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

本研究的重点是了解与特定功能的对流开始前的错误可以影响对流的预测在美国大平原地区的中尺度可预报性实验（MPEX）。 特别是，这项研究将评估的假设，与上层天气特征，对流层中层湿度，对流层中层直减率，边界层湿度和切变在18-24小时和1小时前对流开始负责对流预报缺乏可预测性的错误。 这些假设将通过运行一系列实验进行验证，其中在最敏感区域内吸收不同的观察结果，并与保留这些观察结果的对照组进行比较。 此外，基于集合的灵敏度技术将应用于3公里天气研究和预报（WRF）模型集合预报，该模型使用循环集合卡尔曼滤波器的初始条件。 从该方法获得的灵敏度也将通过产生与调整感兴趣的特征一致的扰动初始条件（即，对流层中部湿度），并将得到的预报与没有施加扰动的对照进行比较。 从几个案例的结果将相互比较，以评估是否对流预报是一贯敏感的特定领域和功能，以及在何种程度上，每个案件的特点是独特的sensitivity.Intellectual优点：这个项目将提高我们的理解的动力过程，限制了对流系统的可预测性在一系列的情况下，使用对流解决的预报合奏。 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上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上的误差增长为特征的对流。这项研究的结果可以为未来对流事件期间在哪里进行观测提供指导，这些观测可以被同化到数值预报模型中，这将有望产生更好的预报，更长的预警准备时间，并减少生命损失。 研究结果将与业务预报员和研究界的其他人交流，以设计更好的方法来观察大气和评估数值模型。 此外，这项研究还将允许对一名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.