`B-Argo profile 3.1 1.2 1950010100000020210323160013202604230757282902748 2902748 PROVOR PROVOR CHINA ARGO PROJECT CHINA ARGO PROJECT FEI CHAI FEI CHAI PRES FLUORESCENCE_CHLA CHLA BETA_BACKSCATTERING700 BBP700 PRES FLUORESCENCE_CHLA CHLA BETA_BACKSCATTERING700 BBP700 " " AAHZHZ0423_siobio003_290 0423_siobio003_290 2B 2B RRRRRRRARRRAP41305-17CH001 P41305-17CH001 V3.01 V3.01 841 841 11d+g@d+g@Gz<@Gz<@ˡEb@ˡEb@11GPS GPS A Primary sampling: averaged [] Secondary sampling: discrete [] ?33? @fff@ @ @ @ @ A A 0AQAffnAffA33A33A A A A AAB BffBff B#B+B 4B C@CffACffBC3CC ECFCffGC3HC3IC KCLLCLMCNCfOCQC RCSCfTC33VC WC XCYC[C33\C]Cf^C`CffaCbCcCeC fCgC iCLjCffkC3lC nCLoCpCfqC sCfftCuC wCLxCyC3zC33|C}C3~C C3CYC CCf&CكCC33CfC͌Cf&CهCC33CfCC33C3C3C @C3C3CLCC̐CYCCْC3sCf&CٔC C33C3C C @C CCLC CCLC CfCYC C3CffCC3C3sCf&C C͌C @C٥CCLC٧CffC3CYC̩CLC C33C3CٯCf&C C CfCCC3CCfCfC CYC3C3sCf&Cy D `DYDfD3
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9Mi9Q9H9 9f9Y:9Z99)9AX9'L99@?9999&9v9c9H9dH99S99E9ձ}:O9=9CZ9L99V9N9䟦99999ɪ9M9`9M99999X98M999S9⒒9 9=c9G99K9q9?99 99I9I9999U9<9:c:9;9Ό9/9<99 }999Db9<9|94|9Ab9!9T99%9T9;b9So9?T9|9ёo91b9@/b9wrG9rT9XL9(9-fG9 :9b9T,9*,9 TG9@T99999 LG9T9b9Ɵ9GG999wT9>,9|9G9GL,9KF9u99na9 S9S9S89-F9 89:m9YE979l9ҤD9v_9+zQ9&l9 Q9(959$}59B9O98O9A9a 4983939M9229%9(Z9?9l&$9P19s>9O9LǍ9\s9
"909J9^"99L9#/9 A<9q!9d90I9;9 9&k;9z9c-9}9m9M-92-9-9rb9 000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOG OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGPRES FLUORESCENCE_CHLA CHLA BETA_BACKSCATTERING700 BBP700 PRES FLUORESCENCE_CHLA CHLA BETA_BACKSCATTERING700 BBP700 Adjusted values are provided in the core profile file none none none none none none CHLA_ADJUSTED = CHLA_NPQ for PRES in [0,ZMaxFluo], CHLA_ADJUSTED = ((FLUORESCENCE_CHLA-MEDIAN(PRELIM_DARK_CHLA))*SCALE_CHLA)/PHYSIO_RATIO otherwise none none none none none none none none none CHLA_NPQ = [], ZMaxFluo = [], PRELIM_DARK_CHLA = [50,50,50,50,50], SCALE_CHLA = 0.0073, PHYSIO_RATIO = 2.2968 none none none none none none none none none CHLA real time adjustment (specified in http://dx.doi.org/10.13155/35385 and computed with MLD_LIMIT = 0.03, and following recommendations of Roesler et al., 2017 (https://doi.org/10.1002/lom3.10185)) none none 20260423075728 HZ HZ HZ HZ OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOG d+g@d+g@