1. 1-1 September 16, 2008 SWOT SWOT Technology and Expected Performance E. Rodríguez Jet Propulsion Laboratory California Institute of Technology

2. 1-2 September 16, 2008 SWOT Interferometer/Altimeter Heritage Nadir altimeters: TOPEX/Poseidon & Jason & ERS & EnviSAT Altimeters: sea surface topography (1992 to present) –Heritage: Error budget for propagation delays, algorithms for range corrections, water reflectivity near nadir Radar Interferometers: –TOPSAR/AIRSAR: early 1990’s-present: C-band airborne platform –Star3I: X-band airborne radar interferomer (1990’s-present) –GeoSAR: X-band P-band radar interferometer –Europe: DLR airborne IFSSAR, DLR X-band spaceborne interferomer with SRTM –Shuttle Radar Topography Mission: spaceborne land topography (2000) Also imaged rivers and the ocean –Wide-Swath Ocean Altimeter : centimeter level precision concept funded by NASA past design reviews, but deferred due to budget problems –Cryosat: forthcoming studies will investigate the use of cryosat interferometric/altimeter modes for surface water. –WatER: proposal to ESA Earth Explorer. –Heritage: error budget verification, instrument design and manufacture, processing algorithms, ground system, mission management, calibration and validation High-frequency Radars: –CloudSat: the proposed instrument uses technology and leasons learned from the high-frequency CloudSat mission (EIK, High Voltage Powser Supply)

3. 1-3 September 16, 2008 SWOT SRTM Error Map

4. 1-4 September 16, 2008 SWOT KaRIN: Ka-Band Radar Interferometer Ka-band SAR interferometric system with 2 swaths, 60 km each WSOA and SRTM heritage Produces heights and co- registered all-weather imagery required by both communities Additional instruments: –conventional Jason-class altimeter for nadir coverage –AMR-class radiometer (with possible high frequency band augmentation) to correct for wet- tropospheric delay No land data compression onboard (50m resolution) Onboard data compression over the ocean (1km resolution) 1000 km -

5. 1-5 September 16, 2008 SWOT SWOT Configuration CNES conceptual drawing Interferometry SAR Antennae

6. 1-6 September 16, 2008 SWOT Orbit Discussions Ocean meeting has recommended: –22-day repeat orbit as nominal mapping orbit (full coverage) –1-3 day repeat period experimental phase at mission start for 3-6 months Multiple orbits will probably be required during the mission All orbits are not sun-synchronous Orbits can still change: e.g., fast sampling orbit or 74 deg inclination orbit –This may impact orbit altitude, consumables, etc. Orbit study needs to be finalized ASAP

7. 1-7 September 16, 2008 SWOT 22-Day Repeat Temporal Sampling

8. 1-8 September 16, 2008 SWOT 22-Day Repeat, Amazon

9. 1-9 September 16, 2008 SWOT 22-Day Repeat, Amazon

10. 1-10 September 16, 2008 SWOT 22-Day Repeat, Amazon

11. 1-11 September 16, 2008 SWOT 22-Day Repeat, Lena

12. 1-12 September 16, 2008 SWOT 22-Day Repeat, Lena

13. 1-13 September 16, 2008 SWOT 22-Day Repeat, Lena

14. 75S 75N Orbits 74N is OK 66N is not Lena R. Ob R. Yenisey R. 70N 60N Mackenzie R.

15. 1-15 September 16, 2008 SWOT Interferometric Measurement Concept Conventional altimetry measures a single range and assumes the return is from the nadir point For swath coverage, additional information about the incidence angle is required to geolocate Interferometry is basically triangulation Baseline B forms base (mechanically stable) One side, the range, is determined by the system timing accuracy The difference between two sides (  r) is obtained from the phase difference (  ) between the two radar channels.  = 2  r  = 2  sin  h = H - r sin 

16. 1-16 September 16, 2008 SWOT Error Budget Dominant Contributors Orbit Error Media Delay Error (Iono, Tropo, EMB) Phase Error Baseline Roll Error Other error sources (e.g., baseline length, yaw errors) can be controlled so that errors are smaller by an order of magnitude, or more.

17. 1-17 September 16, 2008 SWOT Error Characteristics Errors can be divided into spatially correlated and uncorrelated –Uncorrelated: thermal/speckle noise. Precision improves linearly with the area –Correlated: geophysical, orbit. Precision does not improve significantly with averaging Slope (~velocity) is affected differently than height by spatially correlated errors –Relatively large height errors can result in relatively small slope errors For ocean, geostrophic velocity ~slope. Sea-level rise ~height. Heat content ~height For hydrology, velocity (discharge) ~slope (or assimilated height). Storage ~ height

18. 1-18 September 16, 2008 SWOT Land Error Budget Allocations

19. 1-19 September 16, 2008 SWOT Ocean Measurement Requirements k -3 k -2 3 cm noise at 1 km 2 average 1cm noise at 1 km 2 average 30 km 10 km Jason pass 132 (147 cycle average) Wavenumber (cycle/km) Power density (cm 2 /cycle/km) Instrume nt noise 1000 100 10 km error spectrum SWOT SSH error spectral requirement (to resolve signals down to 10 km wavelength) Signal spectrum

20. 1-20 September 16, 2008 SWOT Interferometric Phase Error  h = r tan  /(2  B)  Significant advantages to near-nadir geometry: SRTM vs SWOT tan  : ~0.09 Dominant sources of phase noise: Thermal noise in radar signal (random) Decorrelation of the two returns due to speckle decorrelation of scattered fields (random) Phase imbalance between the two interferometric channels: Temperature driven (slow change) Can be calibrated using calibration loop.

21. 1-21 September 16, 2008 SWOT Random Height Error Validation

22. 1-22 September 16, 2008 SWOT Random Height Error Validation

23. 1-23 September 16, 2008 SWOT KaRIN Random Noise Performance

24. 1-24 September 16, 2008 SWOT Media Delay Errors dh = -  r cos  Similar to nadir altimeter range errors (although there is no tracker error since no estimate of the waveform leading edge is necessary). Sources of range error: Ionospheric delay Dry and wet tropo delays EM Bias

25. 1-25 September 16, 2008 SWOT iono SSB wet tropo SSH Media errors and sea-state errors have scales larger than 100 km and not affecting submesocale SSH measurement. Measurement error noise Geophysical Correction Spectrum

26. 1-26 September 16, 2008 SWOT Spatial Variability of Media Delay A determination of the spatial variability of the media delays can be made using multi-seasonal TOPEX/Poseidon data. No attempt has been made to remove instrument noise, and that is why the errors at 20 km are so large: wet-tropo, EM bias, and ionospheric correlation lengths are >> 20 km. Correcting for media effects can have significant effects on the calculation of slopes and the high frequency spectra Global altimetry: accuracy (Sub) Mesoscale: precision Total Additional Media Errors: EM Bias + Wet Tropo. + Iono. Random noise component

27. 1-27 September 16, 2008 SWOT Uncompensated Tropospheric Delays Source: S. Kheim, JPL Range delay variability from ground measurements Tropospheric delays have correlation distances > 50 km. Order of magnitude slope biases: 5cm/50km ~ 1cm/10km.

28. 1-28 September 16, 2008 SWOT Attenuation Effects at Ka-Band Rain rate mm/hr 35 GHz Attenuation (5-km path) 36 dB 511.5 dB 1024 dB 2050 dB 3073 dB 100215 dB SWOT will only be able to collect valid data at rain rates smaller than 3-5 mm/hr (depending on surface water brightness) Walsh, et al., Rain and Cloud Effects on a Satellite Dual-Frequency Radar Altimeter System Operating at 13.5 and 35 GHz, IEEE Trans. GRS, 22, 1984

29. 1-29 September 16, 2008 SWOT Rain Probability Petersen WA, Nesbitt SW, Blakeslee RJ, et al. TRMM observations of intraseasonal variability in convective regimes over the Amazon JOURNAL OF CLIMATE 15 (11): 1278-1294 JUN 1 2002 The total data loss will be less than 10% in the tropics. Similar numbers also hold at other latitudes.

30. 1-30 September 16, 2008 SWOT Radar Layover and its Effect on Interferometry Brightness Ratio (land darker than water) Correlation Ratio (land less correlated than water) Volumetric Layover (trees) Surface Layover Points on dashed line arrive at the same time

31. 1-31 September 16, 2008 SWOT What is the Global Topographic Layover Probability?

32. 1-32 September 16, 2008 SWOT Amazon Tree Layover Simulation-1 Amazon vegetation/water mask courtesy of L. Hess, UCSB

33. 1-33 September 16, 2008 SWOT Amazon Tree Layover Simulation-2 Assumed tree height: 20 m Fraction of land which is tree covered: 100%

34. 1-34 September 16, 2008 SWOT Ohio River Valley Topographic Layover Example Cross-track range Along Track

35. 1-35 September 16, 2008 SWOT Baseline Roll Error  h = r sin    An error in the baseline roll angle tilts the surface by the same angle. This is equivalent to introducing a constant geostrophic current in the along-track direction As an order of magnitude, a 0.1arcsec roll error results in a 4.5cm height error at 100km from the nadir point Roll knowledge error sources: Errors in spacecraft roll estimate Mechanical distortion of the baseline (can be made negligible if the baseline is rigid enough)

36. 1-36 September 16, 2008 SWOT Ocean Cross-Over Calibration Concept Roll errors must be removed by calibration Assume the ocean does not change significantly between crossover visits For each cross-over, estimate the baseline roll and roll rate for each of the passes using altimeter-interferometer and interferometer-interferometer cross-over differences, which define an over-constrained linear system. Interpolate along-track baseline parameters between calibration regions by using smooth interpolating function (e.g, cubic spline.)

37. 1-37 September 16, 2008 SWOT Distribution of Time Separation Between Calibration Regions

38. 1-38 September 16, 2008 SWOT Land Residual Roll/Phase Errors Calibration at Coasts

39. 1-39 September 16, 2008 SWOT Land Residual Roll Errors Calibration Using SRTM or other DEMs

40. 1-40 September 16, 2008 SWOT Mitigating Roll Errors: Minimize Spacecraft Dynamics S/V bus Solar array (flat panel, leading & trailing bus, single-axis articulation, S/A angle adjusted only during polar passes with articulation angle remaining fixed during pole-to-pole portion of orbit) nadir velocity Solar array Minimizing high- frequency motion errors can be achieved with an appropriate architecture (e.g., Grace has no moving panels) Both CNES and JPL have determined that a feasible architecture exits where no high- frequency spacecraft component motion will occur during data collection Need to minimize these 700km 70km 7km 0.7km

41. 1-41 September 16, 2008 SWOT Proposed Hydrology Measurement Requirements k -3 k -2 3 cm noise at 1 km 2 average 1cm noise at 1 km 2 average 30 km 10 km Jason pass 132 (147 cycle average) Wavenumber (cycle/km) Power density (cm 2 /cycle/km) Instrume nt noise 1000 100 10 km error spectrum SWOT Hydrology Spectral Requirement: Meet threshold science requirements with coastal calibration Signal spectrum