Ensemble Prediction System Research Articles

Forecast uncertainty and ensemble spread in surface currents from a regional ocean model

An operational ocean Ensemble Prediction System (EPS) for the coastal seas off Northern Norway is evaluated by comparing with high-frequency radar current speed estimates. The EPS is composed of 24 members for which the ocean current is not perturbed nor constrained but forced with an atmosphere ensemble. The ocean ensemble spread stems from (i) accumulated differences in wind-forcing history and (ii) constraints of sea surface temperature by data assimilation. The intention of the ensemble is to reflect the actual uncertainty in initial conditions, which are largely unknown in terms of mesoscale circulation. We find a low but pronounced predictive skill in surface currents along with a good statistic skill. Additionally, current speeds show deterioration of the validation metrics over the forecast range. Further, high-resolution wind forcing seems to provide better forecast skill in currents compared to lower resolution forcing. In general, the ensemble exhibits the ability to predict forecast uncertainty.

A Comparison of Sea Surface Temperature Perturbation Methods for a Convection Permitting Ensemble Prediction System Over the European Arctic

A Comparison of Sea Surface Temperature Perturbation Methods for a Convection Permitting Ensemble Prediction System Over the European Arctic

Evaluating Contour Band Depth as a Method for Understanding Ensemble Uncertainty

Abstract Estimating and predicting the state of the atmosphere is a probabilistic problem for which an ensemble modeling approach often is taken to represent uncertainty in the system. Common methods for examining uncertainty and assessing performance for ensembles emphasize pointwise statistics or marginal distributions. However, these methods lose specific information about individual ensemble members. This paper explores contour band depth (cBD), a method of analyzing uncertainty in terms of contours of scalar fields. cBD is fully nonparametric and induces an ordering on ensemble members that leads to box-and-whisker-plot-type visualizations of uncertainty for two-dimensional data. By applying cBD to synthetic ensembles, we demonstrate that it provides enhanced information about the spatial structure of ensemble uncertainty. We also find that the usefulness of the cBD analysis depends on the presence of multiple modes and multiple scales in the ensemble of contours. Finally, we apply cBD to compare various convection-permitting forecasts from different ensemble prediction systems and find that the value it provides in real-world applications compared to standard analysis methods exhibits clear limitations. In some cases, contour boxplots can provide deeper insight into differences in spatial characteristics between the different ensemble forecasts. Nevertheless, identification of outliers using cBD is not always intuitive, and the method can be especially challenging to implement for flow that exhibits multiple spatial scales (e.g., discrete convective cells embedded within a mesoscale weather system). Significance Statement Predictions of Earth’s atmosphere inherently come with some degree of uncertainty owing to incomplete observations and the chaotic nature of the system. Understanding that uncertainty is critical when drawing scientific conclusions or making policy decisions from model predictions. In this study, we explore a method for describing model uncertainty when the quantities of interest are well represented by contours. The method yields a quantitative visualization of uncertainty in both the location and the shape of contours to an extent that is not possible with standard uncertainty quantification methods and may eventually prove useful for the development of more robust techniques for evaluating and validating numerical weather models.

Influence of Physically Constrained Initial Perturbations on the Predictability of Mei-Yu Heavy Precipitation

Abstract Based on the conditional nonlinear optimal perturbation (CNOP) approach, the predictability of mei-yu heavy precipitation and its underlying physical processes is investigated. As an extension of our previous work, the practical predictability of heavy precipitation events is studied using more realistic initial perturbations than previously considered. The initial perturbation reflects certain physical connections among multiple variables, including zonal and meridional winds, potential temperature (T), and water vapor mixing ratio (Q). Two types of initial perturbations for the CNOP are identified, with similar spatial distributions but opposite signs and resulting effects. The accumulated precipitation is strengthened with mostly positive perturbations in the T and Q components for the CNOP and weakened by negative perturbations. Comparing downscaling (DOWN) perturbations and random perturbations (RPs) with the CNOP, it is found that the CNOP and DOWN perturbations exhibit particularly large- and mesoscale spatial structures, respectively, while the RPs yield a spatial distribution with mostly convective-scale features. Also, the CNOP results in the largest error growth and forecast uncertainty, especially for Q, followed by the DOWN perturbations; those in the RPs are the smallest. These results provide important implications for optimizing the initial perturbations of convection-permitting ensemble prediction systems, especially precipitation forecasts. Moreover, it is suggested that small-scale related variables, that is, those associated with vertical motion and microphysical processes, are much less predictable than thermodynamic variables, and the errors grow through distinct physical processes for the three types of initial perturbations, that is, those with flow-dependent features.

Hybrid Dynamical–Statistical Forecasts of the Risk of Rainfall in Southeast Asia Dependent on Equatorial Waves

Abstract Equatorial waves are a major driver of widespread convection in Southeast Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between 2 and 4 times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast [Met Office Global and Regional Ensemble Prediction System (MOGREPS-G)]. Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby–gravity waves are substantially more skillful than for the eastward-moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical–statistical forecasts of rainfall probability using a Bayesian approach. The Brier skill score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high-impact weather using this method as forecasts of large-scale waves improve.

Development and Evaluation of Multi-week Tropical Cyclone Strike Probability Forecasts in the Philippines

Tropical cyclone (TC) forecast, provided a few weeks in advance, can be beneficial for the preparation and mitigation of disaster risks in TC-vulnerable countries such as the Philippines. In this study, TC strike probability forecasts with a lead time of up to 4 wk were derived by combining the TC tracks from each ensemble member of the three models – namely, NCEP Coupled Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) – to show how likely it is for a TC to form or strike an area within the 300-km radius of the TC center. To assess its performance, real-time-derived TC strike probability forecasts over the Tropical Cyclone Information Domain (TCID) of the Philippines (bounded by 0–27 °N and 110–155 °E) covering the period from 06 Oct 2020–31 Oct 2022 were evaluated. Verification metrics revealed that the skill and reliability of the TC forecasts vary with lead time and the TC being forecasted. Week 1 forecasts are reliable and can be helpful for decision-making, whereas Week 2 forecasts are considered most reliable only up to the 51–60% probability interval. On the other hand, forecasts with 3–4-wk lead times are reliable for probabilities less than 20%. The case study performed using five TCs with different intensity classifications has shown that generally, forecasts for TCs with stronger intensity have higher skill than forecasts for relatively weaker ones. It was also observed that the magnitude of the probability values varies with the intensity changes within the validity period. These findings suggest that multi-model ensemble forecasts can be utilized for the improvement and eventual operationalization of multi-week TC strike probability forecasts over the Philippines.

Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0

Abstract. The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in the operating cost.

Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction

This work is concerned with optimally combining quantiles of several post-processed versions of ensemble solar forecasts, which is new in this field. Numerical weather prediction (NWP) serves grid integration of solar energy by issuing dynamical ensemble irradiance forecasts. However, these ensemble members often suffer from under-dispersion, which motivates statistical calibration via quantile regression (QR) or ensemble model output statistics (EMOS). Given the numerous variants of QR and EMOS, it is generally unclear which variant offers the best performance under what situation, which further motivates combining quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed, and a constrained quantile regression averaging scheme is used to exemplify the framework. Using the strictly proper pinball loss, ensemble irradiance forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System are first post-processed using five QR variants and five EMOS variants, and then combined through a linear program. It is found that combining quantiles is an effective strategy that can further improve the calibrated ECMWF forecasts across all locations herein considered.

Improving the short‐range forecast of storm surges in the southwestern Atlantic continental shelf using 4DEnSRF data assimilation

In this study, the assimilation of tide gauge and altimetry data into a two‐dimensional barotropic numerical model for the southwestern Atlantic continental shelf (SWACS) was developed. To do this, the preoperative 4‐day storm surges ensemble prediction system developed by Dinápoli et al. (2021, Journal of the Royal Meteorological Society 147: 557–572) was implemented for the SWACS. This new configuration, called “Model for Storm Surge Simulations” (M3S), considers a curvilinear grid that covers the SWACS with higher resolution along the shoreline (from 2 to 10 km). M3S was forced with an ensemble of 60 members conformed by the combination of perturbations of the eight principal tidal constituents and of the atmospheric products derived from the Global Ensemble Forecast System. Tidal gauge and altimetry data were assimilated in an asynchronous mode using the four‐dimensional‐ensemble square‐root filter (4DEnSRF). The system was developed and validated forecasting two strong positive storm surges. Results show that 4DEnSRF's innovations produce a positive impact upon the forecast skill up to 2 days. Hence, the 4‐day forecast can be divided into two parts: the first 2 days with a stronger dependence on the initial conditions and the other 2 days purely driven by external forcing. It was found that a symmetric assimilation window of 12 hr length produces the best initial condition. Under this configuration, 4DEnSRF removes biases and improves the timing of the M3S forecasted solutions. The largest improvements were observed at the northern SWACS, where more chaotic processes, such as the atmospheric circulation, explain a large part of the sea‐surface height variability. No significant improvements were found at the southern SWACS, which can be attributed to the strong tidal dynamics that characterise the zone. Our results show that the incorporation of 4DEnSRF into M3S can significantly improve the forecast in the SWACS and also the accuracy of the short‐range detection of storm surges.

Comparison of Three Radar-Based Precipitation Nowcasts for the Extreme July 2021 Flooding Event in Germany

Abstract Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, but their evaluation depends on a variety of factors, namely, the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140–1670 km2). Two deterministic [advection-based and spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a maximum lead time of 3 h were used as input to two hydrological models: a physically based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared with hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results. 1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared with adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7–12 h depending on the benchmark. 2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. 3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.

Area day‐ahead photovoltaic power prediction by just‐in‐time modeling with meso‐scale ensemble prediction system

AbstractPhotovoltaics (PV) output prediction, which is indispensable for power system operation, can affects demand and supply adjustment adversely when large prediction error occurs. Thus, the reduction of large error as well as average error is required in PV power prediction. In 2019, the operation of the Meso‐scale Ensemble Prediction System (MEPS) of numerical weather prediction started from the JapanMeteorological Agency, and the amount of forecasting information would be potentially useful for the improvement of PV power prediction. However, very few studies on inputting multiple meteorological elements of the MEPS have been reported. In this paper, we newly develop the prediction model for an area day‐ahead PV power output composed of Just‐In‐Time Modeling (JIT Modeling) with multiple elements of theMEPS. The developed method achieves precise forecasts with low computational load by both selecting meteorological elements valid for improving prediction accuracy and adequately devising the structure of JIT Modeling. Some numerical examples demonstrating the effectiveness of the developed method are also presented. In particular, the proposed method reduces large error significantly.

A Multimodel Real-Time System for Global Probabilistic Subseasonal Forecasts of Precipitation and Temperature

Abstract A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal Experiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each grid point using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of week-3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, sub-Saharan Africa and Southeast Asia, and over subtropical South America, Africa, and Australia. Near-surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America, and East Africa in all seasons. An example real-time week-3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden–Julian oscillation event as well as weak–moderate ongoing La Niña event. Significance Statement This paper develops a system for forecasting of precipitation and temperatures globally over land, several weeks in advance, with a focus on biweekly averaged conditions between three and four weeks ahead. The system provides the likelihood of biweekly and weekly conditions being below, near, or above their long-term averages, as well the probability of exceeding (or not exceeding) any threshold value. Using historical data, the precipitation forecasts are demonstrated to have skill in many tropical regions, and the temperature forecasts are more widely skillful. While weather and seasonal range forecasts have become quite generally available, this is one of the first examples of a publicly available, calibrated multimodel probabilistic real-time forecasting system for the subseasonal range.

Assessing ocean ensemble drift predictions by comparison with observed oil slicks

Geophysical models are cornerstone pieces in marine forecasting of floating objects and pollution, such as marine surface oil slicks. Trajectory forecasts of oil spills inherit the uncertainties from the underlying geophysical forcing. In this work we compare the forecast capabilities of an ocean ensemble prediction system (EPS) to those from a higher resolution deterministic model on the representation of oil slick drift. As reference, we use produced water (PW) slicks detected and delineated from 41 C–band Sentinel-1A/B satellite synthetic aperture radar images between April and December, 2021. We found that the EPS provided at least equivalent member-wise results relative to simulations forced with the deterministic model. Ensemble verification through rank histograms and spread-error relationship showed that including the ocean fields is necessary to address model uncertainties. Whether considering the ocean field or not, the modeled slicks were counterclockwise rotated between 20° and 30° relative to the ones observed in the satellite images, and these were deflected about 45° to the right of the observed wind direction.

Member Formation Methods Evaluation for a Storm Surge Ensemble Forecast System in Taiwan

The forecast of typhoon tracks remains uncertain and is positively related to the accuracy of the storm surge forecast. The storm surge prediction error increases dramatically when the forecast track error is larger than 100 km. This study aims to develop an ensemble storm surge prediction system using parametric weather models to account for the uncertainty in typhoon track prediction. The storm surge model adopted in this study is COMCOT-SS storm surge forecast system. Two methods are introduced and analyzed to generate the ensemble members in this study. One is from the weather ensemble prediction system (WEPS), and the other is from the error distribution of the deterministic forecasts (EDF). The ensemble prediction results show that the ensemble mean of WEPS performs similarly to the deterministic forecast. However, the maximum surge height of WEPS is often lower than one from EDF. The verification results suggest that, for disaster prevention, EDF provides stronger warnings to the coastal region than WEPS. However, it may provide overestimated forecasts in some cases.

Ensemble Machine Learning greatly improves ERA5 skills for wind energy applications

The skill of ERA5 has been assessed in relation to the prediction of the wind energy associated with 28 SYNOP stations located in Italy for a time span of 20 years (2001–2020). For comparison, a WRF-based high-resolution downscaling (3 km horizontally) was also produced for the same period. We found that simple predictions based on materialized past wind measures outperform the wind energy predictions from ERA5. This result can be ascribed to the particularly complex characteristics of the Italian territory. Motivated by this expected behavior, we have implemented a Quantile Random Forest (QRF) calibration which greatly alleviates the problems encountered in the ERA5 reanalysis dataset. This technique provides a calibrated ensemble prediction system for the wind speed at the station. Surprisingly, the calibrated ERA5 outperforms wind energy estimations from the high-resolution 3-km downscaling. Once properly calibrated, the high-resolution downscaling provides predictions very similar to the calibrated ERA5. Limiting our conclusions to the estimation of wind energy over a long time span (here 20 years), having at disposal a high-resolution wind-field dataset does not necessarily mean greater accuracy. A careful calibration of the original coarser wind-field dataset produces better results than the raw high-resolution dataset.

Performance of a Convective-Scale Ensemble Prediction System on 2017 Warm-Season Afternoon Thunderstorms over Taiwan

Abstract A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses. Significance Statement This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.

Met Office MOGREPS‐G initialisation using an ensemble of hybrid four‐dimensional ensemble variational (En‐4DEnVar) data assimilations

AbstractThe Met Office Global and Regional Ensemble Prediction System–Global (MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial conditions from its operational implementation in September 2008 until December 2019. In 2019, MOGREPS‐G became the first operational atmospheric ensemble to apply hybrid four‐dimensional ensemble variational data assimilation (En‐4DEnVar) to each of the 44 perturbed ensemble members. Other enhancements have also been added, including to the inflation used to improve ensemble spread. The combined impact of these changes on ensemble forecasts is overwhelmingly positive but initially more neutral for deterministic forecasts, which also use the ensemble to represent flow‐dependent forecast errors in their hybrid data assimilation updates. The latter result is not a surprise, because the deterministic forecast's hybrid data assimilation was initially weighted more strongly to the modelled stationary covariance component and not optimised to take full advantage of the upgraded ensemble. A subsequent operational upgrade in December 2020 has introduced shifting in addition to lagging to exploit the ensemble better in the deterministic forecast's hybrid data assimilation by including ensemble members from a previous cycle and also from adjacent forecast lead times to augment the ensemble without having to run additional forecasts. More weight has since been given to the ensemble in the deterministic forecast's hybrid data assimilation in May 2022. A key motive for adopting hybrid 4DEnVar in MOGREPS‐G is to reduce maintenance overheads by virtue of sharing much of the deterministic forecast system's data assimilation code. This also enables the ensemble to assimilate almost all observation types used by the deterministic forecast. The updated system also exploits parallelism better so as to be fast enough for operational use, despite assimilating more observations and being more computationally expensive than the Met Office's ETKF.

Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library

AbstractFlash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation‐based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short‐term rainfall forecasts, which should increase decision‐making times. We implemented an adaptive scale‐dependent ensemble blending method in the open‐source pysteps library, based on the Short‐Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill‐dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48‐member blended forecasts against the Belgian NWP forecast, a 48‐member nowcast, and a simple 48‐member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event‐averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment‐average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.

A Gridded Solar Irradiance Ensemble Prediction System Based on WRF-Solar EPS and the Analog Ensemble

The WRF-Solar Ensemble Prediction System (WRF-Solar EPS) and a calibration method, the analog ensemble (AnEn), are used to generate calibrated gridded ensemble forecasts of solar irradiance over the contiguous United States (CONUS). Global horizontal irradiance (GHI) and direct normal irradiance (DNI) retrievals, based on geostationary satellites from the National Solar Radiation Database (NSRDB) are used for both calibrating and verifying the day-ahead GHI and DNI predictions (GDIP). A 10-member ensemble of WRF-Solar EPS is run in a re-forecast mode to generate day-ahead GDIP for three years. The AnEn is used to calibrate GDIP at each grid point independently using the NSRDB as the “ground truth”. Performance evaluations of deterministic and probabilistic attributes are carried out over the whole CONUS. The results demonstrate that using the AnEn calibrated ensemble forecast from WRF-Solar EPS contributes to improving the overall quality of the GHI predictions with respect to an AnEn calibrated system based only on the deterministic run of WRF-Solar. In fact, the calibrated WRF-Solar EPS’s mean exhibits a lower bias and RMSE than the calibrated deterministic WRF-Solar. Moreover, using the ensemble mean and spread as predictors for the AnEn allows a more effective calibration than using variables only from the deterministic runs. Finally, it has been shown that the recently introduced algorithm of correction for rare events is of paramount importance to obtain the lowest values of GHI from the calibrated ensemble (WRF-Solar EPS AnEn), qualitatively consistent with those observed from the NSRDB.

Impact of land surface processes on convection over West Africa in convection‐permitting ensemble forecasts: A case study using the MOGREPS ensemble

AbstractSoil moisture (SM) affects weather through its impact on surface flux partitioning, influencing vertical atmospheric profiles and circulations driven by differential surface heating. In West Africa, observational studies point to a dominant negative SM‐precipitation feedback, where dry soils help to initiate and maintain convection. In this context, serious concerns exist about the ability of models with parameterised convection to simulate this observed sensitivity of daytime convection to SM. Here, we evaluate the effect of initial SM perturbations in a short‐range ensemble forecast over West Africa, comparing the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS) with parameterised convection (GLOB‐ENS) to its regional convection‐permitting counterpart (CP‐ENS). Results from both models suggest SM perturbations introduce considerable spread into daytime evaporative fraction (EF) and near‐surface temperatures. This spread is still evident on Day 3 of the forecast. Both models also show a tendency to increased afternoon rainfall frequency over negative EF anomalies, reproducing the observed feedback. However, this effect is more pronounced in CP‐ENS than GLOB‐ENS, which illustrates the potential for process‐based forecast improvements at convection‐permitting scales. Finally, we identify persistent biases in rainfall caused by land cover mapping issues in the operational GLOB‐ENS setup, emphasising the need for careful evaluation of different mapping strategies for land cover.