Geodetic Applications of Ocean Platforms

1
Geodetic Applications of Ocean Platforms

• Oscar L. Colombo
• GEST/NASA Goddard SFC

2
Seafloor Geodesy(with JHOD, Japan)
3
(No Transcript)
4
(No Transcript)
5
(No Transcript)
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
ANNUAL VELOCITY OF MYAGI RELATIVE TO EURASIAN
PLATE
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
(No Transcript)
16
(No Transcript)
17
(No Transcript)
18
Tsunami Detectionwith GPS BuoyOff-Shikoku
Island(with ERI/Hitachi, Japan)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
(No Transcript)
23
(No Transcript)
24
(No Transcript)
25
Real Time Kinematic Convergence
26
(No Transcript)
27
Convergence Problem in Wide-Area Differential and
Point Positioning RTK

• Real-time kinematic solutions (i.e.,
  Kalman-filter only) need 40min 1h 30min
  assimilating data before they can begin to
  estimate position precisely.
• Also an issue in post-processed kinematic
  solutions with short surveys, or frequent
  interruptions.
• Main cause of delay Estimating Lc biases and
  receiver tropo corrections.

28
Some Amelioration Techniques

• Fixing Ambiguities with precise ionospheric
  corrections.
• Similar to VRS technique, but much larger
  network. Subject to ionospheric conditions. For
  differential RTK.
• Needs 10-20 minutes to work.
• Fixing ambiguities with Melbourne-Wubbena and
  ion-free (Lc) combinations.
• Successful for fixed-site solutions, some recent
  experiments indicate it may also be useful for
  RTK.
• Limited by data noise, mainly pseudo-range and
  phase multipath. Needs 20 minutes or longer to
  work.
• Mean-sea level constraint.
• Limited by satellite geometry. Needs 20-30
  minutes to work.

29
MEAN SEA LEVEL CONSTRAINT

• In program IT, Kalman filter is updated with
  compressed data and instantaneous
  data,successively.
• Data are averaged over interval Tav (a few
  minutes)
• Compressed data gt updates of slow variables (Lc
  biases, tropo, etc.), and mean position.
• From mean position gt mean sea height hmean
• hmean H0 hrw hmw
• hrw Si Dhi
• smw hw Tw (21/2 p Tav)-1 1 cos(2 p
  Tav )1/2
• H0 Initial mean sea height
• hmv Height of averaged waves
  (noise of mean sea level height)
• hrw Mean sea-height random walk
• Dhi System white noise driving the
  random walk hrw
• hw Mean peak-to-null wave height
  for this period
• Tw Mean wave period (5s - 15s).
• Tav Averaging Interval
• smw RMS of Average Wave Height hmv

30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33

• No lightning-fast solutions to convergence
  problem in wide-area differential or in point
  positioning.
• Different techniques for different situations.
• Combination techniques?
• Better tropo estimation for moving receivers.
• Need to test techniques many times, with
  different data sets to assess performance,
  reliability.
• Create large test data base?

34
Testing RTC techniques

• Test data base with data from receivers at
  different latitudes (polar regions, tropics,
  mid-latitudes).
• Include met data to test different approaches to
  tropo modeling?
• Mostly static data, some from moving receivers.
• Anybody can use the data base, file reports.
• On line report form to be filled by testers.
• Few mandatory items in report. The most optional
  information entered the more serious the report
  is likely to be taken.