Diagnosis of Simulated Deep Convective Upscale Growth Errors and Their Causes

GSTDTAP

项目编号	1661662
	Diagnosis of Simulated Deep Convective Upscale Growth Errors and Their Causes
	Adam Varble
主持机构	University of Utah
项目开始年	2017
	2017-08-15
项目结束日期	2021-07-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	24224(USD)
国家	美国
语种	英语
英文摘要	The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) and Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaigns in late 2018 in central Argentina will study storms that are capable of developing into the tallest and largest in the world according to satellite observations. These field campaigns will focus a large number of environmental and radar observations in a topographically regulated storm initiation hot spot location, where growth of individual storm cells into larger, longer-lived, and higher impact systems commonly occurs in close proximity to initiation locations, limiting unpredictability of storm timing and location that has plagued previous field campaign sampling strategies. Research associated with RELAMPAGO and CACTI is focused around improved understanding and prediction of high impact storms in Latin America, the US, and many other locations around the world. Through comparison of an ensemble of carefully designed model simulations with unique RELAMPAGO and CACTI measurements, this project seeks to identify root causes of storm structure and life cycle biases in high-resolution weather models, so that methods to mitigate these biases can be established. Through this process, the relative roles of various environmental factors in controlling the probability and evolution of isolated storms into more impactful larger and longer-lived systems will be established. Large and long-lived systems have much larger societal impacts than shorter lived, single cell storms because of their association with high-impact weather such as flooding, large hail, damaging winds, and tornadoes, and their significant contribution to rainfall in many regions of the world. Improved representation of their development and life cycle in weather and climate prediction models will improve forecasts that help communities make better-informed decisions that limit their negative impacts and accentuate their positive impacts. The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign, from 1 November to 15 December 2018, and the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) experiment, from 15 August 2018 to 30 April 2019, will provide comprehensive in situ and remote sensing datasets with fixed and mobile platforms capable of high spatiotemporal characterization of the evolution of thermodynamic, kinematic, and microphysical conditions in and around convective storms in central Argentina. This location experiences some of the most intense and organized convective systems in the world with focused and frequent deep convective initiation over the Sierras de Córdoba orographic barrier and predictable convective cell movement with frequent upscale growth into larger and longer-lived systems near the high terrain. In this project, the probability, timing, location, and morphology of deep convective upscale growth into larger, longer-lived mesoscale convective systems with high impact rainfall and severe weather will be examined using RELAMPAGO-CACTI field campaign measurements and an ensemble of carefully designed model simulations. Potential model errors will be related to environmental thermodynamic and kinematic conditions, model resolution, and model parameterization of microphysics. Causes for potential simulated deep convective upscale growth biases will be studied, highlighting the separate roles of (i) environmental thermodynamics and kinematics, (ii) interactions between convective drafts, microphysics, and cold pools, and (iii) mesoscale circulation development in response to the deep convective cloud system. The following hypotheses will be tested: 1. The probability that orographically initiated deep convective cells grow upscale is primarily controlled by cold pool depth and strength coupled with low-mid level vertical wind shear and instability. 2. Overly strong and frequent initial deep convective cells with too much large, rimed ice in simulations produce cold pools that are stronger or more abundant than observed, leading to more frequent upscale growth than observed and overly organized convective features. 3. Simulated mesoscale flows responding to biased latent heating and cooling distribution reinforce model deep convective upscale growth and organization biases. 4. Convective upscale growth biases are most limited in simulations that best reproduce observed convective updraft kinematic and microphysical properties.
来源学科分类	Geosciences - Atmospheric and Geospace Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71494
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Adam Varble.Diagnosis of Simulated Deep Convective Upscale Growth Errors and Their Causes.2017.