Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology
Abstract
:1. Introduction
2. Methods
3. Data
4. Principles for SST Estimates
4.1. SST Retrieval Process
4.2. SSES Determination
4.3. Compositing of Products
- L3C-01hour
- The MERGE algorithm uses hourly products estimated at time . For every point on the full disk, using up to 7 consecutive current and prior L2P observations, (from time ), by choosing the time point that best approximates the expected SST at time , assuming that the SST is linearly trending in the best quality over time.
- L3C-04hour
- The MERGE algorithm also permits four-hourly products that are estimated at time . For every point on the full disk, using five consecutive current and prior L3C-01hour full disk observations, (from time ), by choosing the time point that best approximates the expected SST, assuming linear trending in the best quality SST over time.
- L3C-01day
- Nightly SST products are estimated using the CHOOSE algorithm, which selects the latest best quality hourly L3C-01hour SST during the night, before sunrise, for each point on the full disk.
4.3.1. MERGE Algorithm
- PREPARE
- Determine candidate SST at the target time , , by interpolating .
- Given a selection of SST retrievals ordered over time T, at location X, , of quality :
- (a)
- Choose an appropriate Land/Ice mask to identify observation locations that are within scope.
- (b)
- Quality control SST, such that:
- SST is in range, (271 K 330 K).
- SST change over consecutive time periods, , is not too large (−10 K 100 K). Note warm-to-cold transitions are significantly greater causes for removal than cold-to-warm transitions.
- (c)
- Identify the background SST, , subject to a constraint on Q:
- (d)
- Interpolate SSTBG in T, using the quality_level as an exponential weight:
- (e)
- Identify the foreground SST, , based on the interpolated background:
- (f)
- Interpolate SSTFG in T, using the quality_level as an exponential weight:
- Determine the prepared SST at the target time, , , as approximated by the nearest observed SST, :
- Determine the quality field similarly, .
- If a determination is not possible due to too little data or out of range, is considered to have no value.
- SEED
- Determine the seed domain , which forms the basis of reliable SSTs by identifying connected regions of approximately constant , and significant size. The domain of merged SST is grown from these regions of stability.
- Segregate the prepared SST, , into connected regions of nearest neighbours, such that if two adjacent values differ by 0.2 K or less, they belong to the same connected region, regardless of assigned quality.
- Remove connected regions with an area of less than 20 pixels considering them not large enough to have a confirmed stable value.
- GROW
- Grow the seed domain , to the final merged SST,
- Expand the boundary of by replacing undefined or removed SST values by the inverse distance weighted in a 5 pixel radius (using modified Shepard’s method of radius 5 with on a Euclidean metric in native pixel coordinates).
- Repeat this process 15 times, forming .
- Consider the observation with the value closest to the determined domain as before,
4.3.2. CHOOSE Algorithm
- Start with an undefined set of merged values, with undefined quality.
- If the component SST exists as a night SST, and is of sufficient quality (greater than or equal to the current quality), record this as the best choice SST, along with quality and time.
- Repeat for all identified components within the temporal range.
4.4. Validation
5. Results
5.1. L2P SST Product
5.2. Coverage and Bias for L2P SST
5.2.1. Temporal and Spatial Bias
5.2.2. Annual and Diurnal Bias
5.2.3. Validation of Full Disk L2P SSTs
5.3. L3C SST Product
6. Application of Himawari-8 SSTs
7. Discussion and Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dimension | Lower Limit | Upper Limit | Bin Size | Number of Bins | |
---|---|---|---|---|---|
Daytime | Path Length | 1.0 | 2.4 | 0.35 | 4 |
Spectral PDF 1 | SZA | 0.0 | 95 | 2.5 | 3.8 |
0.64 μm | 0.0 | 1.0 | 0.01 | 100 | |
0.86 μm | 0.0 | 1.0 | 0.01 | 100 | |
Daytime | Path Length | 1.0 | 2.4 | 0.35 | 4 |
Spectral PDF 2 | NWP SST | 271 | 304 | 1.0 | 33 |
10.4–12.4 μm BT difference | −1 | 9 | 0.2 | 50 | |
10.4 μm BT-SST | −20 | 10 | 1.0 | 30 | |
Night-time | Path Length | 1.0 | 2.4 | 0.35 | 4 |
Spectral PDF | NWP SST | 270 | 305 | 2.5 | 14 |
3.9–10.4 μm BT difference | −6 | 10 | 0.2 | 80 | |
10.4–12.4 μm BT difference | −1 | 9 | 0.2 | 50 | |
10.4 μm BT-NWP SST | −20 | 10 | 1.0 | 30 | |
Textural PDF | Day/Night | 0 | 180 | 90 | 2 |
Path Length | 1.0 | 2.4 | 0.35 | 4 | |
10.4 μm LSD | 0 | 2 | 0.005 | 400 |
Level | Meaning | P(c) | Sens | Other | |
---|---|---|---|---|---|
0 | No data | Invalid data, land | |||
1 | Bad data | <0.5 | <0.5 | >3 | SST < −2 °C; SST > 50 °C; Bad NWP |
2 | Worst quality | <0.8 | <0.9 | >2 | Limb pixel (satellite zenith ) |
3 | Low quality | <0.9 | <0.95 | >1 | Twilight () |
4 | Acceptable quality | Not used for Himawari-8 | |||
5 | Best quality |
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Govekar, P.; Griffin, C.; Embury, O.; Mittaz, J.; Beggs, H.M.; Merchant, C.J. Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology. Remote Sens. 2024, 16, 3381. https://doi.org/10.3390/rs16183381
Govekar P, Griffin C, Embury O, Mittaz J, Beggs HM, Merchant CJ. Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology. Remote Sensing. 2024; 16(18):3381. https://doi.org/10.3390/rs16183381
Chicago/Turabian StyleGovekar, Pallavi, Christopher Griffin, Owen Embury, Jonathan Mittaz, Helen Mary Beggs, and Christopher J. Merchant. 2024. "Himawari-8 Sea Surface Temperature Products from the Australian Bureau of Meteorology" Remote Sensing 16, no. 18: 3381. https://doi.org/10.3390/rs16183381