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transects of Drake Passage using underway acoustic Doppler current profiler (ADCP) data. They extracted the upper-ocean ageostrophic velocity profile from this dataset by removing an estimate of the geostrophic velocity, assumed constant with depth, taken as the ADCP velocity at 98 m. This method was based on a favorable comparison, at that depth, between the vertical shear of the ADCP cross-track velocities and independent geostrophic cross-track velocity shear estimated from expendable
transects of Drake Passage using underway acoustic Doppler current profiler (ADCP) data. They extracted the upper-ocean ageostrophic velocity profile from this dataset by removing an estimate of the geostrophic velocity, assumed constant with depth, taken as the ADCP velocity at 98 m. This method was based on a favorable comparison, at that depth, between the vertical shear of the ADCP cross-track velocities and independent geostrophic cross-track velocity shear estimated from expendable
occurred within a month of the same time of year, because you wouldn't expect that fall maps and midwinter maps would be much alike anyway, and hoping maybe I could find a few cases where the difference between them or some measure (say rms difference) between the two fields was only half of that between two randomly chosen fields. But the best that I found of these few hundred thousand comparisons was one case I think where it was 62%. It didn't seem like a very good analog somehow but it was enough
occurred within a month of the same time of year, because you wouldn't expect that fall maps and midwinter maps would be much alike anyway, and hoping maybe I could find a few cases where the difference between them or some measure (say rms difference) between the two fields was only half of that between two randomly chosen fields. But the best that I found of these few hundred thousand comparisons was one case I think where it was 62%. It didn't seem like a very good analog somehow but it was enough
lag cross correlation of MEOT profiles peaks at 6, which suggests a lag of 6 months, similar to the one observed in the map sequences. Comparison of the temporal profiles with teleconnection indices confirms that pair MEOT1–MEOT3 relates the most to the ENSO event ( Table 4 ) as evidenced by correlations with the Niño-3.4 index of 0.82 and 0.73 for MEOT1 and MEOT3, respectively. Results indicate that all other climate indices have correlations that are below the 0.5 threshold ( Table 4 and Fig
lag cross correlation of MEOT profiles peaks at 6, which suggests a lag of 6 months, similar to the one observed in the map sequences. Comparison of the temporal profiles with teleconnection indices confirms that pair MEOT1–MEOT3 relates the most to the ENSO event ( Table 4 ) as evidenced by correlations with the Niño-3.4 index of 0.82 and 0.73 for MEOT1 and MEOT3, respectively. Results indicate that all other climate indices have correlations that are below the 0.5 threshold ( Table 4 and Fig
relatively small number of spatial patterns that account for large fractions of variance of thefield. Similar analysis techniques such as canonical correlation analysis (CCA) and singular value decomposition (SVD) analysis that identify pairs of spatial patterns from two data fields are also becoming popular.Singular value decomposition, in general, is a basic matrix operation in linear algebra, whereas the SVD analysis discussed in the present study refers to the technique that isolates pairs of spatial
relatively small number of spatial patterns that account for large fractions of variance of thefield. Similar analysis techniques such as canonical correlation analysis (CCA) and singular value decomposition (SVD) analysis that identify pairs of spatial patterns from two data fields are also becoming popular.Singular value decomposition, in general, is a basic matrix operation in linear algebra, whereas the SVD analysis discussed in the present study refers to the technique that isolates pairs of spatial
of the 300-mb surface, 0400 GCT 31 October 1945. Heavy lines same as in fig. 18.from summer to winter, if any, is small, and Aumomentum does not vary with height in the mean. Inindividual cases, it sometimes increases, sometimesdecreases, upward. The intensity of Au-momentumgenerally is about double that of A I er I -momentum,even though Iv( exceeds u near the subtropical ridge.Preliminary comparison between the hemispherictrends and individual maps shows good agreementwith the expected long
of the 300-mb surface, 0400 GCT 31 October 1945. Heavy lines same as in fig. 18.from summer to winter, if any, is small, and Aumomentum does not vary with height in the mean. Inindividual cases, it sometimes increases, sometimesdecreases, upward. The intensity of Au-momentumgenerally is about double that of A I er I -momentum,even though Iv( exceeds u near the subtropical ridge.Preliminary comparison between the hemispherictrends and individual maps shows good agreementwith the expected long
first-order stations for the most recent 10 yravailable (1959-68) with the 1931-60 values. Althoughqualifying t'heir conclusion since no specific, eff0rt.s weremade t'o select the stations carefully with respect, toexposure, station history, etc., they found a tendencyt,oward the departure patt,erns of the 1850s and 1860s.The core zones of temperature depart,ure identified foreach mid-19th century period did not exactly correspondin location, but a composite map for both periods wouldput the region
first-order stations for the most recent 10 yravailable (1959-68) with the 1931-60 values. Althoughqualifying t'heir conclusion since no specific, eff0rt.s weremade t'o select the stations carefully with respect, toexposure, station history, etc., they found a tendencyt,oward the departure patt,erns of the 1850s and 1860s.The core zones of temperature depart,ure identified foreach mid-19th century period did not exactly correspondin location, but a composite map for both periods wouldput the region
, we will use the predictionoperator X t,hat transforms F(t) into F(t+At). In termsof this operator, (3) becomes an algebraic equation(1--i/3At)X2"2i~tX-(l+i~At)=0, (4)and this equation gives the two solutionsX= at %'1+p2At2-a2At2. (5)1-$AtThe properties of the two solutions depend on the ar2At2 5 1 +@'At2. (6)If this condition is satisfied, the magnitude of the tworoots will be unity, and the numerical integration willremain stable.Consider now a high-frequenc,y perturbation where
, we will use the predictionoperator X t,hat transforms F(t) into F(t+At). In termsof this operator, (3) becomes an algebraic equation(1--i/3At)X2"2i~tX-(l+i~At)=0, (4)and this equation gives the two solutionsX= at %'1+p2At2-a2At2. (5)1-$AtThe properties of the two solutions depend on the ar2At2 5 1 +@'At2. (6)If this condition is satisfied, the magnitude of the tworoots will be unity, and the numerical integration willremain stable.Consider now a high-frequenc,y perturbation where
and One solution to this system is the classic Eady edge wave solution: where the frequency ω is given by This leaves the total leading order solution: 4. Next-order corrections Substituting Φ 0 from (31) into Eqs. (23) for F 1 and G 1 leads to correspondent equivalent representations: Taking into account the properties of Φ e and after some algebra and numerous applications of the identity we find the solutions for the curl potentials: Here F̃ and G̃ are homogeneous solutions to
and One solution to this system is the classic Eady edge wave solution: where the frequency ω is given by This leaves the total leading order solution: 4. Next-order corrections Substituting Φ 0 from (31) into Eqs. (23) for F 1 and G 1 leads to correspondent equivalent representations: Taking into account the properties of Φ e and after some algebra and numerous applications of the identity we find the solutions for the curl potentials: Here F̃ and G̃ are homogeneous solutions to
A Fast Mean-Preserving Spline for Interpolating Interval Data, its 10% bound tolerance will equate to 2°C, in other words, no single interpolated value within the interval can exceed 2°C of the original interval mean. 3. Results and discussion a. Accuracy and comparison with other methods In this section we compare our newly proposed interpolation method with existing mean-preserving smoothing algorithms including the polynomial method with mixed boundary conditions described by D03 and the recursive algebraic method of RM01 . We evaluate each
, its 10% bound tolerance will equate to 2°C, in other words, no single interpolated value within the interval can exceed 2°C of the original interval mean. 3. Results and discussion a. Accuracy and comparison with other methods In this section we compare our newly proposed interpolation method with existing mean-preserving smoothing algorithms including the polynomial method with mixed boundary conditions described by D03 and the recursive algebraic method of RM01 . We evaluate each
. Price, is noltoo difiicult for the graded public schools.Mr. Price first provides by means of suitably ruled pages foithe systematic record of personal observations of the weatheiconditions, the clouds, the winds, end the prominent featuresof storms. Then follow observations by the aid of such instru-mens as the barometer, hygrometer, and thermometers; andfinally, by means of the daily weather maps, the observed localconditions are correlated with the general weather conclitioiifin the United States
. Price, is noltoo difiicult for the graded public schools.Mr. Price first provides by means of suitably ruled pages foithe systematic record of personal observations of the weatheiconditions, the clouds, the winds, end the prominent featuresof storms. Then follow observations by the aid of such instru-mens as the barometer, hygrometer, and thermometers; andfinally, by means of the daily weather maps, the observed localconditions are correlated with the general weather conclitioiifin the United States
