Multi-Sensor Sea Surface Temperature Products from the Australian Bureau of Meteorology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.1.1. AVHRR
2.1.2. VIIRS
2.1.3. Ancillary Fields
- Wind Speed
- Sea Ice Fraction
- Foundation SST Analysis
2.2. SST Processing Methods
2.2.1. IMOS HRPT AVHRR L2P
2.2.2. IMOS HRPT AVHRR L3U
2.2.3. IMOS FRAC AVHRR and VIIRS L3U
- Subskin SST from the original data producers is converted to skin SST by subtracting 0.17 K;
- Ancillary fields are replaced by the sources used for standard IMOS SST products (Section 2.1.3);
- l2p_flags are redefined using modified ancillary fields to conform with the standard IMOS L3U format [12];
- Sensor-Specific Error Statistics (SSES; [16] are maintained from the original sources as different retrieval methods are used by the original data producers;
- Quality level [16] is defined differently for each data source. It is not a reflection of the proximity to clouds, as is the case for IMOS HRPT AVHRR L3U SSTs [12,39]. To make the data from different sources comparable, the quality is redefined using the method based on the supplied quality level, SSES bias and SSES standard deviation, described below in Section 2.2.5.
2.2.4. IMOS L3C SST
2.2.5. Quality Redefining (QR) Method
2.2.6. IMOS Multi-Sensor L3S SST
2.3. Validation
- In situ measurements are located over the time period corresponding to satellite observation;
- For each in situ measurement, all satellite observations within the requisite distance (10 km) and time (6 h) difference are selected;
- Matches are examined in groups, grouped by in situ observation, and the best match is determined for each in situ observation based on time and space difference and observation quality and retained.
- Unique observation measurements are generated per the previous algorithm for each L2P, L3U and L3C;
- Matches are collected over the entire scope (multiple L2P, L3U, L3C matches), then aggregated by satellite observation and in situ instrument identity;
- The best match is retained for each satellite observation and in situ instrument identity combination.
3. Results
3.1. IMOS L3C SSTs
3.2. IMOS Multi-Sensor L3S SSTs
3.3. Case Study: The Great Barrier Reef
4. Discussion
- The BoM compositing algorithm uses sses_bias, sses_standard_deviation and degrees of freedom as parametric quality assessments and quality_level as a non-parametric measure. Only the highest non-parametric quality data are combined parametrically. Thus, we need an effective way to compare, in absolute terms, the quality of data streams from a non-parametric standpoint;
- It is necessary to be able to track degradations in quality over the platform’s life. This allows us to combine “old” platforms with “new” platforms with appropriate quality assessment;
- It allows us to reflect upon the greater uncertainty of measurement and degraded quality as the uncertainty and deviation from in situ measurement increases. Both lead to greater uncertainty so that the skin measurement follows the validation, and the method degrades the quality accordingly;
- It allows supplier quality assessment based on other metrics to be included in the discussion. The process of quality remapping does not promote retrieval to higher quality, it only degrades it based on estimates of SSES parameters. This will tend to push quality assessments down, but they remain closer to an absolute (over time) assessment.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AVHRR | The Advanced Very-High-Resolution Radiometer |
BoM | Australian Bureau of Meteorology |
CSIRO | Commonwealth Scientific and Industrial Research Organisation |
ECMWF | European Centre for Medium-Range Weather Forecasts |
ECT | Equatorial Crossing Times |
ESA | European Space Agency |
FRAC | Full-Resolution Area Coverage |
HRPT | High-Resolution Picture Transmissions |
IMOS | Integrated Marine Observing System |
IMS | IMOS Multi-sensor L3S |
GBR | Great barrier Reef |
GHRSST | Group for High-Resolution Sea Surface Temperature |
GPB | Geo-polar Blended L4 SSTs |
L2P | Level 2P |
L3U | Level 3 Uncollated |
L3C | Level 3 Collated |
L3S | Level 3 Super-Collated |
L4 | Level 4 |
MetOp | Meteorological Operational satellite |
NOAA | National Oceanic and Atmospheric Administration |
N20 | NOAA-20 |
NPOES | NOAA Polar-Orbiting Environmental Satellites |
NPP | National Polar-orbiting Partnership |
OSPO | Office of Satellite and Product Operations |
SSES | Sensor-Specific Error Statistics |
SST | Sea Surface Temperature |
STAR | NOAA/NESDIS Center for Satellite Applications and Research |
VIIRS | Visible Infrared Imaging Radiometer Suite |
QR | Quality Redefining |
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Item | fv01 | fv02 |
---|---|---|
SST retrieval | Regression, fixed coefficients tuned over the first 2 years of platform operation. Separate algorithms for day and night with a large number of terms. | Regression, adaptive coefficients tuned over a rolling 2-year window, updated monthly. Separate standard algorithms for day and night as well as a three-channel unified day and night algorithm. |
SSES generation | Lookup table based on a 60-day rolling window for NOAA-11 to 19 platforms. | Modelled using a one-year rolling window, updated every five days for NOAA 11–19 platforms. |
Time coverage | 1 January 2019 to present. Coverage may be incomplete over some periods, although the L3S daily composites form a close to complete record. | 2 January 1992 to the end of the most recent batch process. Coverage is complete up to navigation and reception issues and subject to the availability of data from all Australian reception stations, from NOAA-11, 12, 14, 15, 16, 17, 18, 19. |
Spatial coverage | Includes reception from Australian continental reception stations for NOAA-18,19 and full domain coverage from N20, NPP and MetOp-B satellites. | Includes reception from both continental and Antarctic stations for NOAA-11, 12, 14, 15, 16, 17, 18, 19. Coverage is enhanced over time periods where earlier and later platforms overlap. Spatial coverage has been enhanced in recent years from fully global SST data from N20, NPP, MetOp-A and MetOp-B satellites. |
Platform coverage | Currently covers NOAA-18, NPP, N20, MetOp-B | NOAA-11, 12, 14, 15, 16, 17, 18, 19, 20, MetOp-A, MetOp-B and NPP. For any given date, all retrievals from the relevant set of active platforms are included in composite L3S files. |
Fields | sea_surface_temperature, quality_level, sst_dtime, dt_analysis, wind_speed, wind_speed_dtime_from_sst, sea_ice_fraction, sea_ice_fraction_dtime_from_sst, l2p_flags, satellite_zenith_angle, sses_bias, sses_standard_deviation, sses_count | sea_surface_temperature, sea_surface_temperature_day_night, quality_level, sst_dtime, dt_analysis, wind_speed, wind_speed_dtime_from_sst, sea_ice_fraction, sea_ice_fraction_dtime_from_sst, l2p_flags, satellite_zenith_angle, sses_bias, sses_standard_deviation, sses_count, sses_quality_level |
Metadata | Default, but tends to be inconsistent in some comment fields. | Default |
AVHRR | VIIRS | |
---|---|---|
−0.2614 | −0.227 | |
0.23 | 0.20 |
Platform | Period |
---|---|
NOAA-18 | 1 January 2012 to 31 December 2020 |
NOAA-19 | 1 January 2012 to 3 September 2018 |
NPP | 1 March 2012 to 31 December 2020 |
N20 | 6 June 2018 to 31 December 2020 |
MetOp-A | 1 January 2012 to 19 January 2016 |
MetOp-B | 20 January 2016 to 31 December 2020 |
Average Spatial Scale n (km) | Fitted Linear Coefficient | Fitted Constant Coefficient |
---|---|---|
0.67 | 0.94 ± 0.04 | 0.083 ± 0.005 |
1.33 | 0.93 ± 0.04 | 0.062 ± 0.004 |
2.67 | 0.93 ± 0.04 | 0.053 ± 0.004 |
5.33 | 0.92 ± 0.03 | 0.042 ± 0.004 |
10.7 | 0.91 ± 0.03 | 0.032 ± 0.003 |
21.3 | 0.89 ± 0.03 | 0.024 ± 0.003 |
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Govekar, P.D.; Griffin, C.; Beggs, H. Multi-Sensor Sea Surface Temperature Products from the Australian Bureau of Meteorology. Remote Sens. 2022, 14, 3785. https://doi.org/10.3390/rs14153785
Govekar PD, Griffin C, Beggs H. Multi-Sensor Sea Surface Temperature Products from the Australian Bureau of Meteorology. Remote Sensing. 2022; 14(15):3785. https://doi.org/10.3390/rs14153785
Chicago/Turabian StyleGovekar, Pallavi Devidas, Christopher Griffin, and Helen Beggs. 2022. "Multi-Sensor Sea Surface Temperature Products from the Australian Bureau of Meteorology" Remote Sensing 14, no. 15: 3785. https://doi.org/10.3390/rs14153785