scholarly journals Long-term sea surface temperature variability in the Aegean Sea

2011 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Nikolaos Skliris ◽  
Sarantis S. Sofianos ◽  
Athanasios Gkanasos ◽  
Panagiotis Axaopoulos ◽  
Anneta Mantziafou ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Aegean Sea ◽  
Sea Surface ◽  
Surface Warming ◽  
In Situ Data ◽  
Nao Index ◽  
Decadal Scale

The inter-annual/decadal scale variability of the Aegean Sea Surface Temperature (SST) is investigated by means of long-term series of satellite-derived and in situ data. Monthly mean declouded SST maps are constructed over the 1985–2008 period, based on a re-analysis of AVHRR Oceans Pathfinder optimally interpolated data over the Aegean Sea. Basin-average SST time series are also constructed using the ICOADS in situ data over 1950–2006. Results indicate a small SST decreasing trend until the early nineties, and then a rapid surface warming consistent with the acceleration of the SST rise observed on the global ocean scale. Decadal-scale SST anomalies were found to be negatively correlated with the winter North Atlantic Oscillation (NAO) index over the last 60 years suggesting that along with global warming effects on the regional scale, a part of the long-term SST variability in the Aegean Sea is driven by large scale atmospheric natural variability patterns. In particular, the acceleration of surface warming in the Aegean Sea began nearly simultaneously with the NAO index abrupt shift in the mid-nineties from strongly positive values to weakly positive/negative values.

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 Comparison of in situ time-series of temperature with gridded sea surface temperature datasets in the North Atlantic

2009 ◽  
Vol 66 (7) ◽  
pp. 1467-1479 ◽  
Author(s):  
Sarah L. Hughes ◽  
N. Penny Holliday ◽  
Eugene Colbourne ◽  
Vladimir Ozhigin ◽  
Hedinn Valdimarsson ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
North Atlantic ◽  
Sea Surface ◽  
The North ◽  
Spatial Coverage ◽  
The North Atlantic

Abstract Hughes, S. L., Holliday, N. P., Colbourne, E., Ozhigin, V., Valdimarsson, H., Østerhus, S., and Wiltshire, K. 2009. Comparison of in situ time-series of temperature with gridded sea surface temperature datasets in the North Atlantic. – ICES Journal of Marine Science, 66: 1467–1479. Analysis of the effects of climate variability and climate change on the marine ecosystem is difficult in regions where long-term observations of ocean temperature are sparse or unavailable. Gridded sea surface temperature (SST) products, based on a combination of satellite and in situ observations, can be used to examine variability and long-term trends because they provide better spatial coverage than the limited sets of long in situ time-series. SST data from three gridded products (Reynolds/NCEP OISST.v2., Reynolds ERSST.v3, and the Hadley Centre HadISST1) are compared with long time-series of in situ measurements from ICES standard sections in the North Atlantic and Nordic Seas. The variability and trends derived from the two data sources are examined, and the usefulness of the products as a proxy for subsurface conditions is discussed.

scholarly journals ANÁLISE DE TEMPERATURA DE PELE DA SUPERFÍCIE DO MAR EM REGIÕES DE ALTA LATITUDE SIMULADA COM O MODELO WRF

2016 ◽  
Vol 38 ◽  
pp. 11
Author(s):  
Alcimoni Nelci Comin ◽  
Otávio Costa Acevedo
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wrf Model ◽  
Sea Surface ◽  
Average Wind Speed ◽  
Shetland Islands ◽  
In Situ Data ◽  
Average Wind ◽  
Nested Grids

The in situ data of sea surface temperature (SST) were measured onboard the Polar Ship Almirante Maximiano in the southern Shetland Islands between 5 and 23 February 2011. For the simulations, three concentric nested grids have been used at the 9 km, 3 km and 1 km spatial resolution in the simulations of the skin sea surface temperature (SSST) with WRF model. The grids are displaced every day, always centered in the middle position of the ship (latitude/longitude) during transect. The SSST is underestimated in comparison with SST on average 1.5°C. The real average wind speed observed was 8.7 ms-1. Therefore the amount of mixing between SST and SSST is greater, and the temperature difference between the two layers is smaller, on average 0.5°C. The underestimation of the model is mean 1°C. This underestimation directly interfere on the amount of ocean evaporation for the atmosphere, which may cause error in the energy balance. The correlation of the SSST with real SST data was 0.84 and root mean square error 1.87. 

scholarly journals Sea‐surface temperature and salinity product comparison against external in situ data in the A rctic O cean

2015 ◽  
Vol 120 (11) ◽  
pp. 7223-7236 ◽  
Author(s):  
J. N. Stroh ◽  
Gleb Panteleev ◽  
Sergey Kirillov ◽  
Mikhail Makhotin ◽  
Natalia Shakhova
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
In Situ Data ◽  
Product Comparison

scholarly journals Long-term sea surface temperature variability in the Aegean Sea

2011 ◽  
Vol 2 (2) ◽  
pp. 125-139 ◽  
Author(s):  
Nikolaos Skliris ◽  
Sarantis S. Sofianos ◽  
Athanasios Gkanasos ◽  
Panagiotis Axaopoulos ◽  
Anneta Mantziafou ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Aegean Sea ◽  
Temperature Variability ◽  
Sea Surface

scholarly journals RETRIEVING COASTAL SEA SURFACE TEMPERATURE FROM LANDSAT-8 TIRS FOR WANGI-WANGI ISLAND, WAKATOBI, SOUTHEAST SULAWESI, INDONESIA

2019 ◽  
Vol 16 (1) ◽  
pp. 13
Author(s):  
Eko Susilo ◽  
Rizki Hanintyo ◽  
Adi Wijaya
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Zone ◽  
Single Band ◽  
Sea Surface ◽  
Superior Performance ◽  
Landsat 8 ◽  
Infrared Sensors ◽  
In Situ Data

The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.

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