1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met.
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Presentation on theme: "1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met."— Presentation transcript:
1. The problem of mixed-phase clouds All models except DWD underestimate mid-level cloud –Some have separate “radiatively inactive” snow (ECMWF, DWD) –Met Office: combined ice/snow but still underestimates cloud fraction –Met Office: mid-level cloud occurs with about the right frequency but with too little cloud fraction or liquid water content Illingworth, Hogan, O’Connor et al. (BAMS 2007) Robin Hogan Observations Mesoscale model www.cloud-net.org
CRM/radar comparisons Radar/lidar observations of glaciating altocumulus Marsham, Dobbie and Hogan (QJRMS 2006) Supercooled water CRM simulates many aspects well but underestimates LWP Falling ice
Mixed-phase clouds cont. Why can CRM simulate altocumulus but not large-scale models? –Insufficient vertical resolution for supercooled liquid layers? –Radiation calls too infrequent so longwave cooling from cloud-top can’t properly interact with dynamics? –Poor representation of microphysics, e.g. ice/liquid ratio depends on temperature alone (not the Met Office model) –No depletion of ice nuclei by glaciation which would allow the subsequent persistence of supercooled water? –Lack of necessary turbulent mixing outside the boundary layer? Ongoing/future work to address this issue –PhD project starting in October –APPRAISE-CLOUDS project –Use spaceborne lidar to estimate global properties of mixed-phase clouds (LITE lidar in 1994 suggested supercooled liquid more common in SH storm track than NH) –More CRM comparisons with radar?
2. Convective cloud properties One-hour scanning-radar animation Red towards radarBlue away from radar
Vertical wind, mass & momentum flux Down-gradient momentum flux Counter-gradient momentum flux Hogan, Halladay and Illingworth (QJRMS 2008, submitted)
3. A-train retrievals Radar reflectivity factor from CloudSat Attenuated lidar backscatter from CALIPSO MODIS/IIR infrared radiances –New variational retrieval combining radar, lidar and IR radiometer –First guess of cloud profile is iteratively refined based on its ability to forward model observations Time [s] Julien Delanoe & Robin Hogan
A priori constraints enable retrieval to vary smoothly in the vertical between clouds detected by just radar or lidar and both Delanoe and Hogan (JGR 2008) Variational radar-lidar retrievals –Visible extinction coefficient –Ice water content –Effective radius
Next steps for spaceborne retrievals Use A-train to evaluate Met Office forecast and climate models Evaluate high-resolution Met Office model in the tropics (CASCADE) Occurrence and properties of liquid clouds, including supercooled The forward models in the variational scheme can also be used as “model-to-observation” simulators for evaluating GCMs: can represent the effects of multiple scattering on radar and lidar Extend algorithm: retrieve the continuum from thin cloud to heavy precipitation, incorporating microwave radiances etc