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Stochastic treatment of cloud related processes in nonhydrostatic weather prediction models

非静水力天气预报模型中云相关过程的随机处理

基本信息

批准号：
175227825
负责人：
Professor Dr. Andreas Bott
金额：
--
依托单位：
Abteilung Meteorologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2010
资助国家：
德国
起止时间：
2009-12-31 至 2014-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/175227825?language=en
关键词：
Stochastic treatment cloud related processes

项目摘要

Cloud processes have a strong influence on the energy and moisture budget of the atmosphere. Since in numerical weather prediction (NWP) models cloud related processes usually are of subgrid scale, they have to be parameterized in a set of parameterization schemes describing e.g. grid scale clouds and precipitation, subgrid scale cumulus convection and atmospheric radiative transfer. The aim of the proposed research project is to develop a new stochastic physics approach (SPA) for the treatment of cloud related processes in nonhydrostatic NWP models. The stochastic approach shall represent unresolved subgrid scale variability and model parameter uncertainties. For this purpose, selected fixed model parameters will be replaced by appropriate stochastic processes.The application of the SPA shall increase the forecast skill of the hosting NWP model. In an ensemble prediction system (EPS) the ensemble spread shall grow to more realistic values and thus help to overcome the current problem of too small spread (underdispersion) when only considering uncertainties in the initial or boundary conditions. The algorithms of the stochastic approach and the EPS will be developed for use in any nonhydrostatic NWP model that includes prognostic microphysics/precipitation, a radiation scheme with fractional cloud cover and an arbitrary mass flux convection scheme.

云过程对大气的能量和水分收支有很大的影响。由于数值天气预报模式中与云有关的过程通常是次网格尺度的，因此必须在一组参数化方案中对它们进行参数化，例如描述网格尺度云和降水、次网格尺度积云对流和大气辐射传输。该研究项目的目的是发展一种新的随机物理方法（SPA），用于处理非静力数值预报模式中与云有关的过程。随机方法应代表未解决的次网格尺度变化和模型参数不确定性。为此目的，选定的固定模式参数将被适当的随机过程所取代。SPA的应用将提高主机NWP模式的预报技巧。在集合预报系统（EPS）中，集合扩散将增长到更现实的值，从而有助于克服当前仅考虑初始或边界条件中的不确定性时扩散太小（欠分散）的问题。随机方法和EPS的算法将被开发用于任何非静力NWP模式，包括预测微物理/降水，辐射计划与分数云量和任意质量通量对流计划。