Use Of Ensemble Forecasts Research Articles

Time‐series‐based ensemble model output statistics for temperature forecasts postprocessing

AbstractThe uncertainty in numerical weather prediction models is nowadays quantified by the use of ensemble forecasts. Although these forecasts are continuously improved, they still suffer from systematic bias and dispersion errors. Statistical postprocessing methods, such as the ensemble model output statistics (EMOS), have been shown to substantially correct the forecasts. This work proposes an extension of EMOS in a time‐series framework. Besides taking account of seasonality and trend in the location and scale parameter of the predictive distribution, the autoregressive process in the mean forecast errors or the standardized forecast errors is considered. The models can be further extended by allowing generalized autoregressive conditional heteroscedasticity. Furthermore, it is outlined how to use these models for arbitrary forecast horizons. To illustrate the performance of the suggested EMOS models in time‐series fashion, we present a case study for the postprocessing of 2 m surface temperature forecasts using five different lead times and a set of observation stations in Germany. The results indicate that the time‐series EMOS extensions are able to significantly outperform the benchmark models EMOS and autoregressive EMOS (AR‐EMOS) in most of the lead time–station cases.

CREST-VEC: a framework towards more accurate and realistic flood simulation across scales

Abstract. Large-scale (i.e., continental and global) hydrologic simulation is an appealing yet challenging topic for the hydrologic community. First and foremost, model efficiency and scalability (flexibility in resolution and discretization) have to be prioritized. Then, sufficient model accuracy and precision are required to provide useful information for water resource applications. Towards this goal, we craft two objectives for improving US current operational hydrological models: (1) vectorized routing and (2) improved hydrological processes. This study presents a hydrologic modeling framework, CREST-VEC, that combines a gridded water balance model and a newly developed vector-based routing scheme. First, in contrast to a conventional fully gridded model, this framework can significantly reduce the computational cost of river routing by at least 10 times, based on experiments at regional (0.07 vs. 0.002 s per step) and continental scales (0.35 vs. 7.2 s per step). This provides adequate time efficiency for generating operational ensemble streamflow forecasts and even probabilistic estimates across scales. Second, the performance using the new vector-based routing is improved, with the median-aggregated NSE (Nash–Sutcliffe efficiency) score increasing from −0.06 to 0.18 over the CONUS (contiguous US). Third, with the lake module incorporated, the NSE score is further improved by 56.2 % and the systematic bias is reduced by 17 %. Lastly, over 20 % of the false alarms on 2-year floods in the US can be mitigated with the lake module enabled, at the expense of only missing 2.3 % more events. This study demonstrated the advantages of the proposed hydrological modeling framework, which could provide a solid basis for continental- and global-scale water modeling at fine resolution. Furthermore, the use of ensemble forecasts can be incorporated into this framework; and thus, optimized streamflow prediction with quantified uncertainty information can be achieved in an operational fashion for stakeholders and decision-makers.

Use of Ensemble Forecasts to Investigate Synoptic Influences on the Structural Evolution and Predictability of Hurricane Alex (2016) in the Midlatitudes

Abstract Hurricane Alex was an extremely rare hurricane event, the first North Atlantic hurricane to form in January since 1938. Alex developed from an extratropical low pressure system that formed over the western North Atlantic basin, and then underwent tropical transition after moving to the eastern basin. It subsequently underwent anomalous extratropical transition (ET) just north of the Azores Islands. We examine the factors affecting Alex’s structural evolution and the predictability of that evolution. Potential scenarios of structural development are identified from a 51-member forecast ensemble from the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF-EPS), initialized at 0000 UTC 10 January 2016. The EPS forecasts are clustered using a regression mixture model based on the storm’s path through the cyclone phase space. Composite maps constructed from these clusters are used to investigate the role of synoptic-scale features on the evolving structure of Hurricane Alex as it interacted with the midlatitude flow. Results suggest that the crucial factor affecting this interplay was the behavior of a large extratropical cyclone and its associated cold front and likely warm conveyor belt upstream of Alex; the intensity of these structures determined whether Alex underwent a typical cold-core ET (as observed) or a warm-seclusion ET. The clustering and compositing methodology proposed not only provides a nuanced analysis of the ensemble forecast variability, helping forecasters to analyze the predictability of future complex tropical–midlatitude interactions, but also presents a method to investigate probable causes of different processes occurring in cyclones.

The application of ensemble precipitation forecasts to reservoir operation

Abstract Although inflow forecasts are useful information that can be used for improving reservoir operation efficiency, uncertainty is still a challenge for getting sound operation results. Ensemble precipitation forecasts can take uncertainties under consideration, so they have been a research topic for improving reservoir operations. In this paper, a rainfall–runoff model, combined of a multiple linear regression model (for non-flood seasons) and the Xinanjiang model (for flood seasons), for inflow forecasts and a stochastic dynamic programming model for reservoir operations are developed in order to effectively use the ensemble precipitation forecast. To explore the best way for using the ensemble precipitation forecast, two post-processing techniques, i.e., ensemble forecast averaging (EFA) and interval value (IV), are tested. The ensemble precipitation forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF) was chosen due to its high accuracy, and the Huanren reservoir, located in China, was used to test the newly developed models. The results show that, compared with the traditional rule curve, hydropower generation increases by 4.86% and 4.55%, respectively, when EFA and IV are used, which indicates that the use of ensemble forecasts facilitates considerable improvements in operating performance.

Europeans shine in weather forecasting

The US global model lags the performance of two European competitors in predicting weather up to two weeks ahead.

Flood forecasting on the Tocantins River using ensemble rainfall forecasts and real‐time satellite rainfall estimates

AbstractThe Tocantins River, located at the northern region of Brazil with over 300 000 km2 of drainage area, is an important water body in terms of hydropower production. The occurrence of floods along the Tocantins River is a relatively frequent event that affects hydropower plant operations and several cities and their inhabitants. Motivated by recent flooding issues, a hydrological forecasting system was developed in order to assist the decision making of dam operation for flood control. The model uses merged rainfall information from ground‐based telemetric gauges and real‐time TRMM satellite rainfall estimates. Streamflow forecasts are obtained based on quantitative precipitation forecasts from two different sources, CPTEC Eta 15 km regional deterministic model and the Global Ensemble Forecasting System‐VII, maintained by the National Center for Environmental Prediction‐National Oceanic and Atmospheric Administration. We present here the forecasting system analysis of the 2011/2012 rainy season flood predictions with the use of ensemble forecasts, and comparison results of deterministic and ensemble forecasts for the major flood of 2012/2013.

Short-term optimal operation of water systems using ensemble forecasts

Short-term water system operation can be realized using Model Predictive Control (MPC). MPC is a method for operational management of complex dynamic systems. Applied to open water systems, MPC provides integrated, optimal, and proactive management, when forecasts are available. Notwithstanding these properties, if forecast uncertainty is not properly taken into account, the system performance can critically deteriorate.Ensemble forecast is a way to represent short-term forecast uncertainty. An ensemble forecast is a set of possible future trajectories of a meteorological or hydrological system. The growing ensemble forecasts’ availability and accuracy raises the question on how to use them for operational management.The theoretical innovation presented here is the use of ensemble forecasts for optimal operation. Specifically, we introduce a tree based approach. We called the new method Tree-Based Model Predictive Control (TB-MPC). In TB-MPC, a tree is used to set up a Multistage Stochastic Programming, which finds a different optimal strategy for each branch and enhances the adaptivity to forecast uncertainty. Adaptivity reduces the sensitivity to wrong forecasts and improves the operational performance.TB-MPC is applied to the operational management of Salto Grande reservoir, located at the border between Argentina and Uruguay, and compared to other methods.

Evaluating ensemble forecasts of plant species distributions under climate change

Species distributions models (SDMs) are commonly used to assess potential species’ range shifts or extinction risk under climate change. It has been suggested that the use of ensemble forecasts, where a variety of model algorithms are used to generate consensus predictions, are preferred to individual SDMs by avoiding bias or prediction error inherent in a single modeling approach. Whereas several studies have assessed the performance of ensemble predictions using cross-validation or data-partitioning approaches, few studies have assessed the predictive accuracy of ensemble forecasts under climate change by using temporally independent model validation data. We used five SDM approaches to develop consensus forecasts of distributions of 145 vascular plant species from California in the 1930s and tested their projections against current distributions, a span of approximately 75 years. When evaluated with a portion of the model training data, consensus forecasts were highly accurate with an average AUC value of 0.97. False positive and false negative error rates were also low, exhibiting similar performance to random forest models. However, when evaluated with temporally independent data, the accuracy of consensus forecasts was similar to that of generalized linear and generalized additive models, with an average AUC value of 0.83. Our results suggest that the high levels of predictive accuracy exhibited by consensus forecasts when using data partitioning approaches may not reflect their performance when predicting temporally independent data. We contend that consensus forecasts may not represent the best approach for predicting species distributions under future climatic change, as they may not provide superior predictive accuracy in novel temporal domains compared to traditional modeling approaches that more readily lend themselves to ecological interpretation of model structure.

An ensemble based turbulence forecasting system

ABSTRACTTurbulence is a major source of weather‐related aviation incidents. There are many different indicators used to try and predict where turbulence is likely to occur. The indicators are derived from deterministic models although they are often quoted as probabilities. This paper proposes the use of ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS) to produce a probabilistic indicator of wind shear and convectively induced moderate or greater turbulence. An objective verification scheme using high‐resolution automated aircraft observations from the Global Aircraft Data Set is used to compare the skill with the routinely available World Area Forecast Centre gridded turbulence forecasts. The forecasts are assessed globally over a 12 month period from November 2010 to October 2011 looking at the skill, reliability and economic value of the forecasts.

Background Error Correlation between Surface Skin and Air Temperatures: Estimation and Impact on the Assimilation of Infrared Window Radiances

Abstract This paper makes use of ensemble forecasts to infer the correlation between surface skin temperature Ts and air temperature Ta model errors. The impact of this correlation in data assimilation is then investigated. In the process of assimilating radiances that are sensitive to the surface skin temperature, the Ts–Ta error correlation becomes important because it allows statistically optimal corrections to the background temperature profile in the boundary layer. In converse, through this correlation, surface air temperature data can substantially influence the analysis of skin temperature. One difficulty is that the Ts–Ta correlation depends on the local static stability conditions that link the two variables. Therefore, a correlation estimate based on spatial or temporal averages is not appropriate. Ensembles of forecasts valid at the analysis time provide a novel means to infer the correlation dynamically at each model grid point. Geostationary Operational Environmental Satellite (GOES)-8 and -10 surface-sensitive imager radiances are assimilated with and without the inferred correlations in a 3D variational analysis system. The impact of the correlation on analyses is assessed using independent radiosonde data. The impact on 6-h forecasts is also evaluated using surface synoptic reports. The influence of the correlation extends from the surface to about 1.5 km. Temperature differences in the resulting analyses on the order of 0.3–0.6 K are typical in the boundary layer and may extend over broad regions. These difference patterns persist beyond 6 h into the forecasts.

Comments on “A More Extensive Investigation of the Use of Ensemble Forecasts for Dispersion Model Evaluation”

In discussing the results obtained using the HighResolution Limited-Area Model (HIRLAM), Straume (2001) incorrectly credited to Mesinger (1984) the hybrid coordinate eta, which in the lowermost model layers follows the ground and in the upper layers follows the pressure surfaces. The hybrid coordinate eta with the described properties was proposed by Simmons and Burridge (1981). Although Mesinger considered his procedure to be a definition of a new coordinate and called it ‘‘eta,’’ Mesinger’s ‘‘eta coordinate’’ cannot in a strict sense be considered to be a valid coordinate system, given that it has continuum singularities above the edges

A More Extensive Investigation of the Use of Ensemble Forecasts for Dispersion Model Evaluation

Abstract An ensemble forecast is used as input to a Lagrangian particle dispersion model to study the effect that analysis errors in the numerical weather prediction assimilation cycle have on dispersion modeling. The wind and temperature fields from a control forecast are first compared with radiosonde measurements to quantify the quality of the forecast. It predicts, on average, a less stable atmosphere near the surface, whereas the average deviations in wind speed and wind direction are small. The dispersion model then simulates a tracer puff release from the European Tracer Experiment (ETEX), by using the ensemble forecast as input. The spread in the ensemble forecast is calculated by comparing the predicted puff concentrations for each ensemble member with those from the control forecast. It was found that ensemble members that were close to the control forecast when one statistical measure was calculated were not necessarily close when another was used. The ensemble spread is then quantified by comp...

Assessment of the practical utility of extended range ensemble forecasts

Lagged-average forecast experiments, produced with the Meteorological Office 11-level general circulation model, are used to assess the benefits arising from the use of ensemble forecasts in practical monthly prediction. As measured by anomaly correlation, forecast skill is consistently positive at days 6-15, but very small on average beyond 20 days. For the monthly mean forecast, removal of an independent estimate of the model systematic error improves the average score significantly. Compared to the mean score amongst individual members, a modest increase in skill is achieved by ensemble averaging. Theoretically, however, there remains scope for a much greater impact in the future, through the use of models capable of more accurate forecasts. Local forecast skill is correlated with a measure of the agreement between ensemble members, although the overall relationship is weak beyond the medium range. Nevertheless, for areas of high agreement, the probability of obtaining useful skill is considerably higher than average at days 6-15 and 11-20. The experiments are also assessed as probability forecasts, using the Ranked Probability Score to measure skill. the ensemble probability forecast (EPF) is created by averaging the probability forecasts of the individual members. Relative to the average score for the latter, the EPF gives improved skill. For the medium-range period of the forecast (days 1-10), the use of an optimized EPF, calculated by weighting each ensemble member according to its age, leads to a small increase in skill compared to the individual forecast from the most recent analysis. This effect is not observed in the corresponding anomaly correlation results. the above results for local skill prediction are confirmed, and somewhat enhanced, in the probabilistic formulation, using the ensemble variance of forecast categories as the predictor. A simple method of issuing confidence-based adjustments to the forecast probabilities is discussed.

Assessment of the practical utility of extended range ensemble forecasts

AbstractLagged‐average forecast experiments, produced with the Meteorological Office 11‐level general circulation model, are used to assess the benefits arising from the use of ensemble forecasts in practical monthly prediction.As measured by anomaly correlation, forecast skill is consistently positive at days 6‐15, but very small on average beyond 20 days. For the monthly mean forecast, removal of an independent estimate of the model systematic error improves the average score significantly. Compared to the mean score amongst individual members, a modest increase in skill is achieved by ensemble averaging. Theoretically, however, there remains scope for a much greater impact in the future, through the use of models capable of more accurate forecasts.Local forecast skill is correlated with a measure of the agreement between ensemble members, although the overall relationship is weak beyond the medium range. Nevertheless, for areas of high agreement, the probability of obtaining useful skill is considerably higher than average at days 6‐15 and 11‐20.The experiments are also assessed as probability forecasts, using the Ranked Probability Score to measure skill. the ensemble probability forecast (EPF) is created by averaging the probability forecasts of the individual members. Relative to the average score for the latter, the EPF gives improved skill. For the medium‐range period of the forecast (days 1‐10), the use of an optimized EPF, calculated by weighting each ensemble member according to its age, leads to a small increase in skill compared to the individual forecast from the most recent analysis. This effect is not observed in the corresponding anomaly correlation results. the above results for local skill prediction are confirmed, and somewhat enhanced, in the probabilistic formulation, using the ensemble variance of forecast categories as the predictor. A simple method of issuing confidence‐based adjustments to the forecast probabilities is discussed.