Effects of Cloud/rain on Ka band altimeter J. Tournadre et al Ifremer Brest

Altika : altimeter in Ka band (see Noubel and Verron presentations) Better performances but one major problem at Ka band (35.75 GHz) rain and cloud can strongly attenuate the signal and distort the echo waveform. Important to quantify : –Impact on echo waveforms and geophysical parameters estimate (use of MLE4 algo) –Percentage of data possibly lost due to rain and/or clouds OSTS Hobart, March 2007

Previous studies (Tournadre 1999). In presence of rain larger than 1-2 mm/hr the distortion of the waveform will inhibit the geophysical parameter retrieval. At Ka band the attenuation by cloud droplet is about 1.1 dB/km par g/m 3 (10 time larger than Ku band), not negligible. Cloud at Ka = rain at Ku. Cloud more frequent than rain. Necessity to analyze in detail the effect of cloud on the signal –Waveform modeling in presence of cloud Estimation of attenuation, off-nadir angle, leading edge slope at 1Hz and 20 Hz as a function of cloud parameters (IWC, height, diameter,..) –Estimation of the impact of cloud on the geophysical parameters (ssh,s0, SWH) retrieval: waveform modeling and MLE4 (Stenou et al). Determination of data availibility –Rain/cloud flag definition based on the signal analysis (Altika is single-frequency)

Analytical model of waveform based on Brown model + an attenuation term A attenuation by cloud or rain k attenuation coefficient in dB/km rain cloud R rain rate, m v liquid water content (g/m 3 ) Waveform modeling

For clouds Attenuation depends on Integratedd cloud liquid water (ICLW) Simpler than the rain case

Cloud liquid water Typical values for the properties of clouds. The values are merely modal-means. The range of observed values is quite large. The radius of cloud droplets is r (microns), the effective optical radius optically is r', N is the number of droplets per cubic centimetre, L is the liquid water content of the cloud (g/m 3 ). For all clouds, the level of observation is just below the freezing level, except for fog and cirrus. (Hess et al 1998)

Cloud : water droplet and ice cristals Ice has a much lower attenuation coefficient and can be neglected in the computation.

Cloud liquid water content data Several satellite sensors gives estimates of the integrated cloud liquid water content such as SSM/I, AIRS or MODIS on Aqua/Terra. However to have a good modeling we need high resolution data.

Waveform distortion by rain and cloud Ka band : strong attenuation by rain and cloud But more important: strong distortions of the waveform shape : modification of leading edge and plateau slope Altika WF over a 10 km 2.5 mm/hr rain cell Return powerAttenuation

Altika unavailability by rain : Use of JASON ENVISAT,TOPEX rain climatology: same sampling as Altika; Probability of rain greater than mm/hr

Waveform modeling: cloud with gaussian distribution of ICLW ICLW. Cloud radius Distance nadir/cloud Attenuation Off-nadir angle Stronger distortion for small high ICLW cloud

Comparison 1Hz 20Hz Small difference on attenuation for cloud radius larger than the footprint (~8km) For ICLW <0.5kg.m² weak impact of cloud (att<1 dB z<0.01°²)

Quantification of the impact of cloud on geophysical parameter see Desjonquéres Presentation Other approach : definition of a rain and cloud flag Altika : single frequency altimeter : implies a definition of a flag based on the analysis of the signal itself (sigma0 and or waveform). Critical for light to very light rain and cloud with high water content. Radiometer liquid water estimate are not sufficient for cloud/rain flagging (see past experience with Topex and Jason) Flag based on sigma0 and off-nadir angle estimate variations. Test on modeled waveform using satellite cloud liquid water measurements.

Nuage à IWC constant Analysis of  and  ² variations within a cloud. Nuage IWC exponentiel High small scale highly correlated variations of  et 

Modeled waveforms using (10 km resolution 10 km) AIRS liquid water data from AIRS on AQUA attenuation Off-nadir angle variations

Detection of  et  ² variations Method: Wavelet decomposition (here symmlet 8) of  et  Use « matching pursuit » algorithm (Mallat et al, 1997) –Determination of the most significant wavelet from an energy point of view (named atoms) –Each event is characterized by its amplitude and scale (possibility to discriminate by scales)

Exemple AIRS modeled waveforms White noise added to attenuation and  Reconstructedd signal from atoms

Synthesis IWC Attenation Off_nadir + attenuation atoms O  atoms

MODIS05 (1km)

IWC Attenation Off_nadir + attenuation atoms O  atoms

Deformation can be important even for low IWC (~0.2kg/m²) For IWC >0.6 kg/m2 rain most probable Below 0.2 kg/m² few data will be contaminated Distortion depends more strongly n the variability of IWC within the footprint than mean value

To be done : Availability maps Test of the feasibility of an operational rain/cloud flag based on  and  variations Modis06 level3 daily products clouds. Cloud fraction, cloud liquid water histograms

Conclusion Model shows that the mean impact of cloud is weak for CLWC lower than 0.2 kg/m² For higher CLWC it will be necessary to flag the data Wavelet analysis of the sigma0 and off- nadir angle variations can be used to flag the data affected by rain and clouds..

Problem: Definition of a rain flag As Altika certainly mono-frequency the rain flag can not be similar to the JASON or ENVISAT one (dual frequency). Critical for light to very light rain (<1 mm/hr) and some non-raining clouds where microwave radiometer data can not be used for rain detection New rain flag to be defined : modelisation show that it can be based on variation of sigma 0 and off-nadir (plateau) angle Example variation of off-nadir angle induced by rain Off_nadir as function of distance from rain cell and rain cell diameter for 0.5 mm/hr Off-nadir as a function of rain celle diameter