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

用于天气业务预测 (AESOP) 的北极雪发射率

基本信息

批准号：
NE/S009280/1
负责人：
Melody Sandells
金额：
$ 2.85万
依托单位：
Northumbria University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FS009280%2F1
关键词：
Arctic Emissivity Snow Operational Prediction

项目摘要

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.

AESOP将发起诺森比亚大学和气象局之间的合作，以做出必要的基于证据的商业决策，以改变气象局业务天气预报中使用冰雪覆盖的北极地区卫星测量的方式。英国气象局需要改善北极地区的短期业务天气预报，由于英国靠近北极，这将导致英国业务季节预报的增强。目前，在北极地区使用这些卫星测量非常困难，因为在穿透云层所需的微波频率下，卫星同时观察地表和大气。因此，大量可能有用的卫星数据目前被天气预报系统拒之门外。新的数据和最近的理论进展，在表面发射率模型现在提供了潜在的分离表面和大气发射率信号的大气微波亮度温度的顶部，从而使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业务系统变更所需的理由和知识。该职位使用现有的科学和专业知识，利用NERC和欧洲航天局资助的最新科学进展，以满足气象局的两个主要需求。首先，气象局需要地面数据和评估在边缘冰区附近的北极云、雪和海冰测量期间进行的机载亮温测量的模拟（MACSSIMIZE：气象局资助的机载大气测量活动设施）。最近，诺森比亚大学的工作人员收集了雪微观结构的必要地面测量数据并进行了质量控制（由NERC英国和加拿大北极伙伴关系奖学金“用于模拟微波和热特性的北极雪特性”资助），并可供AESOP使用。其次，气象局希望利用Sandells及其同事开发的创新的新雪排放建模方法（SMRT）。这是一个重大的发展，以前使用的单一辐射传输模型的方法，由于多个模型表示在SMRT，它允许估计的模型误差所需的天气预报系统。模型代码和计算专业知识将提供给英国气象局诺森比亚大学，供合作使用，通过和超越这个位置的时间尺度。主办机构将受益于数据和建模专业知识转化为RTTOV辐射传输模型在其业务系统中使用，这个位置将为英国气象局提供明确的证据，证明改变他们的操作模型方案的好处。这是促进气象局业务天气预报未来发展变化的关键一步，因为其业务系统内的完整数据同化试验非常昂贵。