Near Real Time SST Retrievals from Himawari-8 at NOAA Using ACSPO System

Near Real Time SST retrievals from Himawari-8 at NOAA using ACSPO system

M. Kramar1,2, A. Ignatov1, B. Petrenko1,2, Y. Kihai1,2, and P. Dash1,3

1NOAA STAR, 2GST, Inc., 3CIRA

ABSTRACT

Japanese Himawari-8 (H8) satellite was launched on October 7, 2014 and placed into a geostationary orbit at ~ 140.7ºE. The Advanced Himawari Imager (AHI) onboard H8 provides full-disk (FD) observations every 10 minutes, in 16 solar reflectance and thermal infrared (IR) bands, with spatial resolution at nadir of 0.5-1 km and 2 km, respectively. The NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) SST system, previously used with several polar-orbiting sensors, was adapted to process the AHI data. The AHI SST product is routinely validated against quality controlled in situ SSTs available from the NOAA in situ SST Quality monitor (iQuam). The product performance is monitored in the NOAA SST Quality Monitor (SQUAM) system. Typical validation statistics show a bias within ±0.2 K and standard deviation of 0.4-0.6 K. The ACSPO H8 SST is also compared with the NOAA heritage SST produced at OSPO from the Multifunctional Transport Satellite (MTSAT-2; renamed Himawari-7, or H7 after launch) and with another H8 SST produced by JAXA (Japan Aerospace Exploration Agency). This paper describes the ACSPO AHI SST processing and results of validation and comparisons. Work is underway to generate a reduced volume ACSPO AHI SST product L2C (collated in time; e.g., 1-hr instead of current 10-min) and/or L3C (additionally gridded in space). ACSPO AHI processing chain will be applied to the data of the Advanced Baseline Imager (ABI), which will be flown onboard the next generation US geostationary satellite, GOES-R, scheduled for launch in October 2016.

1 INTRODUCTION

The new generation Japanese geostationary Himawari-8 (H8) satellite was launched on October 7, 2014 and placed into operational position at 140.7º E. The Advanced Himawari Imager (AHI) onboard H8 provides 5500×5500 full- disk (FD) observations in 16 spectral bands, including five thermal infrared (IR) bands in the atmospheric transmission windows which can be used for SST retrievals (see Table 1). The AHI represents a significant improvement upon the imager onboard the previous generation Multifunctional Transport Satellite (MTSAT-2, renamed Himawari-7, or H7 after launch; positioned at 145ºE), in the number of spectral bands (16 vs. 5), spatial resolution (2 vs. 4 km in thermal IR bands, at nadir), temporal sampling (10 min vs. 30/60 min for VIS/IR FD, respectively), and in the superior radiometric, geolocation and band-to-band and FD-to-FD co-registration performance. The AHI is a close proxy of the Advanced Baseline Imager (ABI), soon to be flown onboard the Geostationary Operational Environmental Satellites -R Series (GOES-R; scheduled for launch in October 2016).

Table 1. H8 AHI bands used in ACSPO Clear Sky Mask and SST processing

Band Central Wavelength (μm) Bandwidth (μm) Spatial Resolution (km) Use in ACSPO 3 0.64 0.03 0.5 Not used 4 0.86 0.02 1.0 ACSM 7 3.90 0.22 2.0 ACSM 11 8.60 0.32 2.0 ACSM & SST 13 10.40 0.30 2.0 ACSM & SST 14 11.20 0.20 2.0 ACSM & SST 15 12.35 0.30 2.0 ACSM & SST

Historically, the NOAA polar and geostationary SST systems have evolved differently and diverged over time. The polar system, Advanced Clear-Sky Processor for Oceans (ACSPO) [1], is used to operationally process at OSPO (Office of Satellite Products and Operations; operational arm of NESDIS) and reprocess at STAR (Center for Satellite Applications and Research; research arm of NESDIS) data of VIIRS onboard S-NPP and several AVHRRs onboard NOAA (4 km resolution Global Area Coverage, GAC) and MetOp (1 km Full Resolution Area Coverage, FRAC) satellites. Experimental ACSPO SST products are also generated at STAR from two MODISs, onboard Terra and Aqua. The geostationary SST system, on the other hand, has been used to process data of NOAA GOES, European Meteosat Second Generation (MSG), and the Japanese H7 satellites [2-3]. Following the launch of H8, and in anticipation of GOES-R launch in October 2016, NOAA began consolidation of the two systems into one enterprise NOAA SST system, capable of producing consistent, high quality SST products from the current and future polar and geostationary platforms. As a first step towards this objective, the ACSPO SST system was adapted to process the H8 AHI data.

Upon the successful launch and on-orbit verification of H8, the JMA (Japan Meteorological Agency) announced its plan to transition its operations from H7 (which was launched in February 2006, and had the expected life time of 10 years) to H8 in December 2015. Following a request from the NOAA users for continuity of the Himawari SST product, ACSPO SST team has established an experimental production of H8 SST in April 2015. Upon discontinuation of the H7 operations at JMA on 4 December 2015, and transition to H8, the ACSPO SST processing was handed over to the Algorithm Scientific Software Integration and System Transition (ASSIST) Team within STAR, which now supports the routine pre-operational ACSPO SST processing and serves the NOAA users’ needs, pending transition to the OSPO operations, which is expected to occur in 2017.

The second objective of the H8 ACSPO SST project is therefore a risk reduction and readiness for the GOES-R launch in October 2016. GOES-R will carry an Advanced Baseline Imager (ABI) onboard, which is near-identical to the AHI. Upon extensive testing the ACSPO SST processing with H8 AHI, it will be applied to the ABI data, to produce a high quality, consistent SST product over the US domain.

This paper describes the current AHI processing chain at NOAA and the experimental ACSPO H8 L2P product. Validation against the quality controlled (QCed) in situ SSTs obtained from the NOAA in situ SST Quality Monitor (iQuam; www.star.nesdis.noaa.gov/sod/sst/iquam/; [4]) and comparisons with two other L2 SST products from H7 and H8, produced by the NOAA OSPO and JAXA, respectively, in another NOAA near-real time online system, SST Quality Monitor (SQUAM; www.star.nesdis.noaa.gov/sod/sst/squam/; [5]). Ongoing work includes improving all elements of the processing chain (clear-sky mask, and SST and error characterization algorithms) and producing condensed ACSPO AHI SST products, L2C (collated in time; e.g., reported at 1 hr instead of the current 10min) and L3C (additionally gridded in space). Preparation for the launch of the GOES-R in October 2016 is also discussed.

2 AHI L1b Data and ACSPO L2 Processing

To distribute AHI L1b data to users, JMA has established an Internet cloud service, "HimawariCloud" [6]. NOAA STAR had established access to the HimawariCloud in January 2015. The L1b data are accessed in a real time and stored in the STAR Central Data Repository (SCDR). As of this writing, L1b data from 8 April 2015 – onward are available in the geo-SCDR. Note that the geo-SCDR was specifically set up to support the GOES-R launch, by storing a large rotated buffer of data for internal use in STAR. Currently, the geo-SCDR continues accumulation of AHI L1b data, but this will be reprioritized after the launch of GOES-R in October 2016, at which time the H8 buffer will be likely reduced, to order of several months.

AHI L1b FD "Himawari Standard Data" (HSD) come in "Himawari Standard Format" [7], with each band divided into 10 segments. Each segment represents a file compressed by bzip2, so that each 10-min FD comprises 10 compressed files for each of the 16 bands, 160 files total. Of those, the ACSPO reads 70 files for seven bands listed in Table 1, with cumulative compressed size of ~320 MB. Nominally, there are 142 FD granules per day (two 10- min slots, at 2:40 and 14:40 UTC, are saved for the AHI housekeeping operations).

In STAR, ACSPO AHI code runs once a day at 02:00 UTC and processes all 142 granules from the previous day. It takes about 8 clock hours to process the 142 L1b granules (70x142 files) into ACSPO L2P output granules. The processing is done in parallel mode on a Linux computer with two Intel Xeon CPU E5-2695 v2. The processing at its peak occupies about ~20 GB of RAM. Since the launch of this near real time processing in April 2015, several significant improvements to the code have been made resulting in a substantial reduction of the computer system usage. ACSPO L2P output consists of ~142 FD granules per day in the GHRSST GDS2 format [8], with a total size of ~45 GB/day. The data are made available to users via the STAR anonymous ftp ftp://ftp.star.nesdis.noaa.gov/pub/sod/sst/acspo_data/l2/ahi/. Upon transition of JMA operations from H7 to H8 on 4 December 2015, the ACSPO AHI code was handed over to the STAR Algorithm Scientific Software Integration and System Transition (ASSIST) Team, for pre-operational production of the SST L2P product in real time and its distribution to the STAR geo-polar blended team, via the OSPO data deliver server (DDS). The operational production at OSPO is expected to commence in 2017.

3 ACSPO Algorithms: Clear-Sky Mask (ACSM), SST, Sensor-Specific Error Statistics (SSES)

The ACSPO processing is organized into four major interleaved blocks: (1) forward radiance calculations using the NOAA Community Radiative Transfer Model (CRTM), with the Canadian Meteorological Center (CMC) L4 SST analysis [9] and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS; www.emc.ncep.noaa.gov/index.php?branch=GFS) first guess fields as inputs [10]; (2) identification of clear-sky ocean pixels suitable for SST retrievals [11]; (3) calculation of SST using the regression equations [12-13]; and (4) calculation of the sensor-specific error statistics [14].

The corresponding ACSPO algorithms have been documented in the above listed references, and the main specific of the H8 is a different form of the SST algorithm which takes the following form [15]:

TS = 0.876435*T13 – 0.826084×(T13 – T15) + 0.511226×(T13 – T11)×S + 0.561733×(T13 – T14)×S – 0 0 – 0.020591×(T13 – T11)×(TS – 273.15) – 0.034769×(T13-T14)×(TS -273.15) + 0 + 0.078092×(T13 – T15)×(TS – 273.15) + 38.112282 (1)

Here T11, T13, T14 and T15, are BTs observed in the AHI bands 11, 13, 14, and 15, respectively; θ is satellite view 0 zenith angle; S=sec(θ); TS is the first guess SST (in K) obtained from CMC. The coefficients of the equation were derived using a representative match-up data set between AHI BTs and iQuam in situ SSTs, from mid-April to mid- May 2015.

The motivation for this form of the non-linear SST (NLSST) equation is discussed in [15]. The major difference from the polar SST algorithms is that the AHI algorithm is not stratified by day and night. The reason is twofold: 1) one single equation is used to minimize the possible day-night discontinuities and 2) the band 7 centered at 3.9 µm was found to be suboptimal for SST retrievals (possibly due to its shift towards longer wavelengths, from its nominal position at 3.7 µm on polar sensor [16]). As opposed to the current ACSPO polar algorithm, the AHI NLSST includes three bands 13-15 in the 10-13 µm window (in contrast to only two bands, available on all current polar sensors), and additionally the band 11 centered at 8.6 µm. (Note that the latter band is available on the VIIRS and MODIS sensors and its information potential is currently being explored.)

4 ACSPO AHI Product, Validation and Comparison with H7/OSPO and H8/JAXA SSTs

Figure 1 shows examples of ACSPO SST nighttime composites obtained from the polar S-NPP/VIIRS and geo H8/AHI instruments (see ftp://ftp.star.nesdis.noaa.gov/pub/sod/sst/acspo_data/l2/ahi/composites/ for more routine daily composites). Grey color represents cloudy areas. In low-to-mid-latitudes, there are typically 1 to 2 VIIRS overpasses per day and per night, over the same area. The number of S-NPP overpasses increases towards high latitudes. At the same time, 10 minutes AHI imagery can provide up to 142 SST values over a pixel per 24hr interval. Separated by day and night, there are typically up to ~70 looks per each. As a result, the AHI composites typically have fewer gaps than the VIIRS composites. Also, AHI superior temporal sampling allows us to have a larger clear sky area during several hours of observation, as the clouds also move.

Figure 2 shows an example histogram of differences between ACSPO AHI SST and drifters plus tropical moorings for July 15, 2015, for two observation times approximately representing the local noon and midnight, at the H8 sub- satellite point. There are from 160-180 matchups for a FD, for the matchup space-time window of (5km, 1hr) from the buoys location to the center of the SST pixel. Both histograms are close to a Gaussian shape, with a mean of +0.19 and standard deviation (SD) of ~0.51 K during local day, and +0.03 and 0.43 K during local night, respectively. Note that the coefficients in the NLSST equation were tuned against in situ SSTs, but they do not account for the effect of diurnal thermocline – i.e., the biases between the buoy SST measured at 15-20 cm depth by drifters, and ~1 m depth by the moorings, and the sub-skin ACSPO SST. As a result, a small warm bias is expected during the daytime (cf. +0.19 K) and close to zero bias at night (cf. +0.03 K). The SD is expected to be smaller at night (0.43 K) compared to the daytime (0.51 K). Figure 3 shows corresponding validation results for the H7/OSPO product. The space-time window for this product was set heuristically and was doubled to provide sufficient number of matchups to calculate meaningful FD statistics. Recall that the H7/OSPO product is derived from a 4 km resolution sensor, at hourly frequency (cf. 10-min in H8/ACSPO). There are ∼300 matchups for H7/OSPO) (compared with ~160-180 for H8/ACSPO). Work is currently underway to analyze and more objectively select the H8 and H7 space-time windows. While the H7/OSPO and H8/ACSPO histograms are similar at local night, the local-day H7/OSPO histogram is much wider, with the mean of −0.09 and SD of ~0.88 K. Recall that at night, the H7/OSPO uses a more accurate three-channel multichannel SST (MCSST) algorithm in conjunction with the transparent 3.7 µm band, whereas during the daytime a two-channel NLSST is used in conjunction with two longwave bands centered at 10.8 and 12 µm. In contrast, the H8/ACSPO algorithm uses four longwave bands during both day and night, and a more uniform and accurate performance of the H8 algorithm is expected.

Time series of the mean and SD statistics for H8/ACSPO and H7/OSPO are shown on Figure 4, for one week in July 2015. The H8/ACSPO SST is typically within ±0.2 K, with maxima of ~+0.1 K and minima of ~-0.1 K occurring at the local day and local night, respectively. The use of only nighttime and daytime with high wind data in the match- up dataset for training the SST coefficients may be tested, to more clearly isolate the effect of the diurnal thermocline during the daytime.

Figure 5 re-plots the time series from Figure 4, but this time using the CMC L4 SST as a reference. Recall that unlike the in situ SST, which varies diurnally (although with a reduced amplitude at drifters/moorings depth, compared to the satellite sub-skin SST), the CMC is one “foundation” SST value per day. As such, one expects that the satellite SST be close to the CMC at night, and show a positive bias during the daytime, due to the diurnal warming. Indeed, both H8 and H7 SSTs are close to the CMC at night, to within ~-0.05 K (for H8/ACSPO) and ~+0.05 K (for H7/OSPO). The H8/ACSPO SST shows a larger diurnal cycle with typical amplitude of ~0.45 K whereas the diurnal signal in H7/OSPO is only ~0.2 K. (Note that these numbers are estimates of the average diurnal warming over the FD retrieval domain, calculated as a function of the UTC (rather than local) time. In some regions with clear-skies and high Sun, it may be larger whereas in cloudy areas and low Sun it is less pronounced.) More analyses are needed to verify and validate these two estimates, which differ by a factor of more than 2.

The time series of the SD for the H8 and H7 products shown on Figures 4 and 5 suggest a more uniform performance of the H8/ACSPO SST compared to the OSPO H7 product that shows large diurnal variations in the SD across the data. At local night, the two products show comparable SDs, but during the daytime, the H7/OSPO SST shows a significantly increased noise in the retrievals.

The improved performance of the H8 SST is expected because the H8 sensor has more spectral bands for accurate SST retrievals, and superior radiometric and co-registration performance. Towards this end, it is instructive to compare performances of the two H8 SST products produced by the NOAA and JAXA [17]. Note that JAXA started producing their product on 7 July 2015, and it was immediately included in the NOAA SQUAM system. Since then, JAXA Team has made a critical improvement to their product in mid-December 2015. Therefore ACSPO and JAXA SST products have been compared for the first week of January 2016.

Figures 6 and 7 (similar in their structure to Figures 2 and 3) plot histograms for the two H8 SST products, produced by NOAA and JAXA, for two observation times approximately representing the local noon and midnight at H7 and H8 sub-satellite points of January 7, 2016. The number of match-ups has reduced in the ACSPO product (~120- 140), compared with 200-250 matchups in the JAXA product. Given the same match-up criteria in the two products (5km, 1hr), the larger number of matchups in JAXA SST may come from the fact that JAXA is a L3, not a L2 product, resampled onto 6001x6001 latitude-longitude grid within an area covering less than FD while ACSPO uses native L1b 5500x5500 swath resolution that covers rectangular area larger than the FD. The performance of the ACSPO SST did not change much from July 2015. There is a near-zero (-0.03 K) bias at night and slightly positive (+0.15 K) during the daytime. Recall that JAXA produces a skin SST. A negative bias of -0.17 K is expected at night and at high winds, which may be offset by the effects of the diurnal warming during the daytime, at high insolation and low winds conditions. The JAXA data indeed show negative biases, from -38 K at night to -0.16 K during the daytime. These estimates are approximately -0.2 K below expected numbers. The SDs are ~0.45-0.48 K in ACSPO, compared with the 0.67-0.70 K in the JAXA product. Corresponding time series of the mean and SDs validation statistics for the two H8 SSTs are shown in Figure 8. They confirm that the JAXA product is biased negative with respect to in situ data by up to -0.3 K. The SDs are typically in the range of 0.45 K for the ACSPO, compared with ~0.65 K for the JAXA SSTs.

Figure 9 re-plots Figure 8, but with respect to CMC L4 SSTs. The encouraging result is the consistency of the amplitude of the diurnal cycle (~0.40 K) between the two H8 SST products. This cross-validation adds confidence in the H8 estimate, compared with the reduced amplitude in the H7/OSPO product. The JAXA product continues to show a negative bias of ~-0.3 K, and increased SD compared to the NOAA product.

Figure 10 shows full time series of the H8/ACSPO, H8/JAXA and H7/OSPO minus CMC SSTs. As discussed earlier, the lower envelope of the bias is expected to be at ~0, because the best correspondence between the satellite sub-skin SST and foundation CMC is expected to be at night. The two NOAA products consistently agree with this expected pattern. In contrast, the JAXA product has been biased cold. Initially, the bias was close to -0.7 K, and after the fix in mid-December 2015, it has reduced to ~-0.3 K. In terms of SDs, the H8/ACSPO has consistently outperformed the H7/OSPO SST, before this product was discontinued on 4 December 2015. Initially, the JAXA SDs has been consistently smaller than the H7/OSPO SDs and only a little larger than the H8/ACSPO SDs. However, after the H8/JAXA fix in mid-December 2015, its SDs have increased and became consistently larger than in the H8/ACSPO product.

The improved/degraded product validation statistics may be counterbalanced by its reduced/increased retrieval domain, respectively. To verify this hypothesis, Figure 11 shows the time series of percent coverage by clear sky pixels over the ocean for all three analyzed SST products. In its initial implementation, the H8/JAXA provided coverage comparable with that of H7/OSPO product. After the fix in mid-December 2015, the coverage from the two H8 products became more consistent. As the prior analyses suggest, this increased coverage in H8/JAXA SST has resulted in somewhat degraded SDs. The coverage in H8/ACSPO remained relatively stable with typical coverage ranging from ~12 to ~30 %, and with long term fluctuations which apparently correlate in all three products.

5 CONCLUSION AND FUTURE WORK

The ACSPO system has been adapted to process the AHI data. Experimental near real time L2P SST product with native AHI spatial (2km at nadir) and temporal (10 min) resolution has been produced at STAR since April 2015, and posted at ftp://ftp.star.nesdis.noaa.gov/pub/sod/sst/acspo_data/l2/ahi/. There are 142 FD ACSPO granules per day in GDS2 format, with a total daily volume of ~45 GB. Due to the large data volume, a rotated buffer of approximately 5-6 months is kept on the STAR anonymous ftp.

The product is routinely monitored and validated against in situ data in the NOAA SST Quality Monitor for geostationary satellites (geo-SQUAM; www.star.nesdis.noaa.gov/sod/sst/squam/GEO/). The validation statistics against drifters plus tropical moorings shows a typical bias within ±0.2 K and standard deviation within ~0.4-0.6 K. These numbers are close to the NOAA requirements for H8 SST, which have been chosen consistently with the JPSS (Joint Polar Satellite System) requirements [18]. The ACSPO AHI SST shows a clear, smooth diurnal cycle, with the average FD amplitude of ~0.4 K.

For generating a SST from a geostationary sensor, AHI offers an unprecedented, for a geostationary SST performance – spatial resolution and the number of spectral bands (along with their superior radiometric, geolocation, and co-registration characteristics), which are approaching the best polar sensors (e.g., cf. 0.75-1 km spatial resolution at nadir for VIIRS, MODIS and AVHRR FRAC). Furthermore, it provides the unseen so far for a geostationary sensor, 10 min refresh rate for the entire FD, thus offering a unique opportunity to use this temporal information and improve the clear-sky coverage.

Comparison with the NOAA heritage H7/OSPO product suggests improved coverage and performance statistics in the H8/ACSPO SST. At least some part of this improved performance is likely due to the improved AHI sensor, compared to the imager onboard the H7 satellite. Comparison with H8/JAXA SST product suggests that the H8/ACSPO product provides comparable coverage, and improved validation statistics. These latter comparisons confirm results of two other independent studies [15, 19], which have shown that the AHI 3.9 µm band is suboptimal for SST retrieval. Another JAXA skin experimental SST was produced based on 8.6, 10.4, and 11.2 µm bands, and validated using 3-months of matchups with iQuam in situ SST data [19]. The 3-month average validation statistics have substantially improved (bias of -0.15 K, SD of 0.56 K). The new bias is now consistent with the expected mean skin-bulk difference of -0.17 K, and the new SD is in line with the typical H8/ACSPO SDs.

It is felt that the H8/ACSPO product has room for improvement. The future work will fall along two major lines: the completion of the full processing chain, and improvements of its all elements. The first and immediate priority is the development of a L2C (collated in time, e.g., 1-hr instead of the current 10-min, in the native swath spatial resolution) product, in which the data volume will be reduced from the current 45 GB to 7-8 GB per day, while preserving the maximum information contained in the 142 FD data, and reducing the noise in the data. The need for smaller volume H8 data has been clearly articulated by the H8 SST users. Also, the GHRSST data archives – the Physical Oceanography Distributed Active Archive Center (PO.DAAC) and the NOAA National Centers for Environmental Information (NCEI) – indicated that data volume should be reduced at least an order of magnitude, to allow efficient data storage and access. Depending on how successfully the planned L2C objectives are achieved, a further step, L3C product may be required, which will further aggregate the L2C data (which recall are reported in the original-swath projection) in space. Once the L2C/L3C functionality is fully completed, all elements of the processing chain (including the ACSPO Clear-Sky Mask and the SST algorithms) and will be revisited and improved. The ACSPO code continues to be modularized and optimized to facilitate its maintenance and further development, and more efficient operations and storage given the challenging data volumes.

Once the ACSPO H8 L2C/L3C products are finalized and another iteration on improvements of all its elements are made, we plan to reprocess all AHI L1b from April 2015 – forward, and discuss their archival with the GHRSST archives at PO.DAAC and NCEI.

6 ACKNOWLEDGMENTS

This work is conducted under the Himawari-8 and GOES-R SST Projects funded by the NOAA Product System Development and Implementation (PSDI; Manager, Tom Schott) Program, GOES-R Program Office (STAR Algorithm Working Group Manager, Jaime Daniels), and the NOAA Ocean Remote Sensing Program (Paul DiGiacomo, Manager, and Marilyn Yuen-Murphy, Deputy Manager). Thanks go to our SST Colleagues (Irina Gladkova, XingMing Liang, Yanni Ding, John Sapper, John Stroup, Xinjia Zhou, Feng Xu), STAR Integration Team (Walter Wolf, Shanna Sampson, Aiwu Li, Meizhu Fan, Veena Jose), and the NOAA Calibration Working Group (Fred Wu, Fangfang Yu, Aaron Pearlman, Frank Padula) for helpful discussions and feedback. Also thanks to JMA for providing the AHI data. The views, opinions, and findings contained in this paper are those of the authors and should not be construed as an official NOAA or US Government position, policy, or decision.

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Figure 1: SST nighttime SST composites based on: (left) H8/AHI; (right) S-NPP/VIIRS data.

Figure 2: Histogram “H8/ACSPO minus in situ” SST on July 15, 2015 for two times: (left) 02:20UTC; (right) 14:20UTC (approximately corresponding to the local noon and midnight, respectively, at the H8 sub-satellite point).

Figure 3: Histogram of “H7/OSPO minus in situ” SST on July 15, 2015 for two times: (left) 02:30UTC; (right) 14:30UTC (approximately corresponding to the local noon and midnight, respectively, at the H7 sub-satellite point).

Figure 4: Time series of (left) median and (right) SD of “satellite minus in situ” SSTs for (green) H8/ACSPO and (red) H7/OSPO. Vertical lines mark local (dotted) noon and (dashed) midnight for H8 sub-satellite point.

Figure 5: Time series of (left) median and (right) SD of “satellite minus CMC” SSTs for (green) H8/ACSPO and (red) H7/OSPO. Vertical lines mark local (dotted) noon and (dashed) midnight for H8 sub-satellite point.

Figure 6: Histogram “H8/ACSPO minus in situ” SST on January 7, 2016 for two times: (left) 02:30 UTC; (right) 14:30UTC (approximately corresponding to the local noon and midnight, respectively, at the H8 sub-satellite point).

Figure 7: Histogram “H8/JAXA minus in situ” SST on January 7, 2016 for two times: (left) 02:30 UTC; (right) 14:30 UTC (approximately corresponding to the local noon and midnight, respectively, at the H8 sub-satellite point).

Figure 8: Time series of (left) median and (right) SD of “satellite minus in situ” SSTs for (green) H8/ACSPO and (blue) H8/JAXA. Vertical lines mark local (dotted) noon and (dashed) midnight for H8 sub-satellite point.

Figure 9: Time series of (left) median and (right) SD of “satellite minus CMC” SSTs for (green) H8/ACSPO and (blue) H8/JAXA. Vertical lines mark local (dotted) noon and (dashed) midnight for H8 sub-satellite point.

Figure 10: Time series of (left) median and (right) SD of “satellite derived minus CMC” SSTs, for three satellite SST products: (green) H8/ACSPO, (blue) H8/JAXA, and (red) H7/OSPO.

Figure 11: Time series of percentage of clear-sky pixels over the ocean for (green) H8/ACSPO, (blue) H8/JAXA, and (red) H7/OSPO. (Left): Original time series. (Right): obtained from the left by one-day moving averaging.