scholarly journals Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Important Variable ◽  
Sea Surface ◽  
Analysis Method ◽  
Validation Process ◽  
Indonesian Seas

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

scholarly journals Sea Surface Temperature Daytime Climate Analyses Derived from Aerosol Bias-Corrected Satellite Data

2006 ◽  
Vol 19 (3) ◽  
pp. 410-428 ◽  
Author(s):  
Nicholas R. Nalli ◽  
Richard W. Reynolds
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Bias Correction ◽  
Tropical Rainfall Measuring Mission ◽  
Stratospheric Aerosol ◽  
Sea Surface ◽  
Diurnal Warming

Abstract This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.

scholarly journals Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations

2008 ◽  
Vol 25 (7) ◽  
pp. 1197-1207 ◽  
Author(s):  
Anne G. O’Carroll ◽  
John R. Eyre ◽  
Roger W. Saunders
Keyword(s):  
Standard Deviation ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Data Sources ◽  
Independent Data ◽  
Earth Observing System ◽  
Along Track

Abstract Using collocations of three different observation types of sea surface temperatures (SSTs) gives enough information to enable the standard deviation of error on each observation type to be derived. SSTs derived from the Advanced Along-Track Scanning Radiometer (AATSR) and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instruments are used, along with SST observations from buoys. Various assumptions are made within the error theory, including that the errors are not correlated, which should be the case for three independent data sources. An attempt is made to show that this assumption is valid and that the covariances between the different observations because of representativity error are negligible. Overall, the spatially averaged nighttime AATSR dual-view three-channel bulk SST observations for 2003 are shown to have a very small standard deviation of error of 0.16 K, whereas the buoy SSTs have an error of 0.23 K and the AMSR-E SST observations have an error of 0.42 K.

scholarly journals High resolution 3-D temperature and salinity fields derived from in situ and satellite observations

Ocean Science ◽  
2012 ◽  
Vol 8 (5) ◽  
pp. 845-857 ◽  
Author(s):  
S. Guinehut ◽  
A.-L. Dhomps ◽  
G. Larnicol ◽  
P.-Y. Le Traon
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Sea Surface ◽  
Temperature Fields ◽  
Global Ocean ◽  
In Situ Observations ◽  
Temperature Observations

Abstract. This paper describes an observation-based approach that efficiently combines the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 yr) are merged with the lower accuracy but high-resolution synthetic data derived from satellite altimeter and sea surface temperature observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations, and salinity fields from altimeter observations, through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolutionary nature of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method, and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50% of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30% of the signal can be reconstructed from altimeter observations, making the in situ observing system essential for salinity estimates. The in situ observations (step 2 of the method) further reduce the differences between the gridded products and the observations by up to 20% for the temperature field in the mixed layer, and the main contribution is for salinity and the near surface layer with an improvement up to 30%. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to describe qualitatively the temperature variability patterns for the period from 1993 to 2009.

scholarly journals Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1

2020 ◽  
pp. 1-47
Author(s):  
Boyin Huang ◽  
Chunying Liu ◽  
Viva Banzon ◽  
Eric Freeman ◽  
Garrett Graham ◽  
...  
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Cold Bias ◽  
Global Average ◽  
Optimum Interpolation ◽  
The Indian Ocean

AbstractNOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST) version 2.0 (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian Ocean, South Pacific, and South Atlantic due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the Traditional Alphanumeric Codes (TAC) to the Binary Universal Form for the Representation of meteorological data (BUFR). The cold bias against Argo was about -0.14°C on global average and -0.28°C in the Indian Ocean from January 2016 to August 2019.We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5 m depth. The impact of using satellite MetOp-B instead of NOAA-19 was notable on high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying freezing-point instead of regressed ice-SST proxy.This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to -0.07°C (-0.04°C) and -0.14°C (-0.08°C) in the global and Indian Ocean, respectively, when compared against independent (dependent) Argo observations. The difference against the Group for High Resolution SST (GHRSST) multi-product ensemble (GMPE) product is reduced from -0.09°C to -0.01°C in the global oceans and from -0.20°C to -0.04°C in the Indian Ocean.

Daily High-Resolution-Blended Analyses for Sea Surface Temperature

2007 ◽  
Vol 20 (22) ◽  
pp. 5473-5496 ◽  
Author(s):  
Richard W. Reynolds ◽  
Thomas M. Smith ◽  
Chunying Liu ◽  
Dudley B. Chelton ◽  
Kenneth S. Casey ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temporal Resolution ◽  
Large Scale ◽  
Sea Surface ◽  
In Situ Data ◽  
Satellite Sst ◽  
Avhrr Data

Abstract Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

scholarly journals Immense variability in the sea surface temperature near macro tidal flat revealed by high-resolution satellite data (Landsat 8)

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Seung-Tae Lee ◽  
Yang-Ki Cho ◽  
Duk-jin Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tidal Flat ◽  
Sea Water ◽  
Sea Surface ◽  
Landsat 8 ◽  
Open Sea ◽  
In Situ Observations

AbstractSea surface temperature (SST) is crucial for understanding the physical characteristics and ecosystems of coastal seas. SST varies near the tidal flat, where exposure and flood recur according to the tidal cycle. However, the variability of SST near the tidal flat is poorly understood owing to difficulties in making in-situ observations. The high resolution of Landsat 8 enabled us to determine the variability of SST near the macro tidal flat. The spatial distribution of the SST extracted from Landsat 8 changed drastically. The seasonal SST range was higher near the tidal flat than in the open sea. The maximum seasonal range of coastal SST exceeded 23 °C, whereas the range in the open ocean was approximately 18 °C. The minimum and maximum horizontal SST gradients near the tidal flat were approximately − 0.76 °C/10 km in December and 1.31 °C/10 km in June, respectively. The heating of sea water by tidal flats in spring and summer, and cooling in the fall and winter might result in a large horizontal SST gradient. The estimated heat flux from the tidal flat to the seawater based on the SST distribution shows seasonal change ranging from − 4.85 to 6.72 W/m2.

scholarly journals High Resolution 3-D temperature and salinity fields derived from in situ and satellite observations

2012 ◽  
Vol 9 (2) ◽  
pp. 1313-1347 ◽  
Author(s):  
S. Guinehut ◽  
A.-L. Dhomps ◽  
G. Larnicol ◽  
P.-Y. Le Traon
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Satellite Observations ◽  
Sea Surface ◽  
Temperature Fields ◽  
In Situ Observations ◽  
Temperature Observations

Abstract. This paper describes an observation-based approach that combines efficiently the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 years) are merged with the lower accuracy but high-resolution synthetic data derived from altimeter and sea surface temperature satellite observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations and salinity fields from altimeter observations through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolution of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50 % of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30 % of the signal can be reconstructed from altimeter observations, making the in situ observing system mandatory for salinity estimates. The in situ observations (step 2 of the method) reduce additionally the error by up to 20 % for the temperature field in the mixed layer and the main contribution is for salinity and the near surface layer with an improvement up to 30 %. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to qualitatively describe the temperature variability patterns for the 1993 to 2009 time period.

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