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Patrick A. Reinecke and Dale Durran

Abstract

The tendency of high-resolution numerical weather prediction (NWP) models to overpredict the strength of vertically propagating mountain waves is explored. Discrete analytic mountain-wave solutions are presented for the classical problem of cross-mountain flow in an atmosphere with constant wind speed and stability. Time-dependent linear numerical solutions are also obtained for more realistic atmospheric structures. On one hand, using second-order-accurate finite differences on an Arakawa C grid to model nonhydrostatic flow over what might be supposed to be an adequately resolved 8Δx-wide mountain can lead to an overamplification of the standing mountain wave by 30%–40%. On the other hand, the same finite-difference scheme underestimates the wave amplitude in hydrostatic flow over an 8Δx-wide mountain. Increasing the accuracy of the advection scheme to the fourth order significantly reduces the numerical errors associated with both the hydrostatic and nonhydrostatic discrete solutions. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) model is used to generate two 70-member ensemble simulations of a mountain-wave event during the Terrain-Induced Rotor Experiment. It is shown that switching from second-order advection to fourth-order advection leads to as much as a 20 m s−1 decrease in vertical velocity on the lee side of the Sierra Nevada, and that the weaker fourth-order solutions are more consistent with observations.

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Daniel Hodyss, Jeffrey L. Anderson, Nancy Collins, William F. Campbell, and Patrick A. Reinecke

Abstract

It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.

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