The altimetric Wet Tropospheric Correction : Progress since the ERS-1 mission

1. The altimetric Wet Tropospheric Correction :Progress since the ERS-1 mission Laurence Eymard CNRS/UPMC - IPSL/LOCEAN

2. The wet tropospheric correction • « dry » term provided by meteorological models with a good accuracy • « wet » term highly variable, ranging from a few cm to about 50 cm

3. The microwave radiometer specifications • To provide the wet tropospheric corrrection with an RMS error ≤ 1cm, with no systematic error with time and space • Microwave radiometry chosen because: • Compact and assessed technology (first radiometers launched at the beginning of the seventies) • Retrieval methods available, based on the use of 3 channels since SMMR. • ERS1/MWR first microwave radiometer dedicated to the altimeter path correction

4. Microwave radiometers - altimetry missions

5. Retrieval of the tropospheric correction • Statistical methods: synthetic data base built using radiative transfer modelling on atmospheric profiles (radiosoundings - TMR ; ECMWF - EMWR) • TMR/JMR retrieval in two steps: « wind » estimate, then linear combination of the 3 channel Tbs, coefficients depending on the « wind » and « wet tropo » classes • ERS/MWR algorithm : loglinear combination of the 2 channel Tbs and the altimeter wind • ENV/MWR algorithm : neural network combination of the 2 channel Tbs and the altimeter backscatter coefficient in Ku band. • Final uncertainty on the retrieved dh depends both on the retrieval method and the instrument calibration: they are not independent, since the radiometer calibration (Tbs) and the algorithm coefficients must be fine tuned to achieve the required accuracy

6. Major calibration issues • Tb absolute calibration must be within about 0.5K (radiometric sensitivity) to achieve the required dh accuracy (1cm) - more stringent for the 21 - 23.8 Ghz channel • Calibration problems: • ERS2/MWR: gain drop by 10 dB (loss of an amplifier?) • JMR (Jason) and TMR(T/P) : yaw mode effect on Tbs (thermal gradient) • ENV/MWR: drift of calibration counts (LO ageing?) • JMR : diode stability • Corrections: empirical methods based on Tbs time series and intercomparison of instruments • Remaining impact negligible for points 1 and 2 (within the specifications) - might be problematic for points 3 and 4 • Warning: the calibration correction and tuning leads to a globally mean adjustment of Tbs

7. Calibration problems with impact on the tropospheric correction homogeneity (1) Side lobes (Envisat and Jason radiometers) • Contribution of the earth in the far side lobes (circle of radius 3000 km around the footprint center) : from 130-140K (cold ocean) to about 300K (continents) • ENV/MWR and JMR : high level of side lobes (>3% for ENV/MWR at 23.8 GHz) • If not corrected for, error up to 10K, with a non-uniform geographic distribution • Empirical correction based on actual ERS2/MWR measurements in the same frequencies (Obligis et al, 2006)

8. Calibration problems with impact on the tropospheric correction homogeneity (2) Long term drift • Microwave radiometers have been proved very stable with time • TMR: detailed long term analysis of trend • Drift on dH identified at the end 90s. Several studies have confirmed the 18 GHz channel trend to be about .2K/year (Ruf et al, 2000, Scharroo et al, 2004, Eymard et al, 2005, …) • Suspected cause: increase loss in a ferrite switch • Similar problem on ERS2/MWR (0.27K/year) • Drift in the ENV/MWR due to receiver • Largest drift at low temperature except on ENV/MWR : determination method based on statistics of coldest ocean data (value stable with time for each channel), as shown by Ruf et al, 2000 • Drift and other calibration problems at high temperature monitored using tropical rain forests : Tbs and derived surface emissivity (using Ts and atmospheric analyses) shown to evidence anomalous behavior (Eymard et al, 2005) Empirical correction function (of time and temperature) applied to Tbs for SSH long term monitoring (see Scharroo et al, 2004)

9. Retrieval of the tropospheric correction: issues • ENV algorithm: no more systematic bias at low / high water vapor content • Validation against radiosoundings on coasts, small islands and ships • Also using satellite intercomparisons: TMR/JMR, TMR/EMWR, ERS1/ERS2/ENV EMWRs, with SSMI and TMI (poster SWT N. Tran) • Also using ECMWF analyses (available immediately after launch)

10. Application of algoritms on the validation data base (ECMWF profiles and simulated Tbs) Loglinear algorithm Neural network algorithm Obligis et al, 2006 Validation of ENV dh using shipborne RS 3 years needed to get 5600 points!

11. TMR higher 8.7 mm JMR higher 5.5 mm Obligis et al, 2004 • Results different from those of Brown et al (2004), based on islands radiosoundings (no bias between TMR and JMR, JMR bias error less than 4 mm) • Different comparison methodologies • Effect of coast contamination?

12. ALTIMETER TRACK RADIOMETER PIXEL CONTAMINATION BY LAND IBIZA ISLAND NO CONTAMINATION Dh TMR Path delay bias due to island overpass, and to coast vicinity Ibiza island French coast Algerian coast Poster SWT C. Desportes Cape de Creus

13. Systematic errors on dh • Coast proximity : • Tbs first biased by side lobe contribution (if not corrected), then main lobe affected (< 50 - 60 km) • Induced bias depending on the algorithm (TMR algorithm may be used closer than ERS/ENV ones) • Effect of the statiscal retrieval: • Bias depending on the surface wind • Geographic biases in regions where the atmosphere profile largely differs from the mean one Bias between ECMWF dh and retrieved dh using EMWR algorithm using ECMWF simulated Tbs, due to atmosphere stratification See Obligis’ talk - SWT workshop Subtropical areas

14. Is the radiometer actually useful? • Technical problems can occur, reducing the quality of measurements • The wet tropospheric path uncertainty cannot be reduced, due to the instrument sensitivity, and due to limitations of the reference in situ data (radio-soundings) • The footprint size is wide compared with the altimeter, and does not allow to use altimetry near coasts • ECMWF model (and others) assimilate in situ and satellite data over oceans (SSMI), so the ECMWF dh is statistically equivalent to radiometer products. • Why to add a microwave radiometer???

15. Some model limitations (to date) • Water vapor analyses suffers from the model spin-up of the hydrological cycle, which reduces the impact of assimilated SSM/I data • In tropical latitudes (high water vapor content), bias and too low variance characterize ECMWF fields, compared with radiometers. • Model version changes are generally accompanied by significant mean differences (ex January 2002) • The horizontal resolution is still worse than the radiometer one, with smoothing over 1 degree (two grid meshes), so abrupt changes are poorly depicted • In coastal areas, global model performances are not better, due to the lack of measurements and grid spacing

16. Variance improvement by using TMR instead of ECMWF at cross-orbit points (4 cycles)

17. Conclusions: what have we learnt since ERS1? • Despite calibration problems, TMR and ERS radiometers have been shown to provide the required accuracy on the wet tropospheric correction for a long time (13 years for TMR). The final uncertainty is acceptable for SSH long term monitoring • We know much better how to control the in-flight calibration and its stability (absolute calibration, side lobes, drifts). Use of vicarious natural targets promising for homogeneous incalibration of radiometers • ERS/ENV and TOPEX radiometers based on well assessed design. Any new design should be strongly evaluated before being used…

18. Conclusions: what have we learnt since ERS1? (2) • Retrieval methods could be still improved (wind and atmosphere stratification dependence) • A major issue for the future is the wet tropospheric path correction in coastal areas, and over inland waters. Use of combination of classical radiometers, high frequency humidity sounders could be explored in coastal areas • Meteorological model improvements (horizontal resolution, water cycle) expected • Promising use of assimilation / variational and neural network + guess techniques