B-Argo profile 3.1 1.2 1950010100000020180903200006202604230740532902748 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_028 0423_siobio003_028 2B 2B RRRRRRRARRRAP41305-17CH001 P41305-17CH001 V3.01 V3.01 841 841 11~@~@w/-@@w/-@@h|?b@h|?b@11GPS GPS A Primary sampling: averaged [] Secondary sampling: discrete [] >33? @ `@ @ @ @ @ AA 0A PAffnA A̘ĄA33A AAA33A B B Bff B $B ,B 4B C ACCC EC GCfHC KCMC OCfPC SC UC WC YC [C]C _C aC cC eCgC iC kC mCfnC qCfrCftCfvC yCC CffC3sC͌C͌C C3sC C͌CC͌C͌C͌C3sCYC C͌C CCfC C͌C C C C D @D D @D
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:.bY9Cx}9ege9fe9lq9Ð9#PM9>PM93]e9~:59s9JY9*Wq9d98(9 9L9͠ 999 9ǥ(9+9f 9@9rX9ř 9Ș 9p4949Ν(9"(9 9}(9К(9(9(9j(9ڏ 9J949(9}9+9^ 9@9o 9(99 9CX9QL9p9 9=49?X9949<9 9(9 9 9[@9@9Q 9p9u9ˋ(9{9p9s9 9 9L9x9@9֥X9O(9} 9X9| 9~n9Rt(9\p49b49T49C49-4949/9h39X?9W9XK9;{9n9n9mV9-29z9M=9rI9ɬ9a9NT9]l985$9 `9.;9ә9v;91:9^9C.9!-9GxQ9.89Ƒ 9\9e\9RD9ob\9N!\9C94tg9]79#[9'b*9#*959~59])9M9i)9V591W}9S|;d@9X9LX9(9(949Gm(9"Y(9 0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOG OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGPRES 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 = 2.2798 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 20260423074053 HZ HZ HZ HZ OGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOGOG ~@~@