A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image, and their size and geometry to classify each individual image. The classification task is achieved with a two-component Gaussian mixture model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labeling the subset of images on which the model was fitted. An overall accuracy and a Cohen kappa score of 99.4 % and 98.8 %, respectively, are achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antarctica and during a winter in Davos, respectively, is presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.
Abstract. A new method to automatically discriminate between hydrometeors and blowing snow particles on Multi-Angle Snowflake Camera (MASC) images is introduced. The method uses four selected descriptors related to the image frequency, the number of particles detected per image as well as their size and geometry to classify each individual image. The classification task is achieved with a two components Gaussian Mixture Model fitted on a subset of representative images of each class from field campaigns in Antarctica and Davos, Switzerland. The performance is evaluated by labelling the subset of images on which the model was fitted. An overall accuracy and Cohen's Kappa score of 99.4 and 98.8 %, respectively, is achieved. In a second step, the probabilistic information is used to flag images composed of a mix of blowing snow particles and hydrometeors, which turns out to occur frequently. The percentage of images belonging to each class from an entire austral summer in Antartica and during a winter in Davos, respectively, are presented. The capability to distinguish precipitation, blowing snow and a mix of those in MASC images is highly relevant to disentangle the complex interactions between wind, snowflakes and snowpack close to the surface.
On a daily basis, MeteoSwiss provides a wide range of automatic weather forecasts to the general public, the aviation and to private customers. These data are provided by an ensemble of heterogeneous individual system wich forces the end-user to choose between different sources and sometimes to combine them despite a limited knowledge of their quality and shortcomings. In addition, the forecasts of the different systems are provided in different formats and through different channels, making combined use even more difficult. Furthermore, from a scientific point of view, combining nowcasting and post-processing approaches in a single step using statistical and/or machine learning methods has been shown to give the best forecast performance for twelve hour precipitation forecasts Deep learning for twelve-hour precipitation forecasts. Nat Commun 13, 5145 (2022)). In such an approach, there is no need for a separate system for the nowcasting range, and therefore no need to combine different forecasts a posteriori, which requires further assumptions and introduces further source of errors and inconsistencies. MeteoSwiss has therefore initiated a project to build a system that will integrate data from different weather observation and weather forecasts data (ECMWF, MeteoSwiss regional ICON implementation) in order to provide a consolidated and easily accessible weather forecast dataset. We aim as well for probabilistic gridded forecasts that are seamless in space and time which can be used by a wide range of application, like hydrological models, automatic generation of warning proposals for forecasters, probabilistic animation of precipitation for the MeteoSwiss App. This project is very much oritented toward end-users and a large effort will be made to assess and meet their needs.
<div> <p><span data-contrast="auto">Forecasting winds at the local scale can be challenging due to the highly variable and complex nature of wind patterns, particularly in the case of complex terrain. In such cases, the accuracy of numerical weather prediction models (NWPs) is often limited by the quality of their initial conditions and their grid resolution. This is where the use of observational data through statistical postprocessing techniques can help to improve the quality of forecasts.</span><span data-ccp-props="{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}">&#160;</span></p> </div> <div> <p><span data-contrast="auto">Statistical postprocessing is nowadays an established component in operational weather forecasting that is used to improve the accuracy, resolution, and calibration of NWP ensemble forecasts with historical observations. In recent years, machine learning techniques have shown great potential in the field of postprocessing, thanks to their ability to deal with increasingly large volumes of data, and the capacity to capture complex relationships between forecasts and observations that are not explicitly represented in traditional postprocessing methods.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}">&#160;</span></p> </div> <div> <p><span data-contrast="auto">To capitalize on machine learning for weather applications, and for it to gain acceptance and become a reliable technology for operational use, it is also crucial to consider the technical and engineering challenges that arise when implementing machine learning in a productive environment. MLOps, or Machine Learning Operations, is a set of practices that are used to manage and streamline the deployment, monitoring, and maintenance of machine learning models in production.&#160;</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}">&#160;</span></p> </div> <div> <p><span data-contrast="auto">We will present our recent experience with the development and operationalization of a statistical postprocessing system based on the use of neural networks to predict the probability distribution of forecasts of surface winds. Following MLOps best practices, our framework aims to improve the reproducibility and automation of most common tasks in a machine learning-based system, such as efficient data loading and manipulation, the monitoring and visualization of prediction quality, and the automation of model training and deployment pipelines.</span><span data-ccp-props="{&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}">&#160;</span></p> </div>
<p>MeteoSwiss has developed and is currently implementing a NWP postprocessing suite for providing &#160;automated weather forecasts at any location in Switzerland. The aim is a combined postprocessing of high resolution limited area and global model ensembles with different forecast horizons to enable seamless probabilistic forecasts over two weeks leadtime. Further, the output should be coherent in space and provide predictions at any location of interest, including sites without observations. We use the full archive of MeteoSwiss&#8217; operational local area models (COSMO-1 and COSMO-E) over the past four years and the corresponding IFS-ENS medium range predictions of ECMWF to develop postprocessing routines for temperature, precipitation, cloud cover and wind. Here we present selected key results on the performance of various postprocessing methods we applied but also on practical aspects of their implementation into operational production.</p><p>Both ensemble model output statistics (EMOS) and machine learning (ML) approaches are able to improve the forecasts in terms of CRPS by up to 30% as compared to the direct output of the local area model. The skill increase obtained by postprocessing varies depending on the parameter, region and season, with best results for temperature and wind in areas of complex orography and only marginal improvements for precipitation during seasons with a high fraction of convective situations. Particularly for temperature, the combined postprocessing of COSMO and IFS-ENS resulted in a skill benefit over postprocessing the COSMO models alone. Locally optimized postprocessing would allow further skill improvements, but only at sites where observations are available. However, the ability of non-local postprocessing approaches to provide calibrated forecast at any point in space is a key advantage for providing automated forecasts to the general public via the internet and smartphone app. Furthermore, the computational efficiency of these non-local approaches makes them attractive for operationalization in a realtime context.&#160;</p>
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