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Marie-Dominique Leroux, Matthieu Plu, David Barbary, Frank Roux, and Philippe Arbogast


The rapid intensification of Tropical Cyclone (TC) Dora (2007, southwest Indian Ocean) under upper-level trough forcing is investigated. TC–trough interaction is simulated using a limited-area operational numerical weather prediction model. The interaction between the storm and the trough involves a coupled evolution of vertical wind shear and binary vortex interaction in the horizontal and vertical dimensions. The three-dimensional potential vorticity structure associated with the trough undergoes strong deformation as it approaches the storm. Potential vorticity (PV) is advected toward the tropical cyclone core over a thick layer from 200 to 500 hPa while the TC upper-level flow turns cyclonic from the continuous import of angular momentum.

It is found that vortex intensification first occurs inside the eyewall and results from PV superposition in the thick aforementioned layer. The main pathway to further storm intensification is associated with secondary eyewall formation triggered by external forcing. Eddy angular momentum convergence and eddy PV fluxes are responsible for spinning up an outer eyewall over the entire troposphere, while spindown is observed within the primary eyewall. The 8-km-resolution model is able to reproduce the main features of the eyewall replacement cycle observed for TC Dora. The outer eyewall intensifies further through mean vertical advection under dynamically forced upward motion. The processes are illustrated and quantified using various diagnostics.

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Huaqing Cai, Wen-Chau Lee, Michael M. Bell, Cory A. Wolff, Xiaowen Tang, and Frank Roux


Uncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy.

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Jason C. Knievel, Yubao Liu, Thomas M. Hopson, Justin S. Shaw, Scott F. Halvorson, Henry H. Fisher, Gregory Roux, Rong-Shyang Sheu, Linlin Pan, Wanli Wu, Joshua P. Hacker, Erik Vernon, Frank W. Gallagher III, and John C. Pace


Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

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