Sources and Growth of Initial Condition Errors in Convection-resolving Forecasts in the Mesoscale Predictability Experiment (MPEX)

中尺度可预测性实验（MPEX）中对流解析预测中初始条件误差的来源和增长

• 批准号: 1239787
• 负责人: Ryan Torn
• 金额: $ 32.63万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-11-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1239787&HistoricalAwards=false
• 关键词: Sources; Growth; Initial; Condition; Errors

This research focuses on understanding how errors associated with particular features prior to convective initiation can influence forecasts of convection over the Great Plains region of the United States within the Mesoscale Predictability Experiment (MPEX). In particular, this study will evaluate the hypothesis that errors associated with upper-level synoptic features, midtropospheric moisture, midtropospheric lapse rate, and boundary layer moisture and shear at 18-24 h and 1 h prior to convective initiation are responsible for the lack of predictability in convection forecasts. These hypotheses will be validated by running a series of experiments whereby different sets of observations are assimilated within the most sensitive regions and compared to the control where these observations are withheld. In addition, the ensemble-based sensitivity technique will be applied to 3 km Weather Research and Forecasting (WRF) model ensemble forecasts that use initial conditions from a cycling ensemble Kalman filter. The sensitivities that are obtained from this method will also be evaluated by producing perturbed initial conditions that are consistent with adjusting a feature of interest (i.e., midtropospheric moisture) and comparing the resulting forecasts against the control where no perturbation is applied. The results from several cases will be compared to each other to evaluate whether convective forecasts are consistently sensitive to particular fields and features and the degree to which each case is characterized by unique sensitivities.Intellectual Merit:This project will enhance our understanding of the dynamical processes that limit the predictability of convective systems over a range of cases using an ensemble of convection-resolving forecasts. Moreover, this study will also demonstrate whether sensitivity analysis, which has typically been applied to phenomena characterized by linear error growth over longer time scales can be applicable to convection, which is characterized by non-linear dynamics and error growth over short time scales.Broader Impacts:The results from this study could provide guidance of where to take observations during future convective events that could be assimilated into numerical prediction models, which will hopefully produce better forecasts, greater lead time for warnings, and reduced loss of life. The results will be communicated with operational forecasters and others in the research communities to design better ways to observe the atmosphere and evaluate numerical models. Moreover, this study will also allow for the training of a graduate student in predictability, convective dynamics and data assimilation.

本研究的重点是了解对流开始前与特定特征相关的误差如何影响中尺度可预测性实验（MPEX）对美国大平原地区对流的预报。特别是，本研究将评估这样一个假设，即对流预报缺乏可预测性的原因是与高层天气特征、对流层中部湿度、对流层中部递减率以及对流开始前18-24 h和1 h的边界层湿度和切变有关的误差。这些假设将通过运行一系列实验来验证，在这些实验中，不同的观察结果在最敏感的区域内被吸收，并与这些观察结果被保留的控制进行比较。此外，基于集合的灵敏度技术将应用于使用循环集合卡尔曼滤波器初始条件的3公里天气研究与预报（WRF）模式集合预报。通过产生与调整感兴趣的特征（即对流层中层湿度）相一致的扰动初始条件，并将结果预测与不应用扰动的控制进行比较，也将通过这种方法获得的灵敏度进行评估。几个案例的结果将相互比较，以评估对流预报是否对特定领域和特征具有一致的敏感性，以及每个案例具有独特敏感性的程度。智力优势：该项目将增强我们对动态过程的理解，这些过程限制了对流系统在一系列情况下使用对流解析预测的可预测性。此外，本研究还将证明，敏感性分析通常适用于以较长时间尺度的线性误差增长为特征的现象，是否也适用于以非线性动力学和短时间尺度的误差增长为特征的对流。更广泛的影响：这项研究的结果可以为在未来对流事件中进行观测提供指导，这些观测可以被吸收到数值预测模型中，这有望产生更好的预测，更长的预警时间，并减少生命损失。结果将与业务预报员和研究团体的其他人员交流，以设计更好的方法来观察大气和评估数值模式。此外，这项研究也将允许在可预测性、对流动力学和数据同化方面训练一名研究生。