Comparisons of GFDL Time slice and CRCM cloud simulations with GOES Satellite Observations
Microwave observations in the presence of cloud and ...Cloud screening Use clear-sky...
Transcript of Microwave observations in the presence of cloud and ...Cloud screening Use clear-sky...
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Slide 1
Microwave observations in the presence of cloud and precipitation
Alan Geer
Thanks to: Bill Bell, Peter Bauer, Fabrizio Baordo, Niels Bormann
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 1
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Slide 2
Overview
Clear-sky microwave (yesterday)
- The microwave spectrum
- Microwave instruments
- Imager channels and the surface
- Temperature sounding channels
- AMSU-A channel 5 at ECMWF
All-sky microwave (today)
- Interaction of particles with microwave radiation
- Solving the scattering radiative transfer equation
- Cloud screening for clear-sky assimilation
- All-sky assimilation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 2
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Slide 3
Size parameter
Atmospheric particles and microwave radiation
30 GHz frequency ↔ 10mm wavelength (λ)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 3
Particle type Size range, r Size parameter, x Scatteringsolution
Cloud droplets 5 – 50 μm 0.003 – 0.03 Rayleigh
Drizzle ~100 μm 0.06 Rayleigh
Rain drop 0.1 – 3 mm 0.06 – 1.8 Mie
Ice crystals 10 – 100 μm 0.006 – 0.06 Rayleigh, DDA
Snow 1 – 10 mm 0.6 – 6 Mie, DDA
Hailstone ~10 mm 6 Mie, DDA
rx
2
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Slide 4
Spherical particles interacting with radiation
Negligible scattering (x < 0.002):
- at low microwave frequencies, liquid water cloud acts like a gaseous absorber
Rayleigh scattering (0.002 < x < 0.2):
- an approximate solution for spherical particles much smaller than the wavelength
Mie scattering (0.2 < x < 2000):
- a complete solution for the interaction of a plane wave with a dielectric sphere
- valid for all x, but depends on a series expansion in Legendre polynomials - can be slow to compute
Geometric optics (x > 2000):
- e.g. the rainbow solution for visible light
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 4
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Slide 5
Discrete dipole approximation (DDA):
Other solution methods exist, e.g. T-matrix
Non-spherical particles interacting with radiation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 5
1. Create a 3D model of a snow or ice particle
2. Discretise into many small polarisable points (dipoles). Grid spacing << wavelength
3. Solve computationally – can be slow
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Slide 6
Scattering properties – single particles
Scattering cross-section: [m2]
- the amount of scattering as an equivalent “area”
Absorption cross-section: [m2]
- particles can absorb as well as scatter radiation
Scattering phase function:
- the amount of scattering in a particular direction (expressed in polar coordinates)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 6
s
a
),( P
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Slide 7
Sum or average over:
- Different orientations (or, in some ice clouds, a preferred orientation)
- Size distribution
- Particle habits: snow and ice shapes, graupel, cloud drops, rain etc.
Extinction coefficient [1/km] :
Single scatter albedo:
- Ratio of scattering to extinction
Asymmetry parameter – the average scattering direction
- g = 1 → completely forward scattering (~ not scattered at all)
- g = 0 → as much forward as back scattering (not necessarily isotropic)
- g = -1 → complete back-scattering (physically unlikely)
Scattering properties - bulk
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 7
sae
sa
ss
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Slide 8
H() - radiative transfer model
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 8
Sensor Given p,T, q, cloud and precipitation on each atmospheric level, we can compute:
• τ - Layer optical depth
(absorption and scattering)
• ωs - Single scattering albedo
• - Scattering phase function
(usually represented only by g, the asymmetry parameter)
= direction vector in solid angle
Now just solve the equation of radiativetransfer (discretized on the vertical grid):
4
''ˆ)'ˆ,ˆ(4
1ˆˆ
dIPBId
dI ss
)'ˆ,ˆ( P
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Slide 9
This can be complex to solve:
- , the radiance in one direction, depends on radiance from all other directions:
- and all levels depend on each other
But in non-scattering atmospheres (no cloud and precipitation) it’s easier:
- ωs = 0 , so
- we can do each layer sequentially
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 9
Radiative transfer solversScattering phase function
4
''ˆ)'ˆ,ˆ(4
1ˆˆ
dIPBId
dI ss
BId
dI
ˆˆ
I
'I
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Slide 10
Scattering solvers
Reverse Monte-Carlo
- Great for understanding and visualisation
- An easy way to do 3D radiative transfer
- Much too slow for operational use: many thousands of “photons” are required for convergence to a useful level of accuracy
Accurate but too slow for use in assimilation:
- DISORT (Discrete ordinates) – a generalisation of the two-stream approach
- Adding / doubling, SOI (Successive Orders of Interaction)
Two-stream and delta-Eddington approaches
- What we use operationally at ECMWF (in RTTOV-SCATT)
- Though quite crude methods, the accuracy is usually more than sufficient: the main R/T error sources in NWP are rarely in the solver
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 10
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Slide 11
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 11
Single scatteralbedo
Rain and snow flux [g m2 s-1]Cloud mixing ratio [0.1 g kg-1]
Extinction coefficient βe
[km-1]
Asymmetry
Snow
Rain
Water cloud
Altitude [km]
Cloud and precipitation at 91 GHzStrong scattering in deep convection
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Slide 12
Cloud and precipitation at 91 GHzStrong scattering in deep convection
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 12
Rain
Sn
ow
Clo
ud
SSA [0-1]
No. of emissions
To sensor
[km]
[km
]
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Slide 13
Cloud screening
Use clear-sky radiative transfer but remove situations where the observations are cloudy or precipitating
Cloud screening methods:
- FG departures in a window channel
- Simple LWP or cloud retrieval
Sounders: Grody et al. (2001, JGR)
Imagers: Karstens et al. (1994, Met. Atmos. Phys.)
Imagers: 37 GHz polarisation difference, Petty and Katsaros
(1990, J. App. Met.)
- Scattering index (SI)
Over land, or in sounding channels affected by ice/snow
scattering, not cloud absorption: Grody (1991, JGR)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 13
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Slide 14
AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess (bias corrected), ocean surfaces
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 14
K
Cloud shows up as a warm emitter over a radiatively cold ocean surface
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Slide 15
AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess, ocean surfaces
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 15
FG departure [K]
3K limit
Warm tail = cloud
20% of observations removed
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Slide 16
AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess (bias corrected), ocean surfaces
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 16
K
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Slide 17
AMSU-A channel 4 departures for cloud screening52.8 GHz observation minus clear-sky first guess (bias corrected), land surfaces < 500m; sea-ice
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 17
K
Ice and snow hydrometeors show up as cold scatterers over a radiatively warm land or ice surface
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Slide 18
AMSU-A channel 4 departures for cloud screening52.8 GHz observation minus clear-sky first guess, land surfaces < 500m
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 18
FG departure [K]
0.7K limit
Poor surface emissivity
Cloud or poor surface emissivity
35% of observations removed
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Slide 19
An approximate radiative transfer equation for window channels (no scattering)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 19
ASA TTTT )1()1(1
,ST
AT)1(
Surface temperature, emissivity
AT)1(
Upwelling atmospheric contribution
Downwelling atmospheric contribution
ST
Observed brightness temperature
AT)1(1 Reflected downwelling radiation
Surface emission
Atmospheric transmittance
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Slide 20
LWP and cloud retrievals
Simplify further by assuming surface and effective atmospheric temperature are identical
An even more approximate radiative transfer equation for microwave imager channels:
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 20
)1(1 20 TT
Observed TB at frequency ν
Surface emissivityAtmospheric
transmittance
Effective atmospheric and surface temperature
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Slide 21
Polarisation difference
Observe v and h polarisations separately:
Compute the difference in brightness temperature
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 21
)1(1 20 VV TT
)1(1 20 HH TT
HVHV TTT 20
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Slide 22
Polarisation difference at 37 GHz (SSMIS)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 22
37v
[K]
37h
[K]
37v -37h
[K]
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Slide 23
Approximate cloud and precipitation retrievals
Polarisation difference, e.g. 37v – 37h
- 40K threshold typically used to indicate precipitation
- Petty and Katsaros (1990, J. App. Met.)
Simple LWP retrieval:
-
- Imagers: Karstens et al. (1994, Met. Atmos. Phys.)
- Sounders (additional zenith angle dependence): Grody et al. (2001, JGR)
- Homework for the keen – derive this by assuming:
(where k=mass absorption coefficient [m-1 (kg m-3) -1]; θ=zenith angle, LWP, TCWV are
column integrated amounts of cloud and water vapour [kg m-2])
Scattering index (SI) – e.g. Grody (1991, JGR)
- Simple SI used at ECMWF for SSMIS over land: 90 GHz – 150 GHz
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 23
v3703v22021 lnln TTcTTccLWP
cosTCWVLWPexp VapourCloud kk
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Slide 24
Reasons to assimilate cloud and precipitation-related observations in global NWP
Extending satellite coverage: to gain more information on temperature and water vapour in cloudy regions
Improving cloud and precipitation analyses and short-range forecasts: assimilating cloud and precipitation directly
Inferring wind information through 4D-Var
Validating and constraining the model: confronting the model with observations
24ECMWF/EUMETSAT satellite course 2015: Microwave 2
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Slide 25
Model Observations
Clear-sky assimilation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 25
×
×
?
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Slide 26
Model Observations
All-sky assimilation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 26
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Slide 27
All-sky assimilation components in 4D-Var
Observation minus first-guess* departures in clear, cloudy and precipitating conditions
Observation operator including cloud and precipitation (RTTOV) - TL/Adjoint
Moist physics - TL/Adjoint
Forecast model - TL/Adjoint
Control variables (winds and mass at start of assimilation window) optimised by 4D-Var
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 27
*FG, T+12, background…
Rest of the global observing system
Background constraint
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Slide 28
28 ECMWF/EUMETSAT satellite
All-sky 4D-Var SSM/I - example
First guess departures (observation minus model)
All-sky observations (37GHz v-pol )
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Slide 29
Is the zero-gradient issue a problem? No
Cloud
Total moisture
Microwave radiance
Total moisture
Cloud
Precipitation
Clear-sky
Model
Observation
Model
Observation
29ECMWF/EUMETSAT satellite course 2015: Microwave 2
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Slide 30
Mislocation between observations and model is a problem
30ECMWF/EUMETSAT satellite course 2015: Microwave 2
Observed TB [K] First guess TB [K]
FG Departure [K](observation minus FG)
220km
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Slide 31
Clear-clear
Cloud-cloud
Obs Cloud- FG clearObs Clear- FG cloud
Watch out for sampling error
Observations cloudy (47%):biased sampling
Observations or FGCloudy (59%):correct sampling
All-sky (100%)
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Slide 32
Asymmetric and symmetric sampling
Cloud amountNormalised 37GHz polarisation difference
as a function of mean of observed and forecast cloud
as a function of forecast cloud
as a function of observed cloud
ECMWF/EUMETSAT satellite course 2015: Microwave 2
Slide 32
Mean channel 19v departures
[K]
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Slide 33
Observation errors as a function of cloud amount
33ECMWF/EUMETSAT satellite course 2015: Microwave 2
Mean of observed and FG cloud amountderived from 37GHz radiances
Standard deviation of
AMSR-E channel 24h departures
[K]
‘Symmetric’ observation error model
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Slide 34
Gaussian
Departures4.3K
Non-Gaussianity
34ECMWF/EUMETSAT satellite course 2015: Microwave 2
Departuressymmetric error
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Slide 35
35ECMWF/EUMETSAT satellite course 2015: Microwave 2
All-sky 4D-Var departures: Quality Control
SSM/I 37v departure Normalised by symmetric error
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Slide 36
Assimilating observations in all-sky conditions
Essentials
- (4D-Var) Tangent linear and adjoint moist physics model
- Scattering radiative transfer code
Attention to “detail”: scattering parameters; cloud overlap
- Only assimilate in channels (and in areas) where the forecast model and the observation operator are accurate and are not affected by systematic errors
- Observation error model
smaller errors in clear skies and higher errors in cloudy and precipitating situations
- Symmetric treatment of biases, observation errors, quality control
Not essential, but still useful
- (4D-Var) Cloud and precipitation variables in the control vector
36ECMWF/EUMETSAT satellite course 2015: Microwave 2
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Slide 37
Applications of all-sky assimilation
Imager channels (e.g. 19, 24, 37, 89 GHz)
- TMI, SSMIS and (AMSRE): all-sky assimilation operational at ECMWF since 2009 over ocean surfaces
Water vapour sounding channels (e.g. 183 GHz)
- Over ocean, land and sea-ice
- SSMIS and MHS 183 GHz all-sky assimilation
- ATMS, MWHS-2 183 GHz all-sky in development
Temperature sounding channels (e.g. 50-60 GHz)
- Clear-sky approach remains the best for now
- AMSU-A channel 4 (52.8 GHz) is like an imager channel, with mainly a cloud sensitivity. Duplicates microwave imager information content.
- AMSU-A channel 5 (53.6 GHz) gained little benefit from all-sky assimilation in initial testing at ECMWF:
Not much additional coverage
Cross-talk between cloud and temperature signals
Scattering radiative transfer is relatively computationally expensive
37ECMWF/EUMETSAT satellite course 2015: Microwave 2
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Slide 38
Impact of all-sky SSMIS and TMI on wind 4 months of forecast verification against own analysis
38ECMWF/EUMETSAT satellite course 2015: Microwave 2
Increased size of wind increments –short range “noise”
Normalised change in RMS forecast error in windCross-hatching indicates statistical significance
RMS error increased = bad (but not always!)
RMS error decreased = good
Up to 2-3% improvement in wind scores in the midatitudes
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Slide 39
ECMWF/EUMETSAT satellite course 2015: Microwave 2
Slide 39
Clear-sky MHS
MHSbad
MHSgood
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Slide 40
ECMWF/EUMETSAT satellite course 2015: Microwave 2
Slide 40
All-sky MHS
MHSbad
MHSgood
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Slide 41
Cloud and precipitation in the microwave
Interaction of particles with microwave radiation
- Compute optical properties using DDA or Mie theory
Cloud-clearing and simple retrievals
- Window channel FG departures, polarisation difference, scattering index or LWP retrieval
Scattering radiative transfer simulations
- RTTOV-SCATT
All-sky assimilation
- Applicable to all imager and moisture channels: cloud, precipitation and water-vapour information
- Benefits to dynamical forecasts through 4D-Var tracing effect
- Temperature channels are best assimilated using clear-sky approach for the moment
41ECMWF/EUMETSAT satellite course 2015: Microwave 2