Microwave observations in the presence of cloud and precipitation

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1 Microwave observations in the presence of cloud and precipitation Alan Geer Thanks to: Bill Bell, Peter Bauer, Fabrizio Baordo, Niels Bormann Slide 1 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 1

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 Slide 2 ECMWF/EUMETSAT satellite course 2015: Microwave 2 2

3 Atmospheric particles and microwave radiation 30 GHz frequency 10mm wavelength (λ) Size parameter x 2r Particle type Size range, r Size parameter, x Scattering solution Cloud droplets 5 50 μm Rayleigh Drizzle ~100 μm 0.06 Rayleigh Rain drop mm Mie Ice crystals μm Rayleigh, DDA Snow 1 10 mm Mie, DDA Hailstone ~10 mm 6 Slide 3 Mie, DDA ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 3

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 Slide 4 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 4

5 Non-spherical particles interacting with radiation Discrete dipole approximation (DDA): 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 5 Other solution methods exist, e.g. T-matrix ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 5

6 Scattering properties single particles Scattering cross-section: [m 2 ] - the amount of scattering as an equivalent area Absorption cross-section: [m 2 ] - particles can absorb as well as scatter radiation Scattering phase function: Slide 6 - the amount of scattering in a particular direction (expressed in polar coordinates) s a ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 6 P(, )

7 Scattering properties - bulk 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 e a s s s a s 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) Slide 7 - g = -1 complete back-scattering (physically unlikely) ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 7

8 H() - radiative transfer model Sensor Given p,t, q, cloud and precipitation on each atmospheric level, we can compute: τ - Layer optical depth (absorption and scattering) ω s - Single scattering albedo P( ˆ, ˆ ') - Scattering phase function (usually represented only by g, the asymmetry parameter) ˆ = direction vector in solid angle ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 8 Now just solve the equation of radiative transfer (discretized on the vertical grid): di d Slide 8 ˆ ˆ s I 1 ( ˆ, ˆ ') ˆ s B P I ' 4 4 d'

9 Radiative transfer solvers di d This can be complex to solve: - I ˆ, the radiance in one direction, depends on radiance from all other directions: I ˆ' - and all levels depend on each other Scattering phase function ˆ ˆ s I 1 B P( ˆ, ˆ ') I ˆ ' s 4 4 d' But in non-scattering atmospheres (no cloud and precipitation) it s easier: B di ˆ - ω s = 0, so I ˆ d - we can do each layer sequentially Slide 9 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 9

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 Slide is 10 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

11 Cloud and precipitation at 91 GHz Strong scattering in deep convection Altitude [km] Single scatter albedo Snow Asymmetry Water cloud Rain Rain and snow flux [g m 2 s -1 ] Cloud mixing ratio [0.1 g kg -1 ] Slide 11 Extinction coefficient β e [km -1 ] ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 11

12 Cloud and precipitation at 91 GHz Strong scattering in deep convection To sensor SSA [0-1] Cloud [km] Rain Snow [km] Slide 12 No. of emissions ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 12

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 Slide 13 scattering, not cloud absorption: Grody (1991, JGR) ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 13

14 AMSU-A channel 3 departures for cloud screening 50.3 GHz observation minus clear-sky first guess (bias corrected), ocean surfaces K Slide 14 Cloud shows up as a warm emitter over a radiatively cold ocean surface ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 14

15 AMSU-A channel 3 departures for cloud screening 50.3 GHz observation minus clear-sky first guess, ocean surfaces 3K limit 20% of observations removed Warm tail = cloud Slide 15 FG departure [K] ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 15

16 AMSU-A channel 3 departures for cloud screening 50.3 GHz observation minus clear-sky first guess (bias corrected), ocean surfaces K Slide 16 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 16

17 AMSU-A channel 4 departures for cloud screening 52.8 GHz observation minus clear-sky first guess (bias corrected), land surfaces < 500m; sea-ice K Slide 17 Ice and snow hydrometeors show up as cold scatterers over a radiatively warm land or ice surface ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 17

18 AMSU-A channel 4 departures for cloud screening 52.8 GHz observation minus clear-sky first guess, land surfaces < 500m 0.7K limit 35% of observations removed Cloud or poor surface emissivity Poor surface emissivity Slide 18 FG departure [K] ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 18

19 An approximate radiative transfer equation for window channels (no scattering) Reflected downwelling radiation 1 (1 )TA Surface emission T S Upwelling atmospheric contribution ( 1 )T A Downwelling atmospheric contribution ( 1 )T A Atmospheric transmittance Observed brightness temperature T T S, Surface temperature, emissivity Slide 19 1 (1 ) TA TS (1 ) TA ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 19

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: T 2 T0 1 (1 ) Observed TB at frequency ν Atmospheric transmittance Surface emissivity Effective atmospheric and surface temperature Slide 20 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 20

21 Polarisation difference Observe v and h polarisations separately: T T 2 1 (1 V T 0 V 2 1 (1 H T 0 H ) ) Compute the difference in brightness temperature T V T H T 0 2 V H Slide 21 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 21

22 Polarisation difference at 37 GHz (SSMIS) 37v [K] 37h [K] 37v -37h Slide 22 [K] ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 22

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: - LWP c T T c T T 1 c2ln 0 22v 3 ln - 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 Slide (1991, 23 JGR) - Simple SI used at ECMWF for SSMIS over land: 90 GHz 150 GHz 0 37v exp k LWP k TCWV cos Cloud Vapour ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 23

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 shortrange forecasts: assimilating cloud and precipitation directly Inferring wind information through 4D-Var Validating and constraining the model: confronting the Slide 24 model with observations ECMWF/EUMETSAT satellite course 2015: Microwave 2 24

25 Clear-sky assimilation Model Observations Slide 25? ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 25

26 All-sky assimilation Model Observations Slide 26 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 26

27 All-sky assimilation components in 4D-Var Observation minus first-guess* departures in clear, cloudy and precipitating conditions *FG, T+12, background Observation operator including cloud and precipitation (RTTOV) - TL/Adjoint Rest of the global observing system Moist physics - TL/Adjoint Forecast model - TL/Adjoint Background constraint Control variables (winds and mass Slide 27 at start of assimilation window) optimised by 4D-Var ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 27

28 All-sky observations (37GHz v-pol ) All-sky 4D-Var SSM/I - example First guess departures (observation minus model) Slide ECMWF/EU METSAT

29 Is the zero-gradient issue a problem? No Cloud Observation Model Total moisture Microwave radiance Model Observation Slide 29 Precipitation Cloud Clear-sky Total moisture ECMWF/EUMETSAT satellite course 2015: Microwave 2 29

30 Mislocation between observations and model is a problem Observed TB [K] First guess TB [K] 220km FG Departure [K] (observation minus FG) Slide 30 ECMWF/EUMETSAT satellite course 2015: Microwave 2 30

31 Watch out for sampling error All-sky Observations (100%) or FG Cloudy cloudy (47%): (59%): correct biased sampling Clear-clear Cloud-cloud Obs Clear- FG cloud Obs Cloud- FG clear Slide 31 ECMWF/EUMETSAT satellite course 2015: Microwave 2 31

32 Asymmetric and symmetric sampling as a function of observed cloud Mean channel 19v departures [K] as a function of mean of observed and forecast cloud Slide 32 Cloud amount Normalised 37GHz polarisation difference as a function of forecast cloud ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide 32

33 Observation errors as a function of cloud amount Standard deviation of AMSR-E channel 24h departures [K] Symmetric observation error model Slide 33 Mean of observed and FG cloud amount derived from 37GHz radiances ECMWF/EUMETSAT satellite course 2015: Microwave 2 33

34 Non-Gaussianity Departures symmetric error Departures 4.3K Gaussian Slide 34 ECMWF/EUMETSAT satellite course 2015: Microwave 2 34

35 All-sky 4D-Var departures: Quality Control SSM/I 37v departure Normalised by symmetric error Slide 35 ECMWF/EUMETSAT satellite course 2015: Microwave 2 35

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 Slide 36 ECMWF/EUMETSAT satellite course 2015: Microwave 2 36

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 GHz all-sky in development Temperature sounding channels (e.g 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 Slide 37 Cross-talk between cloud and temperature signals Scattering radiative transfer is relatively computationally expensive ECMWF/EUMETSAT satellite course 2015: Microwave 2 37

38 Impact of all-sky SSMIS and TMI on wind 4 months of forecast verification against own analysis Increased size of wind increments short range noise RMS error increased = bad (but not always!) Up to 2-3% improvement in wind scores in the midatitudes Normalised change in RMS forecast error in wind Cross-hatching indicates statistical significance Slide 38 RMS error decreased = good ECMWF/EUMETSAT satellite course 2015: Microwave 2 38

39 Clear-sky MHS MHS bad Slide 39 MHS good Slide 39 ECMWF/EUMETSAT satellite course 2015: Microwave 2

40 All-sky MHS MHS bad Slide 40 MHS good Slide 40 ECMWF/EUMETSAT satellite course 2015: Microwave 2

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 Slide 41 ECMWF/EUMETSAT satellite course 2015: Microwave 2 41

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