1. Salinity Interpolation in Corpus Christi Bay
Presented by Ernest To
May 1, 2007.

2. Corpus Christi Bay Testbed
NCDC station
TCOON stations
TCEQ stations
Hypoxic Regions
Montagna stations
USGS gages
SERF stations
National Datasets (National HIS)
Regional Datasets (Workgroup HIS)
USGS
NCDC
TCOON
Dr. Paul Montagna
TCEQ
SERF ET

3. Datacube diagrams

4. Salinity in Corpus Christi Bay
08/02/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21
Salinity in Corpus Christi Bay

5. Salinity in Corpus Christi Bay
08/16/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21
Salinity in Corpus Christi Bay

6. Salinity in Corpus Christi Bay
08/23/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21
Salinity in Corpus Christi Bay

7. Salinity in Corpus Christi Bay
08/30/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21
Salinity in Corpus Christi Bay

8. 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.

9. Test for Normality
Tests also performed on subsets of the data.

10. Examples of Variograms (Variograms for salinity data collected on 8/2/2005)
Major azimuth = N60E
Major range = 15,000 m
Sill = 6
Minor azimuth = N150E
Minor range = 1800 m
Dip angle = 0
Vertical range = 1 m

11. Process flowchart Probability plots Database Perform normality tests
(IDL)
transformation
parameters, λ
Variograms
Plot Variograms
(IDL)
Variogram parameters,
e.g. range, sill
anisotropy, azimuth, dip,
variogram model, etc.
Perform 3D
kriging
(IDL)
Gamv.exe
(GSLIB)
KT3D.exe
(GSLIB)
Visualize results
using voxels (IDL)

12. 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

13. Kriging Results for Aug 2, 2005.
08/02/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21

14. Kriging Results for Aug 16, 2005.
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21

15. Kriging Results for Aug 23, 2005.
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21
08/23/2005

16. Kriging Results for Aug 30, 2005.
08/30/2005
Ingleside
Port Aransas
Packery
Channel
Laguna
Madre
Oso
Bay 1 2 12 11
10/D 14 18 39 34 30 9 24 8 21

17. Space-Time Integration
timeline ? ? ?
What happened in between the observations?

18. 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.

19. 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.

20. Questions?

21. Backup slides

22. Variance