Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

Huiqin Hu Jingyi Liu Lianglong Da Wuhong Guo Kun Liu Baolong Cui

Huiqin Hu, Jingyi Liu, Lianglong Da, Wuhong Guo, Kun Liu, Baolong Cui. Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1738-x
Citation: Huiqin Hu, Jingyi Liu, Lianglong Da, Wuhong Guo, Kun Liu, Baolong Cui. Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1738-x

doi: 10.1007/s13131-021-1738-x

Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea

Funds: The National Natural Science Foundation of China under contract Nos 41705081 and 41906005; the Innovation Special Zone Project under contract No.18-H863-05-ZT-001-012-06; the Open Project Fund of the Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2019A05.
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    Corresponding author: Email: llda@qnlm.ac
  • ╀ mean the authors contributed equally to this work and should be considered as co-first authors.
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    ╀ mean the authors contributed equally to this work and should be considered as co-first authors.
  • Figure  1.  Model domain and topography (m) of model grids (a), and topography (m) of the mid-western part of the Yellow Sea (b). The black solid lines A and B in a indicate locations of the validation sections; the box with black solid line (Box A) in b indicates the location of the selected verification area.

    Figure  2.  Monthly averaged SST in February, May, August, and November (from left to right) from the climatological simulation of model (upper row, a–d), and the multi-year averaged SST in the corresponding months from the satellite data of MODIS (bottom row, e–h).

    Figure  3.  Vertical sections of monthly averaged temperature in August along 35°N (left column, a, c) and 124°E (right column, b, d), for observation from ocean atlas data (upper row, a, b) and model simulation (bottom row, c, d).

    Figure  4.  Locations (colored dots) of the identified sensitive areas and the climatological background currents (vectors) for cases of the 21st (a), 23rd (b) and 25th (c) climatology years, respectively. The sensitive areas are identified based on CNOP-type errors of vertically-integrated temperature after being normalized with their maximum values.

    Figure  5.  Temperature prediction errors (shaded) at water depth of 20 m over the verification area for experiments with adding initial random perturbations on four different areas at four different prediction times. Four different areas are the verification area (Exp_A_1, a1–4), sensitive area (Exp_A_2, b1–4), area to east of the verification area (Exp_A_3, c1–4), and area to northeast of the verification area (Exp_A_4, d1–4), respectively. Four different prediction times are the first, third, fifth and seventh prediction day, respectively.

    Figure  6.  Temporal evolution of root mean square errors of area-averaged temperature profile over the verification area for experiments with adding initial random perturbations on the verification area (Exp_A_1, black line), sensitive area (Exp_A_2, red line), area to east of the verification area (Exp_A_3, blue line), area to northeast of the verification area (Exp_A_4, green line), respectively.

    Figure  7.  Temporal evolution of root mean square errors of area-averaged temperature profile over the verification area (a), and corresponding prediction benefits (b) for sensitivity experiments based on removing different initial random errors. Exp_R_1 and Exp_R_2 denote experiment with removing initial random errors from the verification area and sensitive area, respectively. Ctrl Run in a denotes experiment without removing initial errors from any areas.

    Table  1.   Three different cases for identifying sensitive areas

    Case nameCase 1Case 2Case 3
    Initial timeAugust of the 21st yearAugust of the 23rd yearAugust of the 25th year
    下载: 导出CSV

    Table  2.   Sensitivity experiments based on adding initial random perturbations

    Experiment
    name
    Initial
    conditions
    Location of adding
    perturbations
    True RunICtrueno
    Exp_A_1ICtrueverification area
    Exp_A_2ICtruesensitive area
    Exp_A_3ICtrueeast of the verification area
    Exp_A_4ICtruenortheast of the verification area
    下载: 导出CSV

    Table  3.   Sensitivity experiments based on removing initial random errors

    Experiment
    name
    Initial
    conditions
    Location of removing initial
    random errors (replace ICpg with ICtrue)
    True RunICtrueno
    Ctrl RunICpgno
    Exp_R_1ICpgverification area
    Exp_R_2ICpgsensitive area
    下载: 导出CSV

    Table  4.   Experimental design of balanced/imbalanced initial conditions (ICs)

    Experiment nameIC_meanIC_23IC_25IC_comb
    ICclimatological mean of model
    output from 6th to 25th
    model output of the
    23rd climatic year
    model output of the
    25th climatic year
    combined ICs from
    IC_23 and IC_251)
    Note: 1) The ICs are from model output of the 23rd climatic year at the model grid points (i, j) where (i+j) are odd and are from model output of the 25th climatic year at the model grid points (i, j) where (i+j) are even.
    下载: 导出CSV
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  • 收稿日期:  2020-06-23
  • 录用日期:  2020-08-27
  • 网络出版日期:  2021-05-07

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