Transcript Document
Joint ECMWF-University meeting on interpreting data from spaceborne radar and lidar: AGENDA 09:30 Introduction University of Reading activities • 09:35 Robin Hogan - Overview of CloudSat/CALIPSO/EarthCARE work at University • 09:50 Julien Delanoe - Ice cloud retrievals from CloudSat, CALIPSO & MODIS • 10:05 Lee Smith - Retrieval of liquid water content from CloudSat and CALIPSO 10:20-10:35 Coffee ECMWF Activities • 10:35 Marta Janiskova • 10:50 Olaf Stiller • 11:05 Richard Forbes • 11:20 Maike Ahlgrimm - Overview of CloudSat/CALIPSO activities at ECMWF Estimating representativity errors ECMWF model cloud verification Lidar derived cloud fraction for model comparison 11:35-12:30 Discussion • Retrievals, forward models and error characteristics • Verification of models • Possibilities for collaboration 12:30 Lunch in the canteen Recent CloudSat/CALIPSO/EarthCARErelated work at University of Reading • Forward models and model evaluation – Lidar forward modelling to evaluate the ECMWF model from IceSAT – Multiple scattering model for spaceborne radar and lidar (Hogan) • Retrievals and model evaluation – LITE lidar estimates of supercooled water occurrence – Radar retrievals of liquid clouds (Lee Smith, Anthony Illingworth) – Variational radar-lidar-radiometer retrieval of ice clouds (Delanoe) • ESA “CASPER” project (Clouds and Aerosol Synergy Products from EarthCARE Retrievals) – Defined the required cloud, aerosol and precipitation products – Developed variational ice cloud retrieval for EarthCARE that uses the cloud radar, the “High Spectral Resolution Lidar” (HSRL; the same technology as ADM) and the infrared channels of the multispectral imager Ongoing/future work • Forward models and model evaluation – Use the CloudSat simulator to evaluate the 90-km resolution HiGEM version of the Met Office climate model (Margaret Woodage) – Use the CloudSat simulator to evaluate 1-km large-domain simulations of tropical clouds in “CASCADE” (Thorwald Stein) • Retrievals and model evaluation – Ongoing comparisons with MO and ECMWF models (Smith & Delanoe) – Use of retrievals to evaluate the CASCADE model (Thorwald Stein) • CloudSat, CALIPSO and EarthCARE algorithm development – Develop a “unified” retrieval algorithm for clouds, precipitation and aerosols simultaneously using radar, lidar, infrared radiances and possibly microwave radiances (Nicola Pounder, Hogan, Delanoe) • Science questions – What is the radiative impact of errors in model clouds? Use retrievals, CERES observations and radiative transfer calcs. (Nicky Chalmers) – What is the distribution of supercooled water in the atmosphere and why is it so difficult to model? (Andrew Barrett) ECMWF clouds vs IceSAT using a lidar forward model Wilkinson, Hogan, Illingworth and Benedetti (Monthly Weather Review 2008) • Cloud observations from IceSAT 0.5-micron lidar (first data Feb 2004) • Global coverage but lidar attenuated by thick clouds: direct model comparison difficult Lidar apparent backscatter coefficient (m-1 sr-1) Optically thick liquid cloud obscures view of any clouds beneath Latitude • Solution: forward-model the measurements (including attenuation) using the ECMWF variables ECMWF raw cloud fraction Simulate lidar backscatter: – Create subcolumns with max-rand overlap – Forward-model lidar backscatter from ECMWF water content & particle size – Remove signals below lidar sensitivity ECMWF cloud fraction after processing IceSAT cloud fraction Global cloud fraction comparison ECMWF raw cloud fraction ECMWF processed cloud fraction • Results for October 2003 – Tropical convection peaks too high – Too much polar cloud – Elsewhere agreement is good • Results can be ambiguous – An apparent low cloud underestimate could be a real error, or could be due to high cloud above being too thick IceSAT cloud fraction Examples of multiple scattering • LITE lidar (l<r, footprint~1 km) Stratocumulus Apparent echo from below the surface Surface echo Intense thunderstorm CloudSat radar (l>r) Fast multiple scattering forward model Hogan and Battaglia (J. Atmos. Sci. 2008) • New method uses the timedependent two-stream approximation • Agrees with Monte Carlo but ~107 times faster (~3 ms) • Added to CloudSat simulator CloudSat-like example CALIPSO-like example Combining radar and lidar… Global-mean cloud fraction Cloudsat radar Radar misses a significant amount of ice CALIPSO lidar Preliminary target classification Radar and lidar Radar only Lidar only Insects Aerosol Rain Supercooled liquid cloud Warm liquid cloud Ice and supercooled liquid Ice Clear No ice/rain but possibly liquid Ground “Unified” retrieval framework New ray of data: define state vector Use classification to specify variables describing each species at each gate • Ice: extinction coefficient and N0* • Liquid: liquid water content and number concentration • Rain: rain rate and mean drop diameter • Aerosol: extinction coefficient and particle size Radar model Including surface return and multiple scattering Lidar model Including HSRL channels and multiple scattering Compare to observations Check for convergence Converged Proceed to next ray of data (Black) Ingredients already developed (Delanoe and Hogan JGR 2008) (Red) Ingredients remaining to be developed Radiance model Solar and IR channels Forward model Not converged Gauss-Newton iteration Derive a new state vector Mixed-phase clouds • Supercooled water layers have large radiative impact • Poorly modelled LITE lidar showed more supercooled water in SH than NH Two independent methods from MODIS show the same thing Hogan et al. (GRL 2004) What does CALIPSO show? What is the explanation? How can we model mixedphase clouds? Discussion points • Is the intention to assimilate cloud radar and lidar directly? – If so, are fast radar and lidar forward models of interest? • If retrievals are to be assimilated, what variables are needed? • Do you need error covariances, averaging kernels and information content? Straightforward to calculate, but: – Complicated to store (state vector is a different size for each profile) – Increases the data volume by an order of magnitude • What are best diagnostics for assessing model performance? – Means, PDFs, skill scores… • ECMWF model variables are required by retrievals – What is the error of model temperature, pressure and humidity? CloudSat simulator (Bodas et al) • Simulated radar reflectivity from sub-grid model • Simulated radar reflectivity averaged to model grid – How would this look with high-res model? • Observed CloudSat radar reflectivity Example of mid-Pacific convection MODIS 11 micron channel Height (km) Height (km) Retrieved extinction (m-1) CloudSat radar CALIPSO lidar Deep convection penetrated only by radar Cirrus detected only by lidar Mid-level liquid clouds Time since start of orbit (s) Supercooled water in models • A year of data from the Met Office and ECMWF – Easy to calculate occurrence of supercooled water with > 0.7 Prognostic ice and liquid+vapour variables Prognostic cloud water: ice/liquid diagnosed from temperature