Understanding and Representing Atmospheric Convection across Scales - ParaCon Phase 2
Understanding and Representing Atmospheric Convection across Scales - ParaCon Phase 2
批准号:
NE/T003871/1
负责人:
Robert Plant
金额:
$122.64万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
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英文摘要
Cumulus clouds are produced by the vigorous ascent of buoyant air, a process known as convection. The weather and climate of the tropics are dominated by cumulus clouds, and severe weather at all latitudes involves convection. Convection communicates heat and moisture from the Earth's surface throughout the atmosphere. It is the main process controlling the change of temperature and moisture content with height in the tropical atmosphere. On the global scale, cumulus clouds are responsible for the majority of the rainfall, and convection is a crucial component in the overall pattern of the Earth's atmospheric flows.Computer modelling of the atmosphere is essential for both numerical weather prediction (NWP) and climate projections. Society benefits enormously from their outputs to inform decision making on all scales from the individual member of the public to weather-sensitive business activities, the energy sector, the emergency services, and government policy on climate risks. Computer models for NWP and for climate projection divide the atmosphere into boxes with typical horizontal sizes of 10km and 100km respectively. Convective elements such as thunderstorms, on the other hand, are typically only around 1km in size so they cannot be explicitly represented in the models. Instead we must somehow estimate what cumulus clouds will be present in each of the boxes and what their collective effects will be on the larger-scale atmosphere. This is known as a cumulus parameterization.Cumulus parameterization is a stubborn and difficult problem and is the largest single uncertainty that we face. It is a severe and unforgiving test of just how well we understand the fundamental science of convection and its role in the atmosphere. Defects in the existing parameterizations are known to translate into serious deficiencies in weather and climate models. These include errors in the distribution, timing, and intensity of convective rainfall, as well as the behaviour of larger-scale weather systems that are coupled to convection.ParaCon Phase 2 is a wide-ranging plan to redesign the convection parameterization for the Met Office Model, to demonstrate clear improvements in model fidelity and performance, and to lay the groundwork for the next generation of parameterization research.In Phase 1 we have developed a new convection scheme infrastructure called CoMorph, which enables many of the assumptions that are made in such parameterizations to be relaxed, removed or generalized and we have begun the process of developing a formulation based on alternative and more general assumptions. Also in Phase 1 we have performed promising investigations into radically different formulations based on modelling convection as a manifestation of turbulence, and on a multi-fluid approach that relaxes the usual assumptions even further than CoMorph does.In Phase 2 we will continue the development of CoMorph with a view to its adoption for operational forecasting. Building on the work in Phase 1, improved formulations for the components of the scheme will be developed and implemented. The performance of CoMorph will be evaluated in a wide range of test cases. These will include comparison with a suite of high-resolution simulations of idealized convective archetypes conducted in Phase 1, as well as a range of operational-style configurations.In Phase 2 we will also continue to develop the turbulence-based and multi-fluid-based approaches and to evaluate their potential for representing convection in atmospheric models. A key goal will be to clarify the relationship between the three approaches and to understand the extent to which some unification or combination of the approaches might be possible and beneficial.
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A Machine-Learning-Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection
作为积云对流参数化的机器学习辅助随机云种群模型
DOI:
10.1029/2021ms002808
发表时间:
2022
期刊:
Journal of Advances in Modeling Earth Systems
影响因子:
6.8
作者:
[Hagos S]
通讯作者:
Hagos S
Improving heavy precipitation forecasting over the western Mediterranean: Benefits of stochastic techniques for model error sampling
改进地中海西部强降水预报:模型误差抽样随机技术的好处
DOI:
10.5194/egusphere-egu21-6225
发表时间:
2021
期刊:
影响因子:
--
作者:
[Hermoso A]
通讯作者:
Hermoso A
Evaluating the CoMorph-A parametrization using idealized simulations of the two-way coupling between convection and large-scale dynamics
使用对流和大规模动力学之间双向耦合的理想化模拟来评估 CoMorph-A 参数化
DOI:
10.1002/qj.4547
发表时间:
2023
期刊:
Quarterly Journal of the Royal Meteorological Society
影响因子:
8.9
作者:
[Daleu C]
通讯作者:
Daleu C
DOI:
10.1029/2020gl090460
发表时间:
2020
期刊:
Geophysical Research Letters
影响因子:
5.2
作者:
[Gu J]
通讯作者:
Gu J
Radiation, Clouds, and Self-Aggregation in RCEMIP Simulations
RCMIP 模拟中的辐射、云和自聚集
DOI:
10.5194/egusphere-egu23-1071
发表时间:
2023
期刊:
影响因子:
--
作者:
[Holloway C]
通讯作者:
Holloway C
共 9 条
Putting the morph into CoMorph: Adapting convection parametrisation for the hard grey zone
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批准号:NE/X018512/1
-
项目类别:Research Grant
-
资助金额:$116.52万
-
财政年份:2023
-
负责人:Robert Plant
-
依托单位:
Revolutionizing Convective Parameterization
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批准号:NE/N013743/1
-
项目类别:Research Grant
-
资助金额:$95.37万
-
财政年份:2016
-
负责人:Robert Plant
-
依托单位:
GREYBLS: modelling GREY-zone Boundary LayerS
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批准号:NE/K011502/1
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项目类别:Research Grant
-
资助金额:$31.52万
-
财政年份:2013
-
负责人:Robert Plant
-
依托单位:
Stochastic Parameterization of Deep Convection in Short-Range Ensemble Weather Forecasts
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批准号:NE/D011493/1
-
项目类别:Research Grant
-
资助金额:$32.43万
-
财政年份:2007
-
负责人:Robert Plant
-
依托单位:
海外基金