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Abstract. Agricultural production is largely determined by weather conditions during the crop growing season. An important aspect of crop yield estimation concerns crop growth development. The occurrence of meteorological events such as frosts, droughts or heat stress during the crop life cycle or during certain phenological stages helps explain yield fluctuations of common arable crops. We developed a methodology and visualisation tool for risk assessment, and tested the workflow for drought and frost risk for winter wheat, winter barley and grain maize in Belgium. The methodology has the potential to be extended to other extreme weather events and their impacts on crop growth in different regions of the world.

<p>The international meteorological community is on a journey to embrace open data policies. Using our experiences from meteoblue, we give examples of how open data policies have fostered innovation, created private sector capabilities, and can “jump-start” meteorological value chains. These examples are typical and variations of them can be found in many companies and countries that implement effective open data policies.</p><p>meteoblue as a company wouldn't have been created and might not be sustainable without the benefit of open data. Already prior to forming the company the availability of WRF, GDAS assimilations, and GFS global model output enabled an initial NWP chain. Over time, the evolving processing chain provided an improved, easily accessible forecast for central Europe, in particular the Alps. Additional accuracy was achieved with running models at higher resolutions and tuning them. The development also led to an automatic post-processing and web-based visualisation chain with numerous innovative diagrams and maps.</p><p>The increased availability of open NWP data from national weather models and their further improvement allowed meteoblue to automatically evaluate an increasing number of forecasts for a given location, compare them to weather station data, compute a consensus forecast, and quantify the uncertainties of local forecasts. All information could be made available to end users in straightforward diagrams that in part of the world are used by illiterate farmers.</p><p>Availability of open weather station data allowed meteoblue to devise learning methods and further improve local forecast accuracy. Verification results are publicly available and allow users to assess how valuable the forecasts can be to them. Open radar data form the basis of a high accuracy nowcast and short term rain forecasts.</p><p>With its multi-model capability the processing chain is open to ingest additional models as well as precipitation radar and weather station data, giving their providers (and their users) instant access to all meteoblue post-processing capabilities. The multi-model processing chain is highly resilient against individual model or other data feed failures. Together, these capabilities allow partners, e.g. from small national weather services to both provide immediate access to their local models and service their communities and customers without interruptions. Therefore, meteorological value chains can be started up very quickly.</p>

The climate is changing, and not for the better – this is old news and will surprise nobody. Immediate action to mitigate and adapt to climate change is necessary on all scales from single households to the continental level, including companies.  In recent years, governments worldwide have been expanding their policies to evaluate the environmental sustainability of economic activities, particularly by asking big companies to assess both their effect on the climate and the climate’s effect on them. This has, in some cases, been formalised under official regulations, such as the ”Corporate Sustainability Reporting Directive (CSRD)” or the “EU taxonomy regulation (2020/852)” of the European Union. Advances in climate prediction have been helping generate increasingly detailed and confident information on the climate means, variability, and extremes in the future, providing a basis for the assessment of climate hazards at company locations. However, stakeholders outside the atmospheric sciences will need assistance in interpreting the data and reducing it to key information related to physical hazards. The process from climate model prediction output to actionable information, used in support of decision-making, is a new climate service provided by meteoblue AG.  While the topics for which the risk should be evaluated, for instance for the EU taxonomy, are clear, several issues remain. Firstly, climate projections at a local scale are not straightforward to obtain, and they are generally not suitable for products that need to be inter-comparable worldwide. Secondly, regulations require the assessment of single topics for which even the present hazard is unknown or the uncertainty in the future evolution remains high. Thirdly, the topics go beyond what is explicitly covered by climate models. All these issues need to be appropriately addresses and resolved in standardised processes to provide a product that is valid for any possible company location worldwide. In this presentation we will focus on how we tailor climate data to meet the clients’ requirements to be able to assess the climate risks at their locations, plan accordingly and pass mandatory company audits related to climate change.