Improved Hybrid Geoid Modeling and the FY 2000 Geoid Models

Improved Hybrid Geoid Modelingand the FY 2000 Geoid Models

Dr. Daniel R. Roman

January 16, 20019:30 - 10:30

Conference Room 9836

OUTLINE

• Introduction• G99SSS• GPSBM’s, alternative

geoid height point data• Residual values from

GPSBM’s - G99SSS• Overview of LSC• GEOID99

• Overview of studies• Iterative-LSC• Multi-Matrix-LSC• Summary of Modeling• FY2000 Geoid Models• Conclusions• Future Research

Introduction

• GEOID90/GEOID93 used gravimetric data

• GEOID96/GEOID99 were hybrids created from gravimetric & other geoid height data

• FOCUS: on the approach taken to combine these different data sources and the best solutions for modeling remaining signal

• Data are the same as used for the GEOID99 computation to facilitate comparison

The gravimetric geoid model, G99SSS

• Derived from more than three million terrestrial, marine and altimeter gravity data

• EGM96 gravity removed to create residuals• Gridded at one arcminute to maximize the

resolution of the gravity field• Reference datum is ITRF96(1997.0) • Converted to residual geoid height grid with

1D FFT and restored EGM96 geoid values

GPSBM’s, alternative geoid height point data

• GPS-derived ellipsoid heights on spirit-leveled Bench Marks (GPSBM’s) give a spot estimate of the geoid height

• GPS heights are WRT NAD 83 (ellipsoidal)

• Leveling is WRT NAVD 88 (orthometric)

• Accuracy of geoid heights is dependent on the quality of the ellipsoid and orthometric point values

Residual values from GPSBM’s - G99SSS

• Interpolating G99SSS to the GPSBM locations gives two geoid height estimates

• The differences between them should be zero values assuming perfect models, observations, and interpolation algorithms

• Any residuals derive from errors in the gravimetric geoid, the GPS-derived ellipsoid heights, and/or the spirit-leveling

• Simply gridding residuals yields no error analysis - what is signal and what is noise?

• Must find broader signal in the residuals that correlates over longer distances

• Signal amplitude matches the auto-correlated variance (A0) of the residuals

• The character of the correlated signal drop off with distance (D) is defined by A0, the correlation length (L) and a constant ()

Overview of LSC

100%

50%

Correlation (L) length

Cor

rel a

ted

Si g

nal

Pow

er (

cm2 )

Distance (D) from Reference Point (km)

Elements of a Correlation Curve

0%

signal amplitude (A0)

0

if: Dll = L

then: CL = 0.5 A0

increasing distance =>

ll llC

if: Dll = L

then:

0 5 0

0 7 1

0

0

.

.

A

A

it is easier to thinkin terms of cm than cm2, so use standarddeviation instead ofthe variance

C A ell

DLll

0

2

12 1 2ln .

Overview of LSC (cont.)

• Find the best fitting L and A0 values in Mode 1:

• Iterate for a posteriori data sigma ( ) in Mode 2:

• Use the correlation parameters determined between the 6169 GPSBM points to find the expected correlation at the nodes of the desired grid (s) in Mode 3:

C All

DLll

0

2

e

~l C C D lll ll n 1

s C C D lsl ll n 1

da ta2

• A national bias of 51.7 cm & trend of 0.15 ppm (azimuth = 327o) were removed from the GPSBM-G99SSS residuals

• Best fit parameters of A0 = (18.2 cm)2, L = 400 km & = (4.6 cm)2 were determined for the remaining residual signal

• Note the discrepancy between the empirical data (+) and the modeled values (line)

GEOID99

da ta

2

Empirical (+) Versus Modeled (-) Correlation

GEOID99 (cont.)

• The conversion surface contains data at 30’ intervals but was regridded to 1’

• G99SSS - conversion surface = GEOID99

• GEOID99 is then compared to GPSBM’s to determine final residual values for analysis

• of the 4.6 cm final RMS difference, 2.6 cm is correlated with a 23 km correlation length

Overview of studies

• Iterative-LSC (lower then upper)

• Minimum Curvature (MC) of GPSBM-G99SSS residuals

• MC of GEOID99 LSC point estimates

• Single-pass LSC with corr. length = 33 km

• Iterative-LSC (left then right)

• MC of GPSBM-GEOID99 residuals

• Weighted-LSC of GPSBM-G99SSS res.

• Weighted-LSC of GPSBM-GEOID99 res.

• Multi-Matrix-LSC

• A0 = (15.0 cm)2, L = 550 km, & = (5.2 cm)2 were chosen for best fit of the broader signal in the GPSBM-G99SSS residuals (lower hump)

• The resulting grid, the national trend & bias, and a conversion from ITRF96 to NAD 83 are all used to create a conversion surface

• The conversion surface is removed from G99SSS to create the intermediate geoid

Iterative-LSCda ta

2

Iterative-LSC (cont.)

• Revised residuals are generated by removing interpolated values from the intermediate geoid from the GPSBM’s (GPSBM’s - inter. geoid = rev. residuals)

L=33 km, A0=(3.0 cm)2 and = (2.3 cm)2

were selected to best fit these residuals

• Note the uncorrelated signal component

• Resulting grid = 2nd conversion surface

da ta

2

Iterative-LSC (cont.)

• The second conversion surface is removed from the intermediate hybrid geoid to create the final hybrid geoid model

• Heights from this model are removed from the GPSBM’s for final residuals

• of the 3.3 cm final RMS difference, 2.4 cm is correlated with a 14 km correlation length

Multi-Matrix-LSC

• The combination of two or more correlation matrices that best model all the signal in the GPSBM-G99SSS residuals (both humps)

• Matrices:

• Adding 2 positive definite matrices yields a positive definite matrix

• The combined matrix is used in the LSC solution

C Al l

DL

1 110

1

2

e

C C Cll l l l l 1 1 2 2

C Al l

DL

2 220

2

2

e

Multi-Matrix-LSC (cont.)

• Correlation length and amplitude for each matrix are varied to find the overall best fit

• 1st matrix: A0 = (14.0 cm)2 and L = 650 km

• 2nd matrix: A0 = (11.6 cm)2 and L = 100 km

• The resulting grid, national trend & bias, and ITRF96 conversion are combined into a conversion surface

Multi-Matrix-LSC (cont.)

• This conversion surface is removed from G99SSS to create the final hybrid geoid


• of the 3.0 cm final RMS difference, 1.7 cm is correlated with an 8 km correlation length

Summary of Modeling Studies

• Two approaches gave improved results in modeling GPSBM-G99SSS residual signals

• The iterative-LSC process models broader signal with a single matrix, generating an intermediate geoid and revised residuals that are modeled with another single matrix

• Multi-Matrix-LSC uses multiple matrices in a single pass to best fit the initial residuals

Summary of Relevant StatisticsCharacteristics of the Data and Models Characteristics of the

Hybrid Geoid

CASENAME

ITER.NUM.

BIAS

(cm)

TREND

(ppm/deg)

RMSDIFF.(cm)

AMPL.

(cm)

CORR.LEN.(km)

DATAERR.(cm)

RMSDIFF.(cm)

CORR.AMPL.

(cm)

CORR.LEN.(km)

GEOID99 1 51.7 0.15/327 21.7 18.2 400 4.6 4.6 2.6 23

Iterative-LSC

12

51.7-

0.15/327-

21.75.2

15.03.0

55033

5.22.3

-3.3

-2.4

-14

Multi-Matrix-LSC

1 51.7 0.15/327 21.7 14.0/11.6 650/100 2.9 3.0 1.7 8

FY 2000 Geoid Models

• Two hybrid geoids were created using FY 2000 GPSBM data and G99SSS

• The first, XUSHG2000A, was generated using the same methods as for GEOID99 (single-pass and single-matrix)

• The other, XUSHG2000B, was generated using Iterative-LSC (multi-pass and single-matrix)

FY 2000 GPSBM Data

• Pulled on September 15, 2000

• 7775 total points = 254 rejected + 7521 kept

• Of the 7521 retained points, 1358 were new covering more regions than FY 1999

• More FBN/CBN values with increased accuracies for ellipsoid heights (12 states)

XUSHG2000A

• Model: single-pass, single-matrix

• Correlation Parameters: A0 = (17.7 cm)2, L = 400 km and = (4.5 cm)2

• Comparison with FY 2000 GPSBM’s: of the final 4.5 cm RMS difference, 2.7 cm correlated with a 22 km correlation length

da ta

2

XUSHG2000B

• Model: multi-pass, single-matrix

• Parameters first iteration: A0 = (14.5 cm)2, L = 550 km and = (5.0 cm)2

• About the same as for Iterative-LSC(99)

• Parameters second iteration: A0 = (2.0 cm)2, L = 50 km and = (3.3 cm)2

• Truncated to minimize uncertainties in the short wavelengths of the residuals

da ta

2

da ta

2

XUSHG2000B (cont.)

• The two conversion surfaces are removed from G99SSS to create XUSHG2000B


• of the final 3.5 cm RMS difference, 1.9 cm correlated with an 11 km correlation length

Statistical Comparison of FY 1999 and 2000 Hybrid Geoids

Characteristics of the Data and Models Characteristics of theHybrid Geoid

CASENAME

ITER.NUM.

BIAS

(cm)

TREND

(ppm/deg)

RMSDIFF.(cm)

AMPL.

(cm)

CORR.LEN.(km)

DATAERR.(cm)

RMSDIFF.(cm)

CORR.AMPL.

(cm)

CORR.LEN.(km)

GEOID99 1 51.7 0.15/327 21.7 18.2 400 4.6 4.6 2.6 23

Iterative-LSC(99)

12

51.7-

0.15/327-

21.75.2

15.03.0

55033

5.22.3

-3.3

-2.4

-14

XUHSG2000A

1 51.0 0.13/323 20.9 17.7 400 4.5 4.5 2.7 22

XUSHG2000B(alternate)

122a

51.0--

0.13/323--

20.95.25.2

14.52.02.8

5505035

5.03.32.6

-3.53.4

-1.92.6

-1114

Conclusions for Modeling

• It is possible to incorporate more of the residual signal between gravimetric and GPSBM data into a hybrid geoid model

• Iterative-LSC modeled increasingly shorter correlation lengths, which also aided in analyzing signals in different bandwidths

• Multi-Matrix-LSC modeled the most of the signal in one pass with the best results

Conclusions for FY 2000 Geoids

• FY 1999 and 2000 GPSBM data have been culled to about the same level

• XUSHG2000A is very similar to GEOID99

• Use of iterative-LSC permits selective inclusion of residual signal

• XUSHG2000B does incorporate more of the correlated residual signal than the FY 1999 iterative-LSC model.

Future Research

• Source(s) of residual values

• Focus will be on relationship with nodes in Basic Net A of NAVD 88

• Better understanding of sources will aid in determining how much of the signal to use in future hybrid models

Improved Hybrid Geoid Modeling and the FY 2000 Geoid Models

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