Arctic Emissivity of Snow for Operational Prediction (AESOP) of Weather

AESOP将启动诺森比亚大学和英国气象局之间的合作，以做出必要的基于证据的商业决策，以改变在冰雪覆盖的北极地区使用卫星测量的方式，用于气象局的业务天气预报。英国气象局（Met Office）需要改善北极地区的短期业务天气预报，由于英国离北极很近，这将提高英国的业务季节性预报能力。目前，在北极地区使用这些卫星测量非常困难，因为卫星需要在微波频率上穿透云层，同时观测地表和大气。因此，目前天气预报系统拒绝了大量可能可用的卫星数据。地面发射率模式的新数据和最新理论进展现在提供了将地表和大气发射率信号分离到大气微波亮度温度顶部的可能性，从而允许以前丢弃的卫星观测用于业务数值天气预报。AESOP将为主办组织提供在其开发周期的适当时间点实施操作系统变更所需的理由和知识。该项目利用现有的科学和专业知识，利用由NERC和欧洲航天局资助的最新科学进展，满足气象局的两个主要需求。首先，英国气象局需要地面数据和评估在测量北极云、雪和边缘冰带附近的海冰期间进行的机载亮度温度测量的模拟（MACSSIMIZE：英国气象局资助的机载大气测量设施）。诺森布里亚大学的工作人员最近收集了雪微观结构的必要地面测量数据，并对其进行了质量控制（由NERC英国和加拿大北极伙伴关系奖学金资助的“北极雪的特征用于模拟微波和热特性”），并提供给AESOP。其次，气象局希望利用桑德尔斯及其同事开发的一种创新的新雪排放建模方法（SMRT）。由于SMRT中有多个模型表示，这是对以前使用的单一辐射传输模型方法的重大发展，可以估计天气预报系统所需的模型误差。运行它的模型代码和计算专业知识将由诺森比亚大学提供给英国气象局，供其在实习期间和之后合作使用。主办机构将受益于将数据和建模专业知识转化为其操作系统中使用的RTTOV辐射传输模型，并且这一安置将为英国气象局提供明确的证据，证明改变其操作建模方案的好处。这是促进气象局天气预报业务未来发展变化的关键一步，因为在其业务系统内进行全面的数据同化试验非常昂贵。

AESOP will initiate a collaboration between Northumbria University and the Met Office to make an evidence-based business decision necessary to change the way satellite measurements in the snow-covered Arctic are used in Met Office operational weather forecasts. The Met Office needs to improve short-range operational weather forecasts in the Arctic, which will lead to enhanced operational seasonal forecasts in the UK due to the close proximity of the UK to the Arctic. It is currently extremely difficult to use these satellite measurements over the Arctic because at the microwave frequencies needed to see through the clouds, the satellites observe both the surface and atmosphere together. Consequently, vast quantities of potentially usable satellite data are currently rejected by weather forecasting systems. New data and recent theoretical advances in surface emissivity models now provides potential for separation of surface and atmospheric emissivity signals to top of atmosphere microwave brightness temperature, thus allowing previously discarded satellite observations to be used for operational numerical weather prediction. AESOP will provide the host organisation with the justification and knowledge needed to implement changes in the operational system at the appropriate point in its development cycle.This placement uses existing science and expertise, leveraging recent scientific advances funded by NERC and the European Space Agency to meet two Met Office primary needs. Firstly, the Met Office requires ground-based data and evaluation of simulations of airborne brightness temperature measurements taken during Measurements of Arctic Clouds, Snow, and Sea Ice nearby the Marginal Ice ZonE (MACSSIMIZE : Met Office funded Facility for Airborne Atmospheric Measurements campaign). The necessary ground measurements of snow microstructure have recently been collected and quality-controlled by staff at Northumbria University (funded by NERC UK and Canada Arctic Partnership bursary 'Characterisation of Arctic snow for modelling microwave and thermal properties') and are available to AESOP. Secondly, the Met Office would like to harness an innovative new snow emission modelling approach (SMRT ) developed by Sandells and colleagues. This is a substantial development over single radiative transfer model approaches previously used due to multiple model representations in SMRT, which allows an estimate of the model error needed for weather forecast systems. Model code and computational expertise to run it will be provided to the Met Office from Northumbria University for collaborative use through and beyond the timescale of this placement.The host organisation will benefit from translation of data and modelling expertise into the RTTOV radiative transfer model used in their operational system, and this placement will provide clear evidence to the Met Office of the benefits of making changes to their operational modelling scheme. This is a critical step to contribute to future developmental changes in the operational weather prediction at the Met Office, as full data assimilation trials within their operational system are hugely expensive.