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krn Constant Altitude Plan Position Indicator (CAPPI) maps would outline these cores. [The technique of these CAPPI maps has been de scribed more recently by Marshall and Ballantyne (1975)]. Good correlation was found in time and azimuth between the recorded sferics and the cores as displayed on the 7 km CAPPI maps, which we shall refer to as "Larsen regions." (Further comparison was 0021-8952/78/02o6-o212505.0o ~) 1978 American Meteorological Societymade on strip charts with azimuth and time as
krn Constant Altitude Plan Position Indicator (CAPPI) maps would outline these cores. [The technique of these CAPPI maps has been de scribed more recently by Marshall and Ballantyne (1975)]. Good correlation was found in time and azimuth between the recorded sferics and the cores as displayed on the 7 km CAPPI maps, which we shall refer to as "Larsen regions." (Further comparison was 0021-8952/78/02o6-o212505.0o ~) 1978 American Meteorological Societymade on strip charts with azimuth and time as
conditions. In Brink's model the response istherefore dependent on location; in MF's model it isnot. Also, Brink's boundary conditions limit the zonalfetch, whereas MF's model has unlimited fetch. Brinksucceeds in reproducing characteristic features of hisobserved coherence maps. In this paper we show thatMF's model also reproduces the characteristic featuresof observed coherence maps. One reason why we recalculate coherence maps using MF's model is that itis algebraically simpler because it assumes
conditions. In Brink's model the response istherefore dependent on location; in MF's model it isnot. Also, Brink's boundary conditions limit the zonalfetch, whereas MF's model has unlimited fetch. Brinksucceeds in reproducing characteristic features of hisobserved coherence maps. In this paper we show thatMF's model also reproduces the characteristic featuresof observed coherence maps. One reason why we recalculate coherence maps using MF's model is that itis algebraically simpler because it assumes
chosen fromindicated maps. Comparison values for hemisphere are fromTables 1, 20, 25. Units are m2. None (climatology)Point E' E" Predictor mapsPresent map Present mappast map past map past progE~ E' E~ Ett1 21595 24164 4212 4315 2752 28082 22758 19566 4131 3983 2606 26023 2895 2159 816 543 779 5474
chosen fromindicated maps. Comparison values for hemisphere are fromTables 1, 20, 25. Units are m2. None (climatology)Point E' E" Predictor mapsPresent map Present mappast map past map past progE~ E' E~ Ett1 21595 24164 4212 4315 2752 28082 22758 19566 4131 3983 2606 26023 2895 2159 816 543 779 5474
, B. J. Etherton , and Z. Toth , 2002b : Adaptive sampling with the ensemble transform Kalman filter. Part II: Field program implementation. Mon. Wea. Rev. , 130 , 1356 – 1369 . Majumdar , S. J. , S. D. Aberson , C. H. Bishop , R. Buizza , M. S. Peng , and C. A. Reynolds , 2006 : A comparison of adaptive observing guidance for Atlantic tropical cyclones. Mon. Wea. Rev. , 134 , 2354 – 2372 . Noble , B. , and J. W. Daniel , 1988 : Applied Linear Algebra . 3d
, B. J. Etherton , and Z. Toth , 2002b : Adaptive sampling with the ensemble transform Kalman filter. Part II: Field program implementation. Mon. Wea. Rev. , 130 , 1356 – 1369 . Majumdar , S. J. , S. D. Aberson , C. H. Bishop , R. Buizza , M. S. Peng , and C. A. Reynolds , 2006 : A comparison of adaptive observing guidance for Atlantic tropical cyclones. Mon. Wea. Rev. , 134 , 2354 – 2372 . Noble , B. , and J. W. Daniel , 1988 : Applied Linear Algebra . 3d
. radar at the location of a gage even if the Z-Rrelation, the map projection, and the raingage locationson the map are perfect. However, the resulting error inestimating point rainfall should not be systematic.5. Results The raingage (G) and radar observations (R~) permitted 50 comparisons, none of which involved seededclouds. These comparisons are tabulated in. Table 1 andsummarized in Table 2. Using the raingage results asthe standard, the mean absolute difference is about 30%while the mean
. radar at the location of a gage even if the Z-Rrelation, the map projection, and the raingage locationson the map are perfect. However, the resulting error inestimating point rainfall should not be systematic.5. Results The raingage (G) and radar observations (R~) permitted 50 comparisons, none of which involved seededclouds. These comparisons are tabulated in. Table 1 andsummarized in Table 2. Using the raingage results asthe standard, the mean absolute difference is about 30%while the mean
. 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
. 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
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
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
, 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
, 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
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
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
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
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
