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Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to the global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Accurate streamflow predictions are essential for water resources management. Recent studies have examined the use of hybrid models that integrate machine learning models with process‐based (PB) hydrologic models to improve streamflow predictions. Yet, there are many open questions regarding optimal hybrid model construction, especially in Mediterranean‐climate watersheds that experience pronounced wet and dry seasons. In this study, we performed model benchmarking to (a) compare hybrid model performance to PB and machine learning models and (b) examine the sensitivity of hybrid model performance to PB model parameter calibration, structural complexity, and variable selection. Hybrid models were generated by post‐processing process‐based models using Long Short‐Term Memory neural networks. Models were benchmarked within two northern California watersheds that are managed for both municipal water supplies and aquatic habitat. Though model performance varied substantially by watershed and error metric, calibrated hybrid models frequently outperformed both the machine learning model (for 72% of watershed‐model‐metric combinations) and the calibrated process‐based models (for 79% of combinations). Furthermore, hybrid models were relatively insensitive to PB model calibration and structural complexity, but sensitive to PB model variable selection. Our results demonstrate that hybrid models can improve streamflow prediction in Mediterranean‐climate watersheds. Additionally, hybrid model insensitivity to PB model parameter calibration and structural complexity suggests that uncalibrated or less complex PB models could be used in hybrid models without any loss of streamflow prediction accuracy, improving model construction efficiency. Moreover, hybrid model sensitivity to the selection of PB model variables suggests a strategy for diagnosing poorly performing PB model components. Key Points Hybrid streamflow prediction models frequently outperformed both machine learning and process‐based (PB) models Hybrid models were relatively insensitive to PB model calibration and structural complexity, but sensitive to PB model variable selection Hybrid models can improve streamflow prediction accuracy, efficiency, and diagnostics in Mediterranean‐climate watersheds

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post-Processing of the Society for Cardiovascular Magnetic Resonance (SCMR). The aim of the Task Force is to recommend requirements and standards for image interpretation and post-processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate. It is an update of the original recommendations published 2013.

The most mature aspect of applying artificial intelligence (AI)/machine learning (ML) to problems in the atmospheric sciences is likely post-processing of model output. This article provides some history and current state of the science of post-processing with AI for weather and climate models. Deriving from the discussion at the 2019 Oxford workshop on Machine Learning for Weather and Climate, this paper also presents thoughts on medium-term goals to advance such use of AI, which include assuring that algorithms are trustworthy and interpretable, adherence to FAIR data practices to promote usability, and development of techniques that leverage our physical knowledge of the atmosphere. The coauthors propose several actionable items and have initiated one of those: a repository for datasets from various real weather and climate problems that can be addressed using AI. Five such datasets are presented and permanently archived, together with Jupyter notebooks to process them and assess the results in comparison with a baseline technique. The coauthors invite the readers to test their own algorithms in comparison with the baseline and to archive their results. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post‐process the ensembles. This post‐processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post‐processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post‐processing method and evaluate four possible strategies: only using the raw ensembles without post‐processing, a one‐step strategy where only the weather ensembles are post‐processed, a one‐step strategy where we only post‐process the power ensembles and a two‐step strategy where we post‐process both the weather and power ensembles. Results show that post‐processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post‐processing the weather ensembles does not necessarily lead to increased forecast performance.

Ensembles provide a wealth of information to aid forecasters in their day‐to‐day operations, but with increasing ensemble size and complexity, there is rarely time to fully interrogate their outputs. Clustering ensemble members into distinct scenarios based on the co‐location of hazardous weather features has previously shown promise when applied to global ensemble outputs. However, it is currently unclear whether further value can be gained when applying clustering to convection‐permitting ensemble (CPE) outputs. This study compares precipitation clusters between the operational MOGREPS‐G driving ensemble and the nested MOGREPS‐UK CPE run at the (UK) Met Office during summer 2023. When applied over the UK domain, CPE clustering does not provide clear value compared to global ensemble clustering. Instead, clusters become increasingly similar with leadtime, strongly indicating that CPE clusters are most sensitive to the synoptic forcing common between the two ensembles and that the presence of convective‐scale detail has little influence. However, when focussed on a region impacted by hazardous convection, CPE clustering identified distinct precipitation scenarios and provided improved probabilistic value compared to driving‐ensemble clustering. Finally, by comparing clusters with radar observations, it is demonstrated that the fraction of members supporting a particular scenario is a reliable quantitative prediction of the probability that the given scenario will be the most accurate. We recommend that global ensemble clustering is sufficient over larger domains, while CPE clustering is most useful when applied at regional scales. Does clustering on convection‐permitting ensembles improve the identification of distinct forecast scenarios compared to clustering on the driving global ensemble? Here we show that both ensembles provide reliable clusters, but that the improvements are limited to regional cases.

Purpose To investigate the impact of digital image post‐processing algorithms on various image quality (IQ) metrics of radiographic images under different exposure conditions. Methods A custom‐made phantom constructed according to the instructions given in the IAEA Human Health Series No.39 publication was used, along with the respective software that automatically calculates various IQ metrics. Images with various exposure parameters were acquired with a digital radiography unit, which for each acquisition produces two images: one for‐processing (raw) and one for‐presentation (clinical). Various examination protocols were used, which incorporate diverse post‐processing algorithms. The IQ metrics’ values (IQ‐scores) obtained were analyzed to investigate the effects of increasing incident air kerma (IAK) on the image receptor, tube potential (kVp), additional filtration, and examination protocol on image quality, and the differences between image type (raw or clinical). Results The IQ‐scores were consistent for repeated identical exposures for both raw and clinical images. The effect that changes in exposure parameters and examination protocol had on IQ‐scores were different depending on the IQ metric and image type. The expected positive effect that increasing IAK and decreasing tube potential should have on IQ was clearly exhibited in two IQ metrics only, the signal difference‐to‐noise‐ratio (SDNR) and the detectability index (d’), for both image types. No effect of additional filtration on any of the IQ metrics was detected on images of either type. An interesting finding of the study was that for all different image acquisition selections the d’ scores were larger in raw images, whereas the other IQ metrics were larger in clinical images for most of the cases. Conclusions Since IQ‐scores of raw and their respective clinical images may be largely different, the same type of image should be consistently used for monitoring IQ constancy and when results from different X‐ray systems are compared.

In this study, a novel spatiotemporal hydrodynamic prediction task framework, named single frame prediction, was developed. The framework could generate results based on boundary conditions and a single flood map from the last time step, relying on hydrodynamic principles rather than historical trends, and doesn't require the assistance of traditional hydrodynamic models. Moreover, a post‐processing method based on physical laws was developed to refine the outputs of deep learning models at each time step, aiming to reduce accumulated errors in long‐term predictions. The performance of a widely used convolutional neural network‐based model, U‐Net, was evaluated to assess the feasibility of single frame prediction and the impact of the proposed post‐processing method. The experiments showed that single frame prediction could produce accurate flood maps, demonstrating the feasibility of the novel framework. Furthermore, the results indicated that the physics‐based post‐processing method could mitigate errors at each step, thereby enhancing prediction accuracy across entire flood event, showing strong effectiveness and applicability in flood prediction. Additionally, an ablation experiment was conducted to assess the effectiveness of each step in the method. The single frame prediction provided a more comprehensive and interpretable depiction of flood prediction processes with essential hydrodynamic variables, including water depth and unit discharge on all grid cells. The post‐processing method significantly reduced the accumulated error in the later stages of single frame prediction to an acceptable range with an average root‐mean‐square error of 0.041 m for water depth and 0.003 m2/s for unit discharge, suggesting a new technique for long‐term flood predictions.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.

Additive manufacturing (AM) has revolutionized production across industries, yet challenges persist in achieving optimal part quality. This paper studies the enhancement of post-processing techniques to elevate the overall quality of AM-produced components. This study focuses on optimizing various post-processing methodologies to address prevalent issues such as surface roughness, dimensional accuracy, and material properties. Through an extensive review, this article identifies and evaluates a spectrum of post-processing methods, encompassing thermal, chemical, and mechanical treatments. Special attention is given to their effects on different types of additive manufacturing technologies, including selective laser sintering (SLS), fused deposition modeling (FDM), and stereolithography (SLA) and their dedicated raw materials. The findings highlight the significance of tailored post-processing approaches in mitigating inherent defects, optimizing surface finish, and enhancing mechanical properties. Additionally, this study proposes novel post-processing procedures to achieve superior quality while minimizing fabrication time and infrastructure and material costs. The integration of post-processing techniques such as cleaning, surface finishing, heat treatment, support structure removal, surface coating, electropolishing, ultrasonic finishing, and hot isostatic pressing (HIP), as steps directly within the additive manufacturing workflow can immensely contribute toward this direction. The outcomes displayed in this article not only make a valuable contribution to the progression of knowledge regarding post-processing methods but also offer practical implications for manufacturers and researchers who are interested in improving the quality standards of additive manufacturing processes.