This report analyzed oceanographic patterns of sea surface temperature (SST) and chlorophyll concentration using remotely sensed data at global and regional scales. At the global scale, SST and chlorophyll distributions followed expected patterns driven by ocean currents and upwelling/downwelling zones. Regionally along the Australian East Coast, SST and chlorophyll patterns revealed the influence of the East Australian Current, with seasonal and interannual variability observed. Comparison of remote sensing and in situ data showed good agreement for SST near the surface but limitations for subsurface measurements, highlighting the need for calibration and depth profiling through field work.
Greetings all,
This month’s newsletter is devoted to ocean indices aiming at a better understanding of the state of the ocean climate. Ocean
climate indices can be linked to major patterns of climate variability and usually have a significant social impact. The estimation of
the ocean climate indices along with their uncertainty is thus crucial: It gives an indication of our ability to measure the ocean. It is
as well a useful tool for decision making. Ocean climate indices also provide an at-a-glance overview of the state of the ocean
climate, and a way to talk to a wider audience about the ocean observing system. Several groups of experts are now working on
various ocean indicators using ocean forecast models, satellite data and reanalysis models in observing system simulation
experiments, among which the OOPC, NOAA and MERSEA/Boss4Gmes communities for example:
http://ioc3.unesco.org/oopc/state_of_the_ocean/index.php
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/
http://www.aoml.noaa.gov/phod/cyclone/data/method.html
http://www.mersea.eu.org/Indicators-with-B4G.html
Scientific articles about Ocean indices in the present Newsletter are displayed as follows: The first article by Von Schuckmann et
al. is dealing with the estimation of global ocean indicators from a gridded hydrographic field. Then, Crosnier et al. are describing
the need to conduct intercomparison of model analyses and forecast in order for experts to provide a reliable scientific expertise
on ocean climate indicators. The next article by Coppini et al. is telling us about ocean indices computed from the Mediterranean
Forecasting System for the European Environment Agency and Boss4Gmes. Then Buarque et al. are revisiting the Tropical
Cyclone Heat Potential Index in order to better represent the ocean heat content that interacts with Hurricane. The last article by Greiner et al. is dealing with the assessment of robust ocean indicators and gives an example with oceanic predictors for the
Sahel precipitations.
The next July 2009 newsletter will review the current work on data assimilation and its techniques and progress for operational
oceanography.
We wish you a pleasant reading.
Modification and Climate Change Analysis of surrounding Environment using Rem...iosrjce
This review is presented in three parts. The first part explains such terms as climate, climate change,
climate change adaptation, remote sensing (RS) and geographical information systems (GIS). The second part
highlights some areas where RS and GIS are applicable in climate change analysis and adaptation. Issues
considered are snow/glacier monitoring, land cover monitoring, carbon trace/accounting, atmospheric
dynamics, terrestrial temperature monitoring, biodiversity conservation, ocean and coast monitoring, erosion
monitoring and control, agriculture, flood monitoring, health and disease, drought and desertification. The
third part concludes from all illustrated instances that climate change problems will be less understood and
managed without the application of RS and GIS. While humanity is still being plagued by climate change effects,
RS and GIS play a crucial role in its management for continued human survival. Key words: Climate, Climate
Change, Climate Change Adaptation, Geographical Information System and Remote Sensing.
Simulated versus Satellite Retrieval Distribution Patterns of the Snow Water ...Agriculture Journal IJOEAR
Abstract— Snow is a very important component of the climate system which controls surface energy and water balances. Its high albedo, low thermal conductivity and properties of surface water storage impact regional to global climate. The various properties characterizing snow are highly variable and so have to be determined as dynamically active components of climate. However, on large spatial scales the properties of snow are not easily quantified either from numerical modelling or observations. Since neither observations (ground measurements or satellite retrievals) nor models alone are capable of providing enough adequate information about the time space variability of snow properties, it becomes necessary to combine their information. In the presented study the obtained with the regional climate model RegCM snow water equivalent (SWE) on monthly basis over Southeast Europe for a time window of 14 consecutive winters is compared with the Globsnow satellite product. The concordance between both datasets is evaluated with number of statistical scores. The result reveals the principal agreement between the two products, but however, with very significant discrepancies, mainly overestimations, for some years and gridcells.
Greetings all,
This month’s newsletter is devoted to ocean indices aiming at a better understanding of the state of the ocean climate. Ocean
climate indices can be linked to major patterns of climate variability and usually have a significant social impact. The estimation of
the ocean climate indices along with their uncertainty is thus crucial: It gives an indication of our ability to measure the ocean. It is
as well a useful tool for decision making. Ocean climate indices also provide an at-a-glance overview of the state of the ocean
climate, and a way to talk to a wider audience about the ocean observing system. Several groups of experts are now working on
various ocean indicators using ocean forecast models, satellite data and reanalysis models in observing system simulation
experiments, among which the OOPC, NOAA and MERSEA/Boss4Gmes communities for example:
http://ioc3.unesco.org/oopc/state_of_the_ocean/index.php
http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/
http://www.aoml.noaa.gov/phod/cyclone/data/method.html
http://www.mersea.eu.org/Indicators-with-B4G.html
Scientific articles about Ocean indices in the present Newsletter are displayed as follows: The first article by Von Schuckmann et
al. is dealing with the estimation of global ocean indicators from a gridded hydrographic field. Then, Crosnier et al. are describing
the need to conduct intercomparison of model analyses and forecast in order for experts to provide a reliable scientific expertise
on ocean climate indicators. The next article by Coppini et al. is telling us about ocean indices computed from the Mediterranean
Forecasting System for the European Environment Agency and Boss4Gmes. Then Buarque et al. are revisiting the Tropical
Cyclone Heat Potential Index in order to better represent the ocean heat content that interacts with Hurricane. The last article by Greiner et al. is dealing with the assessment of robust ocean indicators and gives an example with oceanic predictors for the
Sahel precipitations.
The next July 2009 newsletter will review the current work on data assimilation and its techniques and progress for operational
oceanography.
We wish you a pleasant reading.
Modification and Climate Change Analysis of surrounding Environment using Rem...iosrjce
This review is presented in three parts. The first part explains such terms as climate, climate change,
climate change adaptation, remote sensing (RS) and geographical information systems (GIS). The second part
highlights some areas where RS and GIS are applicable in climate change analysis and adaptation. Issues
considered are snow/glacier monitoring, land cover monitoring, carbon trace/accounting, atmospheric
dynamics, terrestrial temperature monitoring, biodiversity conservation, ocean and coast monitoring, erosion
monitoring and control, agriculture, flood monitoring, health and disease, drought and desertification. The
third part concludes from all illustrated instances that climate change problems will be less understood and
managed without the application of RS and GIS. While humanity is still being plagued by climate change effects,
RS and GIS play a crucial role in its management for continued human survival. Key words: Climate, Climate
Change, Climate Change Adaptation, Geographical Information System and Remote Sensing.
Simulated versus Satellite Retrieval Distribution Patterns of the Snow Water ...Agriculture Journal IJOEAR
Abstract— Snow is a very important component of the climate system which controls surface energy and water balances. Its high albedo, low thermal conductivity and properties of surface water storage impact regional to global climate. The various properties characterizing snow are highly variable and so have to be determined as dynamically active components of climate. However, on large spatial scales the properties of snow are not easily quantified either from numerical modelling or observations. Since neither observations (ground measurements or satellite retrievals) nor models alone are capable of providing enough adequate information about the time space variability of snow properties, it becomes necessary to combine their information. In the presented study the obtained with the regional climate model RegCM snow water equivalent (SWE) on monthly basis over Southeast Europe for a time window of 14 consecutive winters is compared with the Globsnow satellite product. The concordance between both datasets is evaluated with number of statistical scores. The result reveals the principal agreement between the two products, but however, with very significant discrepancies, mainly overestimations, for some years and gridcells.
Monitoring Changes in Antarctic Sea Ice Phenology: 1990-2015priscillaahn
Tan & Le Drew (2016) presented an innovative methodology to detect both temporal and spatial changes in Arctic sea ice phenology using sea ice concentration (SIC) indices derived from remotely sensed data. This project attempts to apply this analysis to the still ambiguous Antarctic sea-ice pack for the years 1990-2015.
Geological Investigation of the Mono Basing using ArcGISSachin Mehta
The various geological activity taking place around the Mono Basin Region is extraordinary—and it being the oldest lake in North America, our analysis in this paper makes it even more astounding. Determining the relationships among the geologic points in the region and whether they had effects on one another answered a major question that could help in later studies, research, and work in the area. The particular toolsets used from ArcGIS, such as the ‘Generate Near Table’ analysis was just one of the many incredible and useful ways to gather such important data. Finding that the lake levels of Mono Lake fluctuate a great deal was another important output that occurred from our analyses with ArcGIS. With the Mono-Inyo and volcanic craters occurring in and around the region, our analysis on these particular geological features could possibly help future studies take place.
Sachin Mehta Reno Nevada
Streamflow simulation using radar-based precipitation applied to the Illinois...Alireza Safari
This paper describes the application of a spatially distributed hydrological model WetSpa (Water and Energy Transfer between Soil, Plants and Atmosphere) using radar-based rainfall data provide by the United States Hydrology Laboratory of NOAA's National Weather Service for a distributed model intercomparison project. The model is applied to the
river basin above Tahlequah hydrometry station with 30-m spatial resolution and one hour time--step for a total simulation period of 6 years. Rainfall inputs are derived from radar. The distributed model parameters are based on an extensive database of watershed characteristics available for the region, including digital maps of DEM, soil type, and land use. The model is calibrated and validated on part of the river flow records. The simulated hydrograph shows a good correspondence with observation (Nash efficiency coeffiecient >80%, indicating that the model is able to simulate the relevant hydrologic processes in the basin accurately.
Monitoring Changes in Antarctic Sea Ice Phenology: 1990-2015priscillaahn
Tan & Le Drew (2016) presented an innovative methodology to detect both temporal and spatial changes in Arctic sea ice phenology using sea ice concentration (SIC) indices derived from remotely sensed data. This project attempts to apply this analysis to the still ambiguous Antarctic sea-ice pack for the years 1990-2015.
Geological Investigation of the Mono Basing using ArcGISSachin Mehta
The various geological activity taking place around the Mono Basin Region is extraordinary—and it being the oldest lake in North America, our analysis in this paper makes it even more astounding. Determining the relationships among the geologic points in the region and whether they had effects on one another answered a major question that could help in later studies, research, and work in the area. The particular toolsets used from ArcGIS, such as the ‘Generate Near Table’ analysis was just one of the many incredible and useful ways to gather such important data. Finding that the lake levels of Mono Lake fluctuate a great deal was another important output that occurred from our analyses with ArcGIS. With the Mono-Inyo and volcanic craters occurring in and around the region, our analysis on these particular geological features could possibly help future studies take place.
Sachin Mehta Reno Nevada
Streamflow simulation using radar-based precipitation applied to the Illinois...Alireza Safari
This paper describes the application of a spatially distributed hydrological model WetSpa (Water and Energy Transfer between Soil, Plants and Atmosphere) using radar-based rainfall data provide by the United States Hydrology Laboratory of NOAA's National Weather Service for a distributed model intercomparison project. The model is applied to the
river basin above Tahlequah hydrometry station with 30-m spatial resolution and one hour time--step for a total simulation period of 6 years. Rainfall inputs are derived from radar. The distributed model parameters are based on an extensive database of watershed characteristics available for the region, including digital maps of DEM, soil type, and land use. The model is calibrated and validated on part of the river flow records. The simulated hydrograph shows a good correspondence with observation (Nash efficiency coeffiecient >80%, indicating that the model is able to simulate the relevant hydrologic processes in the basin accurately.
Getting Started with Altmetrics at Your Journal: An Editor's GuideScholastica
Altmetrics, a type of alternative impact indicator, have arisen as a way to show broader impacts of research online. Journals can employ altmetrics to see how their research is being used both in and outside of academia, as well as assess the performance of their promotional efforts. This slideshow, co-produced by Scholastica and Altmetric, breaks down the benefits of altmetrics for journals, common misconceptions about them, and ways to start tracking altmetrics impact at your publication.
Remote sensing data driven bathing water quality assessment using sentinel-3nooriasukmaningtyas
In this paper we are investigating the possibility of usage of remote sensing satellite data, more precisely sentinel-3 OLCI and SLSTR data, for assessment of bathing water quality. In this research we used data driven approach and analysis of data in order to pinpoint aspects of remote sensing data that can be useful for bathing water quality assessment. For this purpose we collected satellite images for period from start of June till end of September of 2019 and results of in-situ measurement for the same period . Results of in-situ measurement were correlated with satellite images bands and analyzed. We propose a simple method for rapid assessment of possible deterioration of bathing water quality to be used by public health authorities for better planning of in situ measurements. Results of implementation of predictive models based on k-nearest neighbour (KNN) and decision tree (DT) are described.
Describe and explain satellite remote sensing mission for monitoring.pdfalshaikhkhanzariarts
Describe and explain satellite remote sensing mission for monitoring water, carbon and global
climate change.
Solution
In recent years, the subjects of water, carbon, and global climate change
have attracted worldwide attention by scientists and the media. Climate
change, whether associated with human- induced or natural
variations, has and will continue to be important to policy makers and the
public. It is clear from reports such as that by the Intergovernmental Panelon Climate Change
(IPCC) [1] that
Earth observations play a critical role in providing information for assessment
and modeling. Improving these observations, better quality and newvariables, is a goal of most
national
and intergovernmental space agencies. Major initiatives are under waythat will result in benefits
to a broad
range of our global society. In the United States, a decadal study [2] was recently completed by
the
Committee on Earth Science and Applications of the US National Research
Council. The committee called for a commitment from the U.S. administration
to Earth observations to secure benefits for mankind. The report gives both
direction and a large boost to U.S. satellite programs as it recommended NOAA
to restore key observational capabilities of satellite missions and also that NASA
and NOAA launch 17 new satellite missions in the next 10 years. The study also
adds an additional focus to these missions: societal benefits.
Other countries have also been expanding their Earth observation programs
with numerous advanced concept satellite missions. Of particular relevance to
this issue are articles describing the
Earth observing programs of the
European Space Agency (ESA),
the Japanese Aerospace Exploration
Agency (JAXA), the China National
Space Administration (CNSA), the
Canadian Space Agency (CSA), and
the National Space Program Office
(NSPO) of Taiwan.
The ESA has a long history of
Earth observation from space that
began with meteorological missions
and has included a series of increasingly
sophisticated radars that have
provided valuable data about climate
and the changing environment. The
ESA’s current and future Earth observing
is under its Living Planet
Programme and includes the Earth
Explorer, meteorological, and Sentinel
missions.
JAXA has supported a wide range
of satellite-based instruments and
platforms that have and will provide
global Earth observations for water,
carbon, and climate. Of particular
note for the future is the commitment
to the Global Change Observation
Mission (GCOM) that will launch a
series of two types of satellites (water
and climate) to provide consistent and
continuous observations of key variables
over a 15-year period.
In addition to the United States,
ESA, and JAXA programs, there are
strong satellite-based Earth observing
programs in Canada, China, Argentina,
Taiwan, and Brazil.
In this Special Issue some of the
most significant recent and future Earth
observing satellites planned to monitor
water, carbon and global clima.
Describes latest observations of climate by satellites and ground stations and assesses them relative to the possible causes of 'greenhouse gases', world energy use, and latent heat transfer by crop irrigation.
GIS based spatial distribution of Temperature and Chlorophyll-a along Kalpakk...IJERA Editor
This paper briefly describes the status of Temperature and Chlorophyll-a trend in Kalpakkam Coast, discusses its ecological and temperature impacts recommending measures to achieve long term sustainability using advanced tools like Geographic Information System (GIS). Present study reveals the monthly spatial distribution of Temperature and Chlorophyll-a at Kalpakkam. Transect based in-situ Temperature and Chlorophyll-a collected at 200m, 500m and 1 km distance into the sea was interpolated using the Inverse Distance Weightage (IDW) method in ARC GIS. Data revealed the extent of spatial distribution of thermal effluent in Kalpakkam. It could be found that temperature range of 26.2 – 31.9°C provided substantial Chlorophyll-a concentration between 0.8 – 2.9 mg/m3 for surface and bottom waters. Further, increase of Chlorophyll-a levels did not lead to higher productivity. Combined temperature and chlorophyll a showed little synergistic effects. It is concluded that the effect of thermal discharge from the power plant into the receiving water body is quite localized and productivity of the coastal waters are not affected. From the results obtained, the spatial data has been found to be useful in determining zones of safe use of seawater and to understand the extent of relationship between the relatable parameters.
Editorial – October 2011 – Three of the MyOcean long time series reanalysis products
Greengs all,
This month’s newsleer is devoted to three of the MyOcean long me series Reanalysis products: the In Situ temperature and salinity CORA reanalysis
(1990 to 2010), the reanalysis of the North Atlanc ocean biogeochemistry (1998-2007) and the Arcc Ocean sea-ice dri/ reanalysis (1992-
2010).
The first product described here is the In Situ temperature and salinity CORA reanalysis (1990 to 2010). A new version of the comprehensive and
qualified ocean in-situ dataset (the Coriolis dataset for Re-Analysis - CORA) is released for the period 1990 to 2010. This in-situ dataset of temperature
and salinity profiles, from different data types (Argo, GTS data, VOS ships, NODC historical data...) on the global scale, is meant to be used for
general oceanographic research purposes, for ocean model validaon, and also for inializaon or assimilaon of ocean models. This product is
available from the MyOcean web portal (hp://www.myocean.eu/).
The second product is the reanalysis of the North Atlanc ocean biogeochemistry (1998-2007). A system assimilang Ocean Colour SeaWiFS data
during the period 1998-2007 has been designed to construct a reanalysis of the North Atlanc ocean biogeochemistry based on a coupled physicalbiogeochemical
model at eddy-admi:ng resoluon. The aim of this study is, on the one hand to develop the skeleton of a pre-operaonal coupled
physical-biogeochemical system with real-me assimilave/forecasng capability, and on the other hand to operate this prototype system for producing
a biogeochemical reanalysis covering the 1998-2007 period. This product is not available from the MyOcean web portal yet.
The third reanalysis product is the 1992-2010 winter Arcc Ocean sea ice dri/ me series made at Ifremer/CERSAT from satellite measurements
which consists of several products: the Level 3 products from single sensors and the L4 products from the combinaon of sensors. They are available
at 3, 6 and 30 day-lag with a 62.5 km-grid size during winter. This dataset is available for oceanic and climate modelling as well as various scienfic
studies in the Arcc. The me series is ongoing and will connue for Arcc long term monitoring using the next MetOp/ASCAT operaonal
scaerometers, planned to be operated for the next 10 years. This product is available from the MyOcean web portal (hp://www.myocean.eu/).
The next January 2012 issue will be dedicated to various applicaons using the Mercator Ocean products.
We wish you a pleasant reading!
This is a personal data visualization project based on the data collected during the Saildrone circumnavigation around Antarctica in 2019.
The full project, including the Jupyter Notebook is available here: https://github.com/mlnrt/saildrone-2019-antarctica-circumnavigation
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Satellite Oceanography
Introduction
The global oceans are just one component of the coupled earth system, constantly changing according to the
physical forces and feedbacks which influence a current state. Both biotic and abiotic indicators can be used in
ocean assessment, which in turn provides insight to the overall functioning of the earth system. In the past, global
ocean evaluation was limited by spatial and temporal coverage, however relatively recent remote sensing
technology has improved this resolution. By utilising satellite platforms which capture visible, near infrared, thermal
infrared and microwave reflectance; spatially extensive and temporally regular datasets have become available for
physical and biological ocean parameter analysis (Pandey et al. 2008). Sea surface temperature (SST) and chlorophyll
are two examples of these parameters, and when combined with in situ data collection, the analysis of ocean
conditions at global and regional scales can be both accurate and precise. Such analysis can reveal patterns and
relationships within the current state of global and regional oceanic systems, and provide prediction of future
conditions according to the current momentum of system change.
This report will discuss the oceanographic patterns of SST and chlorophyll which were observed within remotely
sensed raster images at global and regional scales. Firstly, the data processing method will be summarised. This will
be followed by a discussion regarding the patterns of SST and chlorophyll variability within the global oceans and
along the Australian East Coast (AEC). Finally, remotely sensed and in situ data collection will be compared to assess
the precision and accuracy of remotely sensed analysis.
Methods
Remotely sensed and in situ data sets were acquired from the NASA Ocean Colour Website, the UQ Biophysical
Oceanography Group (UQBOG), and the North Stradbroke Island National Reference Station (NRS). All image
creation utilised SeaDAS 7.3.1; and common procedures included the customisation of colour pallets, the assignment
of land masks to no data pixels, and gridline overlay. The analysis of the AEC also included the overlay of a 200m
shapefile. Global parameter analysis required the acquisition of raster datasets captured by the MODIS multi spectral
imaging device on board the Aqua Satellite. SST utilised a range between -2°C and 35°C for January 2015 and 2002-
2015 climatology, while chlorophyll analysis involved a range of 0.01 mg/m3
to 20mg/m3
over the same temporal
resolutions.
The AEC analysis required MODIS imagery from the Aqua and Terra satellites, and UQBOG pre-processing to provide
daily SST images at a 1km resolution. SST and chlorophyll rasters for 2010-2014 climatology and 2015 mean were
created for January and June, with ranges of 14°C to 33°C and 0.01 mg/m3
to 10 mg/m3
respectively. Parameter
anomalies were then calculated to compare current observations to average patterns. Pixel extraction of MODIS SST
and chlorophyll data allowed further processing in Excel, and a comparison to NRS in situ observations during 2011-
2012. Monthly means for SST were calculated at 0m, -20m, and -63m; while chlorophyll observations were limited
2. 42853288
1
to -20m and -60m depths. Graphs were then generated comparing MODIS and in situ data for both parameters over
the range of applicable depths.
Results
Global Sea Surface Temperature
Figure 1 The month of January 2015- Global mean sea surface temperature distribution generated from NASA Ocean Colour Website MODIS
data
Figure 2 January climatology for 2002 to 2015 - global mean sea surface temperature distribution generated from NASA Ocean
Colour Website MODIS data
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Global Chlorophyll Concentrations
Figure 3- The month of January 2015- Global mean chlorophyll distribution generated from NASA Ocean Colour Website MODIS data
Figure 4 January Climatology 2002-2015- Global mean chlorophyll distribution generated from NASA Ocean Colour Website MODIS
data
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Monthly Sea Surface Temperature Climatology
Monthly Sea Surface Temperature 2015 Means
Figure 6 January SST 2010-2014 Climatology for the
Australian East Coast. Generated from NASA Ocean Colour
Website MODIS data, pre-processed by UQBOG
Figure 5 June SST 2010-2014 Climatology for the Australian
East Coast. Generated from NASA Ocean Colour Website
MODIS data, pre-processed by UQBOG
Figure 8 January SST 2015 mean for the Australian East
Coast. Generated from NASA Ocean Colour Website
MODIS data, pre-processed by UQBOG
Figure 7 June SST 2015 mean for the Australian East
Coast. Generated from NASA Ocean Colour Website
MODIS data, pre-processed by UQBOG
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Anomaly- Sea Surface Temperature
Monthly Chlorophyll Climatology
Figure 10 SST January anomaly between the 2015 mean and
2010-2014 climatology data. Generated from NASA Ocean
Colour Website and pre-processed UQBOG MODIS data
Figure 9 SST June anomaly between the 2015 mean and
2010-2014 climatology data. Generated from NASA Ocean
Colour Website and pre-processed UQBOG MODIS data
Figure 11 June Chlorophyll 2010-2014 climatology for the
Australian East Coast. Generated from NASA Ocean Colour
Website MODIS data, pre-processed by UQBOG
Figure 12 January Chlorophyll 2010-2014 climatology for the
Australian East Coast. Generated from NASA Ocean Colour
Website MODIS data, pre-processed by UQBOG
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Monthly Chlorophyll 2015 Means
Anomaly- Chlorophyll Concentrations
Figure 13 June Chlorophyll 2015 mean for the Australian
East Coast. Generated from NASA Ocean Colour Website
MODIS data, pre-processed by UQBOG
Figure 134 June Chlorophyll 2015 mean for the Australian
East Coast. Generated from NASA Ocean Colour Website
MODIS data, pre-processed by UQBOG
Figure 15 Chlorophyll January anomaly between the 2015
mean and 2010-2014 climatology data. Generated from
NASA Ocean Colour Website and pre-processed UQBOG
MODIS data
Figure 14 Chlorophyll June anomaly between the 2015
mean and 2010-2014 climatology data. Generated from
NASA Ocean Colour Website and pre-processed UQBOG
MODIS data
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NRS In Situ Sea Water Temperature
NRS In Situ Chlorophyll Concentrations
Monthly Sea Surface Temperature for MODIS and NRS In Situ Data at Varied Depth
15
17
19
21
23
25
27
29
1/01/2011 11/04/2011 20/07/2011 28/10/2011 5/02/2012 15/05/2012 23/08/2012 1/12/2012
Temperature(degC)
Time
NRS In Situ Sea Water Temperature Measurements Vs MODIS Remotely Sensed Data-
2011 to 2012
0m In Situ 20m In Situ 63m In Situ MODIS
Figure 16- Time series of NRS in situ data collection of SST for the period of 2011-2012. Data collected using a mooring array instrument, east of
North Stradbroke Island, at depths of at 0m, -20m and -63m.
Figure 17- Time series of NRS in situ data collection of chlorophyll concentrations for the period of 2011-2012. Data collected using a mooring
array instrument, east of North Stradbroke Island, at depths of -20m and -60m
Figure 18- Time series of NRS in situ data collection of SST for the period of 2011-2012, and MODIS measurements for the same period
obtained through pixel extraction. Data collected using a mooring array instrument, east of North Stradbroke Island, at depths of 0m, -
20m and -63m.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1/01/2011 25/06/2011 17/12/2011 9/06/2012 1/12/2012
Chlorophyll(mg/m3)
Time
NRS In Situ Chlorophyll Measurements at Varied Depth between 2011 and 2012
20m Day 20m Month 60m Day 60m Month
15
20
25
30
1/01/2011 11/04/2011 20/07/2011 28/10/2011 5/02/2012 15/05/2012 23/08/2012 1/12/2012
Temperature(degC)
Time
NRS In Situ Sea Water Temperature Measurements at Varied Depth- 2011 to 2012
0m Month 0m Day 20m Month 20m Day 63m Month 63m Day
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Comparison of Monthly MODIS and NRS In Situ Chlorophyll Concentrations
Discussion
Global Scale Analysis
The global SST images illustrate the spatial distribution of temperature within the ocean system. Generally, the
equator is characterised by high temperature waters, which decline with latitude change toward the poles. Due to
the convergence of geostrophic gyres, the warmest locations are at the approximate centre of major oceans. The
current directions and heat load within transverse, eastern and western boundary currents are variable according to
the clockwise/anticlockwise circulation patterns of the specific hemisphere in which they exist. Overall, these ocean
circulations are driven by a balance of physical forces, and the dynamic equilibrium of global energy redistribution.
Regional circulation patterns can also be observed at this global scale. The Agulhas Current extends as a warm
intrusion into the Antarctic Circumpolar Current, illustrating the strength of south westerly currents along the South
Africa coast. At the Benguela Region, cold uplifted waters characterise the continental shelf for approximately 15° of
latitude. While off the Argentinian east coast, a northern cold water intrusion illustrates the Brazil-Malvinas
confluence region and the mixing of the Malvinas and Brazil Currents (Gyory, Mariano & Ryan 2013). These regional
currents exist within the January mean and climatology images, suggesting a certain degree of spatial stability within
temporal change.
Global chlorophyll distributions are concentrated outside the unproductive centres of the five major gyres. The
convergent centres of these gyres cause down welling, which limits nutrient availability for phytoplankton blooms.
The highest chlorophyll concentrations are along the continental shelves of the Peruvian, Californian, Canary,
Humboldt, Benguela and Somali up welling regions. Similar productivity also occurs along other coastlines and
protected bays. Excluding the South Eastern Patagonian coast and its spring to autumn upwelling features (Valla &
Piola 2015), western boundary currents depict relatively low chlorophyll concentrations.
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1/01/2011 25/06/2011 17/12/2011 9/06/2012 1/12/2012
Chlorophyll(mg/m3)
Time
NRS In Situ Chlorophyll Measurements at Varied Depth Vs MODIS Remotely Sensed
Data- 2011/2012
20m In Situ 60m In Situ Modis
Figure 19- Time series of NRS in situ data collection of chlorophyll concentrations for the period of 2011-2012, and MODIS measurements for
the same period obtained through pixel extraction. Data collected using a mooring array instrument, east of North Stradbroke Island, at depths
of -20m and -63m.
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Regional Scale Analysis
Regional SST images of the AEC illustrated the spatial and temporal dynamics of the East Australian Current (EAC).
Often defined as the South Pacific Gyre ‘s western boundary current (Roughan & Middleton 2002), warm tropical
waters flow south along the coast and decline in temperature as latitude increases. Observable January EAC
features include offshore currents and eddy formations at approximately 24°S and 28°S, the north-easterly turn of
the Tasman Front at approximately 32°S to 33°S, and a regular eddy field just below this location (Ridgway & Hill
2009). During June, general EAC characteristics remain the same, however water temperatures decrease and
southern penetration reduces. This seasonal change is due to decreased solar insolation, and the weakening of
current intensity from south-easterly trade wind dominance.
The SST anomalies revealed variations between average patterns and 2015 observations. In January, the Tasman
Current location and the continental shelf between 28°S and 36°S increased by approximately 1.5°C to 2.5°C. Harvey
Bay, Moreton Bay, and the offshore eddies fields at 24°S to 26°S also display positive anomalies. In winter, a decline
in average temperature appears at 34°S to 36°S which corresponds to the eddy field below the Tasman Current
meander, and may indicate an upwelling of cold ocean waters from depth. Overall, January and June SST anomalies
suggest 2015 conditions which were significantly different to average patterns of the last five years. The monthly
variation in positive and negative anomalies appears to be influenced by the spatial characteristics of currents, and
the upwelling or down-welling characteristics of eddies.
Chlorophyll analysis of the AEC indicates the inland continental shelf as the most productive during both summer
and winter. Prime locations include Moreton and Harvey Bay, and the central NSW coast between 29°S and 33°S.
Roughan and Middleton (2002) suggest that these upwelling processes are driven by wind, continental shelf
intrusion of the EAC, diversion of the EAC off the coast, or EAC acceleration from variable bathymetry. In summer,
longer days provide increased sunlight for phytoplankton blooms, and if combined with north-easterly dominate
monsoon winds, the EAC intensifies which is reflected in upwelling processes (Berkelmans, Weeks & Steinberga
2010). The pattern of continental shelf upwelling and coastal productivity is similar in winter. Offshore however,
chlorophyll concentrations increase marginally over the entire Tasman Sea.
Chlorophyll anomalies for both months demonstrate a slight decline in concentrations for the greater part of the
offshore continental shelf. In January however, the inland coastal waters between 30°S to 33°S increase by up to
3.5mg/m3
. This location is just north of the highly populated and industrialized Sydney/Newcastle region, and may
represent the wet season nutrient influx from terrestrial run off and related phytoplankton blooms. Minor positive
anomalies are also located offshore at 34°S, which again corresponds to the eddy circulations below the Tasman
Current meander. During winter, continental shelf negative anomalies remain, but January positive anomalies of the
central coast no longer dominate. The 34°S positive anomalies of January are still present; however this trend has
extended over the majority of the Tasman Sea. This can relate to Tasman Sea eddy formation, and the deepening of
10. 42853288
9
surface mixing which causes low summer chlorophyll concentrations and blooms during autumn and spring (Ridgway
& Hill 2009).
Remotely Sensed and In Situ Data Comparison
Daily and monthly in situ data depict similar results at the surface and -20m samples, yet substantially lower
temperature at a depth of -63m. Generally, the monthly trend line follows daily temperatures; however the
resolution of the averaged data leads to a loss of maximum and minimum temperature fluctuations. This is also
illustrated in the daily and monthly chlorophyll comparisons. Such simplification does not represent the extensive
temporal dynamics of a given ocean location, however still provides a general insight to the average patterns of
change.
The comparison of SST MODIS data and in situ measurements revealed similar responses at the surface and a depth
of -20m. Remote sensing measurement of SST only capture the ocean surface, with penetration of 20 micrometres
for infrared and a few millimetres for microwave radiometers (Remote Sensing Systems 2016). The -63m in situ
temperature was significantly colder, which suggests a stratified ocean temperature gradient over depth. The
comparison for chlorophyll MODIS and in situ measurements indicated unpredictable accuracy at both depths, and
illustrates the variability of concentrations within the water column. Therefore, MODIS measurements for both the
SST and chlorophyll appear restricted to surface layer conditions.
Remote sensing allows data collection over extensive spatial and temporal resolutions which would be inaccessible
through in situ field sampling. However, there are a number of issues with measurements under the ocean surface.
Considering the dynamic nature of physical and biological parameters within the three dimensions of ocean
structure, field work is still required for initial sensor calibration and the collection of measurements at depth.
Overall, remote sensing increases the potential of large scaled global and regional research, and future sensor
technology and algorithm development will only increase application possibilities.
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References
Berkelmans, R, Weeks, SJ & Steinberga, CR 2010, 'Upwelling Linked to Warm Summers and Bleaching on
the Great Barrier Reef', Limnology and Oceanography, vol. 55, no. 6, pp. 2634-44.
Gyory, J, Mariano, A & Ryan, E 2013, Surface Currents in the Atlantic Ocean- The Malvinas Current, viewed
19th of May 2016, <http://oceancurrents.rsmas.miami.edu/atlantic/malvinas.html>.
Pandey, PC, Kumar, R, Varma, AK, Mathur, AK & Chaturvedi, N 2008, 'Remote Sensing Applications to
Coastal Oceanography', in Springer Netherlands, Dordrecht, pp. 45-67, DOI 10.1007/978-1-4020-8327-3_5,
<5okNEnoainqUod6mburnTe5hsOqgIRiMjfQW3392Fsi1P4BKxjTDw>.
Remote Sensing Systems 2016, Sea Surface Temperature, viewed 20th of May 2016,
<http://www.remss.com/measurements/sea-surface-temperature>.
Ridgway, K & Hill, K 2009, The East Australian Current, 978-1-921609-03-9, CSIRO Marine and Atmospheric
Research,, viewed 20 of May 2016.
Roughan, M & Middleton, JH 2002, 'A Comparison of Observed Upwelling Mechanisms off the East Coast of
Australia', Continental Shelf Research, vol. 22, no. 17, pp. 2551-72.
Valla, D & Piola, AR 2015, 'Evidence of Upwelling Events at the Northern Patagonian Shelf Break: Upwelling
at the Patagonian Shelf Break', Journal of Geophysical Research: Oceans, vol. 120, no. 11, pp. 7635-56.