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Potential of Simulator Assessments led by GRP

?. Potential of Simulator Assessments led by GRP. Hiro Masunaga Hydrospheric Atmospheric Research Center, Nagoya University. Satellite data simulator. Satellite data simulators Simulate satellite data, of course. T, q v , q r , … → satellite measured radiance

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Potential of Simulator Assessments led by GRP

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  1. ? Potential of Simulator Assessments led by GRP HiroMasunaga Hydrospheric Atmospheric Research Center, Nagoya University

  2. Satellite data simulator • Satellite data simulators • Simulate satellite data, of course. • T, qv, qr, … → satellite measured radiance • Radiative transfer code + user interfaces • Sub-grid cloud generator (COSP) • Antenna pattern convolution, PSD library (SDSU) • Growing need for multiple sensor package • TRMM/GPM: PR/DPR + TMI/GMI (+ VIRS) • A-Train: CPR + CALIOP + MODIS + AMSR-E + AMSU + AIRS … • Applications • Climate/Cloud-resolving model evaluation • Retrieval algorithm development for future missions • Radiance based data assimilation • … GRP 22nd meeting, Tokyo

  3. Multi-sensor simulator packages • COSP: CFMIP Observation Simulator Package • CFMIP (http://cfmip.metoffice.com/COSP.html) • CRTM: Community Radiative Transfer Model • NOAA (http://www.star.nesdis.noaa.gov/smcd/spb/CRTM/) • ECSIM: EarthCARE Simulator • ESA (Voors et al, 2007) • J-simulator: Joint Simulator for Satellite Sensors • JAXA/U Tokyo (http://www22.atwiki.jp/j-simulator/pages/14.html) • RTTOV: Radiative Transfer Model for TOVS • UK MetOffice/ECMWF (Matricardi et al. 2004; Bauer et al., 2006) • SDSU: Satellite Data Simulator Unit • Nagoya U (http://precip.hyarc.nagoya-u.ac.jp/sdsu/) • Goddard SDSU • NASA GSFC (http://atmospheres.gsfc.nasa.gov/cloud_modeling/sdsu.html) • ISSARS: Instrument Simulator Suite for Atmos Remote Sensing • JPL (under development) GRP 22nd meeting, Tokyo

  4. Who would need it? • Algorithm developers? • Most likely have their own RT codes already. • GCM/CRM developers • Would be happy if user-friendly simulators are available. • Best (or least?) motivated to diagnose and refine model performance. • GCM/CRM users • Would be also happy with simulators. • Best available to spend time on model assessment. Satellite simulators have potential user demands primarily from the modeling communities. GRP 22nd meeting, Tokyo

  5. Why do we need it? N(D), ρp,… N(D), ρp,… Masunaga et al., BAMS (2010) GRP 22nd meeting, Tokyo

  6. Goddard SDSU applied to a • WRF simulation • AMSR-E 36.5 GHz V (top) • MODIS 11 m (middle) • CloudSatdBZ(bottom) • Masunaga et al., BAMS (2010) GRP 22nd meeting, Tokyo

  7. Cloud and Precip Top Heights (CTH and PTH)  TRMM PR&VIRS  NICAM+SDSU  CloudSat CPR  NICAM+SDSU CTH CTH MJO wet MJO dry MJO wet MJO dry CPR echo-top height Infrared Tb PTH PTH CPR 10-dBZ height PR echo-top height Too much snow In the cloud model ? ? MJO wet MJO dry MJO wet MJO dry GRP 22nd meeting, Tokyo

  8. Missing 94-GHz Echoes above 8 km The 94-GHz back-scattering coefficient begins to be saturated due to non-Rayleigh scattering as snow content increases. 94-GHz GRP 22nd meeting, Tokyo

  9. Rayleigh regime Wavelength >> 2πr Geometric optics regime Wavelength << 2πr 2r GRP 22nd meeting, Tokyo

  10. A Modification to snow microphysics Snowflake mass spectrum = m(D)n(D)=aDb N0exp(-lD) where a=2.5x10-2 kg m-2 and b=2 (original=Grabowski, 1998) a=5x10-4 kg m-1 and b=1 (modified) Less saturated SWC=1g/m3 0.1 g/m3 Smaller snowflakes GRP 22nd meeting, Tokyo

  11. PSD Impact on the CTH/PTH Histogram TRMM MJO wet MJO dry MJO wet MJO dry NICAM Modified Original MJO wet MJO wet MJO dry MJO dry MJO wet MJO wet MJO dry MJO dry GRP 22nd meeting, Tokyo

  12. Model assessment with simulators Model Grid resolution, PBL schemes,… Satellite data simulator GCM cumulus/cloud parameterizations, … Particle Scattering Size distribution, Crystal habit, … CRM cloud microphysics, … Measuring principles Vis, IR, or Microwave Passive or active GRP 22nd meeting, Tokyo

  13. Tasks • Close communication is crucial among scientists with different background (modeling vs. remote sensing) to foster new ideas to develop assessment metrics. • Simulator-related sessions in int’l conferences • ex.) AGU fall meetings in a past few years. • GRP led efforts for simulator-based assessment • … GRP 22nd meeting, Tokyo

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