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

利用卫星应用和技术（AIRSAT）进行区域预测的空气污染创新

• 批准号: NE/Y005147/1
• 负责人: Richard Pope
• 金额: $ 31.01万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY005147%2F1
• 关键词: Air; pollution; Innovation; Regional; forecasts

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).

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造成的最大环境压力。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，它们会导致呼吸系统和心血管疾病等健康疾病。据估计，在全球范围内，空气污染是每年约900万人过早死亡的原因，而在英国，空气污染每年导致约4万人过早死亡。因此，英国气象局(UKMO)使用其空气质量预测模型，为公众(例如患有呼吸系统疾病的个人)和政府部门/机构(例如NHS为应对医院排放增加做准备)提供危险AQ事件的预警。UKMO使用地面站点(称为自动城乡网络，AURN)的观测值来评估其AQ预报的性能，并开发了一个统计方案来修正它们(例如，如果模式地面臭氧浓度与AURN观测值相比太大，则该地区的预测值将被降低)。虽然这是提高UKMO AQ预报技能的有力方法，但地面网络仅包括约100个站点。因此，英国各地存在着巨大的数据差距，这些预测无法得到验证。然而，在过去的十年里，从太空观测空气污染有了快速的发展和进步。我们现在有卫星仪器，可以探测污染热点(例如城市)，空间分辨率为几公里。卫星平台可以提供全球(以及英国)的日常覆盖，并提供利用这些数据进行模式预报评估和改进的令人兴奋的机会。因此，这些卫星AQ产品可以整合到与地面AURN观测相同的统计方案中，以提供每日预报订正和更新。这项建议旨在分三步进行：1.开发必要的处理和数据分析工具(例如编程代码)，以便将过去的模式预报与现成的卫星数据产品进行比较。这将有助于确定哪些卫星空气质量产品对模式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有关整个大气中空气污染物的信息，因此可以利用大气和表面污染之间的已知统计关系来提取重要的表面信息。3.将卫星表面信息纳入联合气象组织针对历史案例研究的统计偏差校正计划(例如，对以前AQ事件的预测)，然后可以根据AURN观测对其进行独立评估。一旦完全功能脱机，英国气象组织就可以将卫星部分同化到他们的业务统计偏差校正方案中，以改进公众空气质量预报。最终，该项目的目的是将卫星数据集整合到英国气象组织的业务空气质量预报中，以提高这一重要公共服务的质量。由于很少有国家气象机构(例如，包括英国气象局)将地球观测(EO)产品纳入其对空气质量预报模式的例行评估，这是一个创新的步骤，将卫星空气质量产品的用途超越了它们在学术界的最常见用途(例如，用于科学研究)。