Calibration and Validation of HY-2 Altimeter Wave Height
1. Introduction
Ocean surface winds and waves are of much interest to the oceanography and ocean engineering communities. They interact with each other in a very complicated manner. On the one hand, winds generate waves through the component of pressure in quadrature with the wavy surface (e.g., Young 1999). On the other hand, waves influence sea surface drag and consequently change the flux transferred by winds and the very winds that generated them (e.g., Janssen 2004; Babanin and Makin 2008). When waves propagate against local winds, wave decay is also expected (Donelan 1999). To investigate winds and waves, apart from building analytical theories and numerical models, in situ or remote sensing measurements of them is crucial. Among the diverse methods to observe winds and waves, satellite radar altimeters play a special role. Although having a limited time resolution when compared with buoys or other platforms, altimeters provide an excellent global coverage and by now satellite data have been available for three decades (e.g., Young et al. 2011, 2015). It is well known that the earliest global wave measurements by satellite altimetry can be dated back to 1985, when Geosat was launched (e.g., Zieger et al. 2009). So far, with the efforts of agencies from different countries, a total of 11 satellite altimeter missions have been operational, including the most recent ones by Jason-2 (Dumont et al. 2011), CryoSat-2 (ESRIN/MSSL 2012), Satellite with Argos and ALtiKa (SARAL; Bronner et al. 2013), and Hai Yang-2 (HY-2; NSOAS 2013). HY-2 (also termed HY-2A), launched on 16 August 2011, is China’s first dynamic environmental satellite. It is positioned in a sun-synchronous orbit at an altitude of 971 km and an inclination of 99.34°. Its nodal period is 104.46 min, which translates into an exact repeat ground-track cycle of 14 days (see also Table 1). In addition to a radar altimeter, HY-2 is also equipped with three other microwave sensors: a scatterometer, a scanning radiometer, and a calibration radiometer. This enables it to simultaneously monitor multiple ocean surface dynamic parameters, such as wind speed and direction, wave height, sea surface height, and temperature. Similar to Jason-2, the HY-2 altimeter operates at dual-frequency bands: Ku band (13.58 GHz) and C band (5.25 GHz). Note that although HY-2 was initially planned for 3 years of operation, at present it is still fully operational. To provide an uninterrupted ocean monitoring service, the Chinese National Satellite Ocean Application Service (NSOAS) has started developing follow-on satellite missions, namely, HY-2B and HY-2C, which are scheduled to launch in 2017 and 2018, respectively.
Summary of altimeter missions and data used in this paper including orbit parameters, data duration and source, and the percentage of flagged records, etc. Note that CryoSat-2 here denotes its low-resolution, nadir-looking altimeter mode, and except for the repeat period of 369 days, it also has a subcycle period of 30 days.
Retrieving the 10-m wind speed (
Altimeter-derived 1-Hz 
To sum up, by covering long periods of time and extended spatial scales, altimeter-estimated 
There have been several published works assessing HY-2 
Summary of previous studies on calibrating HY-2 
Compared to the extensive attention paid to 
This article is organized as follows. Section 2 presents the data and methods we used to evaluate HY-2 
2. Data and methods
a. Quality control
We analyzed geophysical records from four satellite altimeters: HY-2, CryoSat-2, Jason-2, and SARAL. Data from HY-2 and CryoSat-2 are denoted as the interim geophysical data records (IGDRs), while data for the other two missions are referred to as the geophysical data records (GDRs). Table 1 summarizes the information regarding these data, including their sources, data periods, and the orbit parameters of each mission. Except for SARAL, all the missions have provided records for more than 3 yr.
As far as the calibration of altimeter products is concerned, the first step is to quality control them—that is, to remove the erroneous or suspicious records normally through using a number of empirical criteria. These criteria should not be so rigorous or loose that the calibration and statistics we finally obtain are unrepresentative (Cotton et al. 2003). Basically, the methods of quality control can be roughly categorized into two groups. Both groups rely on the basic flags for altimeter records; for example, land, ice, rain flags, other 1-Hz quality flags, and the number of valid waveforms that make up the 1-Hz sample. The first group, furthermore, depends on additional auxiliary information, such as range R, off-nadir angle, peakiness, and the standard deviation (std) of 
The second group uses a more straightforward and also a very efficient method. Under the assumption that the sea state will not change dramatically within a limited geophysical scale say less than ~200 km, altimeter records along the track are divided into blocks of ~25 observations for a further statistical consistency check (Young and Holland 1996; Zieger et al. 2009; Queffeulou and Croizé-Fillon 2016). Spikes or outliers within each block—that is, values that deviate significantly from the mean value of the block—are flagged as bad. Not only intuitive, methods of this kind can also be directly applied to various missions with a slight modification of block size that depends only on the ground scanning speed of each satellite altimeter. A combination of these two groups is also feasible (Abdalla and Hersbach 2004). Following Young and Holland (1996), we chose the second method for the purpose of quality control. For further details of this three-pass procedure, please also refer to Zieger (2010). The proportion of the “flagged” records for the four altimeters used in our work is also shown in Table 1 (last column). Note that HY-2 has the highest percentage (21%).
b. Calibration against in situ measurements
Although not free of errors, the in situ measurements are always regarded as ground truth and were applied to calibrate satellite measurements and to validate wave models (e.g., Bidlot et al. 2002; Li and Saulter 2014). Of all globally operating buoy networks, data from the buoys maintained by the U.S. National Data Buoy Center (NDBC) are most widely used and referenced in the literature due to their excellent quality and long duration, dating back to the early 1970s. After being quality controlled by NDBC staff, the historical NDBC data are archived at the National Oceanographic Data Center (NODC). For this paper, we obtained data for the period from 1 October 2011 to 31 November 2014, which nearly covers the durations of all available altimeter records. Only buoys located more than 40 km offshore were considered in order to avoid the heavily seaward sampling problem when a buoy is too close to a coastline (Greenslade and Young 2004). This criterion finally yielded 63 stations, whose locations are shown in Fig. 1. Although the buoy data were checked by a number of quality-control procedures, a few erroneous values are still unflagged (not shown). Therefore, an additional quality control was carried out in a fashion similar to the method described in Caires and Sterl (2003). More precisely, wave measurements that (i) are too extreme—that is, 
The locations of the 63 NDBC buoys (empty circles) used in the study. Only buoys more than 40 km offshore are listed and some buoys could have drifted somewhat or been redeployed during the period of study.
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
c. Cross validation against other altimeters
The altimeter–buoy collocated data were basically limited to an area off the U.S. coastline; therefore, altimeter measurements needed to be further validated against other simultaneously operating altimeters on a global scale. For HY-2 validations, three other missions were available: Jason-2, CryoSat-2, and SARAL. Jason-2 and CryoSat-2 operate on the same Ku-band frequency as HY-2. SARAL operates in the high-frequency Ka band (35.75 GHz), which reduces the size of the altimetry footprint and provides better spatial and vertical resolutions. This Ka band–operated altimeter is expected to provide more accurate 
To confidently use 
d. Further verification against wave model
Although the period of the altimeter data we considered was relatively long (e.g., >3 yr for HY-2), the altimeter–buoy and altimeter–altimeter collocated measurements were still limited in total number, usually of the order of O(103) (section 3). In addition, high sea states (
The calibration results for the 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
3. Results
a. Calibration against buoys
An overall comparison of 
1) HY-2
Performance of the HY-2 
2) Other three altimeters
There are many studies that address the quality of wave height measurements as estimated by the Jason-2, CryoSat-2, and SARAL altimeters (e.g., Queffeulou et al. 2011; Ray and Beckley 2012; Sepùlveda et al. 2015; Zhang et al. 2015). The differences in the calibration results from these studies are usually caused by inconsistent methods of data processing. Therefore, we applied the same calibration procedure to these three altimeters for consistency and show their performances in Figs. 2b–d.
A total of 3164 collocations between Jason-2 and buoys (Fig. 2b) show a significant correlation (0.99), an RMSE as low as 0.14 m, and a negligible bias (0.02 m). The RMA regression is very close to the 1:1 line. All the metrics demonstrate the high quality of Jason-2 
Among the four altimeters considered in this article, measurements from the SARAL mission are of the highest quality. In Fig. 2d, SARAL measurements show the lowest RMSE (0.13 m) and scatter index (0.07). But they have a slightly higher bias (0.06 m) than that of Jason-2 measurements. Also, the RMA regression can further decrease the RMSE to 0.11 m. Sepùlveda et al. (2015) also pointed out that SARAL measurements has a slightly larger bias but lower RMSE than Jason-2 measurements. Since, as mentioned in section 2b, SARAL is capable of providing a high spatial and vertical resolution, it is not surprising that SARAL is superior to the Ku-band altimeters. Most importantly, its performance along coastlines is also fairly encouraging. Hithin et al. (2015) analyzed SARAL 
To summarize briefly, among the four altimeters, SARAL 
b. Cross validation against other altimeters
For HY-2 
The cross-validation results for calibrated 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
The October 2011–2014 time series of the differences between calibrated 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
Inspection of Fig. 3 shows that the agreement between Jason-2-, CryoSat-2-, and SARAL-calibrated 
Another shortcoming of the HY-2 RMA calibration is displayed in Fig. 4, where the difference between altimeter–altimeter collocated measurements is shown as a function of time. For better visualization, block averages over 20 points have been used (Zieger et al. 2009). Similar to what has been seen above, differences between Jason-2, CryoSat-2, and SARAL (Figs. 4d–f) are relatively insignificant and remain so with time. Only a bias of a few centimeters is to be expected between CryoSat-2 and Jason-2/SARAL (see also Figs. 3e and 3f). A noticeable jump in HY-2-measured 
4. Revised calibration of HY-2 
As explained in the previous section, HY-2 
The best way to calibrate altimeter wave height is undoubtedly to use in situ buoy measurements as we did in section 3a. Nonetheless, for HY-2 measurements, there were only very few buoy collocations when sampling high seas (Fig. 2a) and thus this presented difficulties for our study. Considering the excellent consistency between Jason-2-, CryoSat-2-, and SARAL-calibrated 
In Fig. 3, the negative bias in HY-2 
The revised two-branch calibration of the HY-2 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
The HY-2 measured-wave heights calibrated using Eqs. (7) and (8), were again compared to collocated-buoy measurements and the values from the other three satellite altimeters (calibrated values) in Fig. 6. When compared to buoys (Fig. 6a), HY-2 wave height estimates now are almost unbiased (0.01 m) and have a much lower RMSE of 0.17 m versus 0.30 m for the uncorrected data (Fig. 2a). The RMA best fit is nearly equal to 1, proving that using Jason-2-calibrated 
As in Fig. 3, but for HY-2 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
As in Fig. 4, but for the difference between buoy and HY-2 (a) raw 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
The final calibrations of 
5. Discussion
This section focuses on verifying the practical applicability of our revised calibration provided in the section above. One may argue that the dataset we used to fit the calibrations Eqs. (7) and (8) is limited and less representative for real high seas. Here, we attempt to indirectly prove the validity of the revised calibration. Numerical wave models can provide excellent resolution in both temporal and spatial scales. Comparing altimeter data to a model can definitely increase the sample size of the collocated dataset and, consequently, the probability of representing a wide variety of sea states. However, since wave models are inclined to be less accurate than altimeters, we avoid using them for the ground truth. Instead, we compared both HY-2- and Jason-2-calibrated 
Since we just wanted to demonstrate the validity of our calibration, we used only two-months of model data, consisting of January 2013 from phase 1 and January 2014 from phase 2 of HY-2 measurements. The comparison between Jason-2–HY-2 and the wave model is shown in Fig. 8. For both Jason-2 and HY-2, more than 50 000 instances of collocated measuremnts were obtained for each month. Focusing first on Jason-2 (Figs. 8a and 8d), the agreement between Jason-2 results and the wave model is quite good: for each month, the correlation coefficient is 0.97 and the scatter index is 0.12. The comparison of January 2013 shows a bias of 0.11 m and an RMSE of 0.34 m; while January 2014 gives slightly improved result (a bias of 0.07 m and an RMSE of 0.31 m). These statistics are very similar to the results of Zieger et al. (2015, see the middle panel of their Fig. 13). The difference between Figs. 8a and 8d is that in the latter panel, Jason-2 
Comparison between (top) Jason-2 
Citation: Journal of Atmospheric and Oceanic Technology 33, 5; 10.1175/JTECH-D-15-0219.1
6. Conclusions
The 
- Among the four altimeters, SARAL gives the lowest RMSE (0.13 m) and the lowest scatter index (0.07), followed by Jason-2, CryoSat-2, and HY-2. Despite this, HY-2 obtained
- When compared with in situ buoy measurements, HY-2
- We finally proposed a two-branched calibration for HY-2- and CryoSat-2-estimated
- Our calibration for HY-2
As mentioned in section 1, NSOAS is now reprocessing HY-2 altimeter IGDRs by adopting a different retracking method with the objective to supply a much better sea surface height (h) and wind speed 
The study focused on the ability of satellite-altimeters to derive 
Q. Liu acknowledges the fellowship supported by the China Scholarship Council (CSC). Radar altimeter data were kindly provided by NSOAS, AVISO, and NOAA/NESDIS. The in situ buoy observations were provided by NDBC and NODC. The wave model output provided by the Ifremer IOWAGA project is highly acknowledged. C. Guan and J. Sun appreciate the support from the National Natural Science Foundation of China (Grants 41276010, 41376010 and U1406401). AVB acknowledges support from the Australian Research Council Discovery Grant DP130100227 and the U.S. Office of Naval Research Grant N00014-13-1-0278. We would like to thank the two anonymous reviewers and the editor for their comments and suggestions that have improved this paper.
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