Improving UK Air Quality Forecasts During Heatwaves through Advanced Statistical and Machine Learning Methods

英国气象局，除其他外，使用数值过程模型来预测地面空气污染。这些预测被用来为公共卫生警告提供信息。人们担心，在某些情况下，例如。在热浪期间，空气污染水平可能会上升到异常高的水平，这些预测可能不如理想的准确。该项目旨在发展统计方法，利用观测数据和基于过程的模型的信息来改进预测，从而提出更准确的健康警告。所采用的方法将主要取自极值分析和时间序列建模中使用的现有方法。这些方法中的许多方法，特别是那些用于多变量数据的方法（这就是我们将要使用的方法），实现起来并不简单，并且对于“最佳”模型/方法没有普遍的共识。我们希望使用这些模型来（i）比较/对比基于过程的预测模型预测极端事件的能力，以及（ii）使用第（i）部分的研究结果来开发改进基于过程的统计预测方法。对于第（ii）部分，这可以通过采取缩小规模类型的方法，即。通过调整基于过程的模型输出中的一致偏差或其他错误，或者通过从头开始建立一个全新的统计预测模型。前者肯定需要使用（a）多变量极值分析（将预测模型输出与观测结果进行比较）--这篇论文说明了广义概念，如果不是我们将使用的实际建模细节的话，请参见此处https：//onlinelibrary.wiley.com/doi/full/10.1002/env.2143（B）回归和/或空间技术（原因如下）。对于后者，我们将使用（a）单变量极值模型，纳入趋势和非季节性（即。回归建模）;（B）预测方法，例如。动态线性模型，可能与极值分析相结合;（c）极值空间建模，以便能够汇集信息、更现实的不确定性估计和置信界限，并确保空间一致的预测。空间建模可以采取分层建模方法-见https：//www.example.com第4节，以通过地质统计和多元极值模型相结合的空间极值建模方面的最新发展为基础，详情如下：https：//www.lancaster.ac.uk/martwadswojl/CSE-paper.pdf;（d）多元建模-如果我们达到了试图预测涉及多种污染物的极端事件的地步; projecteuclid.org/download/pdfview_1/euclid.ss/1340110864or

The UK Met Office, amongst others, uses numerical process models to forecast ground-level air pollution. These forecasts are used to inform public-health warnings. There is concern that in certain situations, eg. during heat waves during which air pollution levels can rise to unusually high levels, these forecasts may not be as accurate as would be desirable. This project aims to develop statistical methodology to utilise information from both observational data and process-based models to improve forecasts and hence make more accurate health warnings. Methodology utilised will be, primarily, taken from existing methodology used in extreme value analysis and the modelling of time-series. Many of these methods, particularly those for multivariate data (which is what we would be using), are non-trivial to implement and there is no general consensus on the 'best' model/approach. We would like to use these models to (i) compare/contrast the ability of the process-based forecasting models to predict extreme events and (ii) using the findings from part (i) develop statistical forecasting methods that improve on the process-based ones. For part (ii) this could be by taking a down-scaling type approach, ie. by adjusting for consistent biases or other errors in the process-based model output or by building up a completely new statistical forecasting model from scratch. The former would certainly require the use of (a) multivariate extreme value analysis (to compare the forecasting model output with observations) - a paper which illustrates the broad concept if not the actual modelling details that we would use is herehttps://onlinelibrary.wiley.com/doi/full/10.1002/env.2143(b) regression and/or spatial techniques (for the same reasons as below). For the latter we would be using (a) univariate extreme value models, incorporating trends and non-seasonality (ie. regression modelling);(b) forecasting methodology eg. dynamic linear models, potentially combined with extreme value analysis;(c) spatial modelling of extremes to enable pooling of information, more realistic estimated uncertainty and confidence bounds, and ensure spatially consistent forecasts. Spatial modelling could either take a hierarchical modelling approach - see section 4 ofhttps://projecteuclid.org/download/pdfview_1/euclid.ss/1340110864or be based on recent developments in the modelling of spatial extremes through a combination of geostatistical and multivariate extreme value models as detailed here:https://www.lancaster.ac.uk/~wadswojl/CSE-paper.pdf;(d) multivariate modelling _if_ we get to the point of trying to forecast extreme episodes that involve multiple pollutants;