemble prediction is an indispensable tool of modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has so far remained exclusively as a duty of operational weather agencies. It has been very hard for academia therefore to be able to contribute to this important branch of NWP research using realistic weather models. In order to open up the ensemble prediction research for a wider research community, we have recreated all 50+1 operational IFS ensemble initial states for OpenIFS CY43R3. The dataset (OpenEnsemble 1.0) is available for use under a Creative Commons license and is downloadable from an https-server. The dataset covers one year (December 2016 to November 2017) twice daily. Downloads in three model resolutions (TL159, TL399 and TL639) are available to cover different research needs. An open-source workflow manager, called OpenEPS, is presented here and used to launch ensemble forecast experiments from the perturbed initial conditions. The deterministic and probabilistic forecast skill of OpenIFS (cycle 40R1) using this new set of initial states is comprehensively evaluated. In addition, we present a case study of typhoon Damrey from year 2017 to illustrate the new potential of being able to run ensemble forecasts outside major global weather forecasting centres.

scholarly journals Decomposition of a New Proper Score for Verification of Ensemble Forecasts

2015 ◽  
Vol 143 (5) ◽  
pp. 1517-1532 ◽  
Author(s):  
H. M. Christensen
Keyword(s):  
Standard Deviation ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Brier Score ◽  
Continuous Variables ◽  
Ensemble Forecasts ◽  
Atmospheric Flow ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Medium Range

Abstract A new proper score, the error-spread score (ES), has recently been proposed for evaluation of ensemble forecasts of continuous variables. The ES is formulated with respect to the moments of the ensemble forecast. It is particularly sensitive to evaluating how well an ensemble forecast represents uncertainty: is the probabilistic forecast well calibrated? In this paper, it is shown that the ES can be decomposed into its reliability, resolution, and uncertainty components in a similar way to the Brier score. The first term evaluates the reliability of the forecast standard deviation and skewness, rewarding systems where the forecast moments reliably indicate the properties of the verification. The second term evaluates the resolution of the forecast standard deviation and skewness, and rewards systems where the forecast moments vary from the climatological moments according to the predictability of the atmospheric flow. The uncertainty term depends only on the observed error distribution and is independent of the forecast standard deviation or skewness. The decomposition was demonstrated using forecasts made with the European Centre for Medium-Range Weather Forecasts ensemble prediction system, and was able to identify the source of the skill in the forecasts at different latitudes.

A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems

2005 ◽  
Vol 133 (5) ◽  
pp. 1076-1097 ◽  
Author(s):  
Roberto Buizza ◽  
P. L. Houtekamer ◽  
Gerald Pellerin ◽  
Zoltan Toth ◽  
Yuejian Zhu ◽  
...  
Keyword(s):  
Ensemble Forecasting ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
Global Systems

Abstract The present paper summarizes the methodologies used at the European Centre for Medium-Range Weather Forecasts (ECMWF), the Meteorological Service of Canada (MSC), and the National Centers for Environmental Prediction (NCEP) to simulate the effect of initial and model uncertainties in ensemble forecasting. The characteristics of the three systems are compared for a 3-month period between May and July 2002. The main conclusions of the study are the following:the performance of ensemble prediction systems strongly depends on the quality of the data assimilation system used to create the unperturbed (best) initial condition and the numerical model used to generate the forecasts;a successful ensemble prediction system should simulate the effect of both initial and model-related uncertainties on forecast errors; andfor all three global systems, the spread of ensemble forecasts is insufficient to systematically capture reality, suggesting that none of them is able to simulate all sources of forecast uncertainty.The relative strengths and weaknesses of the three systems identified in this study can offer guidelines for the future development of ensemble forecasting techniques.

Sign in / Sign up

Export Citation Format

Share Document