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

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 20061116

Datacube diagrams

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

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

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

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

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 = N60E Major range = 15,000 m Sill = 6 Minor azimuth = N150E Minor range = 1800 m Dip angle = 0 Vertical range = 1 m

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)

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 Packery Channel Laguna Madre Oso Bay 1 2 12 11 10/D 14 18 39 34 30 9 24 8 21

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

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

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

Space-Time Integration 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

Variance