Air-pollution Innovation in Regional-forecasts utilising operational Satellite Applications and Technologies (AIRSAT)

不断恶化的空气质量（AQ）是全球所有主要经济体面临的持续挑战，现在被认为是对人类健康的最大环境压力。主要的空气污染物包括臭氧（O3），二氧化氮（NO2）和气溶胶（悬浮在空气中的污染颗粒）等气体，这些气体会导致呼吸道和心血管疾病等健康疾病。在全球范围内，据估计，空气污染是每年约900万人过早死亡的原因，而在英国，它导致每年约40，000人过早死亡。因此，英国气象局（UKMO）使用其空气质量预测模型，为公众（例如患有呼吸道疾病的个人）和政府部门/机构（例如NHS准备增加医院排放）提供危险空气质量事件的预警。UKMO使用地面站点的观测结果（称为自动化城市和农村网络，AURN），以评估其AQ预测的性能，并制定了一个统计方案来纠正它们（例如，如果模式的地面臭氧浓度与AURN的观测值相比过大，则该区域的预测值将降低）。虽然这是提高UKMO AQ预测技能的有效方法，地面网络仅由~100个站点组成。因此，英国各地存在大量数据缺口，无法验证预测。然而，在过去十年中，在从空间观测空气污染方面取得了迅速发展和进步。我们现在有卫星仪器，能够以几公里的空间分辨率探测污染热点（例如城市）。这些卫星平台可以提供覆盖地球仪（以及英国）的每日覆盖，并提供利用这些数据进行模型预测评估和改进的令人兴奋的机会。因此，这些卫星AQ产品可以被整合到与地面AURN观测相同的统计方案中，以提供每日预报校正和更新。该提案旨在通过三个步骤做到这一点：1。开发必要的处理和数据分析工具（例如编程代码），以便将过去的模型预测与现有的卫星数据产品进行比较。这将有助于确定哪些卫星AQ产品对模型预报验证和校正最有用。使用成熟的统计方法从卫星数据中提取有用的地表信息。大多数卫星产品提供关于整个大气层中空气污染物的信息，因此，大气和地表污染之间的已知统计关系可用于提取重要的地表信息。3.将卫星表面信息纳入UKMO的历史案例研究统计偏差校正方案（例如，对以前的AQ事件的预测），然后可以根据AURN的观测结果进行独立评估。一旦完全离线运行，UKMO就可以将卫星部分吸收到其业务统计偏差校正计划中，以改善公众的空气质量预测。最终，该项目旨在将卫星数据集整合到UKMO业务空气质量预测中，以改善这项重要的公共服务的质量。由于很少有国家气象机构（例如包括英国气象组织）将地球观测（EO）产品纳入其AQ预测模型的常规评估中，这代表了一个创新的步骤，利用卫星AQ产品，超出其在学术界的最常见用途（例如用于科学研究）。

Deteriorating air quality (AQ) is an ongoing challenge for all major global economies and is now recognised as the largest environmental stress on human health. Key air pollutants include gases like ozone (O3), nitrogen dioxide (NO2) and aerosols (particles of pollution suspended in the air), which can cause health ailments such as respiratory and cardiovascular illness. Globally, it has been estimated that air pollution is the cause of ~9 million premature deaths/year, while in the UK, it results in ~40,000 premature deaths/year. As a result, the UK Met Office (UKMO) uses its AQ forecast model, to provide the forewarning of hazardous AQ episodes for the general public (e.g. individuals with respiratory illnesses) and government departments/bodies (e.g. the NHS to prepare for increases hospital emissions). The UKMO uses observations from surface sites (known as the Automated Urban and Rural network, AURN) to evaluate the performance of their AQ forecasts and developed a statistical scheme to correct them (e.g. if the model surface ozone concentrations are too large when compared to the AURN observations, the forecast values will be lowered in that region).While this is a powerful method to improve the skill of the UKMO AQ forecasts, the surface network only consists of ~100 sites. Therefore, there are large data gaps across the UK where the forecasts cannot be verified. However, in the last decade, there has been rapid development and advancements in observing air pollution from space. We now have satellite instruments which can detect pollution hotspots (e.g. cities) at a spatial resolution of several kilometres. The satellite platforms can provide daily coverage across the globe (and thus the UK) and provide the exciting opportunity to exploit this data for model forecast evaluation and improvement. As such, these satellite AQ products can be integrated into the same statistical scheme as the surface AURN observations to provide daily forecast corrections and updates. This proposal aims to do this in three steps:1. Develop the necessary processing and data analysis tools (e.g. programing codes) to compare past model forecasts with readily available satellite data products. This will help identify which satellite AQ products are most useful for model forecast verification and correction.2. Use well established statistical methods to extract useful surface information from the satellite data. Most satellite products provide information on an air pollutant throughout the atmosphere, so known statistical relationships between atmospheric and surface pollution can be used to extract important surface information. 3. Integrate the satellite surface information into the UKMO's statistical bias correction scheme for historical case studies (e.g. forecasts of previous AQ episodes), which can then be independently assessed against the AURN observations. Once fully functional off-line, the UKMO can then assimilate the satellite component into their operational statistical bias correct scheme to improve the public AQ forecasts.Ultimately, this project aims to integrate satellite data sets into the UKMO operational AQ forecasts to improve the quality of this important public service. As very few national meteorological agencies (e.g. including the UKMO) include Earth observation (EO) products into their routine evaluation of AQ forecast models, this represents an innovative step to utilise satellite AQ products beyond their most common use with in academia (e.g. used in scientific studies).