salinity interpolation in corpus christi bay
DESCRIPTION
Salinity Interpolation in Corpus Christi Bay. Presented by Ernest To May 1, 2007. Corpus Christi Bay Testbed. National Datasets (National HIS). Regional Datasets (Workgroup HIS). USGS. NCDC. TCOON. Dr. Paul Montagna. TCEQ. SERF. NCDC station. TCOON stations. TCEQ stations. - PowerPoint PPT PresentationTRANSCRIPT
Salinity Interpolation in Corpus Christi Bay
Presented by Ernest To
May 1, 2007.
Corpus Christi Bay Testbed
Montagna stations
SERF stations
TCOON stations
USGS gages
TCEQ stations
Hypoxic Regions
NCDC station
National Datasets (National HIS) Regional Datasets (Workgroup HIS)
USGS NCDC TCOON Dr. Paul Montagna TCEQ SERF
ET 20061116
Datacube diagrams
08/02/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
39
34
309
24
8
21
Salinity in Corpus Christi Bay
08/16/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
39
34
309
24
8
21
Salinity in Corpus Christi Bay
08/23/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
39
34
309
24
8
21
Salinity in Corpus Christi Bay
08/30/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
39
34
309
24
8
21
Salinity in Corpus Christi Bay
Kriging
• Kriging is BLUE (best linear unbiased estimator).• Kriging is a function that predicts value and error
estimates by using data values and their spatial configuration as inputs.
• Requires:– Stationarity, i.e. mean and variance are invariant with
translation• to support covariance modeling
– Normality• to support linear estimation
• Advanced Kriging methods can deal with non-normality and non-stationarity.
Test for Normality
Tests also performed on subsets of the data.
Examples of Variograms(Variograms for salinity data collected on 8/2/2005)
Major azimuth = N60EMajor range = 15,000 mSill = 6
Minor azimuth = N150EMinor range = 1800 m
Dip angle = 0Vertical range = 1 m
Process flowchart
Performnormality tests
(IDL)
Plot Variograms(IDL)
Perform 3Dkriging (IDL)
Visualize resultsusing voxels (IDL)
Database
Gamv.exe(GSLIB)
KT3D.exe(GSLIB)
Variogram parameters, e.g. range, sillanisotropy, azimuth, dip, variogram model, etc.
transformationparameters, λ
Probability plots
Variograms
Voxels
• Voxels = volume pixels or 3D pixels• A voxel volume is formed by superpositioning four 3D
arrays:– Red array + Green array + Blue array +Opacity array
• Manipulation of the opacity array can make inner voxels visible
Plotted with data from head.dat from IDL 6.3 examples
Kriging Results for Aug 2, 2005.
08/02/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
3934
309
24
8
21
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
3934
309
24
8
21
Kriging Results for Aug 16, 2005.
Kriging Results for Aug 23, 2005.
08/23/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
3934
309
24
8
21
Kriging Results for Aug 30, 2005.
08/30/2005
Ingleside
Port Aransas
PackeryChannel
Laguna Madre
OsoBay
1
2
12
11
10/D
14
18
3934
309
24
8
21
Space-Time Integration
35
40
45
50
8/1/2005 8/6/2005 8/11/2005 8/16/2005 8/21/2005 8/26/2005 8/31/2005
Salinity (psu) Salinity vs t
timeline
What happened in between the observations?
? ? ?
Conclusion
• Created set of tools to investigate data, perform 3D interpolation and visualize results.
• Analysis framework can be expanded to 4D and modified to incorporate data from deterministic models.
• One step towards understanding space-time integration.
Next steps
• Understand factors causing salinity patterns observed in August 2005.
• Expand on framework to perform space-time kriging
• Incorporate results from salinity models into kriging model using kriging with external drift.
Questions?
Backup slides
35
40
45
50
8/1/2005 8/6/2005 8/11/2005 8/16/2005 8/21/2005 8/26/2005 8/31/2005
Salinity (psu) Salinity vs t
Variance