Preface |
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xi | (4) |
Acknowledgments |
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xv | |
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Chapter 1 Data Acquisition and Recording |
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1 | (158) |
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1 | (1) |
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1.2 Basic sampling requirements |
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2 | (6) |
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3 | (1) |
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4 | (2) |
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6 | (1) |
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1.2.4 Burst sampling versus continuous sampling |
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6 | (1) |
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1.2.5 Regularly versus irregularly sampled data |
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7 | (1) |
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1.2.6 Independent realizations |
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8 | (1) |
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8 | (25) |
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1.3.1 Mercury thermometers |
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9 | (3) |
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1.3.2 The mechanical bathythermograph (MBT) |
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12 | (2) |
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1.3.3 Resistance thermometers (expendable bathythermograph: XBT) |
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14 | (4) |
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1.3.4 Salinity/conductivity-temperature-depth profilers |
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18 | (1) |
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1.3.5 Dynamic response of temperature sensors |
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19 | (3) |
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1.3.6 Response times of CTD systems |
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22 | (1) |
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1.3.7 Temperature calibration of STD/CTD profilers |
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23 | (1) |
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1.3.8 Sea surface temperature |
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24 | (6) |
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1.3.9 The modern digital thermometer |
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30 | (2) |
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1.3.10 Potential temperature and density |
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32 | (1) |
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33 | (9) |
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1.4.1 Salinity and electrical conductivity |
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34 | (5) |
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1.4.2 The practical salinity scale |
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39 | (3) |
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1.4.3 Nonconductive methods |
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42 | (1) |
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42 | (13) |
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1.5.1 Hydrostatic pressure |
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42 | (1) |
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43 | (5) |
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48 | (6) |
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1.5.4 Other depth sounding methods |
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54 | (1) |
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1.6 Sea-level measurement |
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55 | (13) |
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1.6.1 Tide and pressure gauges |
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58 | (4) |
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1.6.2 Satellite altimetry |
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62 | (1) |
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1.6.3 Inverted echo sounder (IES) |
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63 | (4) |
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1.6.4 Wave height and direction |
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67 | (1) |
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68 | (34) |
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1.7.1 Early current meter technology |
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70 | (1) |
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1.7.2 Rotor-type current meters |
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70 | (8) |
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1.7.3 Nonmechanical current meters |
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78 | (5) |
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1.7.4 Profiling acoustic Doppler current meters (ADCM) |
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83 | (11) |
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1.7.5 Comparisons of current meters |
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94 | (1) |
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1.7.6 Electromagnetic methods |
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95 | (1) |
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1.7.7 Other methods of current measurement |
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96 | (1) |
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97 | (2) |
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99 | (3) |
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1.8 Lagrangian current measurements |
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102 | (17) |
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1.8.1 Drift cards and bottles |
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103 | (1) |
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104 | (3) |
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1.8.3 Processing satellite-tracked drifter data |
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107 | (2) |
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109 | (6) |
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1.8.5 Other types of surface drifters |
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115 | (1) |
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116 | (3) |
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1.8.7 Surface displacements in satellite imagery |
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119 | (1) |
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119 | (6) |
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125 | (2) |
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127 | (18) |
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1.11.1 Conventional tracers |
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128 | (10) |
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1.11.2 Light attenuation and scattering |
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138 | (4) |
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1.11.3 Oxygen isotope: XXX(18)O |
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142 | (1) |
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1.11.4 Helium-3; helium/heat ratio |
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143 | (2) |
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1.12 Transient chemical tracers |
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145 | (14) |
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146 | (3) |
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149 | (4) |
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1.12.3 Chlorofluorocarbons |
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153 | (2) |
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155 | (2) |
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1.12.5 Sulfur hexachloride |
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157 | (1) |
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158 | (1) |
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Chapter 2 Data Processing and Presentation |
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159 | (34) |
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159 | (1) |
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160 | (1) |
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161 | (1) |
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162 | (31) |
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162 | (5) |
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167 | (3) |
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170 | (2) |
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172 | (5) |
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177 | (4) |
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2.4.6 Characteristic or property versus property diagrams |
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181 | (4) |
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2.4.7 Time-series presentation |
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185 | (2) |
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187 | (1) |
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2.4.9 New directions in graphical presentation |
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187 | (6) |
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Chapter 3 Statistical Methods and Error Handling |
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193 | (112) |
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193 | (1) |
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194 | (3) |
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197 | (4) |
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3.3.1 Cumulative probability functions |
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200 | (1) |
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3.4 Moments and expected values |
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201 | (6) |
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3.4.1 Unbiased estimators and moments |
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203 | (1) |
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3.4.2 Moment generating functions |
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204 | (3) |
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3.5 Common probability density functions |
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207 | (4) |
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3.6 Central limit theorem |
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211 | (3) |
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214 | (2) |
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216 | (8) |
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3.8.1 Confidence interval for XXX (XXX known) |
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217 | (1) |
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3.8.2 Confidence interval for XXX (XXX unknown) |
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218 | (1) |
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3.8.3 Confidence interval for XXX(2) |
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219 | (1) |
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3.8.4 Goodness-of-fit test |
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220 | (4) |
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3.9 Selecting the sample size |
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224 | (1) |
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3.10 Confidence intervals for altimeter bias estimates |
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225 | (2) |
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227 | (6) |
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3.11.1 Minimum variance unbiased estimation |
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228 | (1) |
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229 | (1) |
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3.11.3 Maximum likelihood |
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230 | (3) |
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3.12 Linear estimation (regression) |
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233 | (10) |
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3.12.1 Method of least squares |
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234 | (4) |
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3.12.2 Standard error of the estimate |
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238 | (1) |
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3.12.3 Multivariate regression |
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239 | (1) |
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3.12.4 A computational example of matrix regression |
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240 | (2) |
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3.12.5 Polynomial curve fitting with least squares |
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242 | (1) |
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3.12.6 Relationship between least-squares and maximum likelihood |
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242 | (1) |
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3.13 Relationship between regression and correlation |
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243 | (6) |
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3.13.1 The effects of random errors on correlation |
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244 | (1) |
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3.13.2 The maximum likelihood correlation estimator |
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245 | (1) |
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3.13.3 Correlation and regression: cause and effect |
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246 | (3) |
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249 | (8) |
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3.14.1 Significance levels and confidence intervals for correlation |
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253 | (1) |
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3.14.2 Analysis of variance and the F-distribution |
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254 | (3) |
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3.15 Effective degrees of freedom |
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257 | (9) |
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3.15.1 Trend estimates and the integral time scale |
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261 | (5) |
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3.16 Editing and despiking techniques: the nature of errors |
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266 | (11) |
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3.16.1 Identifying and removing errors |
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266 | (7) |
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3.16.2 Propagation of error |
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273 | (1) |
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3.16.3 Dealing with numbers: the statistics of roundoff |
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274 | (3) |
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3.16.4 Gauss-Markov theorem |
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277 | (1) |
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3.17 Interpolation: filling the data gaps |
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277 | (13) |
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3.17.1 Equally and unequally spaced data |
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277 | (2) |
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3.17.2 Interpolation methods |
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279 | (7) |
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3.17.3 Interpolating gappy records: practical examples |
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286 | (4) |
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3.18 Covariance and the covariance matrix |
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290 | (4) |
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3.18.1 Covariance and structure functions |
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291 | (1) |
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3.18.2 A computational example |
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291 | (2) |
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3.18.3 Multivariate distributions |
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293 | (1) |
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3.19 Bootstrap and jackknife methods |
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294 | (11) |
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295 | (6) |
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301 | (4) |
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Chapter 4 The Spatial Analyses of Data Fields |
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305 | (66) |
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4.1 Traditional block and bulk averaging |
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305 | (4) |
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309 | (10) |
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4.2.1 Objective mapping: examples |
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314 | (5) |
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4.3 Empirical orthogonal functions |
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319 | (25) |
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4.3.1 Principal axes of a single vector time series (scatter plot) |
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325 | (3) |
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4.3.2 EOF computation using the scatter matrix method |
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328 | (4) |
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4.3.3 EOF computation using singular value decomposition |
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332 | (2) |
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4.3.4 An example: deep currents near a mid-ocean ridge |
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334 | (2) |
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4.3.5 Interpretation of EOFs |
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336 | (4) |
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4.3.6 Variations on conventional EOF analysis |
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340 | (4) |
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344 | (12) |
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4.4.1 Vertical normal modes |
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344 | (3) |
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4.4.2 An example: normal modes of semidiurnal frequency |
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347 | (3) |
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4.4.3 Coastal-trapped waves (CTWs) |
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350 | (6) |
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356 | (15) |
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4.5.1 General inverse theory |
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356 | (5) |
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4.5.2 Inverse theory and absolute currents |
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361 | (5) |
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4.5.3 The IWEX internal wave problem |
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366 | (4) |
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4.4.4 Summary of inverse methods |
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370 | (1) |
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Chapter 5 Time-series Analysis Methods |
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371 | (198) |
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371 | (2) |
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5.2 Stochastic processes and stationarity |
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373 | (1) |
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5.3 Correlation functions |
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374 | (6) |
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380 | (12) |
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5.4.1 Mathematical formulation |
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381 | (3) |
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5.4.2 Discrete time series |
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384 | (3) |
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5.4.3 A computational example |
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387 | (1) |
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5.4.4 Fourier analysis for specified frequencies |
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388 | (2) |
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5.4.5 The fast Fourier transform |
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390 | (2) |
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392 | (12) |
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5.5.1 A least-squares method |
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392 | (3) |
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5.5.2 A computational example |
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395 | (2) |
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5.5.3 Harmonic analysis of tides |
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397 | (1) |
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5.5.4 Choice of constituents |
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398 | (1) |
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5.5.5 A computational example for tides |
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399 | (3) |
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5.5.6 Complex demodulation |
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402 | (2) |
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404 | (60) |
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5.6.1 Spectra of deterministic and stochastic processes |
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409 | (4) |
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5.6.2 Spectra of discrete series |
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413 | (4) |
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5.6.3 Conventional spectral methods |
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417 | (8) |
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5.6.4 Spectra of vector series |
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425 | (7) |
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5.6.5 Effect of sampling on spectral estimates |
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432 | (9) |
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5.6.6 Smoothing spectral estimates (windowing) |
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441 | (9) |
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5.6.7 Smoothing spectra in the frequency domain |
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450 | (4) |
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5.6.8 Confidence intervals on spectra |
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454 | (1) |
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5.6.9 Zero-padding and prewhitening |
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455 | (5) |
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5.6.10 Spectral analysis of unevenly spaced time series |
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460 | (1) |
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5.6.11 General spectral bandwidth and Q of the system |
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461 | (1) |
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5.6.12 Summary of the standard spectral analysis approach |
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461 | (3) |
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5.7 Spectral analysis (parametric methods) |
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464 | (16) |
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5.7.1 Some basic concepts |
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467 | (1) |
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5.7.2 Autoregressive power spectral estimation |
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468 | (10) |
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5.7.3 Maximum likelihood spectral estimation |
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478 | (2) |
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5.8 Cross-spectral analysis |
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480 | (21) |
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5.8.1 Cross-correlation functions |
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480 | (2) |
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5.8.2 Cross-covariance method |
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482 | (1) |
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5.8.3 Fourier transform method |
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482 | (2) |
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5.8.4 Phase and cross-amplitude functions |
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484 | (1) |
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5.8.5 Coincident and quadrature spectra |
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485 | (1) |
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5.8.6 Coherence spectrum (coherency) |
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486 | (4) |
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5.8.7 Frequency response of a linear system |
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490 | (5) |
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5.8.8 Rotary cross-spectral analysis |
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495 | (6) |
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501 | (15) |
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5.9.1 The wavelet transform |
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502 | (2) |
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504 | (1) |
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5.9.3 Oceanographic examples |
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505 | (3) |
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5.9.4 The S-transformation |
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508 | (3) |
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5.9.5 The multiple filter technique |
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511 | (5) |
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516 | (41) |
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518 | |
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517 | (2) |
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519 | (8) |
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5.10.4 Design of oceanographic filters |
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527 | (5) |
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5.10.5 Running-mean filters |
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532 | (3) |
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5.10.6 Godin-type filters |
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535 | (1) |
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5.10.7 Lanczos-window cosine filters |
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536 | (7) |
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5.10.8 Butterworth filters |
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543 | (8) |
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5.10.9 Frequency-domain (transform) filtering |
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551 | (6) |
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557 | (12) |
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5.11.1 The scaling exponent method |
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561 | (1) |
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5.11.2 The yardstick method |
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562 | (1) |
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5.11.3 Box counting method |
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563 | (1) |
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5.11.4 Correlation dimension |
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564 | (1) |
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5.11.5 Dimensions of multifractal functions |
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564 | (3) |
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567 | (2) |
Appendices |
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569 | (24) |
Appendix A Units in physical oceanography |
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570 | (2) |
Appendix B Glossary of statistical terminology |
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572 | (4) |
Appendix C Means, variances and moment-generating functions for some common continuous variables |
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576 | (1) |
Appendix D Statistical tables |
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577 | (8) |
Appendix E Correlation coefficents at the 5% and 1% levels of significance for various degrees of freedom v |
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585 | (1) |
Appendix F Approximations and nondimensional numbers in physical oceanography |
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586 | (7) |
References |
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593 | (24) |
Index |
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617 | |