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Edmund K. M. Chang, Malaquías Peña, and Zoltan Toth
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Warren J. Tennant, Zoltan Toth, and Kevin J. Rae

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The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike.

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Dingchen Hou, Kenneth Mitchell, Zoltan Toth, Dag Lohmann, and Helin Wei

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Hydrological processes are strongly coupled with atmospheric processes related, for example, to precipitation and temperature, and a coupled atmosphere–land surface system is required for a meaningful hydrological forecast. Since the atmosphere is a chaotic system with limited predictability, ensemble forecasts offer a practical tool to predict the future state of the coupled system in a probabilistic fashion, potentially leading to a more complete and informative hydrologic prediction. As ensemble forecasts with coupled meteorological–hydrological models are operationally running at major numerical weather prediction centers, it is currently possible to produce a gridded streamflow prognosis in the form of a probabilistic forecast based on ensembles. Evaluation and improvement of such products require a comprehensive assessment of both components of the coupled system.

In this article, the atmospheric component of a coupled ensemble forecasting system is evaluated in terms of its ability to provide reasonable forcing to the hydrological component and the effect of the uncertainty represented in the atmospheric ensemble system on the predictability of streamflow as a hydrological variable. The Global Ensemble Forecast System (GEFS) of NCEP is evaluated following a “perfect hydrology” approach, in which its hydrological component, including the Noah land surface model and attached river routing model, is considered free of errors and the initial conditions in the hydrological variables are assumed accurate. The evaluation is performed over the continental United States (CONUS) domain for various sizes of river basins. The results from the experiment suggest that the coupled system is capable of generating useful gridded streamflow forecast when the land surface model and the river routing model can successfully simulate the hydrological processes, and the ensemble strategy significantly improves the forecast. The expected forecast skill increases with increasing size of the river basin. With the current GEFS system, positive skill in short-range (one to three days) predictions can be expected for all significant river basins; for the major rivers with mean streamflow more than 500 m3 s−1, significant skill can be expected from extended-range (the second week) predictions. Possible causes for the loss of skills, including the existence of systematic error and insufficient ensemble spread, are discussed and possible approaches for the improvement of the atmospheric ensemble forecast system are also proposed.

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Bo Cui, Zoltan Toth, Yuejian Zhu, and Dingchen Hou

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The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.

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Jie Feng, Jing Zhang, Zoltan Toth, Malaquias Peña, and Sai Ravela

Abstract

Ensemble prediction is a widely used tool in weather forecasting. In particular, the arithmetic mean (AM) of ensemble members is used to filter out unpredictable features from a forecast. AM is a pointwise statistical concept, providing the best sample-based estimate of the expected value of any single variable. The atmosphere, however, is a multivariate system with spatially coherent features characterized with strong correlations. Disregarding such correlations, the AM of an ensemble of forecasts removes not only unpredictable noise but also flattens features whose presence is still predictable, albeit with somewhat uncertain location. As a consequence, AM destroys the structure, and reduces the amplitude and variability associated with partially predictable features. Here we explore the use of an alternative concept of central tendency for the estimation of the expected feature (instead of single values) in atmospheric systems. Features that are coherent across ensemble members are first collocated to their mean position, before the AM of the aligned members is taken. Unlike earlier definitions based on complex variational minimization (field coalescence of Ravela and generalized ensemble mean of Purser), the proposed feature-oriented mean (FM) uses simple and computationally efficient vector operations. Though FM is still not a dynamically realizable state, a preliminary evaluation of ensemble geopotential height forecasts indicates that it retains more variance than AM, without a noticeable drop in skill. Beyond ensemble forecasting, possible future applications include a wide array of climate studies where the collocation of larger-scale features of interest may yield enhanced compositing results.

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Roberto Buizza, P. L. Houtekamer, Gerald Pellerin, Zoltan Toth, Yuejian Zhu, and Mozheng Wei

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; and
  • for 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.

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Sharanya J. Majumdar, Kathryn J. Sellwood, Daniel Hodyss, Zoltan Toth, and Yucheng Song

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The characteristics of “target” locations of tropospheric wind and temperature identified by a modified version of the ensemble transform Kalman filter (ETKF), in order to reduce 0–7-day forecast errors over North America, are explored from the perspective of a field program planner. Twenty cases of potential high-impact weather over the continent were investigated, using a 145-member ensemble comprising perturbations from NCEP, ECMWF, and the Canadian Meteorological Centre (CMC).

Multiple targets were found to exist in the midlatitude storm track. In half of the cases, distinctive targets could be traced upstream near Japan at lead times of 4–7 days. In these cases, the flow was predominantly zonal and a coherent Rossby wave packet was present over the northern Pacific Ocean. The targets at the longest lead times were often located within propagating areas of baroclinic energy conversion far upstream. As the lead time was reduced, these targets were found to diminish in importance, with downstream targets corresponding to a separate synoptic system gaining in prominence. This shift in optimal targets is sometimes consistent with the radiation of ageostrophic geopotential fluxes and transfer of eddy kinetic energy downstream, associated with downstream baroclinic development. Concurrently, multiple targets arise due to spurious long-distance correlations in the ETKF. The targets were least coherent in blocked flows, in which the ETKF is known to be least reliable. The effectiveness of targeting in the medium range requires evaluation, using data such as those collected during the winter phase of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Field Campaign (T-PARC) in 2009.

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Joseph J. Barsugli, Jeffrey S. Whitaker, Andrew F. Loughe, Prashant D. Sardeshmukh, and Zoltan Toth

Can an individual weather event be attributed to El Niño? This question is addressed quantitatively using ensembles of medium-range weather forecasts made with and without tropical sea surface temperature anomalies. The National Centers for Environmental Prediction (NCEP) operational medium-range forecast model is used. It is found that anomalous tropical forcing affects forecast skill in midlatitudes as early as the fifth day of the forecast. The effect of the anomalous sea surface temperatures in the medium range is defined as the synoptic El Niño signal. The synoptic El Niño signal over North America is found to vary from case to case and sometimes can depart dramatically from the pattern classically associated with El Niño. This method of parallel ensembles of medium-range forecasts provides information about the changing impacts of El Niño on timescales of a week or two that is not available from conventional seasonal forecasts.

Knowledge of the synoptic El Niño signal can be used to attribute aspects of individual weather events to El Niño. Three large-scale weather events are discussed in detail: the January 1998 ice storm in the northeastern United States and southeastern Canada, the February 1998 rains in central and southern California, and the October 1997 blizzard in Colorado. Substantial impacts of El Nino are demonstrated in the first two cases. The third case is inconclusive.

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Zoltan Toth, Eugenia Kalnay, Steven M. Tracton, Richard Wobus, and Joseph Irwin

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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 running the forecasts.

A number of graphical forecast products derived from the ensemble are available to the users, including forecasters at the Hydrometeorological Prediction Center and the Climate Prediction Center of NCEP. The products include the ensemble and cluster means, standard deviations, and probabilities of different events. One of the most widely used products is the “spaghetti” diagram where a single map contains all 17 ensemble forecasts, as depicted by a selected contour level of a field, for example, 5520 m at 500-hPa height or 50 m s−1 windspeed at the jet level.

With the aid of the above graphical displays and also by objective verification, the authors have established that the ensemble can provide valuable information for both the short and extended range. In particular, the ensemble can indicate potential problems with the high-resolution control that occurs on rare occasions in the short range. Most of the time, the “cloud” of the ensemble encompasses the verification, thus providing a set of alternate possible scenarios beyond that of the control. Moreover, the ensemble provides a more consistent outlook for the future. While consecutive control forecasts verifying on a particular date may often display large “jumps” from one day to the next, the ensemble changes much less, and its envelope of solutions typically remains unchanged. In addition, the ensemble extends the practical limit of weather forecasting by about a day. For example, significant new weather systems (blocking, extratropical cyclones, etc.) are usually detected by some ensemble members a day earlier than by the high-resolution control. Similarly, the ensemble mean improves forecast skill by a day or more in the medium to extended range, with respect to the skill of the control. The ensemble is also useful in pointing out areas and times where the spread within the ensemble is high and consequently low skill can be expected and, conversely, those cases in which forecasters can make a confident extended-range forecast because the low ensemble spread indicates high predictability. Another possible application of the ensemble is identifying potential model errors. A case of low ensemble spread with all forecasts verifying poorly may be an indication of model bias. The advantage of the ensemble approach is that it can potentially indicate a systematic bias even for a single case, while studies using only a control forecast need to average many cases.

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Zoltan Toth, Mark Tew, Daniel Birkenheuer, Steve Albers, Yuanfu Xie, and Brian Motta
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