Cold Microphysical Effects on Surface Rainfall Variability in the Tropics

0125706 Georgakakos两年研究项目的重点是量化（a）大尺度水汽和热浮力强迫场与（B）风暴微物理学对对流海洋表面降雨变率时空特征发展的相对重要性。 特别感兴趣的是了解冷微物理特性对降雨变率在不同时空域和分辨率的影响。 这项研究研究海洋降雨作为第一步，通过分离大尺度强迫和微物理学从大陆降雨中存在的复杂地形影响来理解降雨变化的原因。 这些研究成果对水文气象学和水文学具有根本的重要性，可用于以下应用：（a）通过遥感或大尺度气候模型获得的降水量估算的缩小或扩大，或（B）需要高时空分辨率的降水量估算的水文设计研究。 将降雨发生和演变的统计-动力数值模式与国家大型试验资料相结合，以实现项目的目标。 利用对流区的现场实验估计的状态变量的典型垂直分布，有效地积分了垂直动量、质量和能量守恒的状态方程。 一个数量ofquantitative假设进行测试的原因在域的256公里长，1公里的空间分辨率和时间分辨率为1分钟。该模型使用的蒙特卡罗框架，结合观测数据的不确定性，大尺度强迫场，和模型微物理参数化，包括可能的反馈从生成的云到近场大尺度强迫。

0125706GeorgakakosThe focus of the two-year research project is to quantify the relativeimportance of (a) the large-scale forcing fields of water vapor andthermal buoyancy versus (b) the storm microphysics for the developmentof the spatio-temporal characteristics of convective oceanic surfacerainfall variability. Of particular interest is to understand theeffects of cold microphysical properties on rainfall variability atvarious spatiotemporal domains and resolutions. The research studiesoceanic rainfall as a first step toward the understanding of the causesof rainfall variability by separating the large scale forcing andmicrophysics from the complex terrain influences present in continentalrainfall. The research results are of fundamental importance forhydrometeorology and hydrology, and they may be used in applicationssuch as (a) downscaling or upscaling rainfall estimates obtained byremote sensing or by large-scale climate models, or (b) hydrologicdesign studies requiring rainfall estimates of high spatio-temporalresolution. Statistical-dynamical numerical models of rainfall genesisand evolution are combined with data from national large-scaleexperiments to achieve the goals of the project. State equations forthe vertical momentum, mass and energy conservation are integratedefficiently using typical vertical profiles of the state variables,estimated from field experiments in convective regimes. A number ofquantitative hypothesis are tested regarding the causes of rainfallvariability in domains of a 256-km length, with spatial resolution of 1km and temporal resolution of 1 min. The models are used in aMonte-Carlo framework that incorporates uncertainties in observed data,large-scale forcing fields, and model microphysical parameterizations,including possible feedbacks from the generated cloud to the near fieldlarge-scale forcing.