cloud and precipitation best estimate… …and things i don’t know that i want to know
DESCRIPTION
Cloud and precipitation best estimate… …and things I don’t know that I want to know. Robin Hogan University of Reading. Unified retrieval. Ingredients developed Done since December Not yet developed. 1. Define state variables to be retrieved - PowerPoint PPT PresentationTRANSCRIPT
Cloud and precipitation Cloud and precipitation best estimate…best estimate…
…and things I don’t know …and things I don’t know that I want to knowthat I want to know
Robin HoganUniversity of Reading
3. Compare to observationsCheck for convergence
Unified Unified retrievalretrieval
Ingredients developedDone since December
Not yet developed
1. Define state variables to be retrievedUse classification to specify variables describing each species at each gateIce and snow: extinction coefficient, N0’, lidar ratio, riming factor
Liquid: extinction coefficient and number concentrationRain: rain rate, drop diameter and melting iceAerosol: extinction coefficient, particle size and lidar ratio
2a. Radar modelWith surface return and multiple scattering
2b. Lidar modelIncluding HSRL channels and multiple scattering
2c. Radiance modelSolar & IR channels
4. Iteration methodDerive a new state vector: Gauss-Newton or quasi-Newton scheme
2. Forward model
Not converged
Converged
Proceed to next ray of data5. Calculate retrieval errorError covariances & averaging kernel
CloudSat
Calipso
Unified retrieval
Ice extinction coefficient
Rain rate
Aerosol extinction coefficient
Simulated EarthCARESimulated EarthCARE• Higher sensitivity than CloudSat gives strikingly better sensitivity
to tropical cirrus• This case perhaps can form the basis for some ECSIM and Doppler
simulations for an EarthCARE Bull Am Met Soc paper
CloudSat
EarthCARE CPR
Extending ice retrievals to riming Extending ice retrievals to riming snowsnow
• Heymsfield & Westbrook (2010) fall speed vs. mass, size & area• Brown & Francis (1995) ice never falls faster than 1 m/s
Brown & Brown & Francis (1995)Francis (1995)
0.9
0.8
0.7
0.6
• Retrieve a riming factor (0-1) which scales b in mass=aDb between 1.9 (Brown & Francis) and 3 (solid ice)
Simulated observations – no Simulated observations – no rimingriming
Simulated retrievals – no Simulated retrievals – no rimingriming
Simulated retrievals – rimingSimulated retrievals – rimingBut retrieval is completely dependent on how well we
can model scattering by rimed snowflakes!
There are known knowns. These are things we know that we know.
There are known unknowns. That is to say, there are things that we know we don't know.
But there are also unknown unknowns. There are things we don't know we don't know.
Donald Rumsfeld
A Rumsfeldian taxonomyA Rumsfeldian taxonomy• The known knowns, things we know so well no error bar is needed
– Drops are spheres, density of water is 1000 kg m-3
• The known unknowns, things we can explicitly assign an well-founded error bar to in a variational retrieval– Random errors in measured quantities (e.g. photon counting errors)– Errors and error covariances in a-priori assumptions (e.g. rain number
conc. parameter Nw varies climatologically with a factor of 3 spread)• The unknown unknowns where we don’t know what the error is in an
assumption or model– Errors in radiative forward model, e.g. radar/lidar multiple scattering– Errors in microphysical assumptions, e.g. mass-size relationship– How do errors in classification feed through to errors in radiation?– How do we treat systematic biases in measurements or assumptions?
• (also the ignored unknowns that we are too lazy to account for!)
How can we move more things into the “known unknowns” category?
Number concentration
Size distribution width/shape
Particle shape
Radar scattering & absorption
Lidar scattering & absorption
Warm liquid droplets
Miles et al. (2000)
Many aircraft campaigns
Sphere Mie Mie
Supercooled droplets
A few aircraft studies?
Same as for warm droplets?
Sphere Attenuation unknown!
Mie
Drizzle Abel and Boutle (2012)
Aircraft studies?
Sphere Mie Mie
Rain Many distrometer studies
Illingworth & Blackman (2002)
Spheroid, known aspect ratio
T-matrix (Mie OK too)
Mie is OK
Ice Delanoe and Hogan (2008)
Delanoe et al. (2005), Field et al. (2005)
Aggregate aspect ratio 0.6
Spheroid agrees with obs (Hogan et al. 2012)
Retrieved lidar ratio encapsulates variations
Snow (possibly rimed)
Same as ice? Same as ice? How do we represent riming?
Scattering uncertain!
Lidar ratio encapsulates variations
Melting ice Lies between snow & rain?
Lies between snow & rain?
Very uncertain
Attenuation uncertain!
Ignore
Aerosol Many aircraft campaigns
Many aircraft campaigns
Dry aerosol shape uncertain
Ignore Lidar ratio encapsulates variations
Melting layer: modellingMelting layer: modelling
• Fabry and Szyrmer (1999) found 10 dB spread in melting-layer reflectivity at 10 GHz although their “Model 5” agreed best with obs
• But what about 94-GHz attenuation?• What we really need is PIA through the melting layer versus rain rate,
with an error
Haynes et al. (2009)Haynes et al. (2009)
• The same Szymer and Zawadzki (1999) model• Rain rate 2 mm h-1
• 6-7 dB 2-way attenuation
Top of melting layer
Base of melting layer
Matrosov (2008)Matrosov (2008)• 2-way radar
attenuation in dB is 2.2 times rain rate in mm h-1
• Attenuation at 2 mm h-1 is 5-5.5 dB
• We need observational constraints!
• E.g. aircraft flying above and below melting layer, each with 94-GHz radar and the one below with airborne distrometer
• Or multi-wavelength ground-based technique?
Matrosov (IEEE Trans. Geosci. Rem. Sens. 2008)
SnowSnow• What’s the 94 GHz
backscatter cross-section of this?
• Spheroid model works up to D ~ , but not for larger particles
• Rayleigh-Gans approximation works well: describe structure simply by area of particle A(z) as function of distance in direction of propagation of radiation
Are
a of
slic
e th
roug
h pa
rticl
e A
(z)
Simulated aggregate (Westbrook et al.)
Self-similar Rayleigh Gans Self-similar Rayleigh Gans approx.approx.
• A spheroid has a very similar A(z) to the mean A(z) over many snow aggregates: but for 1 cm snow at 94 GHz, the spheroid model underestimates backscatter by 2-3 orders of magnitude!
• Radiation “resonates” with structure in the particle on the scale of the wavelength, leading to a much higher backscatter, on average
• Power spectrum of this structure shows that these aggregates are “self similar”
• An equation has been derived for the mean backscatter cross-section of an ensemble of particles
• But all this depends on the realism of the simulated aggregates!
• Can we test observationally?
Hogan and Westbrook (in preparation)
Supercooled Supercooled water water
absorptionabsorption• No laboratory observations of 10-1000-GHz absorption of water colder than –6°C!
• All models in the literature are therefore an extrapolation, and unsurprisingly they differ significantly
• This is of most concern for microwave radiometry
• For EarthCARE, it is of concern in convective clouds, but very unclear whether the observations can usefully tell us about supercooled water in these clouds anyway; perhaps as a contribution to PIA in addition to rain?
Stefan Kneifel et al (submitted)
Summary: the unknown Summary: the unknown unknownsunknowns
We need a best estimate and error for the following:•Snow backscatter cross-section at 94 GHz
– “Self-Similar Rayleigh Gans” equation: test with multi-wavelength radar?
•Structure and radar scattering of riming particles– How do we represent the continuous transition between low-density
aggregates and high density graupel, and validate observationally?•Melting-layer radar attenuation versus rain rate
– Key issue for interpreting PIA in rain: need observational constraints•Super-cooled water in convective clouds
– Radar absorption unknown, as well as vertical distribution•Lidar backscatter of complex ice and aerosol shapes
– Is it sufficient to simply retrieve lidar ratio from the HSRL and so bypass the difficulty in modelling the scattering of such particles?
A comprehensive algorithm inter-comparison project would also help to identify unknown unknowns!
Examples of Examples of snowsnow
35 GHz radar at Chilbolton
1 m/s: no riming or very weak
2-3 m/s: riming?• PDF of 15-min-averaged
Doppler in snow and ice (usually above a melting layer)
Simulated observations – Simulated observations – rimingriming
Power spectrum of snow Power spectrum of snow structurestructure
New formula for backscatterNew formula for backscatter• Rayleigh-Gans formula:
• Fourier-like decomposition of A(z):
• Assume amplitudes decrease at smaller scales as a power-law
• Formula for backscatter:– Wavenumber k– Volume V– Radius zmax
– Power-law parameters &
Prior information about size Prior information about size distribution distribution • Radar+lidar enables us to retrieve two variables: extinction and N0*
(a generalized intercept parameter of the size distribution)• When lidar completely attenuated, N0* blends back to temperature-
dependent a-priori and behaviour then similar to radar-only retrieval– Aircraft obs show
decrease of N0* towards warmer temperatures T
– (Acually retrieve N0*/0.6 because varies with T independent of IWC)
– Trend could be because of aggregation, or reduced ice nuclei at warmer temperatures
– But what happens in snow where aggregation could be much more rapid?
Delanoe and Hogan (2008)