Synergy Algorithms for EarthCARE

EarthCARE 的协同算法

• 批准号: NE/H003894/1
• 负责人: Robin Hogan
• 金额: $ 26.03万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH003894%2F1
• 关键词: Synergy; Algorithms; EarthCARE

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.

数值气候模型之间有一个共识，即地球正在变暖，但它们在预测的变暖规模和全球分布以及相关的降水变化方面存在很大差异。这种分歧主要是由于在模型中如何表示云和气溶胶方面的不确定性;云对气候很重要，因为它们通过与太阳和热红外辐射的相互作用而沉淀，而气溶胶可以与云相互作用来调节这两个过程。因此，最优先的工作是利用详细的观测来测试和改进模型中云、降水和气溶胶的表现。2013年，欧洲和日本航天局（欧空局和日本宇宙航空研究开发机构）将直接解决这一问题，发射地球云、气溶胶和辐射探测器（EarthCARE）卫星，该卫星载有一个雷达、一个激光雷达以及窄带和宽带辐射计。EarthCARE是美国宇航局“A列车”卫星的重大进步;雷达是多普勒的，因此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获得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是A列车雷达的4倍左右。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特性。然而，制定计算机代码以数学上严格的方式考虑所有可用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”方法应用于雷达、激光雷达和辐射计组合的专家，正如他们最近开发的一种从A-Train获得冰云特性的方法所证明的那样。这项工作已经揭示了英国气象局和欧洲中期天气预报中心（ECMWF）预测的云的严重缺陷。在这一项目中，我们将承担一项雄心勃勃的任务，即开发一种检索方法，利用地球关怀项目现有的所有仪器（宽带辐射计除外，它将被用作检索的独立测试），同时得出云、降水和气溶胶的特性。这对于获得对大气特性的最佳估计，从而为测试模型提供必要的信息至关重要。将这些仪器如此全面地结合在一起是以前从未尝试过的，因此，将引起地面和空间多仪器平台的其他用户的极大兴趣。我们将在开源许可证下发布我们灵活的代码，以便它可以适应其他乐器组合。我们的方法的另一个优点是，它产生可靠的估计的不确定性的检索，使他们适合数据同化，天气预报模型能够将所有的观测大气到一个预测的方法。ECMWF是数据同化科学的世界领导者，目前正在研究如何同化来自EarthCARE等卫星的云检索。我们将与他们紧密合作，确保我们的数据产品包含用于同化的所有必要信息，从而改善未来的天气预报。这项工作将使英国处于一个很好的位置，利用EarthCARE来回答气候预测核心的关键科学问题。此外，A-Train的令人兴奋的结果表明，雷达和激光雷达必须在太空中有一个长期的未来，甚至超越EarthCARE。该项目将使英国处于星载雷达和激光雷达研究的最前沿，因此处于领导未来任务的理想位置。

项目成果

Normalized particle size distribution for remote sensing application

• DOI: 10.1002/2013jd020700
• 发表时间: 2014-04
• 期刊: Journal of Geophysical Research: Atmospheres
• 作者: J. Delanoë;A. Heymsfield;A. Protat;Aaron R. Bansemer;Robin J. Hogan

A unified synergistic retrieval of clouds, aerosols and precipitation from EarthCARE: the ACM-CAP product

从 EarthCARE 对云、气溶胶和降水进行统一协同检索：ACM-CAP 产品

• DOI: 10.5194/egusphere-2022-1195
• 发表时间: 2022
• 作者: Mason S

Evaluation of ice cloud representation in the ECMWF and UK Met Office models using CloudSat and CALIPSO data

使用 CloudSat 和 CALIPSO 数据评估 ECMWF 和英国气象局模型中的冰云表示

• DOI: 10.1002/qj.882
• 发表时间: 2011
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Delanoë J

A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product

从 EarthCARE 对云、气溶胶和降水进行统一协同检索：ACM-CAP 产品

• DOI: 10.5194/amt-16-3459-2023
• 发表时间: 2023
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: Mason S

Radar Scattering from Ice Aggregates Using the Horizontally Aligned Oblate Spheroid Approximation

• DOI: 10.1175/jamc-d-11-074.1
• 发表时间: 2012-03
• 期刊: Journal of Applied Meteorology and Climatology
• 影响因子: 3
• 作者: Robin J. Hogan;L. Tian;P. R. A. Brown;Christopher D. Westbrook;A. Heymsfield;J. Eastment