Richard Wobus

THE ECONOMIC VALUE OF ENSEMBLE-BASED WEATHER FORECASTS

Abstract The potential economic benefit associated with the use of an ensemble of forecasts versus anequivalent or higher-resolution control forecast is discussed. Neither forecast systems are post-processed,except a simple calibration that is applied to make them reliable. A simple decision-making model is used where all potential users of weather forecasts are characterized by the ratio between the cost of their action to preventweather-related damages, and the loss that they incur in case they do not protect their operations. It isshown that the ensemble forecast system can be used by a much wider range of users. Furthermore,for many, and for beyond 4-day lead time for all users, the ensemble provides greater potential economicbenefit than a control forecast, even if the latter is run at higher horizontal resolution. It is argued that theadded benefits derive from 1) the fact that the ensemble provides a more detailed forecast probabilitydistribution, allowing the users to tailor their weather forecast...

Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system

Since modern data assimilation (DA) involves the repetitive use of dynamical forecasts, errors in analyses share characteristics of those in short-range forecasts. Initial conditions for an ensemble prediction/forecast system (EPS or EFS) are expected to sample uncertainty in the analysis field. Ensemble forecasts with such initial conditions can therefore (a) be fed back to DA to reduce analysis uncertainty, as well as (b) sample forecast uncertainty related to initial conditions. Optimum performance of both DA and EFS requires a careful choice of initial ensemble perturbations. DA can be improved with an EFS that represents the dynamically conditioned part of forecast error covariance as accurately as possible, while an EFS can be improved by initial perturbations reflecting analysis error variance. Initial perturbation generation schemes that dynamically cycle ensemble perturbations reminiscent to how forecast errors are cycled in DA schemes may offer consistency between DA and EFS, and good performance for both. In this paper, we introduce an EFS based on the initial perturbations that are generated by the Ensemble Transform (ET) and ET with rescaling (ETR) methods to achieve this goal. Both ET and ETR are generalizations of the breeding method (BM). The results from ensemble systems based on BM, ET, ETR and the Ensemble Transform Kalman Filter (ETKF) method are experimentally compared in the context of ensemble forecast performance. Initial perturbations are centred around a 3D-VAR analysis, with a variance equal to that of estimated analysis errors. Of the four methods, the ETR method performed best in most probabilistic scores and in terms of the forecast error explained by the perturbations. All methods display very high time consistency between the analysis and forecast perturbations. It is expected that DA performance can be improved by the use of forecast error covariance from a dynamically cycled ensemble either with a variational DA approach (coupled with an ETR generation scheme), or with an ETKF-type DA scheme.

A Synoptic Evaluation of the NCEP Ensemble

Abstract Ensemble forecasting has been operational at NCEP (formerly the National Meteorological Center) since December 1992. In March 1994, more ensemble forecast members were added. In the new configuration, 17 forecasts with the NCEP global model are run every day, out to 16-day lead time. Beyond the 3 control forecasts (a T126 and a T62 resolution control at 0000 UTC and a T126 control at 1200 UTC), 14 perturbed forecasts are made at the reduced T62 resolution. Global products from the ensemble forecasts are available from NCEP via anonymous FTP. The initial perturbation vectors are derived from seven independent breeding cycles, where the fast-growing nonlinear perturbations grow freely, apart from the periodic rescaling that keeps their magnitude compatible with the estimated uncertainty within the control analysis. The breeding process is an integral part of the extended-range forecasts, and the generation of the initial perturbations for the ensemble is done at no computational cost beyond that of...

Ensemble Transform Kalman Filter-based ensemble perturbations in an operational global prediction system at NCEP

The initial perturbations used for the operational global ensemble prediction system of the National Centers for Environmental Prediction are generated through the breeding method with a regional rescaling mechanism. Limitations of the system include the use of a climatologically fixed estimate of the analysis error variance and the lack of an orthogonalization in the breeding procedure. The Ensemble Transform Kalman Filter (ETKF) method is a natural extension of the concept of breeding and, as shown byWang and Bishop, can be used to generate ensemble perturbations that can potentially ameliorate these shortcomings. In the present paper, a spherical simplex 10-member ETKF ensemble, using the actual distribution and error characteristics of real-time observations and an innovation-based inflation, is tested and compared with a 5-pair breeding ensemble in an operational environment. The experimental results indicate only minor differences between the performances of the operational breeding and the experimental ETKF ensemble and only minor differences to Wang and Bishop’s earlier comparison studies. As for the ETKF method, the initial perturbation variance is found to respond to temporal changes in the observational network in the North Pacific. In other regions, however, 10 ETKF perturbations do not appear to be enough to distinguish spatial variations in observational network density. As expected, the whitening effect of the ETKF together with the use of the simplex algorithm that centres a set of quasi-orthogonal perturbations around the best analysis field leads to a significantly higher number of degrees of freedom as compared to the use of paired initial perturbations in operations. As a new result, the perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations, a feature that can be important in practical applications. Potential additional benefits of the ETKF and Ensemble Transform methods when using more ensemble members and a more appropriate inflation scheme will be explored in follow-up studies.

Three Years of Operational Prediction of Forecast Skill at NMC

Abstract In real time since 1990, the National Meteorological Center (NMC) has been running a system to predict the forecast skill of the medium-range forecasts produced by the NMC global spectral model. The predictors used are the agreement of an ensemble consisting of operational forecasts from various centers, the persistence in the forecast, and the amplitude of the anomalies. These predictors are used in a stepwise regression scheme, with the last 60 days used as training period, and the regional anomaly correlation of the 0000 UTC NMC global forecast is predicted from days 1 to 6. By far the most important predictor of skill is the agreement between the NMC global forecast started at 0000 UTC, out to 6 days, and four other 12-h “older” forecasts (Japan Meteorological Agency, United Kingdom Meteorological Office, and the European Centre for Medium-Range Weather Forecasts, as well as the average of the NMC forecast at 0000 UTC with the previous days forecast). The other predictors have been selected ...

Global Forecast Error Correlation. Part 1: Isobaric Wind and Geopotential

Abstract Results of a thorough study of the correlation structure of observation-minus-forecast increments for mandatory pressure level radiosonde observations of zonal and meridional wind components and geopotential, differenced with NMCs 6-hour global forecasts, are reported. Our work focused on the selection of a representation for spatial lag-correlations to be used in updating the multivariate statistical objective analysis algorithm of the global data assimilation system, with attention given to regional and seasonal dependence of the correlation structure, and on the degree to which the increments are in the same geostrophic balance as the signal and forecast fields individually. We compare the performance of several candidates for representing autocorrelations of geopotential increments, on the one hand, and the auto- and cross-correlations of the wind component increments, on the other, for five mandatory pressure levels, for four regions of the Northern Hemisphere and for the Southern Hemispher...

Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment

AbstractA new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias o...

Spatial Resolution Impacts on National Meteorological Center Nested Grid Model Simulations

Abstract Forecasts from different resolution versions of the National Meteorological Center Nested Grid Model (NGM) are compared for two case studies to assess an optimal ratio of model vertical and horizontal resolutions. Four combinations are considered: 1) 16 layers and 80-km horizontal grid over the United States (the operational version of the model), 2) 32 layers and 80-km horizontal grid, 3) 16 layers and 40-km horizontal grid, and 4) 32 layers and 40-km horizontal grid. Resolution impacts are evaluated for a number of weather system components such as extratropical cyclone evolution, baroclinic and frontal zone structure, jet-stream blow, moisture fields, and precipitation. Resolution impacts for this limited sample are relatively small for synoptic-scale features such as the position of the extratropical cyclone and main jet-stream flows. Larger impacts are noted for smaller-scale horizontal structure and gradients, frontal zone associated circulations and hydrological cycle components. Vertical ...

A synoptic evaluation of normal mode initialization experiments with the NMC nested grid model

Abstract The Regional Analysis and Forecast System at the National Meteorological Center consists of an optimum interpolation objective analysis scheme, an adiabatic nonlinear normal model initialization (NNMI) and a hemispheric Nested Grid Model (NGM) to provide 48 h forecasts. We investigate here the effect NNMI has on the analyses and forecasts produced by this system. An eight vertical mode NNMI procedure led to significant reductions of the divergent component of the analyzed wind field in regions of heavy precipitation. This is shown to contribute to a systematic spinup error in NGM 0–12 h precipitation forecasts, especially from the 0000 UTC runs. Forecasts starting with no initialization had unacceptable noise levels. NNMI using two vertical modes yielded the best combination of noise-free forecasts and unsuppressed initial precipitation rates. A physical interpretation of this result is provided using the vertical structure functions of the normal modes. Tests of the two-mode NNMI in an operation...

Toward the Improvement of Subseasonal Prediction in the National Centers for Environmental Prediction Global Ensemble Forecast System

In order to provide ensemble-based subseasonal (weeks 3 and 4) forecasts to support the operational mission of the Climate Prediction Center, National Centers for Environmental Prediction, experiments have been designed through the Subseasonal Experiment (SubX) project to investigate the predictability in both tropical and extratropical regions. The control experiment simply extends the current operational Global Ensemble Forecast System (GEFS; version 11) from 16 to 35 days. In addition to the control, the parallel experiments will be mainly designed to focus on three areas: (1) improving model uncertainty representation for the tropics through stochastic physical perturbations; (2) considering the impact of the ocean by using a two-tiered sea surface temperature approach; and (3) testing a new scale-aware convection scheme to improve the model physics for tropical convection and Madden-Julian Oscillation (MJO) forecasts. All experiments are initialized every 5 days at 0000 UTC during the period of May 2014–May 2016 (25 months). In the tropics, MJO forecast skill has been improved from an average of 12.5 days (control) to nearly 22 days by combining all three modifications to GEFS. In the extratropics, the ensemble mean anomaly correlation of 500-hPa geopotential height improved over weeks 3 and 4. In addition, the Continuous Ranked Probability Score (of the Northern Hemisphere raw surface temperature (land only) is improved as well. A similar result is found in the Contiguous United States precipitation, although forecast skill is extremely low. Our results imply that calibration may be important and necessary for surface temperature and precipitation forecast for the subseasonal timescale due to the large systematic model errors.