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C. L. BRISTOR

of routinelyproduced barotropic forecasts. One such program pro-duces mean monthly algebraic height error informationover the entire grid from 24-, 48-, and 72-hr. forecasts.Further processing yields t,he resulting zonal wind errorpatterns. Figure 1 presents this information for the in-dividual months. Here one sees the marked tendency forthe forecasts to shift the maximum westerlies to the north.The same general type of error is repeated month aftermonth suggesting a highly systematic error

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Claude Girard
,
Robert Benoit
, and
Michel Desgagné

–15 November 1999) of the Mesoscale Alpine Programme (MAP). An overview of its performance during MAP is given in Benoit et al. (2002a) . In spite of the relatively good performance of the model on real as well as canonical cases, a problem handling finescale topography was becoming increasingly evident. From the beginning ( Denis 1990 ), the model had been showing rather high sensitivity to orographic forcing. In consequence, topography used by the model was smoothed more than what is

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Leo C. Clarke
and
Robert J. Renard

and B,where the quantity MM~ has a discontinuity withthe algebraic sign of MM~ differing to either side.For the purpose at hand, these unique points, denotingthe maximuln and minimum GG~ points, are set equalto zero in numerical calculations and will be referredto as "zero" MM~ points hereafter. Also note thatthe maximum MM~ corresponds with the zero GG~value. Except for the points A and B, the MM~ reflectsthe magnitude of V~. These relationships give significance to the symbolization MM~ which

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Michael C. Tsianco
and
K. Ruben Gabriel

theauthors.2.The temperature dataThe data consist of monthly mean temperatures(units of 10°C) at each of 50 weather stations in North,Central and South America-see the map in Fig. 1(for details, see Tsianco and Gabriel, 1981)-for allmonths of 1951 and 1952. For a preliminary view ofthe data, we present a three-way analysis of variancein Table 1. We see strong effects of stations and monthsas well as interactions between these factors. A modelwith stations, months and station X month effects fitsquite

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Dezső Dévényi
and
Thomas W. Schlatter

the Mesoscale Analysis and Prediction System (MAPS), areal-time data assimilation system, were investigated. Observed residuals are defined as differences betweenrawinsonde observations interpolated vertically to the model levels and the predicted values from MAPS interpolated horizontally to the radiosonde locations. One-point statistical moments up to order 4 (including skewnessand flatness) were computed to investigate the normality of the probability distribution of observed residuals

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Jürgen Willebrand

low frequencies. For the data set discussed in this paper,a direct comparison has not yet been possible as theobservational period does not overlap with that ofavailable weathership records. Nevertheless, a comparison of spectral quantities is meaningful providedthat the statistical properties of the wind field donot change in time. Fig. 8 shows autospectra of the wind componentsfrom the Atlantic Weather Station "C", togetherwith those from the nearest grid point of the synoptic map. The

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Akira Kasahara

results ofthe geostrophic (G) and nongeostrophic (NG) forecasts of hurricane movement are presented here, notonly to facilitate comparison between the behaviorof the two models but also to investigate the forecasterrors with a view to further improvement of theprediction models.374JOURNAL OF METEOROLOGYVOLUME 16.Let us first consider the sources of forecast errors.In general, errors inherent in the forecast may beclassified into the following three types :1. map analysis errors, especially those due

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M. F. Larsen
and
J. Röttger

.g., Handbook for MAP, Bowhill andEdwards 1983, 1984, 1986) and in diverse journalsthat may not be readily accessible to the meteorologicalcommunity. Hocking et al. (1989) have provided themost recent publication on the interpretation, reliability, and accuracy of parameters deduced fromspaced antenna measurements. In this article, we willdescribe the technique and outline some of the potentialadvantages and disadvantages of the method. We willalso describe the handful of comparisons of both thespaced

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Ming Bao
and
John M. Wallace

derived from the five subsets to obtain a single set of clusters. The values of RP are based on spatial correlations between corresponding clusters in the five subsets of the input. As explained in the previous section, each value represents the average of 10 different comparisons. The resulting clusters are shown in Fig. 1 . The first three patterns correspond closely to the regimes G′, A′, and R′ in CW (their Fig. 7). Fig . 1. Composite 500-hPa height anomaly maps of the cluster derived from Ward

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Richard E. Chandler
,
Clair R. Barnes
, and
Chris M. Brierley

this based on a purely visual inspection. Moreover, it is hard to identify any systematic and detailed spatial structure from the maps. This creates difficulties for several potential users of the ensemble. Consider, for example, a regional climate modeler who sees EuroCORDEX as an opportunity to explore the simulation of summer temperatures by different RCMs. Apart from the apparent cool biases noted above, Fig. 1 does not obviously provide useful information to support such a comparison

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