scholarly journals ANALISIS PENGGUNAAN PERSAMAAN MULTICHANNEL SEA SURFACE TEMPERATUR (MCSTT) SPLIT-WINDOW PADA SENSOR SATELIT NOAA-AVHRR UNTUK DETEKSI TEMPERATUR PERMUKAAN AIR LAUT

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Agung Mulyo Widodo ◽  
Nizirwan Anwar
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Normal Distribution ◽  
Great Influence ◽  
Weather Conditions ◽  
Sea Surface ◽  
Noaa Avhrr ◽  
Sensing Technology ◽  
Kolmogorov Smirnov ◽  
The Difference

<em> Sea surface temperatures have a great influence on weather conditions and terrestrial climates. The phenomena that occur in the oceans such as La Nina and El Nino also have a great impact on the changing world weather. For that required data of sea surface temperature up to date. The remote sensing technology can be used to monitor up-to-date seawater temperatures using NOAA's AVHRR-rated NOAA radios that have three thermal infrared channels, namely channel 3 (3.33-3.93μm), channel 4 (10.3 -11,3μm), and channel 5 (11,5 - 12μm) and by using Split-Window Multichannel Sea Surface Temperature (MCSTT) to calculate sea surface temperature. In this research will be an analysis of the accuracy of the use of these equations for tropical perariran in Indonesia. Field verification performed on the coast in Tuban region with geographical coordinates (6.83099<sup>o</sup>- 6.76149<sup>o</sup> SL and 112,029<sup>o</sup>-112,101<sup>o</sup>EL) by measuring the temperature at 30 points for comparison. The results of these measurements are then tested statistics Kolmogorov-Smirnov test and the results of temperature data obtained from the calculation and measurement of both normal distribution then because the normal distribution is done t test with 95% confidence level to compare between the temperature obtained from the calculation by using the value gray pixels with temperature measurements in the field turned out the average population is not the same or different significantly and the difference between the temperature shown image with temperature measurement results of 0.9886 <sup>o</sup>C</em>

scholarly journals Can an Earth System Model simulate better climate change at mid-Holocene than an AOGCM? A comparison study of MIROC-ESM and MIROC3

2013 ◽  
Vol 9 (4) ◽  
pp. 1519-1542 ◽  
Author(s):  
R. Ohgaito ◽  
T. Sueyoshi ◽  
A. Abe-Ouchi ◽  
T. Hajima ◽  
S. Watanabe ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Earth System Model ◽  
Sea Surface ◽  
System Model ◽  
Earth System ◽  
Intercomparison Project ◽  
The Difference ◽  
Precipitation Enhancement

Abstract. The importance of evaluating models through paleoclimate simulations is becoming more recognized in efforts to improve climate projection. To evaluate an integrated Earth System Model, MIROC-ESM, we performed simulations in time-slice experiments for the mid-Holocene (6000 yr before present, 6 ka) and preindustrial (1850 AD, 0 ka) periods under the protocol of the Coupled Model Intercomparison Project 5/Paleoclimate Modelling Intercomparison Project 3. We first give an overview of the simulated global climates by comparing with simulations using a previous version of the MIROC model (MIROC3), which is an atmosphere–ocean coupled general circulation model. We then comprehensively discuss various aspects of climate change with 6 ka forcing and how the differences in the models can affect the results. We also discuss the representation of the precipitation enhancement at 6 ka over northern Africa. The precipitation enhancement at 6 ka over northern Africa according to MIROC-ESM does not differ greatly from that obtained with MIROC3, which means that newly developed components such as dynamic vegetation and improvements in the atmospheric processes do not have significant impacts on the representation of the 6 ka monsoon change suggested by proxy records. Although there is no drastic difference between the African monsoon representations of the two models, there are small but significant differences in the precipitation enhancement over the Sahara in early summer, which can be related to the representation of the sea surface temperature rather than the vegetation coupling in MIROC-ESM. Because the oceanic parts of the two models are identical, the difference in the sea surface temperature change is ultimately attributed to the difference in the atmospheric and/or land modules, and possibly the difference in the representation of low-level clouds.

scholarly journals Sea surface temperature variations in the south-eastern BalticSea in 1960–2015

Baltica ◽  
2018 ◽  
Vol 30 (2) ◽  
pp. 75-85 ◽  
Author(s):  
Viktorija Rukšėnienė ◽  
Inga Dailidienė ◽  
Loreta Kelpšaitė-Rimkienė ◽  
Tarmo Soomere
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Weather Conditions ◽  
Sea Surface ◽  
South Eastern ◽  
Air Temperatures ◽  
Joint Application ◽  
Open Sea ◽  
And Cluster Analysis

This study focuses on time scales and spatial variations of interrelations between average weather conditions and sea surface temperature (SST), and long-term changes in the SST in south-eastern Baltic Sea. The analysis relies on SST samples measured in situ four times a year in up to 17 open sea monitoring stations in Lithuanian waters in 1960–2015. A joint application of non-metric multi-dimensional scaling and cluster analysis reveals four distinct SST regimes and associated sub-regions in the study area. The increase in SST has occurred during both winter and summer seasons in 1960–2015 whereas the switch from relatively warm summer to colder autumn temperatures has been shifted by 4–6 weeks over this time in all sub-regions. The annual average air temperature and SST have increased by 0.03°C yr–1 and 0.02°C yr–1, respectively, from 1960 till 2015. These data are compared with air temperatures measured in coastal meteorological stations and averaged over time intervals from 1 to 9 weeks. Statistically significant positive correlation exists between the SST and the average air temperature. This correlation is strongest for the averaging interval of 35 days.

scholarly journals The Effect of Diurnal Sea Surface Temperature Warming on Climatological Air–Sea Fluxes

2013 ◽  
Vol 26 (8) ◽  
pp. 2546-2556 ◽  
Author(s):  
Carol Anne Clayson ◽  
Alec S. Bogdanoff
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Heat Budget ◽  
Longwave Radiation ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Diurnal Variability ◽  
Diurnal Warming ◽  
Yearly Average ◽  
The Difference

Abstract Diurnal sea surface warming affects the fluxes of latent heat, sensible heat, and upwelling longwave radiation. Diurnal warming most typically reaches maximum values of 3°C, although very localized events may reach 7°–8°C. An analysis of multiple years of diurnal warming over the global ice-free oceans indicates that heat fluxes determined by using the predawn sea surface temperature can differ by more than 100% in localized regions over those in which the sea surface temperature is allowed to fluctuate on a diurnal basis. A comparison of flux climatologies produced by these two analyses demonstrates that significant portions of the tropical oceans experience differences on a yearly average of up to 10 W m−2. Regions with the highest climatological differences include the Arabian Sea and the Bay of Bengal, as well as the equatorial western and eastern Pacific Ocean, the Gulf of Mexico, and the western coasts of Central America and North Africa. Globally the difference is on average 4.45 W m−2. The difference in the evaporation rate globally is on the order of 4% of the total ocean–atmosphere evaporation. Although the instantaneous, year-to-year, and seasonal fluctuations in various locations can be substantial, the global average differs by less than 0.1 W m−2 throughout the entire 10-yr time period. A global heat budget that uses atmospheric datasets containing diurnal variability but a sea surface temperature that has removed this signal may be underestimating the flux to the atmosphere by a fairly constant value.

scholarly journals VARIABILITY OF CHLOROPHYLL-A CONCENTRATION AND SEA SURFACE TEMPERATURE OF NATUNA WATERS

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Bisman Nababan ◽  
Kristina Simamora
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Wide Field ◽  
The West ◽  
Chlorophyll A Concentration ◽  
Sst Variability ◽  
Noaa Avhrr

Variability of chlorophyll-a concentration and sea surface temperature (SST) in Natuna waters were analyzed using satellite data Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). SeaWiFS data with a resolution of 9×9 km2 and AVHRR with a resolution of 4×4 km2 were the monthly average data downloaded from NASA website. Chlorophyll-a concentrations and SST were estimated using OC4v4 and MCSST algorithms. In general, the concentration of chlorophyll-a in Natuna waters ranged between 0.11-4.92 mg/m3 with an average of 0.56 mg/m3 during the west season and 0.09-2.93 mg/m3 with an average of 0.66 mg/m3 during the east season. Chlorophyll-a concentrations were relatively high seen in coastal areas, especially around the mouth of the Kapuas, Musi, and Batang Hari rivers allegedly caused by the high nutrient intake from the mainland. SST variability in Natuna waters ranged from 23.46-30.88 °C during the west season and tended to be lower than that the east season (27.91-31.95 °C). In addition, the SST values tended to be lower in the offshore than that inshore. During the west season (Nov-Feb) and the transitional season (Apr) in the years of Elnino Southern Oscillation (ENSO), the concentration of chlorophyll-a and the SST in Natuna waters was generally higher than that in non-ENSO years. The results of wind analyses showed that ENSO caused the change of direction and speed of wind from its normal conditions.Keywords: Sea surface temperature, chlorophyll-a, Natuna waters, ENSO, SeaWiFS, AVHRR

scholarly journals Impact of Land–Atmosphere Interactions on Sahel Climate

2017 ◽  
Author(s):  
Yongkang Xue
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Land Surface ◽  
General Circulation ◽  
Great Influence ◽  
Climate System ◽  
Sea Surface ◽  
Surface Processes ◽  
Severe Drought ◽  
Land Surface Processes

The Sahel of Africa has been identified as having the strongest land–atmosphere (L/A) interactions on Earth. The Sahelian L/A interaction studies started in the late 1970s. However, due to controversies surrounding the early studies, in which only a single land parameter was considered in L/A interactions, the credibility of land-surface effects on the Sahel’s climate has long been challenged. Using general circulation models and regional climate models coupled with biogeophysical and dynamic vegetation models as well as applying analyses of satellite-derived data, field measurements, and assimilation data, the effects of land-surface processes on West African monsoon variability, which dominates the Sahel climate system at intraseasonal, seasonal, interannual, and decadal scales, as well as mesoscale, have been extensively investigated to realistically explore the Sahel L/A interaction: its effects and the mechanisms involved. The Sahel suffered the longest and most severe drought on the planet in the 20th century. The devastating environmental and socioeconomic consequences resulting from drought-induced famines in the Sahel have provided strong motivation for the scientific community and society to understand the causes of the drought and its impact. It was controversial and under debate whether the drought was a natural process, mainly induced by sea-surface temperature variability, or was affected by anthropogenic activities. Diagnostic and modeling studies of the sea-surface temperature have consistently demonstrated it exerts great influence on the Sahel climate system, but sea-surface temperature is unable to explain the full scope of the Sahel climate variability and the later 20th century’s drought. The effect of land-surface processes, especially land-cover and land-use change, on the drought have also been extensively investigated. The results with more realistic land-surface models suggest land processes are a first-order contributor to the Sahel climate and to its drought during the later 1960s to the 1980s, comparable to sea surface temperature effects. The issues that caused controversies in the early studies have been properly addressed in the studies with state-of-the-art models and available data. The mechanisms through which land processes affect the atmosphere are also elucidated in a number of studies. Land-surface processes not only affect vertical transfer of radiative fluxes and heat fluxes but also affect horizontal advections through their effect on the atmospheric heating rate and moisture flux convergence/divergence as well as horizontal temperature gradients.

scholarly journals Improving Satellite-Derived Sea Surface Temperature Accuracies Using Water Vapor Profile Data

2011 ◽  
Vol 28 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Ian J. Barton
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Clear Correlation ◽  
Transfer Model ◽  
The Difference

Abstract Analyses based on atmospheric infrared radiative transfer simulations and collocated ship and satellite data are used to investigate whether knowledge of vertical atmospheric water vapor distributions can improve the accuracy of sea surface temperature (SST) estimates from satellite data. Initially, a simulated set of satellite brightness temperatures generated by a radiative transfer model with a large maritime radiosonde database was obtained. Simple linear SST algorithms are derived from this dataset, and these are then reapplied to the data to give simulated SST estimates and errors. The concept of water vapor weights is introduced in which a weight is a measure of the layer contribution to the difference between the surface temperature and that measured by the satellite. The weight of each atmospheric layer is defined as the layer water vapor amount multiplied by the difference between the SST and the midlayer temperature. Satellite-derived SST errors are then plotted against the difference in the sum of weights above an altitude of 2.5 km and that below. For the simple two-channel (with typical wavelengths of 11 and 12 μm) analysis, a clear correlation between the weights differences and the SST errors is found. A second group of analyses using ship-released radiosondes and satellite data also show a correlation between the SST errors and the weights differences. The analyses suggest that, for an SST derived using a simple two-channel algorithm, the accuracy may be improved if account is taken of the vertical distribution of water vapor above the ocean surface. For SST estimates derived using algorithms that include data from a 3.7-μm channel, there is no such correlation found.

Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area

2017 ◽  
Vol 862 ◽  
pp. 90-95 ◽  
Author(s):  
Agung Budi Cahyono ◽  
Dian Saptarini ◽  
Cherie Bhekti Pribadi ◽  
Haryo Dwito Armono
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Coastal Development ◽  
Sea Surface ◽  
Distribution Area ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology

The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.

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