Synergy Algorithms for EarthCARE

点击翻译按钮获取中文摘要

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.