scholarly journals DAILY BLENDED MICROWAVE AND INFRARED SEA SURFACE TEMPERATURE COMPOSITION

2013 ◽  
Vol 31 (2) ◽  
pp. 317 ◽  
Author(s):  
Gutemberg Borges França ◽  
Rosa Cristhyna de Oliveira Vieira Paes ◽  
Antônio Do Nascimento Oliveira ◽  
Bianca Couto Ruivo ◽  
Angelo Sartori
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficients ◽  
Sea Surface ◽  
Objective Analysis ◽  
Tropical Atlantic ◽  
Daily Average ◽  
Temporal And Spatial ◽  
Oceanographic Model

ABSTRACT. A simple system for daily cloud free sea surface temperature (SST) composition based on thermal AVHRR and microwave TMI data is presented in this paper. Barnes’ objective analysis is applied as an interpolator to merge these two data sources, which have different spatial and temporal resolutions in a daily SST composition and in a regular grid product. Three comparisons were carried out as follows. First, in situ SST (daily average) measurements from eleven PIRATA’s (Prediction and Research Moored Array in the Tropical Atlantic) buoys were compared. The correlation coefficients results varied from 0.89 to 0.99, and RMSE, MAE and MBE values have not exceeded 0.57 for period from 2002 to 2010. Second, comparisons between daily SST composition and average daily in situ SST collected from twenty three drifting buoys for the period from May 2008 to October 2010. The statistics results are 0.94, 0.25, 0.19 and − 0.002 for correlation, RMSE, MAE and MBE, respectively. Third, SST (daily average) time series generated by OSTIA project was compared. The temporal and spatial RMSE (considering the study area) values ranged from approximately 0.21◦C to 1.50◦C and its average was 0.47◦C for the period from April 1st 2006 to December 31st 2010. Besides, an investigation about the influence of the data homogenization in the SST interpolation is discussed. Validation results are quite consistent (with SST composition accuracy less than 1.0◦C). Thus, aiming to fulfill the numerical oceanographic model assimilation purposes and other oceanographic features studies, the developed SST product may be recommended as a candidate.   Keywords: oceanography, objective analysis, satellites. RESUMO. Este trabalho apresenta uma metodologia para geração de composições diárias de temperatura da superfície do mar (TSM) sem contaminação de nuvens, baseada em dados termais do AVHRR e micro-ondas do TMI. A análise objetiva de Barnes é utilizada como interpolador para mesclar estas duas fontes de dados, que possuem diferentes resoluções espacias e temporais, e gerar uma composição diária de TSM em grade regular. Três tipos de comparações foram feitas com esta composição de TSM, conforme descrito a seguir. 1) Comparação com medidas in situ de TSM (média diária) de onze bóias do PIRATA. Os coeficientes de correlação variaram de 0,89 a 0,99, e os RMSE, MAE e MBE não excederam 0,57 para o período entre 2002 e 2010. 2) Comparação com medidas in situ de TSM (média diária) de vinte e três boias de deriva do PNBOIA para o período entre Maio de 2008 e Outubro de 2010. Os resultados das estatísticas foram: 0,94, 0,25, 0,19 e − 0,002 para a correlação, RMSE, MAE e MBE, respectivamente. 3) Comparação com uma série temporal de TSM gerados pelo projeto OSTIA. A faixa dos valores do RMSE (considerando a área de estudo) variou aproximadamente entre 0,21◦C e 1,50◦C e sua média foi de 0,47◦C para o período de 01 de Abril de 2006 a 31 de Dezembro de 2010. Uma investigação sobre a influência da homogeneização das diferentes fontes de dados antes do processo de interpolação é discutida. Os resultados da validação da TSM são consistentes (com uma acurácia menor que 1,0◦C).   Palavras-chave: oceanografia, análise objetiva, satélites.

DAILY BLENDED MICROWAVE AND INFRARED SEA SURFACE TEMPERATURE COMPOSITION

2014 ◽  
Vol 31 (2) ◽  
Author(s):  
Gutemberg Borges França ◽  
Rosa Cristhyna Paes ◽  
Antônio Nascimento Oliveira ◽  
Bianca Couto Ruivo ◽  
Angelo Sartori Neto
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficients ◽  
Sea Surface ◽  
Regular Grid ◽  
Daily Average ◽  
Temporal And Spatial ◽  
Oceanographic Model ◽  
Drifting Buoys

A simple system for daily cloud free sea surface temperature (SST) composition based on thermal AVHRR and microwave TMI data is presented in this paper. Barnes’ objective analysis (Barnes, 1964) is applied as an interpolator to merge these two data sources, which have different spatial and temporal resolutions in a daily SST composition and in a regular grid product. Three comparisons were carried out as follows. First, in situ SST (daily average) measurements from eleven PIRATA´s buoys were compared. The correlation coefficients results varied from 0.89 to 0.99, and RMSE, MAE and MBE values have not exceeded 0.57 for period from 2002 to 2010. Second, comparisons between daily SST composition and average daily in situ SST collected from twenty three drifting buoys for the period from May 2008 to October 2010. The statistics results are 0.94, 0.25, 0.19 and -0.002 for correlation, RMSE, MAE and MBE, respectively. Third, SST (daily average) time series generated by OSTIA project was compared. The temporal and spatial RMSE (considering the study area) values ranged from approximately 0.21oC to 1.50oC and its average was 0.47oC for the period from January 1st to May 31st, 2006. Besides, an investigation about the influence of the data homogenization in the SST interpolation is discussed. Validation results are quite consistent (with SST composition accuracy less than 1.0oC). Thus, aiming to fulfill the numerical oceanographic model assimilation purposes and other oceanographic features studies, the developed SST product may be recommended as a candidate. 

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 RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

scholarly journals On the impact of atmospheric vs oceanic resolutions on the representation of the sea surface temperature in the South Eastern Tropical Atlantic

2020 ◽  
Vol 54 (11-12) ◽  
pp. 4733-4757 ◽  
Author(s):  
Alba de la Vara ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Dmitry Sidorenko ◽  
Nikolay V. Koldunov ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Tropical Atlantic ◽  
The South ◽  
South Eastern ◽  
The Impact
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