Analog-Based Ensemble Model Output Statistics

Ensemble weather forecasts often under‐represent uncertainty, leading to overconfidence in their predictions. Multi‐model forecasts combining several individual ensembles have been shown to display greater skill than single‐ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say). We propose a flexible multivariate Bayesian postprocessing framework, based on a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from available ensemble forecasts and an estimate of the shared discrepancy, obtained from a collection of past forecast–observation pairs. We also propose a novel approach to selecting an appropriate training set for estimation of the required correction, using synoptic‐scale analogues to obtain a regime‐dependent estimate of the adjustment. The proposed technique is applied to forecasts of surface temperature over the UK during the winter period from 2007 to 2013. Although the resulting parametric multivariate‐normal probabilistic forecasts are marginally less sharp than those of the leading competitor, they capture the spatial structure of the observations better than a correlation structure based on either the ensembles or climatology alone, and are robust to changes in the variables and spatial domain of the forecast, at a greatly reduced computational cost.

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“…; ; Junk et al . ). This means that, given an ensemble of forecasts

({}, y_{1}, . . ., y_{k})

of some weather quantity Y 0 , with the forecasts having sample variance s 2 , the NGR predictive distribution of Y 0 has the form

\begin{matrix} alignleft \\ align-1 Y_{0} \sim N (a + b_{1} y_{1} + … + b_{k} y_{k}, c + d s^{2}) . & align-2 \end{matrix}

…”

Section: Current Methods In Mme Postprocessingmentioning

confidence: 97%

“…; Junk et al . ). We propose a new source of synoptic‐scale analogues for construction of a training dataset, choosing candidate forecasts generated under weather regimes similar to that of the forecast of interest.…”

Section: Introductionmentioning

confidence: 99%

New approaches to postprocessing of multi‐model ensemble forecasts

Barnes

Brierley

Chandler

2019

Quart J Royal Meteoro Soc

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“…This is due to the intrinsic difficulty in simulating all sources of uncertainty. It is then very beneficial to calibrate the EPS to increase its reliability and statistical consistency (Buizza et al, ; McSharry et al, ; Alessandrini et al ., ; Junk et al, ).…”

Section: Methodsmentioning

confidence: 99%

Gridded probabilistic weather forecasts with an analog ensemble

Sperati

Alessandrini

Monache

2017

Quart J Royal Meteoro Soc

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This study extends the analog ensemble (AnEn) technique to generate probabilistic forecasts of 10 m wind speed over a grid. The AnEn has been widely used to generate short‐term probabilistic predictions of meteorological variables, air quality, wind and solar power, and to effectively downscale reanalysis fields. It is based on a historical dataset including observations paired with corresponding deterministic predictions. For each forecast lead time and location, the AnEn is generated using the observations corresponding to the past deterministic predictions that are more similar to the current forecast. So far, the AnEn has been used to generate predictions at specific locations with available observations. Here, it is extended over a two‐dimensional grid, where each grid point is treated independently, using meteorological analysis instead of observations. The forecast range is extended up to 6 days ahead to evaluate the performance of the AnEn in both the short and medium range. The proposed approach allows to extend the AnEn to applications requiring gridded probabilistic products that are often generated with dynamical ensemble models. The AnEn forecasts are generated using the European Centre for Medium‐Range Weather Forecasts (ECMWF) deterministic and analysis model. Also, the ECMWF Ensemble Prediction System (EPS) is used for comparison. Given that the AnEn predictions are generated independently at any location and lead time, the resulting spatial and temporal correlation may be degraded by noise. A reordering technique called Schaake Shuffle (SS) is then applied to the ensemble members to recover the spatio‐temporal correlation. The AnEn outperforms a calibrated version of EPS for the first two days of the prediction. During the third forecast day, the AnEn remains competitive with the calibrated EPS, while the latter is more skilful from 72 to 144 h ahead. The AnEn uses about one sixth of the computational resources necessary to generate the real‐time EPS prediction.

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“…Others have used analogs of past forecasts based 75 on weighted atmospheric predictors to quantify forecast uncertainty (Delle Monache et al, 2013;Junk et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…This approach to obtaining marginal predictive distributions is rather simple, but given the limited amount of data that remained after filtering, we thought that a stable parameter estimation for a more 675 complex model was not warranted. A larger training dataset would allow one to account for forecast biases that vary with wind direction (Eide et al, 2017), or to use an analog-based regression approach similar to the method proposed by Junk et al, (2015), and include analog predictor variables related to atmospheric stability.…”

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confidence: 99%

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Worsnop

2018

Preprint

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Wind power forecasting is gaining international significance as more regions promote policies to increase the use of renewable energy. Wind ramps, large variations in wind power production during a period of minutes to hours, challenge utilities and electrical balancing authorities. A sudden decrease in wind energy production must be balanced by other power 15 generators to meet energy demands, while a sharp increase in unexpected production results in excess power that may not be used in the power grid, leading to a loss of potential profits. In this study, we compare different methods to generate probabilistic ramp forecasts from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model with up to twelve hours of lead time at two tall-tower locations in the United States. We validate model performance using 21 months of 80-m wind speed observations from towers in Boulder, Colorado and near the Columbia River Gorge in eastern 20Oregon.We employ four statistical post-processing methods, three of which are not currently used in the literature for wind forecasting. These procedures correct biases in the model and generate short-term wind speed scenarios which are then converted to power scenarios. This probabilistic enhancement of HRRR point forecasts provides valuable uncertainty information of ramp events and improves the skill of predicting ramp events over the raw forecasts. We compute Brier skill 25 scores for each method at predicting up-and down-ramps to determine which method provides the best prediction. We find that the Standard Schaake Shuffle method yields the highest skill at predicting ramp events for these datasets, especially for up-ramp events at the Oregon site. Increased skill for ramp prediction is limited at the Boulder, CO site using any of the multivariate methods, because of the poor initial forecasts in this area of complex terrain. These statistical methods can be implemented by wind farm operators to generate a range of possible wind speed and power scenarios to aid and optimize 30 decisions before ramp events occur.Wind Energ. Sci. Discuss., https://doi

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Analog-Based Ensemble Model Output Statistics

Cited by 65 publications

References 20 publications

New approaches to postprocessing of multi‐model ensemble forecasts

New approaches to postprocessing of multi‐model ensemble forecasts

Gridded probabilistic weather forecasts with an analog ensemble

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

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