the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Application of quality-controlled sea level height observation at the central East China Sea: Assessment of sea level rise
Abstract. This study presents the state-of-the-art quality control (QC) process for sea level height (SLH) time series observed at the Ieodo Ocean Research Station (I-ROS) in the central East China Sea, a unique in-situ measurement in the open sea for over two decades with a 10-minute interval. The newly developed QC procedure called the Temporally And Locally Optimized Detection (TALOD) method has two notable differences in characteristics from the typical ones: 1) spatiotemporally optimized local range check based on the high-resolution tidal prediction model TPXO9, 2) considering the occurrence rate of a stuck value over a specific period. Besides, the TALOD adopts an extreme event flag (EEF) system to provide SLH characteristics during extreme weather. A comparison with the typical QC process, satellite altimetry, and reanalysis products demonstrates that the TALOD method can provide reliable SLH time series with few misclassifications. Through budget analysis, it was determined that the sea level rise at I-ORS is primarily caused by the barystatic effect, and the trend differences between observations, satellite, and physical processes are related to vertical land motion. It was confirmed through GNSS that ground subsidence of -0.89±0.47 mm/yr is occurring at I-ORS. As a representative of the East China Sea, this qualified SLH time series makes dynamics research possible spanning from a few hours of nonlinear waves to a decadal trend, along with simultaneously observed environmental variables from the air-sea monitoring system in the research station. This TALOD QC method is designed for SLH observations in the open ocean, but it can be generally applied to SLH data from tidal gauge stations in the coastal region.
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RC1: 'Comment on egusphere-2024-3380', Anonymous Referee #1, 05 Dec 2024
Review of “Application of quality-controlled sea level height observation at the central East China Sea: Assessment of sea level rise”
The authors propose a new method, called TALOD, to perform quality control on the sea level height (SLH) measured by the radar located in I-ORS. TALOD detects problems with metadata, out of range data, spikes and stuck data. This new method is compared to the existing IOC method. The quality-controlled data is compared to HYCOM, GLORYS and ERAS5. Thus, TALOD proves to work correctly. The good data is further used to compute sea level rise (SLR), which leads to the conclusion that SLR in this location is due to vertical land movement (VLM).
This manuscript is clear and well written. I appreciate that the authors are adapting the TALOD method for the sea level height observations in open ocean. Also, the type of bad data is even further categorized, which brings much information on how SLH behaves.
Detailed comments:
-Please do an analysis on the tides.
-Please specify the constituents considered in this study.
Fig. 1: characters should be larger.
L250-253: There really should not be so many stuck errors in a modern sensor, is there an explanation for that?
L270: Yes, recurrent spikes make the automatic detection “think” they are good values. Good job, there, solving the issue by computing a local bias.
L420 -HYCOM shows a trend in SLH of -23.86mm/yr, which is quite high. Please consider whether it is a good model to compare to.
Sec 3.3 Great analysis of the contribution to sea level rise on this site.
Citation: https://doi.org/10.5194/egusphere-2024-3380-RC1 -
RC2: 'Comment on egusphere-2024-3380', Anonymous Referee #2, 05 Feb 2025
This study proposes a method of quality control for sea level time series called TALOD, which uses a variety of checks to remove bad data. The study indicates that it performs well against ‘IOC’ QC methods and uses the resulting good data (averaged to daily means) along with various altimetry and model datasets to infer something about long term sea level rise and its forcing factors. The various checks seem to be slight variants of IOC methods. However, a fundamental step of QC seems to have been overlooked in TALOD , which is harmonic analysis. This step removes the dominant tidal variability and makes suspect data more easily identifiable from true observations. In addition, it appears that some good data, associated with extreme event, have been removed which may bias any resulting tends that are inferred. The authors use 20 years of data to infer trends, which is a rather short time series to deduce a robust long-term trend. Assumptions are made about vertical land motion, which seem tenuous.
Line 75 – Open ocean tides are generally easier to analyse than those at the coast, where shallow water effects can distort the tide.
Line 152 S 2.2.1 Meta Check – This terminology is confusing. The term ‘metadata’ seems to be used in a non-traditional way here. It seems that what the authors are actually describing are cross checks between instrumental maintenance records and sea level time series. I would therefore give this check an alternative name
Lines 156-163 are confusing, There is stated to be no maintenance record for the station, but then it is claimed that the sensor was relocated twice and swapped out on another occasion. How did the authors deduce this in the absence of maintenance records?
Line 168 – S 2.2.2 Stuck Check. It is unclear whether this check is performed manually or is automated. In any event, given that step 1 is a manual check, why would these ‘stuck data’ checks not be identified during step 1? Figure 5 d shows that they are quite obvious.
Line 177 – S 2.2.3 Range Check. I don’t understand why the authors would go to the trouble of using predictions from a global tidal model to identify the tidal range within a given month, to then remove an offset to move the model closer to observations and then smooth the model tide to compare it to observations. Surely it would be far simpler to perform Classical Harmonic Analysis of the tide gauge observations to generate a non-tidal residual time series in which any suspect datapoints will be immediately obvious, because they are not masked by the dominant tidal variability? This is the principle on which conventional QC of sea level time series is built and by omitting this step, the authors are making life much more difficult for themselves and may indeed overlook some suspect data.
Figure 5(b) and (c) It isn’t clear to me that the range or spike check has worked as some of the yellow boxes appear visually to be within range. If they are truly out-of-range, that will be apparent in the non-tidal residual time series, which should be presented in Figure 5 instead of the total water level.
Figure 6 I’m not clear on the purpose of the EEF flag. Are the authors trying to remove real variability that is due to typhoons? Some of those datapoints that are flagged look reasonable but whether or not this is truly the case can only be demonstrated in a non-tidal residual time series.
Line 235 the authors state that they have compared their process with the IOC standard methodology, but they do not provide a reference for the IOC QC regimen that has been used nor do they describe the software that was used to do so. Given that harmonic analysis is a fundamental component of the IOC QC methodology, I can’t see that such a comparison is valid.
Line 260 – Are the authors simply flagging real extreme events as bad?
Line 273 mention automatic QC and it isn’t clear to me whether the TALOD QC method is manual or automatic, nor whether the IOC QC protocol that has been used is an automatic or delayed mode process. I’m not sure that the 2 systems are comparable.
Figure 8 – the results reported to be from the IOC methodology do not look correct to me. I would recommend that the authors consult the IOC manual of QC Quality control of in situ sea level observations: a review and progress towards automated quality control, volume 1 - UNESCO Digital Library. A recent publication which might also help is here OS - Delayed-mode reprocessing of in situ sea level data for the Copernicus Marine Service
Line 378-392 – The observed VLM at a tide gauge site from the GNSS receiver is a better indicator than differencing altimetry etc, but in any event the observed VLM from GNSS appear to act in the opposite sense to the one the authors have derived.
Citation: https://doi.org/10.5194/egusphere-2024-3380-RC2
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