Goal: to determine how the Jason waveforms might be retracked to better fit the CODAR synthetic heights
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CALIFORNIAN CODAR DATA SET At least 2 radar overlapping fields are needed to derive the currents Using 2008 & 2009 hourly CODAR measured ocean surface currents on a resampled and post processed 6 km and 2 km grid. Courtesy of Sung Yon Kim, Scripps Institute of Oceanography. Long range Short range Frequencies 4.66 MHz 13.54 MHz Resolution 6 km 2 km Up to 150 km 50 km offshore
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Bathymetry and geography in the coastal transition zon e Data set geography Around Monterey Bay Red Lines : bathymetry, 100 m and 250 m Blue Lines : bathymetry, every 1000 m from 1000 to 4000 m every 1000 m from 500 to 3500 m Green Line : boundary of 6 km CODAR set ~ 150 km from shore In order to retrieve the geostrophic currents from the total velocity surface derived from HF radar, an analysis of the time and spatial scales of the coastal oceanic features is done. These features are expected to vary with the distance to the coastline and or bathymetry, and thus may vary regionally
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CODAR TIME and SPATIAL SCALES Time scale 10 days in the open ocean 3 days near the coast From temporal covariance of velocities at zero spatial lag averaged over 2008 3 day averaging as a first approximation to the geostrophic flow binned according to spatial lag X and Y normalized and averaged over 2008. Looking at the shape of the velocity covariances we will assume that locally, the homogeneous isotropic turbulent model is adequate . Spatial Scale Covariances of CODAR velocity Cuu and Cvv for 10 km width regions Cuu u cross shelf velocity Cvv v along shelf velocity
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Spatial scales For homogeneous isotropic turbulence, velocity covariances are related to the stream-function covariance We use the Waldstat (1991) streamfunction spatial covariance : And find parameters : For zone 50-60 km : a = 50 km, b = 70 km For zone 20-30 km: a = 35 km, b = 50 km C ψψ = ( 1 - (r⁄b) 2 ) exp( -(r⁄a) 2 ) Fitted Observed Optimal Interpolation (OI) [Bretherton et al. (1976)] to derive the estimated streamfunction
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CODAR Geostrophic heights field Region of P221 going through Monteray bay . The complex bathymetry generates the formation of Eddies. And this region is prone to have large SLA variations. 3 day-averaged CODAR total currents minus a Mean current derived from the 0.02° gridded DNSC08 MSS This mean was chosen due to the short span of the CODAR time series. A Mean dynamic topography should be removed, but would have more errors in the coastal region. In the OI, the spatial scales L vary locally as a function of the grid point distance to coast, and only the surrounding observations at a distance less than L are considered. The noise error for the CODAR was chosen to be constant = 15 cm/s although in reality it varies depending on the radar geometry, and weather conditions, and in our case to how close the geostrophic assumption is valid. CODAR ocean currents u,v 3 day- averaged - Mean derived from DNSC08MSS OI varying spatial scales Synthetic height fields
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CODAR 2 km Geostrophic field Right : Effect of using a single spatial scale chosen at the 50 km zone (top), and the improved resolution using a varying spatial scale (bottom) Left : A close up of the improved map underlying the input CODAR vectors cm
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6 & 2 km CODAR SSH Along track P221 At the time of J1 passage More variations in the 2 km set. But the 6 km set enables us to get information further offshore. Later we combine both resolution products.
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CODAR & weekly Merged MSLA Aviso product: Weekly merged MSLA on a 1/3 °x1/3° grid, for 2008 CODAR SSH computed directly on the MSLA grid.
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6-km-Codar and J2 SSH on P221 for 2009 Interpolated at the J along track P221 JASON Corrected SSH Along track P221 Distances more than 20 km off coast Smoothed 25 km Smoothed 50 km Mean of time Series removed CODAR ocean currents u,v 3 day- averaged - Mean current derived from DNSC08MSS OI varying spatial scales Synthetic height fields Comparison
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J2 PISTACH coastal product resampled to 1 Hz using the MLE4 Ocean Brown model retracked range : Sea level relative to the DNSC08 MSS, all corrections applied. We generate two smoothed SSH sets: cut-off frequency of 25 km cut-off frequency of 50 km Codar 6 km Along track SSH amplified by 2 Mean of all time series removed 6-km-Codar and J2 SSH on P221 for 2009 OCE
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6-km-Codar and J2 SSH on P221 for 2009 ( cont.) 2x{Codar} SSH filt 25 km SSH filt 50 km OCE
Red points : statistically non significant Mean( blue points) = 0.65 Correlation Codar 6 km and 50-km-smoothed J2 between Along track distance 50 – 150 km
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J2 PISTACH Retrackers Data : Aviso 20 Hz-J2 PISTACH coastal product Oce : ku Band range: Deep ocean Brown model: MLE4 (range, amplitude, significant wave height & mispointing angle) Red3 : MLE3 Ice3 : 30 % threshold Time period: 2009 Extracted range from 3 retrackers : ( from PISTACH handbook) Principe of the Ice3 algorithm ( from PISTACH handbook) Done on a smaller window selected around the leading edge
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J2 retrackers & 6-km-CODAR for P221 2x{Codar} SSH filt 25 km SSH filt 50 km If we assume Codar SSH to be the best estimate of the geostrophic field, then we can evaluate the retracking techniques. At this point, the shapes are more important than the exact values. cm C006 C007 Along track distance ( km) For these first 4 cycles it seems that Ice3 fits better to the Codar SSH. Although they are quite similar except for the third case on C021, Feb -03 OCE
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J2 retrackers & 6-km-CODAR for P221 Here we only keep the best visually retracker fit either Ice3 or OCE, for the first 16 cycles of 2009 2x{Codar} SSH filt 25 km SSH filt 50 km The averaged correlation is now 0.82 OCE OCE OCE OCE
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Closer to shore use of 20 hz Pistach & Codar 2 km To filter the noisy 20 hz data: iterative median and low Pass filter with a 3-sigma data selection [ Dufau Claire,2011] With wave length L = 21 points ( 7 km ) L = 61 points ( 21 km ) Sea level relative to a MSS, all corrections applied: use of decontaminated water vapor corr, GIMP ionospheric corr, SSB included ( may not be reliable over continental shelf) 3x{ Codar 2 km} Mean of time series removed Ice3 retracked range Good fit Phase shift wiggles
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Jason-2 Ku 20 Hz Waveform time series for P221 median tracker algorithm, Diode acquisition mode In some places the leading edge of the WF deviates from the predetermine tracker gate 31. They correspond with large offsets between Codar and altimetry SSH. The median tracker algorithm uses the WF to update the tracker range. So large deviations could give us a clue to possible changes in WF shape and errors in retracking ranges. Gate number Along track distance from coast
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sig0 and unretracked range relative to MSS MLE4 Ku-band sig0 (dB) Unretracked range relative to MSS Oce retracked – unretracked range Strong correlation ( > 0.7) when sig0 > 15 dB If not lower ( 0.6 ) due to signal to noise level Sig0 = radar return backscattering cross section C019, Jan 15 C031, May 14 C030, May 04
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sig0 and unretracked range relative to MSS J2, Jan 15 J1, Jan 15 Median tracker algorithm Split gate tracker algorithm J2 and J1 in flying phase formation, same orbit , 54 s delay Both tracker algo behave similarly with a slight difference in the sensitivity of the echo shape However the (sig0, unretracked range) relationship disappear when J2 is in the Diode/DEM coupling mode, because the tracking operations do not depend on echoes analysis anymore . Diode/DEM mode J2, Jun12
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Sigma0 blooms Altimetry data degraded by high sig0 [ Tournadre 2006, Thibaut 2007] Occur in presence of weak winds ( cm scale waves absent) surface slick WF may be corrupted due to the non uniform sig0 in the altimeter footprint with localized highly reflecting patches. various possible WF distortions Consecutive 20 Hz WF, 30 km offshore in presence of a blooming event. Trailing edge increasing Peakiness increased V shape or Round pattern similar to rain events J2 C021 Feb03
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Variations in sea states : Sig0 and SWH MLE4 retracked Sig0 (dB) 2x{SWH} (m) Occurrence, size and strength of blooming events variable Dots: regions where Ice3 and Red3 ranges diverge 1 Blooms 30 km and 140 km 4 Blooms before 40 km and after 60 km SWH variable 5 Sig0 > 15 dB SWH high and variable 2 Bloom 30 km SWH high 3 Sig0 < 14 dB No blooms 6 Sig0 ~ 15 dB A lit bloom at 140 km C019, Jan 15 C021, Feb 03 C030, May 04 C034, Jun 12 C031, May 14 C026, Mar 25
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Behavior of Ice3 and Red3 range related to the sea state 3 6 1 Along track distance from coast Along track distance from coast Ice 3 retracker Red 3 retracker C019 Jan 15 C019 Jan 15 No blooming events Ice 3 performs well and better. Sig0 stays below 15 dB SWH < 2.5 m Ice3 behaves well closer than 50 km , then Red3 until a little blooming event around 140 km Sig0 is about 15 dB SWH < 1.5 m Ice3 fits well closer than 50 km when a strong blooming event starts, then Red3, until the next blooming event around 140 km SWH < 2 m
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Behavior of Ice3 and Red3 range related to the sea state 2 5 Along track distance from coast Ice 3 retracker Red 3 retracker After the close to shore blooming event ( < 30 km), Red3 performs better, though it seems noisy. High SWH Sig0 > 15 dB and high ; High variations of SWH Ice3 and Red3 behave in a similar way and do not perform well. A different retracking method should be adapted to this situation.
We have processed the CODAR current derived velocities into synthetic heights using a varying spatial scale OI technique, assuming a locally isotropic and homogeneous field.
We have shown that there is a good relationship between JASON & CODAR SSH, further than 20 km off the coast.
It seems promising as a tool to evaluate the several retracking techniques. They seem to perform differently depending on the sea state.
In the presence of blooming events the altimetry data is corrupted. These could happen more frequently over the Californian coastal areas due to upwelling.
FUTURE WORK
To correct for the blooming events using the Codar SSH as a reference.
Redefine the retracking strategy: using the Codar SSH, find the corresponding retracking gate and deduce what type of retracking technique would fit.
This should allow to make improvements on the coastal altimetry SSH accuracies.
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Questions? Thank you Carolyn Roesler, William J. Emery and Waqas Qazi CCAR Aerospace Eng. Sci. Dept. University of Colorado at Boulder
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