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

2021 ◽  
Vol 14 (4) ◽  
pp. 2143-2160
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

Abstract. Ensemble prediction is an indispensable tool in modern numerical weather prediction (NWP). Due to its complex data flow, global medium-range ensemble prediction has almost exclusively been carried out by operational weather agencies to date. Thus, it has been very hard for academia to contribute to this important branch of NWP research using realistic weather models. In order to open ensemble prediction research up to the 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 licence and is downloadable from an https server. The dataset covers 1 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 of major global weather forecasting centres.

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 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 Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

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 An evaluation of the canadian global meteorological ensemble prediction system for short-term hydrological forecasting

2009 ◽  
Vol 6 (4) ◽  
pp. 4891-4917
Author(s):  
J. A. Velázquez ◽  
T. Petit ◽  
A. Lavoie ◽  
M.-A. Boucher ◽  
R. Turcotte ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Forecast Horizon ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Hydrological Forecasting ◽  
Updating Procedure ◽  
First Time

Abstract. Hydrological forecasting consists in the assessment of future streamflow. Current deterministic forecasts do not give any information concerning the uncertainty, which might be limiting in a decision-making process. Ensemble forecasts are expected to fill this gap. In July 2007, the Meteorological Service of Canada has improved its ensemble prediction system, which has been operational since 1998. It uses the GEM model to generate a 20-member ensemble on a 100 km grid, at mid-latitudes. This improved system is used for the first time for hydrological ensemble predictions. Five watersheds in Quebec (Canada) are studied: Chaudière, Châteauguay, Du Nord, Kénogami and Du Lièvre. An interesting 17-day rainfall event has been selected in October 2007. Forecasts are produced in a 3 h time step for a 3-day forecast horizon. The deterministic forecast is also available and it is compared with the ensemble ones. In order to correct the bias of the ensemble, an updating procedure has been applied to the output data. Results showed that ensemble forecasts are more skilful than the deterministic ones, as measured by the Continuous Ranked Probability Score (CRPS), especially for 72 h forecasts. However, the hydrological ensemble forecasts are under dispersed: a situation that improves with the increasing length of the prediction horizons. We conjecture that this is due in part to the fact that uncertainty in the initial conditions of the hydrological model is not taken into account.

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.

scholarly journals Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

2019 ◽  
Vol 23 (1) ◽  
pp. 493-513 ◽  
Author(s):  
Samuel Monhart ◽  
Massimiliano Zappa ◽  
Christoph Spirig ◽  
Christoph Schär ◽  
Konrad Bogner
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Added Value ◽  
Ensemble Prediction ◽  
Late Winter ◽  
Streamflow Prediction ◽  
Ensemble Prediction System ◽  
Mountainous Regions ◽  
Temperature And Precipitation ◽  
Nwp Model

Abstract. Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700 km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.

scholarly journals Test of a Poor Man’s Ensemble Prediction System for Short-Range Probability Forecasting

2005 ◽  
Vol 133 (7) ◽  
pp. 1825-1839 ◽  
Author(s):  
A. Arribas ◽  
K. B. Robertson ◽  
K. R. Mylne
Keyword(s):  
Short Range ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Small Subset ◽  
Prediction System ◽  
Ensemble Forecasts ◽  
Sea Level Pressure ◽  
Ensemble Prediction System ◽  
To Come ◽  
Prediction Systems

Abstract Current operational ensemble prediction systems (EPSs) are designed specifically for medium-range forecasting, but there is also considerable interest in predictability in the short range, particularly for potential severe-weather developments. A possible option is to use a poor man’s ensemble prediction system (PEPS) comprising output from different numerical weather prediction (NWP) centers. By making use of a range of different models and independent analyses, a PEPS provides essentially a random sampling of both the initial condition and model evolution errors. In this paper the authors investigate the ability of a PEPS using up to 14 models from nine operational NWP centers. The ensemble forecasts are verified for a 101-day period and five variables: mean sea level pressure, 500-hPa geopotential height, temperature at 850 hPa, 2-m temperature, and 10-m wind speed. Results are compared with the operational ECMWF EPS, using the ECMWF analysis as the verifying “truth.” It is shown that, despite its smaller size, PEPS is an efficient way of producing ensemble forecasts and can provide competitive performance in the short range. The best relative performance is found to come from hybrid configurations combining output from a small subset of the ECMWF EPS with other different NWP models.

Assessment of the Regional Arctic System Model Intra-Annual Ensemble Predictions of Arctic Sea Ice

2020 ◽  
Author(s):  
Wieslaw Maslowski ◽  
Younjoo Lee ◽  
Anthony Craig ◽  
Mark Seefeldt ◽  
Robert Osinski ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Weather Prediction ◽  
Arctic Sea Ice ◽  
System Model ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
The Past ◽  
Arctic Sea

<p>The Regional Arctic System Model (RASM) has been developed and used to investigate the past to present evolution of the Arctic climate system and to address increasing demands for Arctic forecasts beyond synoptic time scales. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model configured over the pan-Arctic domain with horizontal resolution of 50 km or 25 km for the atmosphere and land and 9.3 km or 2.4 km for the ocean and sea ice components. As a regional model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which for simulations of the past to present are derived from global atmospheric reanalyses, such as the National Center for Environmental Predictions (NCEP) Coupled Forecast System version 2 and Reanalysis (CFSv2/CFSR). This dynamical downscaling approach allows comparison of RASM results with observations, in place and time, to diagnose and reduce model biases. This in turn allows a unique capability not available in global weather prediction and Earth system models to produce realistic and physically consistent initial conditions for prediction without data assimilation.</p><p>More recently, we have developed a new capability for an intra-annual (up to 6 months) ensemble prediction of the Arctic sea ice and climate using RASM forced with the routinely produced (every 6 hours) NCEP CFSv2 global 9-month forecasts. RASM intra-annual ensemble forecasts have been initialized on the 1<sup>st</sup> of each month starting in 2019 with forcing for each ensemble member derived from CSFv2 forecasts, 24-hr apart from the month preceding the initial forecast date.  Several key processes and feedbacks will be discussed with regard to their impact on model physics, the representation of initial state and ensemble prediction skill of Arctic sea ice variability at time scales from synoptic to decadal. The skill of RASM ensemble forecasts will be assessed against available satellite observations with reference to reanalysis as well as hindcast data using several metrics, including the standard deviation, root mean square difference, Taylor diagrams and integrated ice-edge error.</p>

Advances in the Study of Severe Convection Weather Nowcasting in Central China

2020 ◽  
Author(s):  
Chunguang Cui ◽  
Yanjiao Xiao ◽  
Anwei Lai ◽  
Muyun Du
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Central China ◽  
Dual Polarization ◽  
Specific Difference ◽  
Severe Convection ◽  
Rainfall Forecast ◽  
Short Time ◽  
Differential Reflectivity

<p>Based on the characteristics of sudden and local, short life history, serious disasters and so on, the severe convection weather system is difficult to be captured by the conventional meteorological observation network, and is still challenging for catastrophic weather forecasting. In order to improve the service ability in strong weather monitoring and prediction, the following researches have been carried out recently: (1) The new mesocyclone and tornado vortex feature recognition algorithms are developed and proved to be successfully in identifying tornado vortex characteristics in more than a dozen tornado cases. Extracted from Doppler radar volume scan data, a number of parameters (exceed thirty) have been used in the research on the automatic recognition and warning technology of classified severe convective weather (downburst, tornado, hail and short-time strong precipitation). Based on large sample data and results of a variety of analysis methods, a thunderstorm winds Bayes discriminant model has also been established. The testing results show that its Heidke skill score is 0.836, along with the accuracy rate and hit rate are greater than 95%, and the empty rate is below 5%. (2) Rapid update cycle forecast system can effectively improve the quality of model initial values that is very suitable for short time forecast application. For the sake of improving severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize numerical weather prediction (NWP) at convection-resolving scales. In addition, a new set of simplified and parameterized dual-polarization radar simulators for horizontal reflectivity (Z<sub>H</sub>), differential reflectivity (Z<sub>DR</sub>), specific difference phase (K<sub>DP</sub>), and correlation coefficient (ρ<sub>HV</sub>) have been co-developed, and some preliminary data assimilation experiments have shown that the assimilation of dual polarization variables including differential reflectivity and specific difference phase in addition to radar radial velocity and horizontal reflectivity can help improve the accuracy of initial conditions for model hydrometer variables and ensuing model forecasts. (3) Although not yet mature enough for meteorological application, blending technology which is expected to overcome the deficiency of the quantitative precipitation (QPF) by a mesoscale NWP model for the short term at convective scales and the rapidly descending skill of rainfall forecast based on radar extrapolation method beyond the first few hours is under development and debugging, and also has potential in enhancing the ability of rainfall forecast within the nowcasting period. (4) The above methods and systems were applied and provided technical support for meteorological services during the 7<sup>th</sup> Wuhan World Military Games in 2019, and a good service effect had been achieved.</p>

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