Identification of the sensitive area for targeted observation to improve vertical thermal structure prediction in summer in the Yellow Sea
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Abstract: The sensitive area of targeted observations for short-term (7 d) prediction of vertical thermal structure (VTS) in summer in the Yellow Sea was investigated. We applied the Conditional Nonlinear Optimal Perturbation (CNOP) method and an adjoint-free algorithm with the Regional Ocean Modeling System (ROMS). We used vertical integration of CNOP-type temperature errors to locate the sensitive areas, where reduction of initial errors is expected to yield the greatest improvement in VTS prediction for the selected verification area. The identified sensitive areas were northeast−southwest orientated northeast to the verification area, which were possibly related to the southwestward background currents. Then, we performed a series of sensitivity experiments to evaluate the effectiveness of the identified sensitive areas. Results show that initial errors in the identified sensitive areas had the greatest negative effect on VTS prediction in the verification area compared to errors in other areas (e.g., the verification area and areas to its east and northeast). Moreover, removal of initial errors through deploying simulated observations in the identified sensitive areas led to more refined prediction than correction of initial conditions in the verification area itself. Our results suggest that implementation of targeted observation in the CNOP-based sensitive areas is an effective method to improve short-term prediction of VTS in summer in the Yellow Sea.
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Key words:
- targeted observation /
- sensitive area /
- vertical thermal structure (VTS) /
- conditional nonlinear optimal perturbation (CNOP)
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Figure 1. Model domain and topography (depth, 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 1 Case 2 Case 3 Initial time August of the 21st year August of the 23rd year August of the 25th year 
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Table 2. Sensitivity experiments based on adding initial random perturbations
Experiment
nameInitial
conditionsLocation of adding
perturbationsTrue Run ICtrue no Exp_A_1 ICtrue verification area Exp_A_2 ICtrue sensitive area Exp_A_3 ICtrue east of the verification area Exp_A_4 ICtrue northeast of the verification area
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Table 3. Sensitivity experiments based on removing initial random errors
Experiment
nameInitial
conditionsLocation of removing initial
random errors (replace ICpg with ICtrue)True Run ICtrue no Ctrl Run ICpg no Exp_R_1 ICpg verification area Exp_R_2 ICpg sensitive area
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Table 4. Experimental design of balanced/imbalanced initial conditions (ICs)
Experiment name IC_mean IC_23 IC_25 IC_comb IC climatological mean of model
output from 6th to 25thmodel output of the
23rd climatic yearmodel output of the
25th climatic yearcombined 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.
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