Synergy Algorithms for EarthCARE

数值气候模型中有一个共识，即地球正在变暖，但它们在预测变暖的大小和全球分布以及与降水相关的变化方面存在很大差异。这种分歧在很大程度上是由于在如何在模型中表示云和气溶胶方面的不确定性；云对气候很重要，因为它们可以降水，并通过它们与太阳和热红外辐射的相互作用，而气溶胶可以与云相互作用来调节这两个过程。因此，当务之急是利用详细的观测来测试和改进模型中云、降水和气溶胶的表示。2013年，欧洲和日本宇宙航空研究开发机构(欧空局和日本宇宙航空研究开发机构)将通过发射地球云、气溶胶和辐射探测器(EarthCARE)卫星直接解决这一问题，该卫星携带一部雷达、一部激光雷达以及窄带和宽带辐射计。地球观测是美国国家航空航天局“A系列”卫星的重大进步；雷达是多谱线化的，因此将能够测量云中的垂直运动，而“高光谱分辨率”激光雷达可以比普通激光雷达更可靠地得出光学特性的垂直分布。此外，较低的轨道意味着雷达的灵敏度将比A-Train的雷达高约4倍。EarthCARE的一个非常令人兴奋的方面是协同作用的潜力：当这些仪器一起使用时，可以更准确和全面地估计云的性质。然而，制定计算机代码来以严格的数学方式考虑所有可用的信息是非常具有挑战性的。该项目的PI和PDRA是将严格的变分方法应用于组合雷达、激光雷达和辐射计的专家，他们最近开发的从A-Train获得冰云特性的方法就证明了这一点。这项工作已经揭示了气象局和欧洲中期天气预报中心(ECMWF)模型预测的云的严重缺陷。在这个项目中，我们将承担一项雄心勃勃的任务，即利用EarthCARE上现有的所有仪器(宽带辐射计除外，它将用作一项独立的反演测试)，开发一种能够同时得出云、降水和气溶胶特性的反演方法。这对于获得对大气性质的最佳估计，从而为测试模型提供必要的信息是至关重要的。如此全面地组合这些仪器以前从未尝试过，因此将引起其他多仪器陆基和天基平台用户的极大兴趣。我们将在开源许可下发布我们的灵活代码，这样它就可以适应其他乐器组合。我们方法的另一个优点是，它对反演中的不确定性产生了可靠的估计，使它们适合于数据同化，即天气预报模型能够将所有大气观测纳入预报的方法。ECMWF在数据同化科学方面处于世界领先地位，目前正在研究如何同化来自EarthCARE等卫星的云数据。我们将与他们密切合作，确保我们的数据产品包含所有必要的信息，用于同化，从而改善未来的天气预报。这项工作将使英国在利用EarthCARE回答气候预测核心的关键科学问题方面处于有利地位。此外，来自A-Train的令人兴奋的结果表明，雷达和激光雷达在太空中肯定有长期的未来，甚至超过了EarthCARE。该项目将把英国置于星载雷达和激光雷达研究的前沿，从而处于领导未来任务的理想位置。

There is a consensus amongst numerical climate models that the earth is warming, but they differ substantially in the predicted size and global distribution of both the warming and associated change to precipitation. This disagreement is largely attributable to uncertainties in how to represent clouds and aerosols in models; clouds are important for climate because they precipitate and via their interaction with solar and thermal infrared radiation, while aerosols can interact with clouds to modulate both of these processes. It is therefore of the highest priority to test and improve the representation of clouds, precipitation and aerosols in models using detailed observations. In 2013, the European and Japanese Space Agencies (ESA and JAXA) will address this problem directly with the launch of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite, carrying a radar, a lidar and narrow- and broad-band radiometers. EarthCARE is a significant advance on NASA's 'A-Train' of satellites; the radar is Dopplerized, so will be able to measure vertical motions in clouds, while the 'high spectral resolution' lidar can derive the vertical distribution of optical properties much more reliably than ordinary lidar. Moreover, the lower orbit means that the radar will be around 4 times more sensitive than the radar in the A-Train. A very exciting aspect of EarthCARE is the potential for synergy: when the instruments are used together, much more accurate and comprehensive estimates of cloud properties are possible. However, formulating computer codes to take account of all the available information in a mathematically rigorous way is very challenging. The PI and PDRA on this project are experts in applying rigorous 'variational' methods to combinations radar, lidar and radiometers, as demonstrated by their recent development of a method for deriving the properties of ice clouds from the A-Train. This work has already revealed serious deficiencies in the clouds predicted by the models of the Met Office and the European Centre for Medium Range Weather Forecasts (ECMWF). In this project, we will undertake the ambitious task of developing a retrieval method that can derive the properties of clouds, precipitation and aerosols simultaneously, using all the instruments available on EarthCARE (except the broad-band radiometers, which would be used as an independent test of the retrievals). This is essential to obtain the best possible estimate of atmospheric properties, and thereby to provide the necessary information to test models. Combining such instruments so comprehensively has never been attempted before, and therefore will be of great interest to other users of multiply instrumented ground-based and spaceborne platforms. We will release our flexible code under an open-source license, so that it can be adapted to other combinations of instruments. An additional advantage to our approach is that it yields reliable estimates of the uncertainties in the retrievals, making them suitable for data assimilation, the method by which weather forecast models are able to incorporate all the observations of the atmosphere into a forecast. ECMWF are world leaders in the science of data assimilation, and are currently working on the problem of how to assimilate cloud retrievals from satellites such as EarthCARE. We will work closely with them to ensure that our data products contain all the necessary information to be used for assimilation, and hence to improve weather forecasts in the future. This work will put the UK in an excellent position to exploit EarthCARE in answering the key scientific questions at the heart of climate prediction. Moreover, the exciting results from the A-Train have shown that radar and lidar must have a long-term future in space, even beyond EarthCARE. This project will place the UK at the forefront of spaceborne radar and lidar research and hence in an ideal position to lead future missions.