Ensemble Prediction Research Articles

An extension to ensemble forecast of conditional nonlinear optimal perturbation considering nonlinear interaction between initial and model parametric uncertainties

Initial and model uncertainties are the main sources of forecast errors, making the single and deterministic forecasts unreliable. To estimate these uncertainties, a growing consensus shifts towards ensemble forecasting, aiming to provide the probability density distribution of the atmosphere. However, current ensemble methods either focus on single-source uncertainties or employ a simple superposition of the two, neglecting the nonlinear interaction between them, and thus fail to reflect the real forecast uncertainty. Motivated by this, this study extends the CNOP approach, defined as the optimal growth considering nonlinear interaction between initial and model parameters, to the scenario of ensemble forecasts and proposes an orthogonal CNOPs method (O-CNOP-IPs). This method concerns the nonlinear effect of initial and model parametric uncertainties through a joint optimization strategy and enhances the estimation of this effect by providing diversity and independent CNOPs (via orthogonality). To evaluate the performance of O-CNOP-IPs, extensive experiments are conducted for North Atlantic Oscillation (NAO) ensemble forecasts in the realistically configured Community Earth System Model (CESM). Our findings reveal that the O-CNOP-IPs method outperforms existing methods in forecast skill and reliability, improving deterministic skill by 17.5 % and probabilistic skill by 52 %–63 %. Our dynamic analysis also unveils that this method undergoes rapid development in the early stage and effectively neutralizes errors in control forecasts, significantly enhancing the reliability of ensemble forecasts. It is expected that O-CNOP-IPs plays a significant role in accurately representing the forecast uncertainty of other high-impact weather and climate phenomena.

Physics-Based vs Data-Driven 24-Hour Probabilistic Forecasts of Precipitation for Northern Tropical Africa

Abstract Numerical weather prediction (NWP) models struggle to skillfully predict tropical precipitation occurrence and amount, calling for alternative approaches. For instance, it has been shown that fairly simple, purely data-driven logistic regression models for 24-h precipitation occurrence outperform both climatological and NWP forecasts for the West African summer monsoon. More complex neural network–based approaches, however, remain underdeveloped due to the non-Gaussian character of precipitation. In this study, we develop, apply, and evaluate a novel two-stage approach, where we train a U-Net convolutional neural network (CNN) model on gridded rainfall data to obtain a deterministic forecast and then apply the recently developed, nonparametric Easy Uncertainty Quantification (EasyUQ) approach to convert it into a probabilistic forecast. We evaluate CNN+EasyUQ for 1-day-ahead 24-h accumulated precipitation forecasts over northern tropical Africa for 2011–19, with the Integrated Multi-satellitE Retrievals for GPM (IMERG) data serving as ground truth. In the most comprehensive assessment to date, we compare CNN+EasyUQ to state-of-the-art physics-based and data-driven approaches such as monthly probabilistic climatology, raw and postprocessed ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), and traditional statistical approaches that use up to 25 predictor variables from IMERG and the ERA5 reanalysis. Generally, statistical approaches perform about on par with postprocessed ECMWF ensemble forecasts. The CNN+EasyUQ approach, however, clearly outperforms all competitors in terms of both occurrence and amount. Hybrid methods that merge CNN+EasyUQ and physics-based forecasts show slight further improvement. Thus, the CNN+EasyUQ approach can likely improve operational probabilistic forecasts of rainfall in the tropics and potentially even beyond. Significance Statement Precipitation forecasts in the tropics remain a great challenge despite their enormous potential to create socioeconomic benefits in sectors such as food and energy production. Here, we develop a purely data-driven, machine learning–based prediction model that outperforms traditional, physics-based approaches to 1-day-ahead forecasts of rainfall occurrence and rainfall amount over northern tropical Africa in terms of both forecast skill and computational costs. A combined data-driven and physics-based (hybrid) approach yields further (slight) improvement in terms of forecast skill. These results suggest new avenues to more accurate and more resource-efficient operational precipitation forecasts in the Global South.

A Combined Scheme based on the Multiscale Stochastic Perturbed Parameterization Tendencies and Perturbed Boundary Layer Parameterization for a Global Ensemble Prediction System

Abstract Stochastic representations of model uncertainties are of great importance for the performance of ensemble prediction systems (EPSs). The stochastically perturbed parameterization tendencies (SPPT) scheme with a single-scale random pattern has been used in the operational global EPS of China Meteorological Administration (CMA-GEPS) since 2018. To deal with deficiencies in this operational single-scale SPPT scheme, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is developed. In the combined scheme, the mSPPT component aims to expand model uncertainties characterized by SPPT at mesoscale, synoptic scale, and planetary scale. The SPP-PBL component with six vital parameters is used to capture uncertainties in PBL processes, which is underrepresented by SPPT for the tapering treatment within PBL. Comparisons between the operational SPPT scheme and the mSPPT scheme reveal that the mSPPT scheme can generate more improvements in both ensemble reliability and forecast skills mainly in tropics. Besides, additional benefits from SPP-PBL on top of mSPPT are shown to be primarily distributed in tropics at the lower layers below 850 hPa and surface. Furthermore, the combined scheme of mSPPT and SPP-PBL is suggested to yield better spread–error relationships and forecast skills than the operational SPPT scheme in terms of objective verification scores for standard upper-air variables and surface parameters. A case study for the extreme precipitation event on 20 July 2021 in Henan Province of China also demonstrates the better ability of the combined scheme in forecasting the precipitation intensity and location. Significance Statement A comprehensive and reasonable representation of model uncertainties helps to improve the performance of ensemble prediction systems (EPSs). Despite the popular usage in simulating model uncertainties, the stochastically perturbed parameterization tendencies (SPPT) scheme possesses several shortcomings. To overcome this, a combined scheme based on the multiscale SPPT (mSPPT) scheme and the stochastically perturbed parameterization for the planetary boundary layer (SPP-PBL) scheme is proposed. Based on the global EPS of China Meteorological Administration (CMA-GEPS), additional benefits from the independent usage of mSPPT and SPP-PBL are disclosed. And the combined scheme can inherit the merits of mSPPT and SPP-PBL and generate more improvements on ensemble performance than the original single-scale SPPT scheme. This research provides a guideline for future upgradation of CMA-GEPS.

Forecasting CPI inflation under economic policy and geopolitical uncertainties

Forecasting consumer price index (CPI) inflation is of paramount importance for both academics and policymakers at central banks. This study introduces the filtered ensemble wavelet neural network (FEWNet) to forecast CPI inflation, tested in BRIC countries. FEWNet decomposes inflation data into high- and low-frequency components using wavelet transforms, and incorporates additional economic factors, such as economic policy uncertainty and geopolitical risk, to enhance forecast accuracy. These wavelet-transformed series and filtered exogenous variables are input into downstream autoregressive neural networks, producing the final ensemble forecast. Theoretically, we demonstrate that FEWNet reduces empirical risk compared to fully connected autoregressive neural networks. Empirically, FEWNet outperforms other forecasting methods and effectively estimates prediction uncertainty, due to its ability to capture non-linearities and long-range dependencies through its adaptable architecture. Consequently, FEWNet emerges as a valuable tool for central banks to manage inflation and enhance monetary policy decisions.

An Improved Type-1 Fuzzy Regression Function for COVID-19 Cases Prediction

The deficiency in data collection and reporting has led to the emergence of uncertainty in the data in the pandemic process. These matters cause that traditional statistical and mathematical models have been functionless and unreliable in this issue. In this respect, this study presents an ensemble prediction model with innovative and contemporary properties to model COVID-19 cases in the UK and USA inclusively throughout the whole pandemic process. The proposed ensemble prediction model is composed of an assembly of Type-1 fuzzy regression functions with elastic net regularization (E-T1FRF) and radial basis function neural networks (RBFNNs). Thus, the proposed ensemble model can successfully model the uncertainty in the data with the fuzzy modelling perspective of T1FRF and also thoroughly adapt to the patterns in the data thanks to the flexible modelling ability of RBFNN based on data. With the proposed ensemble prediction model, the cases in the entire pandemic process from the beginning of March 2020 to the end of June 2022 were modelled and predicted for 23 different periods in one-month prediction steps. The proposed model produced predictions with MAPE values below 3% in all but periods except for three periods. Also, the average MAPE values for all periods were obtained at around 2% for the UK and only 1.5% for the US. These results, at a reasonable level of error, demonstrated the practicality of the usage of the proposed community model for other countries and provided valuable information for future action.

Global convection-permitting model improves subseasonal forecast of plum rain around Japan

Abstract In 2020 early summer, a historically severe rainy season struck East Asia, causing extensive damage to life and property. Subseasonal forecast of this event challenges the limits of rainy season predictability. Employing the integrated Atmospheric Model Across Scales (iAMAS) and the Sunway supercomputer, we conducted ensemble one-month forecasts at global 3 km, variable 4-60 km, and global 60 km resolutions. The global convection-permitting forecast accurately captures the rainband, while other forecasts exhibited northward and weaker shifts due to the northward shifts of the atmospheric rivers (ARs) over Japan, attributed to intensified Western North Pacific Subtropical High (WNPSH). Further, the double-ITCZ-like tropical rainfall pattern in Western Pacific (D-ITCZ-WP) in global convection-permitting forecast contributes to a more accurate WNPSH and rainband. In contrast, other forecasts show a single-ITCZ-like pattern in Western Pacific (S-ITCZ-WP), leading to a northward-shifted WNPSH and rainband, advocating the importance of accurately representing tropical convections, as they can significantly affect mid-/high-latitude weather and climate.

Ensemble seasonal forecasting of typhoon frequency over the western North Pacific using multiple machine learning algorithms

Abstract This study introduces an ensemble prediction methodology employing multiple machine learning algorithms for forecasting the frequency of typhoons (TYFs) over the western North Pacific (WNP) during June‒November. Potential predictors were initially identified based on the relationships between the year-by-year variation (DY) of the TYFs and preseason (March–May) environmental factors. These predictors were subsequently further refined, resulting in the selection of eight key predictors. Prediction models were constructed using twenty machine learning algorithms, utilizing data from 1965 to 2010. These trained models were then applied to perform hindcasts of TYFs from 2011 to 2023. The forecasted DY was added to the observed TYF of the preceding year to obtain the current year’s TYF. The results indicate that the TYFs predicted by the multi-model ensemble (MME) closely align with the observation during the hindcast period. Compared to individual models, the MME improves the prediction skill for the DY by at least 5.56% and up to 56.92%. Furthermore, the mean bias of the MME for TYF is notably smaller than that of the ECMWF’s most recent seasonal forecasting system (SEAS5) in the years of 2017‒2023. The superior performance of the ensemble prediction approach was also validated through leave-one-out cross-validation. This research underscores the potential of ensemble prediction approach utilizing multiple machine learning algorithms to improve the forecasting skill of TYF over the WNP.

Development and Validation of NOAA’s 20-year global wave ensemble reforecast

Abstract A new 20-year wave reforecast was generated based on the NOAA Global Ensemble Forecast System, version 12 (GEFSv12). It was produced using the same wave model setup as the NCEP’s operational GEFSv12 wave component, which employs the numerical wave model WAVEWATCH III and utilizes three grids with spatial resolutions of 0.2° and 0.25°. The reforecast comprises 5 members with one cycle per day and forecast range of 16 days. Once a week, it expands to 35 days and 11 members. This paper describes the development of the wave ensemble reforecast, focusing primarily on validation against buoys and altimeters. The statistical analyses demonstrated very good performance in the short range for significant wave height, with correlation coefficients of 0.95-0.96 on day 1, and between 0.86-0.88 within week 1, along with bias close to zero. After day 10, correlation coefficients fall below 0.70. We found that the degradation of predictability and the increase in scatter errors predominantly occur in the forecast lead time between days 4 and 10, in terms of the ensemble mean and individual members, including the control. For week 2 and beyond, a probabilistic spatio-temporal analysis of the ensemble space provides useful forecast guidance. Our results provide a framework for expanding the usefulness of wave ensemble data in operational forecasting applications.

A boundary perturbation approach for regional wave ensemble forecast

In this study, we developed and validated two wave ensemble prediction systems (WEPS) to forecast wave conditions along the southeastern coast of Australia. Using the SWAN model (GEN3 ST6), we integrated complex bathymetric features with an unstructured grid and validated model outputs against buoy observations from Sydney, Port Kembla, and Batemans Bay. The two WEPS, SWAN-WW3 and SWAN-Pert, utilize different methodologies: SWAN-WW3 derives boundary conditions from NCEP’s Global Wave Ensemble System, while SWAN-Pert employs Latin Hypercube Sampling for boundary perturbations based on historical data. Our results demonstrate that both systems effectively predict significant wave height (Hs), with SWAN-Pert showing improved forecast accuracy in certain metrics compared to SWAN-WW3. Despite underdispersion in spread-skill diagrams, both WEPS exhibited good agreement with observed data. Additionally, rank histograms revealed that SWAN-Pert is more reliable at shorter lead times. This study highlights the potential of integrating statistical sampling methods and ensemble systems for enhancing regional wave forecasting accuracy.

High-speed railway express delivery volume forecast based on data-driven ensemble forecast approaches: The China case

Current researches on logistics delivery volume forecast mainly focus on traditional transportation modes such as road, railway, and aviation, with little research on predicting high-speed railway express delivery (HSRED) volume. In this paper, a data-driven ensemble forecast approach is proposed to forecast the HSRED. Firstly, we use four kinds of single methods to predicate the HSRED, include Auto-Regressive Moving Average Model (ARMA), Linear Regression (LR), Random Forest (RF) and Singular Spectrum Analysis (SSA). Secondly, based on the predication results of single methods, we apply two ensemble methods including Arithmetic Mean (AM) and Allocation Integration Operator (AIO) to determine 22 combinations of ensemble forecast approaches. The Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Correlation Coefficient (CC) are used to evaluate the performance of each single and ensemble approach. Next, a real-world case study is conducted by using the HSRED in China as the background, the delivery demand data from Jan. 2014 to Mar. 2023 is used as the input. The results show that: The prediction results based on SSA, ARMA, and RF are close to the actual data. LR has the worst predication performance. The overall performance indicators of the ensemble models are better than those of the single prediction models. The ensemble models by using AIO are generally better than those by using AM. SSA-ARMA ensemble model constructed by AIO shows the best predictive performance. The trend of HSRED changes from 2025 to 2050 is gentle, and there will be no explosive growth, and it will gradually decrease and eventually stabilize. Finally, we distribute the predicted results to each province based on the gravity model and conduct visual analysis. The results show that: Provinces with high demand for HSRED include Shandong, Zhejiang, Jiangsu, Shanghai, and Beijing, mostly concentrated in the eastern and southeastern regions, we can consider strengthening the development and increasing investment of HSRED in these cities. Provinces with less demand, efforts can be made to strengthen publicity, accelerate the formation process of the high-speed railway network, and increase support for the HSRED industry.

Implementation of Ensemble Learning Algorithm - Stacking Regressor on PM2.5 Prediction Model

This study aims to develop a PM2.5 concentration prediction model using ensemble learning techniques, focusing on the application of stacking regressor. The developed model is compared with several other basic models, namely LSTM, Random Forest, XGBoost, and GBM, to evaluate its performance in terms of prediction accuracy. The results show that the stacking regressor model provides more accurate prediction results than these basic models. The LSTM model has a Mean Squared Error (MSE) value of 80.42 and a coefficient of determination (R²) of -0.29, which shows unsatisfactory performance despite being able to capture temporal patterns. Random Forest gave better results with an MSE of 10.90 and R² of 0.83, thanks to its ability to handle heterogeneous data. The XGBoost and GBM models showed similar performance, with MSE of 9.01 and 8.81 respectively and R² of 0.86. However, the stacking regressor model with the RidgeCV meta-learner achieved the best results with an MSE of 8.07 and R² of 0.87, indicating that this technique successfully combines the advantages of various base models to improve prediction accuracy. In conclusion, the stacking regressor model proved effective in improving PM2.5 prediction accuracy, making it a potential tool for air quality monitoring and supporting public health policy making. This research makes an important contribution to the development of ensemble learning-based air quality prediction methods, and the results can serve as a reference for future research

Predicting compound agricultural drought and hot events using a Cascade Modeling framework combining Bayesian Model Averaging ensemble with Vine Copula (CaMBMAViC)

Compound Agricultural Drought and Hot Events (CADHEs) cause more disastrous impacts on water-food-energy-ecology security in comparison with individual drought or hot extremes. Reliable CADHEs predictions are therefore paramount to mitigate the potential risk associated with drought and hot events and their combinations. However, there is no prediction model with multi-predictand variables for reliably predicting CADHEs by considering the compounding influence of diverse confounders. In this study, we first employed a Cascade Modeling (CaM) framework to measure the cascading effects between agricultural droughts and hot events driven by the compounding influence of different confounders. Then, a new ensemble prediction model with multi-predictand variables using a CaM framework coupling Bayesian Model Averaging ensemble with Vine Copula, namely the CaMBMAViC model, was developed to simultaneously predict agricultural droughts, hot events, and CADHEs in the warm season with 1–3-month lead times in China, in which the three most important predictors were selected as the explanatory variables. For these selected typical years (e.g., 2006, 2013, and 2019, as the droughts and hot events in these years swept over most regions of China), the proposed CaMBMAViC model has provided reliable predictions for agricultural droughts, hot events, and CADHEs with 1–3-month lead times in the warm season over most areas of China. Two performance metrics, Kling-Gupta Efficiency (KGE) and Percent Bias (PBIAS), both demonstrated that this model yielded skillful predictions for agricultural droughts (KGE > 0.9 and PBIAS = [1.79%, 1.96%]), hot events (KGE > 0.86 and PBIAS = [2.23%, 2.72%]), and CADHEs (KGE > 0.83 and PBIAS = [–0.95%, 0.36%]) for most areas in China. These findings enhance our confidence in seasonal predictions of compound climate events and help us understand their future dynamic.

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Analysis of carbon peak achievement at the provincial level in China: Construction of ensemble prediction models and Monte Carlo simulation

As China advances toward its carbon peaking goals, many regions face the challenge of balancing rapid economic growth with sustainable development. Evaluating carbon emissions at the provincial level is crucial for formulating effective strategies to achieve China's carbon peak targets. This study aims to construct an accurate model for predicting carbon emissions and to explore the evolution of these emissions across Chinese provinces, as well as their contributions to national carbon peak targets. Using the Environmental Kuznets Curve (EKC) theory, the 30 provinces were categorized into groups. An ensemble carbon emissions forecasting model was developed by integrating time-series models with multifactor models. Three scenarios were established within the framework of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Monte Carlo simulations were employed to explore potential pathways to achieve carbon peaks. The results indicate that China will reach its carbon emission peak between 2030 and 2031, with peak values expected to range between 11,499.65 and 11,629.51 Mt. Significant differences were observed among the provincial groups in their contributions to carbon peaking. Groups II and III are projected to peak in 2030 and 2022, respectively, while Groups I and IV face greater challenges, with peak years projected between 2032–2035 and 2031–2034, respectively. Four tiers with different emission reduction responsibilities were identified by comparing the peak times of the 30 provinces under the three scenarios, and optimal recommendations for achieving carbon peaks were proposed for each province. The accurate prediction models and Monte Carlo simulations provide reliable results for achieving carbon peak targets across Chinese provinces, offering a scientific basis for optimizing national carbon emission reduction policies.

Ensemble Predictions of Rainfall-Induced Landslide Risk under Climate Change in China Integrating Antecedent Soil-Wetness Factors

Ensemble Predictions of Rainfall-Induced Landslide Risk under Climate Change in China Integrating Antecedent Soil-Wetness Factors

A Hybrid Machine Learning Model Architecture with Clustering Analysis and Stacking Ensemble for Real Estate Price Prediction

A Hybrid Machine Learning Model Architecture with Clustering Analysis and Stacking Ensemble for Real Estate Price Prediction

The Crucial Role of the Subpolar North Atlantic for Skillful Decadal Climate Predictions

AbstractWe investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies.

Recent CO2 emission and projections in Chinese provinces: New drivers and ensemble forecasting

Although extensive studies focused on the driver of changing CO2 emission, the roles of labor and capital were largely ignored in shaping spatiotemporal change in CO2 emission and forecasting differences on CO2 emission was few considered, hindering relevant policymaking towards sustainable development in both climate change mitigation and economic growth for developing countries in particular. To fill the gap above, the study explored the roles of capital and labor in contributing to recent CO2 emission in a case of China over 2010–2019 and projecting provincial CO2 emissions to 2030, by proposing two new spatiotemporal logarithmic mean Divisia index models with Cobb-Douglas production function and developing an ensemble forecasting model including machine learning. We found, first, the effects of capital and labor inputs and carbon factor were the positive drivers affecting aggregate CO2 emissions, while the effects of the total-factor productivity and energy intensity were negative drivers. Second, the effects of capital and labor inputs were the negative drivers for narrowing the emission gap. Third, the ensemble forecasting model can improve the generalization ability of CO2 emission predictions. Therefore, we recommend that policymakers focus on optimizing the carbon reduction effects of capital and labor inputs while promoting the development of a circular economy to achieve sustainable economic growth.

Application of weather post-processing methods for operational ensemble hydrological forecasting on multiple catchments in Canada

Hydrological forecasts contain biases that need to be addressed for their effective use in operational decision-making in water resources management. Performing post-processing allows reducing the overall systematic bias while improving the distribution and accuracy of hydrological forecasts. In this study, a Quantile Mapping (QM) post-processing method was applied on weather forecasts following three temporal configurations (monthly, seasonal, and annual) of the quantile mapping scheme. The evaluation encompasses 20 catchments in southern Canada, employing a leave-one-out approach with the QM method on ECMWF ensemble weather forecasts spanning 2015–2020 inclusively. These processed forecasts are subsequently utilized as forcings for eight hydrological models, generating ensemble streamflow forecasts over a 6-year period with a lead time of 10 days and a sub-daily timestep of 6 h. The performance of the QM method is mainly assessed using the Continuous Ranked Probability Score (CRPS) metric, in complement with a forecast reliability score (ABDU) and a forecast sharpness metric (NMIQR). Significant improvements are discerned in precipitation forecasts upon the application of QM. Notably, these improvements are translated into enhanced hydrological forecasts for over half of the catchments studied (55 %). Surprisingly, no discernible differences in performance are observed among the three QM configurations in most catchments. Interestingly, there are watersheds where the implementation of QM exhibit either poorer or no change in performance and sharpness compared to raw forecasts.

Weather to subseasonal prediction from the UFS coupled Global Ensemble Forecast System

Weather to subseasonal prediction from the UFS coupled Global Ensemble Forecast System