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useful to review the results of a purely linear analysis of (2.6) and (2.7) . Neglecting all nonlinear terms yields (after some algebra) The normal modes of the above are extracted by adding (2.9) and (2.10) , the latter having been multiplied by an unknown coefficient, α. The aim of a normal mode analysis is to produce an equation in one variable only. Some straightforward algebra demonstrates that α values of yield two equations of the form ( h ± ) t − β ± ( h ± ) x = − β ± ϕ x , (2
useful to review the results of a purely linear analysis of (2.6) and (2.7) . Neglecting all nonlinear terms yields (after some algebra) The normal modes of the above are extracted by adding (2.9) and (2.10) , the latter having been multiplied by an unknown coefficient, α. The aim of a normal mode analysis is to produce an equation in one variable only. Some straightforward algebra demonstrates that α values of yield two equations of the form ( h ± ) t − β ± ( h ± ) x = − β ± ϕ x , (2
) were used as the second criterion in the proposed methodology. Fig . 4. Data sources used to delimit the aquifer basin boundaries: (a) groundwater resources of the world according to the WHYMAP (BGR and UNESCO; http://www.whymap.org ), (b) the BGR geological units by age (BGR; http://www.bgr.de/karten/igme5000/igme5000.htm ), and (c) the simplified lithology of France (BRGM; http://infoterre.brgm.fr ). The comparison of the BDRHF map ( Fig. 3 ) with the two selected classes of WHYMAP, the IGME
) were used as the second criterion in the proposed methodology. Fig . 4. Data sources used to delimit the aquifer basin boundaries: (a) groundwater resources of the world according to the WHYMAP (BGR and UNESCO; http://www.whymap.org ), (b) the BGR geological units by age (BGR; http://www.bgr.de/karten/igme5000/igme5000.htm ), and (c) the simplified lithology of France (BRGM; http://infoterre.brgm.fr ). The comparison of the BDRHF map ( Fig. 3 ) with the two selected classes of WHYMAP, the IGME
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
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
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
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
–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
–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
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
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
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
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
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
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
.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
.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
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
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
