Author:
Golding Brian,Sun Jenny,Riemer Michael,Yussouf Nusrat,Titley Helen,Robbins Joanne,Ebert Beth,Pagano Tom,Lewis Huw,Dashwood Claire,Boyce Graeme,Peace Mika
Abstract
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.
Publisher
Springer International Publishing
Cited by
1 articles.
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1. Pioneering Climate Forecasting in Tennessee with LSTM-ANN Machine Learning Model;2023 IEEE 20th International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET);2023-12-04