Climate Monitoring with GNSS Radio Occultation

Transcription

1 Climate Monitoring with GNSS Radio Occultation Stephen Leroy Harvard University Fourth FORMOSAT-3/COSMIC Data Users Workshop University Corporation for Atmospheric Research Boulder, Colorado October October 2009 Leroy: Climate Monitoring with GNSS RO 1

2 Outline SI Traceability Definition Test: Cross-platform bias? Test: Trend independent of retrieval process? Trend detection, theoretical considerations Atmospheric trends Natural variability and detection times Optimization Trend detection, observation Kirchengast et al., October 2009 Leroy: Climate Monitoring with GNSS RO 2

3 SI Traceability The International Vocabulary of Basic and General Terms in Metrology (ISO 1993): 6.10 Traceability Property of the result of a measurement or the value of a standard whereby it can be related to stated references, usually national or international standards, through an unbroken chain of comparisons all having stated uncertainties. Source-based traceability: Reproduce the unit of the observation by means of physical laws (e.g., blackbodies & Planck equation) Detector-based traceability: Reproduce the definition of the unit by means other than that of operational observation (e.g., electrical substitution) October 2009 Leroy: Climate Monitoring with GNSS RO 3

4 Two satellites in true polar orbit Launch: ~ Mission Concept Review: February 2010 Project Scientist: Dave Young Science Team Lead: Bruce Wielicki October 2009 Leroy: Climate Monitoring with GNSS RO 4

5 Calibration: Double Differencing Hardy, K.R., G.A. Hajj, and E.R. Kursinski, 1994: Accuracies of atmospheric profiles obtained from GPS occultations. Int. J. Sat. Comm., 12, October 2009 Leroy: Climate Monitoring with GNSS RO 5

6 Redundant Traceability Double-differencing Reference: Ground-based clock (10-15 stability) Error: Ground-based clock itself, high-rate LC link to reference GNSS, low-rate link to ground clock Single-differencing Reference: GNSS clock (~10-14 stability) Error: GNSS clock itself, ionospheric noise in LC link to reference GNSS Zero-differencing Reference: USO on-board LEO (~10-13 stability) Error: On-board USO October 2009 Leroy: Climate Monitoring with GNSS RO 6

7 Verifying a Benchmark Reproducible standards: An SI traceable observable should be reproducible by anyone, anywhere, anytime. Test: Collocated radio occultation soundings from independent satellites should produce identical measurements to within observation error. If not, there is an unaccounted break in SI traceability. Accurate trend reproduction: Uniform application of a retrieval algorithm should produce an accurate trend. Test: Measure trends in refractivity as produced by independent retrieval centers. Disagreement indicates poorly understood uncertainty or poorly understood data type October 2009 Leroy: Climate Monitoring with GNSS RO 7

8 Verifying a Benchmark: Test 1 Reproducibility: Comparing CHAMP to SAC-C Hajj et al., J. Geophys. Res., Possible break in traceability? October 2009 Leroy: Climate Monitoring with GNSS RO 8

9 Verifying a Benchmark: Test 2 Robust trends Ho et al., J. Geophys. Res., In Press October 2009 Leroy: Climate Monitoring with GNSS RO 9

10 Uncertainty: Systematic Error 50 MEG-ECMWF Altitude, km Refractivity deviation, % Ionospheric residual Unknown lower troposphere bias The famous Kursinski paper (1997) Gorbunov et al., To be submitted October 2009 Leroy: Climate Monitoring with GNSS RO 10

11 Non-optimal Trends Ringer and Healy, Geophys. Res. Lett., October 2009 Leroy: Climate Monitoring with GNSS RO 11

12 Optimal fingerprints Leroy et al., J. Geophys. Res., October 2009 Leroy: Climate Monitoring with GNSS RO 12

13 The Record So Far (1) GPS/MET, CHAMP, GRACE, COSMIC Steiner et al., Geophys. Res. Lett., October 2009 Leroy: Climate Monitoring with GNSS RO 13

14 The Record So Far (2) RATPAC-lite, a bias-corrected radiosonde data set (47 lowbias stations) Randel et al., J. Geophys. Res., October 2009 Leroy: Climate Monitoring with GNSS RO 14

15 Summary GNSS RO is a good climate benchmark because of its traceability to international definition of the second. Two empirical tests demonstrate its viability as a climate benchmarking technique. Possibility of local multipath error on CHAMP, SAC-C. The most substantial sources of systematic error are ionospheric residual in the stratosphere and a yet-to-beunderstood bias in the lower troposphere. Early climate monitoring results may be sufficient to evaluate other, longer climate data timeseries October 2009 Leroy: Climate Monitoring with GNSS RO 15