FN Archimer Export Format
PT J
TI Improved Statistical Method for Quality Control of Hydrographic Observations
BT 
AF Gourrion, Jerome
   Szekely, Tanguy
   Killick, Rachel
   Owens, Breck
   Reverdin, Gilles
   Chapron, Bertrand
AS 1:1;2:1;3:2;4:3;5:4;6:5;
FF 1:;2:;3:;4:;5:;6:PDG-ODE-LOPS-SIAM;
C1 OceanScope, Plouzané, France
   Met Office, Exeter, United Kingdom
   Woods Hole Oceanographic Institution, Woods Hole, Massachusetts
   Sorbonne-Université, CNRS/IRD/MNHN (LOCEAN UMR 7159), Paris, France
   LOPS, Ifremer, Plouzané, France
C2 OCEANSCOPE, FRANCE
   MET OFFICE, UK
   WHOI, USA
   UNIV SORBONNE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   copubli-france
   copubli-europe
   copubli-univ-france
   copubli-int-hors-europe
IF 2.075
TC 6
UR https://archimer.ifremer.fr/doc/00628/74031/73359.pdf
LA English
DT Article
DE ;Ocean;Climatology;Salinity;Temperature;Data quality control;Oceanic variability
AB Realistic ocean state prediction and its validation rely on the availability of high quality in situ observations. To detect data errors, adequate quality check procedures must be designed. This paper presents procedures that take advantage of the ever-growing observation databases that provide climatological knowledge of the ocean variability in the neighborhood of an observation location. Local validity intervals are used to estimate binarily whether the observed values are considered as good or erroneous. Whereas a classical approach estimates validity bounds from first- and second-order moments of the climatological parameter distribution, that is, mean and variance, this work proposes to infer them directly from minimum and maximum observed values. Such an approach avoids any assumption of the parameter distribution such as unimodality, symmetry around the mean, peakedness, or homogeneous distribution tail height relative to distribution peak. To reach adequate statistical robustness, an extensive manual quality control of the reference dataset is critical. Once the data have been quality checked, the local minima and maxima reference fields are derived and the method is compared with the classical mean/variance-based approach. Performance is assessed in terms of statistics of good and bad detections. It is shown that the present size of the reference datasets allows the parameter estimates to reach a satisfactory robustness level to always make the method more efficient than the classical one. As expected, insufficient robustness persists in areas with an especially low number of samples and high variability. 
PY 2020
PD MAY
SO Journal Of Atmospheric And Oceanic Technology
SN 0739-0572
PU American Meteorological Society
VL 37
IS 5
UT 000537897000004
BP 789
EP 806
DI 10.1175/JTECH-D-18-0244.1
ID 74031
ER

EF