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Edward H. Barker

Abstract

The aspects of the navy's multivariate optimum interpolation (MVOI) for atmospheric analysis are presented. Included is an overview of how the MVOI is used in the navy's data-assimilation system, and the basic design of the MVOI. The specific features described include: a cursory presentation of the optimization method and some of its deficiencies, the application of the volume method in data selection and program design, the structure models used to represent the prediction errors, and the quality control checks applied to the observations.

Validation experiments that illustrate some of the features of the navy's analysis system are presented. Experiments showing the exactness of the geostrophic constraint, the effect of correlated observation error, the advantage of the geostrophic constraint, and the impact of satellite temperatures on the analysis are presented. These experiments were necessary to catch minor design and programming errors in the analysis system that are too small to be detected through casual inspection, yet which degrade the quality of the analysis. It has been shown that implementation of the volume method has given more precise geostrophic coupling over the gridpoint method, and that inputting satellite temperatures as pressure thicknesses rather than pressure-level heights produces results that agree with the satellite-derived layer thickness values, while it ties the analysis to observations of pressure heights. Finally, the validation experiments were shown to be highly effective at removing subtle errors in the analysis system, which led to rapid implementation and an extended error-free operational lifetime.

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Edward H. Barker

Abstract

Analyzing and balancing wind and mass fields on constant pressure surfaces and then interpolating the results to model coordinates cause significant errors, which lower verification scores and create inertial gravity noise. Although interpolation of geopotential to model coordinates produces less error, the computation of temperature with the model finite difference equations may lead to very large errors, as demonstrated by computation with standard atmosphere profiles.

The solution for temperature using a variational formalism together with the model hydrostatic equation provides a method that greatly decreases the error in computation of pressure height by the model. The procedure is derived and results given for two different forms of Arakawa's hydrostatic equation. One of these forms an ill-conditioned equation set when geopotential is used to compute temperature. The results show that a significant decrease in the errors of geopotential produced by the model occurs when the variational procedure is used.

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David B. Johnson
,
Edward H. Barker
, and
Paul R. Lowe

Abstract

A two-dimensional axially symmetric computer model was used to study downwash-induced fog clearings. In order to produce a clearing the helicopter downwash must reach the ground while the helicopter hovers at or above the top of the fog. The major factors affecting the size and penetration of the downwash are the strength of the helicopter and the buoyancy of the downwash. The clearing can be enlarged beyond the size of the primary downwash by surface-induced divergence and by mixing of dry air into the fog.

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Qin Xu
,
Li Wei
,
Andrew Van Tuyl
, and
Edward H. Barker

Abstract

The statistical analysis of innovation (observation minus forecast) vectors is one of the most commonly used techniques for estimating observation and forecast error covariances in large-scale data assimilation. Building on the work of Hollingsworth and Lönnberg, the height innovation data over North America from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are analyzed. The major products of the analysis include (i) observation error variances and vertical correlation functions, (ii) forecast error autocovariances as functions of height and horizontal distance, (iii) their spectra as functions of height and horizontal wavenumber. Applying a multilevel least squares fitting method, which is simpler and more rigorously constrained than that of Hollingsworth and Lönnberg, a full-space covariance function was determined. It was found that removal of the large-scale horizontal component, which has only small variation in the vertical, reduces the nonseparability. The results were compared with those of Hollingsworth and Lönnberg, and show a 20% overall reduction in forecast errors and a 10% overall reduction in observation errors for the NOGAPS data in comparison with the ECMWF global model data 16 yr ago.

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Qin Xu
,
Binbin Zhou
,
Stephen D. Burk
, and
Edward H. Barker

Abstract

An air–soil layer coupled scheme is developed to compute surface fluxes of sensible heat and latent heat from data collected at the Oklahoma Atmospheric Radiation Measurement–Cloud and Radiation Testbed (ARM–CART) stations. This new scheme extends the previous variational method of Xu and Qiu in two aspects: 1) it uses observed standard deviations of wind and temperature together with their similarity laws to estimate the effective roughness length, so the computed fluxes are nonlocal; that is, they contain the contributions of large-eddy motions over a nonlocal area of O(100 km2); and 2) it couples the atmospheric layer with the soil–vegetation layer and uses soil data together with the atmospheric measurements (even at a single level), so the computed fluxes are much less sensitive to measurement errors than those computed by the previous variational method. Surface skin temperature and effective roughness length are also retrieved as by-products by the new method.

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