Aeolus in heterogeneous atmospheric conditions Gert-Jan Marseille

Polar Stratospheric Clouds Quite a lot over Antarctic in August and Arctic in January 30 km 15 km PSC not always well sampled with the Mie channel

true cloud/aerosol (CALIPSO) Aeolus simulations true HLOS wind (UKMO) true cloud/aerosol (CALIPSO) 30 km Aeolus Rayleigh channel 1 orbit, simulations with LIPAS 30 km Aeolus Mie channel 18 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 wind error can be large depending on Clear atmosphere true atmosphere Mie signal Rayleigh signal 30 m/s 20 m/s 1 km mean wind in bin: 25 m/s measured: nothing measured: < 25 m/s Aeolus Rayleigh wind error can be large depending on binsize bin altitude wind-shear inside the bin COG

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 2 km 1.5 km 1 km

Cloud layer inside Aeolus bin true atmosphere Mie signal Rayleigh signal 30 m/s 20 m/s cross-talk 1 km cloud transmission mean wind in bin: 25 m/s measured: 25 m/s measured: > 25 m/s mean wind in bin: 25 m/s measured: 20 m/s measured: < 25 m/s 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

Mie Rayleigh z  c  RMSE wind error Sun et al., 2014 m/s m/s Mie Rayleigh 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 cloud layer z  Bin height

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

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 Bilt radiosonde 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

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

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

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!

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

Feature detection true OPC OPC

Score sheet good detection false alarm missed detection clear air

OPC layer location estimation Black dots denote OPC layer top/bottom OPC well fits the layer location at a resolution higher than Aeolus bins Outliers for individual measurements

Aeolus main contribution is Rayleigh winds in clear atmosphere 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

backup

Feature detection of true input scattering ratio > 1.15

This is the challenge scattering ratio > 1.15 Noisy

(T,P) from radiosonde database analytical radiosonde (T,P) mean and stddev 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 Use AUXMET to correct for Rayleigh channel height assignment errors

Results largely confirmed for LIPAS applied to the radiosonde database Bin with cloud layer Mie Rayleigh Cloud layer with thickness z and one-way transmission c; linear inside cloud cloud layer z  bias bin z => 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 std.dev RMSE c => Results largely confirmed for LIPAS applied to the radiosonde database

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) 2000 m 1000 m 500 m 250 m

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