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Peter Müller

. Brink (1989) , Samelson (1990) , and later Lippert and Müller (1995) all calculated coherence maps from a simple linear quasigeostrophic model with stochastic wind forcing, for comparison with the observed maps. The model has a flat bottom and no mean currents. The forcing is assumed to be statistically homogeneous and described either by its autocovariance function in separation space or by its spectrum in wavenumber space. Choosing certain idealized but reasonable spectra or autocovariance

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Lindley Graham
,
Troy Butler
,
Scott Walsh
,
Clint Dawson
, and
Joannes J. Westerink

defined as nodes that are wet at any point during the simulation. The nonoptimal QoI map stations , shown in Fig. 12 , identify as a larger event in the parameter space as shown in Fig. 13 . Figure 9 shows that the QoI map formed from the nonoptimal set of stations is highly skewed in comparison to the QoI maps formed from the optimal and near-optimal sets of stations. The estimated volume of the region of interest for the nonoptimal QoI map 1.917 × 10 −2 is about 250% greater than that of

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S. J. Majumdar
,
S. D. Aberson
,
C. H. Bishop
,
R. Buizza
,
M. S. Peng
, and
C. A. Reynolds

, ETKF, and SV techniques, and their similarities and differences are described in section 2 . Guidance maps for two hurricane forecasts are presented in section 3 . A quantitative comparison of guidance from the respective techniques on large and local scales is performed in section 4 . Concluding remarks follow in section 5 . 2. Adaptive observing techniques The five types of adaptive observing guidance for TCs are summarized in Table 1 . The ensemble DLM wind variance only considers the

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FREDERICK G. SHUMAN
and
JOHN D. STACKPOLE

March 1968Frederick G. Shuman and John D. Stackpole157NOTE ON THE FORMULATION OF FINITE DIFFERENCE EQUATIONS INCORPORATING A MAP SCALE FACTOR FREDERICK G. SHUMAN and JOHN D. STACKPOLE National Meteorological Center, Weather Bureau, ESSA, Washington, D.C.ABSTRACTNumerical experimentation with various finite difference formulations of a particular set of differential equationsincorporating a map scale factor indicates that the stability of the calculations is as dependent upon the

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J. K. ANGELL

deviation.I V-V, 1 is indicated in the upper diagram of figure 3.Tlle mode is 2 4 m. sec.-l, the n1edia.n is 10 m. sec-l andtile mean is 12 m. sec.". I n 15 percent of the cases thetleviation is greater than 20m.sec.-l. For comparison,Jfachta found an average ~7alue of 13 m. sec.-l f o r the vectorgeostrophic deviation at 300 mb. during the winternlonths. His chta were obhined by comparing the windand pressure gradient (geostrophic wind) on analyzedsynoptic maps.The absolute magnitudes of the cross

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Filipe Aires
,
Fabrice Papa
,
Catherine Prigent
,
Jean-François Crétaux
, and
Muriel Berge-Nguyen

-resolution surface water maps as well as water height for rivers, lakes, inundated areas, and wetlands. Before the launch of this mission and in the framework of its preparation, long-term datasets of high-spatial-resolution surface water extent are in demand. Would it be possible to develop downscaling methodology to derive high-resolution surface water extent from the existing GIEMS low-resolution dataset? Since GIEMS has a global coverage, the ideal situation would be to develop a downscaling technique

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Sarah T. Gille

sample globally, obtaining mean temperatures and velocities over 9–25-day intervals and therefore are averaging over several full tidal and internal wave cycles. One of the design objectives in deploying ALACE floats was to measure absolute reference velocities for use in refining hydrographic transport estimates. The first goal of this study is to make use of ALACE measurements to map mean temperature and dynamic topography in the Southern Ocean. ALACE temperatures are then compared with

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Michèle De La Chevrotière
and
John Harlim

for sake of comparison. Overall, GC performs the worst with numerical blow up at . The scalar map ME2 produces improved filter estimates over GC except on but it converges for the case of . The vector map ME2 beats these two cases on all counts. Also, the filtering skills of the vector maps ME1 and ME2 are visually indistinguishable. For some of the fields, most notably for , , , and , their skills are close to that of the perfect-model vector map experiment PM . Interestingly, in

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J. M. Schneider
,
J. D. Garbrecht
, and
D. A. Unger

tend to scale linearly with annual precipitation (i.e., wetter climates have larger rmse’s). This average is about 39% of the 30-yr monthly average precipitation for the contiguous United States. Table 1 also includes the ratio of the rmse to the standard deviation of all months in the test period for each forecast division, with the corresponding map shown in Fig. 5 . This is a comparison between method error and the variability of the test data. The unitless ratio ranges from about 0.4 to 0

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K. J. H. Law
and
A. M. Stuart

observations. The first of these is found by means of accurate MCMC simulations and is then characterized by three quantities: its mean, variance, and MAP estimator. It is our contention that, where quantification of uncertainty is important, the comparison of algorithms by their ability to predict (i) is central; however many algorithms are benchmarked in the literature by their ability to predict the truth [(ii)] and so we also include this information. A comparison of the algorithms with the

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