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

本研究的重点是在中尺度可预报性试验(MPEX)中了解与对流启动前的特定特征有关的误差如何影响美国大平原地区的对流预报。特别是，这项研究将评估一个假设，即与高层天气特征、对流层中层湿度、对流层中层下降率以及对流开始前18-24小时和1小时边界层湿度和切变有关的误差是对流预报缺乏预报性的原因。这些假设将通过运行一系列实验来验证，在最敏感的区域内吸收不同的观测集合，并与保留这些观测的对照进行比较。此外，基于集合的敏感性技术将被应用于3公里天气研究和预报(WRF)模式集合预报，该模式集合使用来自循环集合卡尔曼过滤器的初始条件。通过产生与调整感兴趣的特征(即对流层中层湿度)一致的扰动初始条件，并将所得到的预报与没有施加扰动的控制相比较，还将评估从该方法获得的敏感性。几个个例的预报结果将相互比较，以评估对流预报是否始终对特定的场和特征敏感，以及每个个例具有独特敏感性的程度。智力上的好处：这个项目将加强我们对动力过程的理解，这些动力过程限制了对流系统在一系列个例中的预报能力。此外，这项研究还将证明，通常应用于较长时间尺度上的线性误差增长的现象的敏感性分析是否适用于对流，而对流的特征是非线性动力学和短时间尺度上的误差增长。广泛的影响：这项研究的结果可以为在未来对流事件期间进行观测提供指导，这些事件可以被同化到数值预报模式中，这将有望产生更好的预报，更长的预警提前时间，并减少生命损失。结果将与业务预报员和研究界的其他人进行沟通，以设计更好的方法来观察大气和评估数值模式。此外，这项研究还将允许对研究生进行可预测性、对流动力学和数据同化方面的培训。

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.