microwave observations in the presence of cloud and ...cloud screening use clear-sky...
TRANSCRIPT
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
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
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
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
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
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
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
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
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
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
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
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
]
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
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
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
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
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
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
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
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
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
Slide 22
Polarisation difference at 37 GHz (SSMIS)
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 22
37v
[K]
37h
[K]
37v -37h
[K]
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
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
Slide 25
Model Observations
Clear-sky assimilation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 25
×
×
?
Slide 26
Model Observations
All-sky assimilation
ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 26
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
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 )
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
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
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%)
31ECMWF/EUMETSAT satellite course 2015: Microwave 2
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]
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
Slide 34
Gaussian
Departures4.3K
Non-Gaussianity
34ECMWF/EUMETSAT satellite course 2015: Microwave 2
Departuressymmetric error
Slide 35
35ECMWF/EUMETSAT satellite course 2015: Microwave 2
All-sky 4D-Var departures: Quality Control
SSM/I 37v departure Normalised by symmetric error
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
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
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
Slide 39
ECMWF/EUMETSAT satellite course 2015: Microwave 2
Slide 39
Clear-sky MHS
MHSbad
MHSgood
Slide 40
ECMWF/EUMETSAT satellite course 2015: Microwave 2
Slide 40
All-sky MHS
MHSbad
MHSgood
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