Our world is rapidly changing. Societies are facing an increase in the frequency and intensity of high-impact and extreme weather and climate events. These extremes together with exponential population growth and demographic shifts (e.g., urbanization, increase in coastal populations) are increasing the detrimental societal and economic impact of hazardous weather and climate events. Urbanization and our changing global economy have also increased the need for accurate projections of climate change and improved predictions of disruptive and potentially beneficial weather events on kilometer scales. Technological innovations are also leading to an evolving and growing role of the private sector in the weather and climate enterprise. This article discusses the challenges faced in accelerating advances in weather and climate forecasting and proposes a vision for key actions needed across the private, public, and academic sectors. Actions span (i) utilizing the new observational and computing ecosystems; (ii) strategies to advance Earth system models; (iii) ways to benefit from the growing role of artificial intelligence; (iv) practices to improve the communication of forecast information and decision support in our age of internet and social media; and (v) addressing the need to reduce the relatively large, detrimental impacts of weather and climate on all nations and especially on low-income nations. These actions will be based on a model of improved cooperation between the public, private, and academic sectors. This article represents a concise summary of the white paper on the Future of Weather and Climate Forecasting (2021) put together by the World Meteorological Organizations’ Open Consultative Platform.

AbstractAchieving consistency in the prediction of the atmosphere and related environmental hazards requires careful design of forecasting systems. In this chapter, we identify the benefits of seamless approaches to hazard prediction and the challenges of achieving them in a multi-institution situation. We see that different modelling structures are adopted in different disciplines and that these often relate to the user requirements for those hazards. We then explore the abilities of weather prediction to meet the requirements of these different disciplines. We find that differences in requirement and language can be major challenges to seamless data processing and look at some ways in which these can be resolved. We conclude with examples of partnerships in flood forecasting in the UK and wildfire forecasting in Australia.

AbstractResults from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs.

>Despite advances in forecasting and emergency preparedness,weather related disasters continue to cost many lives, to displace populations and to cause wide-spread damage. Therefore, High Impact Weather Project (HIWeather), a 10-year research project

The forecast verification metric known as the Fractions Skill Score (FSS) is typicallycomputed using sliding window operators, which can be computationally expensive. A keycomponent of the score is the computation of fractional event frequencies, which is equivalent to a weighted summation of sub-grids (windows) commonly realized as a convolutionoperation. An alternative approach is to use "summed area tables", which have been used incomputer graphics as a means to quickly compute summations of sub-grids in texture fields.In this paper we describe how a summed area table can effectively reduce the computationtime of the FSS while also allowing the score to generalize to include the time dimension.We demonstrate the methodology on idealized cases from the Spatial Verification MethodsInter-comparison Project and explore the properties of the score on a high-resolution NWPdataset.

Leading NWP centers have agreed to create a database of their operational ensemble forecasts and open access to researchers to accelerate the development of probabilistic forecasting of high-impact weather.Objectives and cOncept.During the past decade, ensemble forecasting has undergone rapid development in all parts of the world.Ensembles are now generally accepted as a reliable approach to forecast confidence estimation, especially in the case of high-impact weather.Their application to quantitative probabilistic forecasting is also increasing rapidly.In addition, there has been a strong interest in the development of multimodel ensembles, whether based on a set of single (deterministic) forecasts from different systems, or on a set of ensemble forecasts from different systems (the so-called superensemble).The hope is that multimodel ensembles will provide an affordable approach to the classical goal of increasing the hit rate for prediction of high-impact weather without increasing the false-alarm rate.This is being taken further within The Observing System Research and Predictability Experiment (THORPEX), a major component of the World Weather Research Programme (WWRP) under the World Meteorological Organization (WMO).