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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

, although the technique is limited to relatively calm atmospheric conditions in the lower atmosphere (e.g., Kusunoki 2002 ). For instance, Kawabata et al. (2007) reproduced the entire life cycle of an isolated cumulonimbus by assimilating clear-air echoes as environmental observations. Rennie et al. (2011) reported that assimilating clear-air echoes in 3D-Var had small but positive impacts for three convective cases. Another available technique for this purpose is Doppler wind lidar (DWL), which

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Nicholas A. Gasperoni and Xuguang Wang

1. Introduction Data assimilation of observations in both time and space improves a numerical weather prediction (NWP) forecast, in an average sense. However, it is important to determine the value added to a forecast from a specific subset of observations. In this way, we can investigate by instrument type, observation type, and location, which observations are the most impactful on a forecast. Additionally, we can avoid using observations that have negative impacts on a forecast. Evaluating

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Daryl T. Kleist and Kayo Ide

left) velocity azimuth display (VAD) winds (blue), lidar wind profilers (red), and pilot balloon (pibal) winds (green), and (bottom right) Special Sensor Microwave Imager (SSM/I)-derived surface wind speeds. 4. Experiment design To test the impact of the various components and aspects of including a hybrid EnVar component to the system, it is necessary to first produce a fully cycled 3DVar run as the control experiment utilizing simulated observations from the Joint OSSE ( section 3 ). The model

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