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Aeolus in heterogeneous atmospheric conditions

This study investigates the accuracy of wind retrievals from the Aeolus satellite in the presence of clouds and aerosols. The results show that while Rayleigh winds in clear conditions are well-characterized, wind errors can be large in the presence of cloud and aerosol layers. The development of the Optical Properties Code (OPC) is discussed, which aims to estimate particle backscatter and extinction in order to improve wind retrievals in heterogeneous atmospheres.

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Aeolus in heterogeneous atmospheric conditions

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  1. Aeolus in heterogeneousatmospheric conditions Gert-Jan Marseille

  2. Polar Stratospheric Clouds • Quite a lot over Antarctic in August and Arctic in January 30 km 15 km • PSC notalways well sampledwiththe Mie channel

  3. Aeolus simulations true HLOS wind (UKMO) true cloud/aerosol (CALIPSO) 30 km • Rayleigh clear Mie, cloud/aerosol => complementary • Clear area dominates => Rayleigh channel is most important for Aeolus • No winds below (optically) dense clouds Aeolus Rayleigh channel 1 orbit, simulations with LIPAS 30 km Aeolus Mie channel 18 km

  4. Clear atmosphere true atmosphere Mie signal Rayleigh signal Aeolus Rayleigh wind error can be large depending on • binsize • bin altitude • wind-shear inside the bin 30 m/s 1 km COG measured:< 25 m/s measured: nothing 20 m/s mean wind in bin: 25 m/s

  5. Rayleigh wind error in clear atmosphere • Rayleigh channel height assignment error is height dependent • Typical atmosphere example • Stratosphere, 2 km Rayleigh bin, wind-shear 0.01 s-1 • H=40 m ~ 0.4 ms-1 bias • Biases exceed mission requirement in more extreme scenes (tropopause jet stream, PBL) if height assignment error is not corrected height assignment error as function of Rayleigh channel bin size 1 km 1.5 km 2 km

  6. Cloud layer inside Aeolus bin true atmosphere Mie signal Rayleigh signal Aeolus wind error can be large depending on (i) bin size, (ii) cloud/aerosol layer location inside the Aeolus bin, (iii) layer size, (iv) layer transmission and (v) wind-shear over the bin 30 m/s cross-talk 1 km cloud transmission mean wind in bin: 25 m/s measured:> 25 m/s measured:< 25 m/s measured: 25 m/s measured: 20 m/s 20 m/s mean wind in bin: 25 m/s

  7. Bin height RMSE wind error Sun et al., 2014 m/s m/s  Mie Rayleigh z cloud layer z  c • Rayleigh HLOS insensitive to z, but sensitive to particle layer transmission c • c can be obtained from Rayleigh channel signal • Rayleigh winds are under control • Mie HLOS however sensitive to z

  8. model vs. real atmosphere • Models are very smooth relative to the real atmosphere as measured by radiosonde • ECMWF underestimates real atmospheric wind shear by a factor of 3 ECMWF radiosonde Houchi et al., 2010

  9. Radiosonde database • Radiosondes provide wind, temperature, humidity and pressure at high (10-m) resolution • cloud layers detected from humidity along the radiosonde path (Zhang et al., 2010). Applied to De Biltradiosonde • One year (2007) database of high resolution (u, v, T, P) and cloud for Aeolus testing and L2Bp algorithm development • Retrieval of aerosol and cloud properties from radiosonde data remains a challenge • Focus on cloud layers, assuming simplified back- scatter and extinction

  10. Cloud layer statistics from radiosondes • Aeolus height bins are typically 1 km • But, 1/3 of cloud layers are thinner than 400m • Such layers cause non-uniform Mie backscatter and extinction • Mean backscatter height is uncertain • Wind and wind shear will be biased(mean shear = 4 m/s per km height) • Advanced retrieval methods will be needed Sun et al., 2014 Mie wind error

  11. Optical Properties Code (OPC) • Data assimilation requires estimates of the errors of Mie and Rayleigh channel winds • Pretty well known in clear air (Rayleigh winds) • Less well known in heterogeneous atmosphere • This requires estimates of particle layer size, location and transmission • Optical Properties Code as part of the L2Bp • Main purpose: feature detection as input for classification before accumulation from measurement to observation level • Spin-off: estimation of particle backscatter and extinction and (sometimes) the location of the layer inside the Aeolus bin • Needs Rayleigh channel signal only! • Still under development

  12. LITE scene – tropical cirrus over Indonesia • Measured signal is at very low resolution and noisy • Thin layers hard to detect, even by eye ?? quite a challenge!

  13. OPC – feature detection • Input: • AUX_MET: ECMWF forecast of P,T,u,v as a function of z • AUX_CAL: Rayleigh channel calibration info • Estimate the Rayleigh channel signal in clean (no aerosol/cloud) air • Compare with measured signal • Detect features

  14. Feature detection OPC true OPC

  15. Score sheet good detection false alarm missed detection clear air

  16. OPC layer location estimation • Black dotsdenote OPC layer top/bottom • OPC well fits thelayerlocationat a resolutionhigherthanAeolus bins • Outliersforindividualmeasurements

  17. Conclusions • Aeolus main contribution is Rayleigh winds in clear atmosphere • Error characteristics are well-known • Large signal from cloud and aerosols => good Mie SNR • But location of layer inside bin not known => large Mie wind errors, which cannot easily be estimated and/or corrected • Mie winds may not be very useful for NWP • Rayleigh winds in cloudy/aerosol conditions more reliable • But depending on layer size and transmission • Optical properties code (OPC) provides estimates • Feature detection (as part of OPC) is critical to decide between clear/cloud (aerosol) bins

  18. backup

  19. Feature detection of true input scattering ratio > 1.15

  20. This is the challenge • Noisy scattering ratio > 1.15

  21. (T,P) from radiosonde database • 1 year radiosonde data over De Bilt => (T,P) => m(z) =>w(z) => COG • Molecular (attenuated) backscatter profile smaller than from analytical expression • Height assignment errors are slightly larger than from analytical expressions • Not very sensitive to T errors analytical radiosonde (T,P) mean and stddev Use AUXMET to correct for Rayleigh channel height assignment errors

  22. Bin with cloud layer Mie Rayleigh • Cloud layer with thickness z and one-way transmission c; linear inside cloud • H can not be corrected: cloud location and thickness are unknown • H for Rayleigh ch. relative insensitive for z, more to c but that can be obtained from optical prop. code • We can estimate Rayleigh wind bias, not for Mie; Rayleigh wind bias smaller, in particular for c > 0.8  z cloud layer bias bin z => std.dev RMSE c => Results largely confirmed for LIPAS applied to the radiosonde database

  23. Radiosonde database – Mie wind error • 309 radiosonde launches in De Bilt • About 25% of cloud layers is assigned ice cloud and generally thin: • 25% < 300 m • 60% < 1 km • Mie wind error reduces for smaller bin sizes • For 1 km bins: std.dev = 1-1.5 ms-1 • Slightly below the analytical calculations (1.66 ms-1) 250 m 500 m 1000 m 2000 m

  24. Radiosonde database – Rayleigh wind error • 309 radiosonde launches in De Bilt • Rayleigh wind error reduces for smaller bin sizes • For 1 km bins: std.dev = 0.2-0.6 ms-1 in free troposphere • Compatible with analytical calculation (0.4 ms-1) • But classification may further reduce Rayleigh ch. wind error Particle-free

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