cloud and precipitation best estimate… …and things i don’t know that i want to know

24
Cloud and precipitation Cloud and precipitation best estimate… best estimate… …and things I don’t know that …and things I don’t know that I want to know I want to know Robin Hogan University of Reading

Upload: masao

Post on 19-Mar-2016

52 views

Category:

Documents


0 download

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 Presentation

TRANSCRIPT

Page 1: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 2: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 3: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

CloudSat

Calipso

Unified retrieval

Ice extinction coefficient

Rain rate

Aerosol extinction coefficient

Page 4: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 5: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)

Page 6: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

Simulated observations – no Simulated observations – no rimingriming

Page 7: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

Simulated retrievals – no Simulated retrievals – no rimingriming

Page 8: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

Simulated retrievals – rimingSimulated retrievals – rimingBut retrieval is completely dependent on how well we

can model scattering by rimed snowflakes!

Page 9: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 10: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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?

Page 11: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 12: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 13: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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

Page 14: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)

Page 15: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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.)

Page 16: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)

Page 17: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)

Page 18: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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!

Page 19: Cloud and precipitation  best estimate… …and things I don’t know that I want to know
Page 20: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)

Page 21: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

Simulated observations – Simulated observations – rimingriming

Page 22: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

Power spectrum of snow Power spectrum of snow structurestructure

Page 23: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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 &

Page 24: Cloud and precipitation  best estimate… …and things I don’t know that I want to know

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)