The complexity and dimensionality of making deterministic photovoltaic power forecasts from ensemble numerical weather prediction

Hydrological ensemble prediction systems

For streamflow forecasting, calibration of ensemble numerical weather prediction (NWP) models has long been considered a necessary evil. Necessary, because NWP forecasts are usually too biased to force calibrated hydrological models, they often produce unreliable ensembles and may produce forecasts that are less accurate than simple climatology at longer lead times. Evil, because calibration adds complexity to any forecasting system and the calibration process destroys spatial, temporal and inter-variable correlations in the ensemble, which then must be reconstructed in various and usually unsatisfying ways. As ensemble NWPs improve, the degree to which calibration is ‘necessary’ declines.Here we investigate recent versions of two ensemble NWP models – the European Centre for Medium-range Weather Forecasts ensemble NWP (ECMWF-ens) and the Bureau of Meteorology’s Australian Community Climate and Earth-System Simulator Global Ensemble (ACCESS-GE) NWP. The models are tested over Tasmania, where CSIRO is working with Hydro Tasmania, Australia’s largest generator of hydropower, to establish new ensemble streamflow forecasting systems. Tasmania is mountainous and temperate and features strong rainfall gradients. We apply an existing calibration method – the Catchment-scale Hydrological Precipitation Processor (CHyPP) – which uses a Bayesian Joint Probability model to calibrate ensemble precipitation forecasts.We show that CHyPP improves reliability in both the ECMWF-ens and ACCESS-GE ensembles, but these improvements come at the cost of a slight reduction in skill at short lead times. Uncalibrated ACCESS-GE forecasts generally produce more biased and less reliable forecasts than ECMWF-ens, and we conclude that calibration is necessary for the ACCESS-GE model, both to reduce biases and improve reliability. However, the improvements in bias from calibrating the ECMWF-ens are negligible in some catchments, with the main benefit being improved reliability at longer lead times. This brings into question the need for calibration of the ECMWF-ens model with CHyPP. We note that these findings may not hold outside the Tasmanian catchments tested, where high resolution ensemble NWP forecasts generally perform well. We discuss the implications of these findings with respect to streamflow forecasts.

This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

Deterministic numerical weather prediction (NWP) models and ensemble NWP models are routinely run worldwide to assist weather forecasting. Deterministic forecasts are capable of capturing more detailed spatial features, while ensemble forecasts, often with a coarser resolution, have the ability to predict uncertainty in future conditions. A comparative understanding of the performance of these two types of forecasts is valuable for both users of NWP products and model developers. Past published comparisons tended to be limited in scope, for example, for only specific locations and weather events, and involving only raw forecasts. In this study, we conduct a comprehensive comparison of the performance of a deterministic model and an ensemble model of the Australian Bureau of Meteorology in forecasting daily precipitation across Australia over a period of 3 years. The deterministic model has a horizontal grid spacing of approximately 25 km, and the ensemble model 60 km. Despite the coarser resolution, the ensemble forecasts are found to be superior by a number of measures, including correlation, accuracy and reliability. This finding holds true for both raw forecasts from the NWP models and forecasts post‐processed using the recently developed seasonally coherent calibration (SCC) model. Post‐processing is shown to greatly improve the forecasts from both models; however, the improvement is greater for the deterministic model, narrowing the performance gap between the two models. This study adds strong evidence to the general notion that coarser‐resolution ensemble NWP forecasts perform better than deterministic forecasts.

Under the two-step framework of photovoltaic (PV) power forecasting, that is, forecasting first the irradiance and then converting it to PV power, there are two chief ways in which one can account for the uncertainty embedded in the final PV power forecast. One of those is to produce probabilistic irradiance forecast through, for example, ensemble numerical weather prediction (NWP), and the other is to pass the irradiance forecast through a collection of different irradiance-to-power conversion sequences, which are known as model chains. This work investigates, for the first time, into the question: Whether pairing ensemble NWP with ensemble model chain is better than leveraging any individual method alone? Using data from 14 utility-scale ground-mounted PV plants in Hungary and the state-of-the-art global mesoscale NWP model of the European Centre for Medium-Range Weather Forecasts, it is herein demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, owing to the higher-quality probabilistic forecasts, the point forecast accuracy is also improved substantially through pairing. Overall, the recommended paring strategy achieves a mean-normalized continuous ranked probability score and a root mean square error of 18.4% and 42.1%, respectively.

The skill of operational deterministic turbulence forecasts is impacted by the uncertainties in both weather forecasts from the underlying numerical weather prediction (NWP) models and diagnoses of turbulence from the NWP model output. This study compares various probabilistic turbulence forecasting approaches to quantify these uncertainties and provides recommendations on the most suitable approach for operational implementation. The approaches considered are all based on ensembles of NWP forecasts and/or turbulence diagnostics, and include a multi-diagnostic ensemble (MDE), a time-lagged NWP ensemble (TLE), a forecast-model NWP ensemble (FME), and combined time-lagged MDE (TMDE) and forecast-model MDE (FMDE). Both case studies and statistical analyses are provided. The case studies show that the MDE approach that represents the uncertainty in turbulence diagnostics provides a larger ensemble spread than the TLE and FME approaches that represent the uncertainty in NWP forecasts. The larger spreads of MDE, TMDE, and FMDE allow for higher probabilities of detection for low percentage thresholds at the cost of increased false alarms. The small spreads of TLE and FME result in either hits with higher confidence or missed events, highly dependent on the performance of the underlying NWP model. Statistical evaluations reveal that increasing the number of diagnostics in MDE is a cost-effective and powerful method for describing the uncertainty of turbulence forecasts, considering trade-offs between accuracy and computational cost associated with using NWP ensembles. Combining either time-lagged or forecast-model NWP ensembles with MDE can further improve prediction skill and could be considered if sufficient computational resources are available.

An analog ensemble (AnEn) is constructed by first matching up the current forecast from a numerical weather prediction (NWP) model with similar past forecasts. The verifying observation from each match is then used as an ensemble member. For at least some applications, the advantages of AnEn over an NWP ensemble (multiple real-time model runs) may include higher efficiency, avoidance of initial condition and model perturbation challenges, and little or no need for postprocessing calibration. While AnEn can capture flow-dependent error growth, it may miss aspects of error growth that can be represented dynamically by the multiple real-time model runs of an NWP ensemble. To combine the strengths of the AnEn and NWP ensemble approaches, a hybrid ensemble (HyEn) is constructed by finding m analogs for each member of a small n-member NWP ensemble, to produce a total of m × n members. Forecast skill is compared between the AnEn, HyEn, and an NWP ensemble calibrated using logistic regression. The HyEn outperforms the other approaches for probabilistic 2-m temperature forecasts yet underperforms for 10-m wind speed. The mixed results reveal a dependence on the intrinsic skill of the NWP members employed. In this study, the NWP ensemble is underspread for both 2-m temperature and 10-m winds, yet displays some ability to represent flow-dependent error for the former and not the latter. Thus, the HyEn is a promising approach for efficient generation of high-quality probabilistic forecasts, but requires use of a small, and at least partially functional, NWP ensemble.

Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncertainty. Limited computational resources constrain the size of ensembles, so choices must be made about which members to include. No known objective methodology exists to guide users in choosing which combinations of physics parameterizations to include in an NWP ensemble, however. This study presents such a methodology.The authors build an NWP ensemble using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). This 24-member ensemble varies physics parameterizations for 18 randomly selected 48-h forecast periods in boreal summer 2009. Verification focuses on 2-m temperature and 10-m wind components at forecast lead times from 12 to 48 h. Various statistical guidance methods are employed for down-selection, calibration, and verification of the ensemble forecasts. The ensemble down-selection is accomplished with principal component analysis. The ensemble PDF is then statistically dressed, or calibrated, using Bayesian model averaging. The postprocessing techniques presented here result in a recommended down-selected ensemble that is about half the size of the original ensemble yet produces similar forecast performance, and still includes critical diversity in several types of physics schemes.

An archived dataset from the ECMWF Ensemble Prediction System for probabilistic solar power forecasting

China has highly emphasized the research and operational application of numerical weather prediction. This paper determines the objective function parameters, such as CAPE and SRH, to apply an ensemble numerical prediction model in weather forecasting. Preprocessing and evaluating rainfall data is necessary to construct the WRF-ARW numerical weather prediction model. The WRF-ARW model is applied to simulate the weather forecasts in Henan Province, and the difficulties and challenges faced in the efficient implementation of the parameterized scheme are outlined. The WRFARW model’s prediction errors for the maximum rainfall and total rainfall in Henan Province range from 1.78%-13.51% and 0.16%-3.78%, respectively, which are significantly less than 15%, and the model is more predictive than the others. The raw data test set’s credibility ranges from 0.957 to 0.997, which is close to 1, indicating that the raw data collected in this paper are highly credible. The WRF-ARW model’s qualification rates for forecasting maximum rainfall and total rainfall are 86.7% and 93.3%, respectively, and its overall accuracy is grade B and grade A, respectively. The pass rates for the peak occurrence time of maximum rainfall and total rainfall were 93.3% and 86.7%, respectively, and the overall prediction accuracy was Grade A and Grade B, respectively. The WRF-ARW model is effective in weather forecasting throughout Henan Province. In summary, the WRF-ARW model is very effective in improving the efficiency of ensemble numerical weather prediction and parameterization schemes in Henan Province.

Abstract. Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture the complex variability of these fields with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter, leading to local systematic biases). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate but suffer from widespread systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high-resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of precipitation estimates by radar and gauges, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the particle filter and ensemble Kalman filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski-resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local-scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data.

In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

Typhoon rainfall is one of the most important water resources in Taiwan. However, heavy rainfall during typhoons often leads to serious disasters. Therefore, accurate typhoon rainfall forecasts are always desired for water resources managers and disaster warning systems. In this study, the quantitative rainfall forecasts from an ensemble numerical weather prediction system in Taiwan are used. Furthermore, a novel strategy, which is based on the use of a self-organizing map (SOM) based cluster analysis technique, is proposed to integrate these ensemble forecasts. By means of the SOM-based cluster analysis technique, ensemble forecasts that have similar features are clustered. That is helpful for users to effectively combine these ensemble forecasts for providing better typhoon rainfall forecasts. To clearly demonstrate the advantage of the proposed strategy, actual application is conducted during five typhoon events. The results indicate that the ensemble rainfall forecasts from numerical weather prediction models are well categorized by the SOM-based cluster analysis technique. Moreover, the integrated typhoon rainfall forecasts resulting from the proposed strategy are more accurate when compared to those from the conventional method (i.e., the ensemble mean of all forecasts). In conclusion, the proposed strategy provides improved forecasts of typhoon rainfall. The improved quantitative rainfall forecasts are expected to be useful to support disaster warning systems as well as water resources management systems during typhoons.

Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.

Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction