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Microwave observations in the presence of cloud and precipitation

Microwave observations in the presence of cloud and precipitation. Alan Geer Thanks to: Bill Bell, Peter Bauer, F abrizio Baordo , Niels Bormann. O verview. Clear-sky microwave (yesterday) T he microwave spectrum Microwave instruments Imager channels and the surface

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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, FabrizioBaordo, Niels Bormann ECMWF/EUMETSAT satellite course 2013: Microwave 2

  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 • RTTOV practical: AMSU-A weighting functions and TBs • 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 2013: Microwave 2

  3. Atmospheric particles and microwave radiation Size parameter 30 GHz frequency ↔ 10mm wavelength ECMWF/EUMETSAT satellite course 2013: Microwave 2

  4. Spherical particles interacting with radiation • Rayleigh scattering (x < 0.2): • an approximate solution for spherical particles much smaller than the wavelength • particle can be treated as an induced dipole in the wave’s electric field • makes for very simple radiative transfer when dealing with liquid water cloud in the microwave • 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 2013: Microwave 2

  5. Non-spherical particles interacting with radiation • Discrete dipole approximation (DDA): • Other solution methods exist, e.g. T-matrix 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 ECMWF/EUMETSAT satellite course 2013: Microwave 2

  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 2013: Microwave 2

  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 • 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) ECMWF/EUMETSAT satellite course 2013: Microwave 2

  8. H() - radiative transfer model Sensor • Given p,T, q, cloud and precipitation on each atmospheric level, we can compute: • τ - Layeroptical 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 radiative transfer (discretized on the vertical grid): ECMWF/EUMETSAT satellite course 2013: Microwave 2

  9. Radiative transfer solvers Scattering phase function • 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 2013: Microwave 2

  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 • 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 • 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) ECMWF/EUMETSAT satellite course 2013: Microwave 2

  11. Cloud and precipitation at 91 GHzStrong scattering in deep convection Altitude [km] Single scatter albedo Snow Asymmetry Water cloud Rain Rain and snow flux [g m2 s-1] Cloud mixing ratio [0.1 g kg-1] Extinction coefficient βe [km-1] ECMWF/EUMETSAT satellite course 2013: Microwave 2

  12. Cloud and precipitation at 91 GHzStrong scattering in deep convection SSA[0-1] To sensor [km] Snow Cloud Rain [km] No. of emissions ECMWF/EUMETSAT satellite course 2013: Microwave 2

  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 2013: Microwave 2

  14. AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess, ocean surfaces K Cloud shows up as a warm emitter over a radiatively cold ocean surface ECMWF/EUMETSAT satellite course 2013: Microwave 2

  15. AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess, ocean surfaces 3Klimit 20% of observations removed Warm tail = cloud FG departure [K] ECMWF/EUMETSAT satellite course 2013: Microwave 2

  16. AMSU-A channel 3 departures for cloud screening50.3 GHz observation minus clear-sky first guess, ocean surfaces K ECMWF/EUMETSAT satellite course 2013: Microwave 2

  17. AMSU-A channel 4 departures for cloud screening52.8 GHz observation minus clear-sky first guess, land surfaces < 500m K Ice and snow hydrometeors show up as cold scatterers over a radiatively warm land or ice surface ECMWF/EUMETSAT satellite course 2013: Microwave 2

  18. AMSU-A channel 4 departures for cloud screening52.8 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 FG departure [K] ECMWF/EUMETSAT satellite course 2013: Microwave 2

  19. An approximate radiative transfer equation for window channels (no scattering) Upwelling atmospheric contribution Surface emission Reflected downwelling radiation Downwelling atmospheric contribution Atmospheric transmittance Surface temperature, emissivity Observed brightness temperature ECMWF/EUMETSAT satellite course 2013: Microwave 1

  20. LWP and cloud retrievals • Simplify further by assuming surface and effective up- and down-welling atmospheric temperature are all identical • An even more approximate radiative transfer equation for microwave imager channels: Surface emissivity Observed TB at frequency ν Atmospheric transmittance Effective atmospheric and surface temperature ECMWF/EUMETSAT satellite course 2013: Microwave 2

  21. Polarisation difference • Observe v and h polarisations separately: • Compute the difference in brightness temperature ECMWF/EUMETSAT satellite course 2013: Microwave 2

  22. Polarisation difference at 37 GHz (SSMIS) 37v [K] 37h [K] 37v -37h [K] ECMWF/EUMETSAT satellite course 2013: Microwave 2

  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 2013: Microwave 2

  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, and inferring wind information through 4D-Var • Validating and constraining the model: confronting the model with observations ECMWF/EUMETSAT satellite course 2013: Microwave 2

  25. The rest of the observing system: radiosondes, aircraft, clear-sky radiances, AMVs, GPSRO … All-sky 4D-Varassimilation Other observation operators, e.g. clear-sky radiative transfer Radiances (all-sky) Assimilated variable Scattering radiative transfer model RTTOV-SCATT Model variables at observation time Cloud and precipitation , wind, mass, humidity 4D-Var including atmospheric model Control variables at beginning of window Wind, mass, humidity ECMWF/EUMETSAT satellite course 2013: Microwave 2

  26. All-sky observations (37GHz v-pol ) All-sky 4D-Var SSM/I - example First guess departures (observation minus model) ECMWF/EUMETSAT satellite course 2013: Microwave 2

  27. The zero-gradient problem? No Cloud Observation Model Total moisture Microwave radiance Precipitation Observation Cloud Model Clear-sky Total moisture ECMWF/EUMETSAT satellite course 2013: Microwave 2

  28. The nonlinearity problem? NoSingle microwave imager observation in 4D-Var in tropical convective rain Nonlinearity and resolution error ECMWF/EUMETSAT satellite course 2013: Microwave 2

  29. Yes: Mislocationbetween observations and model Observed TB [K] First guess TB [K] 220km FG Departure [K] (observation minus FG) ECMWF/EUMETSAT satellite course 2013: Microwave 2

  30. Yes: sampling error Observations or FG Cloudy (59%): correct sampling Observations cloudy (47%): biased sampling All-sky (100%) Clear-clear Cloud-cloud Obs Cloud- FG clear Obs Clear- FG cloud ECMWF/EUMETSAT satellite course 2013: Microwave 2

  31. 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 as a function of forecast cloud Cloud amount Normalised 37GHz polarisation difference ECMWF/EUMETSAT satellite course 2013: Microwave 2

  32. 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 ECMWF/EUMETSAT satellite course 2013: Microwave 2

  33. Observation errors as a function of cloud amount ‘Symmetric’ observation error model Standard deviation of AMSR-E channel 24h departures [K] • Mean of observed and FG cloud amount • derived from 37GHz radiances ECMWF/EUMETSAT satellite course 2013: Microwave 2

  34. Non-Gaussianity Departures symmetric error Departures 4.3K Gaussian ECMWF/EUMETSAT satellite course 2013: Microwave 2

  35. All-sky 4D-Var departures: Quality Control Normalised by symmetric error SSM/I 37v departure ECMWF/EUMETSAT satellite course 2013: Microwave 2

  36. 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) • SSMIS 183 GHz all-sky assimilation to go operational over oceans, later this year (cycle 39r1). Land surfaces to follow (cycle 39r2?). • MHS may soon be transferred from clear-sky to the all-sky approach • 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 ECMWF/EUMETSAT satellite course 2013: Microwave 2

  37. 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!) Normalised change in RMS forecast error in wind Cross-hatching indicates statistical significance Up to 2-3% improvement in wind scores in the midatitudes RMS error decreased = good ECMWF/EUMETSAT satellite course 2013: Microwave 2

  38. AMSU-A channel 4: all-sky vs. clear-skyMean change in temperature at 925hPa Clear-sky AMSU-A channel 4 assimilation minus control Undetected cloud aliased into temperature All-sky AMSU-A channel 4 assimilation minus control ECMWF/EUMETSAT satellite course 2013: Microwave 2

  39. AMSU-A channel 4: all-sky vs. clear-skyMean change in temperature at 850hPa Clear-sky AMSU-A channel 4 assimilation minus control Clear-sky AMSU-A channel 4 All-sky AMSU-A channel 4 All-sky AMSU-A channel 4 assimilation minus control All-sky affects temperature ECMWF/EUMETSAT satellite course 2013: Microwave 2

  40. What about AMSU-A channel 5 in all-sky?RMS of FG departure - ignoring cloud (“clear sky”) - including cloud radiative transfer (“all sky”) Cloudy radiative transfer improves fits by modelling emission from frontal cloud Cloudy radiative transfer brings worse fits through “double penalty” – less predictable convective systems ECMWF/EUMETSAT satellite course 2013: Microwave 2

  41. Cloud and precipitation in the microwave • Interaction of particles with microwave radiation • Compute optical properties using Rayleigh, 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 ECMWF/EUMETSAT satellite course 2013: Microwave 2

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