Your SlideShare is downloading. ×
0
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements

410

Published on

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
410
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  1. Effects of Uncertainty in Cloud Microphysics on Passive Microwave Rainfall Measurements<br />Ju-Hye Kim and Dong-Bin Shin*<br />Department of Atmospheric Sciences<br />Yonsei University, Seoul, Republic of Korea<br />jhkim07@yonsei.ac.kr, dbshin@yonsei.ac.kr<br />
  2. Outline<br />1. Introduction (motivation)<br />2. Methodology (characteristics of different microphysics schemes)<br />3. Impacts of microphysics on a-priori databases <br />4. Impacts of microphysics on PMW rainfall retrievals<br />5. Conclusions<br />
  3. Cloud water + DSD<br />Rain water + DSD<br />Snow + , DSD<br />Graupel + , DSD<br />Cloud ice + DSD<br />Hail + , DSD<br />Water Vapor Temperature<br />Introduction<br />Current physically-based PMW rainfall algorithms heavily rely on CRM simulations.<br />Simulated TB<br />RTM<br />e.g., Plane-Parallel , MC models<br />Forward models provide prior information<br />Cloud Model<br />* e.g., Goddard Cumulus Ensemble Model (GCE),. ....<br />Assumptions in some parameters (e.g., microphysics)<br />
  4. observed<br />simulated<br />Introduction<br />CRM-based rainfall retrieval algorithms have been evolved to use CRMs and observations simultaneously.<br />e.g., The parametric rainfall algorithm: Cloud model + TRMM PR/TMI observations<br />(1st version, Shin & Kummerow, 2003)<br />Simulated precipitation field<br />TB computation<br />simulated<br />observed<br />Realistic set of 3-D geophysical parameters are created from combination of TRMM PR/TMI and CRM.<br />Figure at left is a comparison of surface rainfall from TRMM PR and simulator.<br />Once 3-D geophysical parameters are constructed, TB can be computed for any current or planned sensor.<br />Figure at right is a comparison of Tb from TRMM TMI and simulator.<br />
  5. Obs. Tb vsSim. Tb<br /><ul><li>The liquid portion of the </li></ul>profile is matched, the CRMs <br />consistently specify ice <br />particles of an incorrect size <br />and density, which in turn <br />leads to lower than observed Tb. <br />10 GHz H<br />19 GHz H<br />10 GHz V<br /><ul><li>A better choice would be to </li></ul>continue the development of <br />the Cloud Resolving Model <br />physics to insure that <br />simulations properly match the <br />observed relationship between ice scattering and the rainfall <br />column. <br />19 GHz V<br />21 GHz v<br />37 GHz H<br />85 GHz H/V<br />37 GHz V<br />Assumptions in microphysics still have great impacts on CRM+OBS.-based DBs.<br />
  6. Introduction<br />Cloud Resolving Model <br />Simulations<br />Passive Microwave <br />Rainfall<br />Observations<br />TRMM field campaigns <br /><ul><li>The Kwajalein Experiment (KWAJEX)
  7. The South China Sea Monsoon Experiment (SCSMEX)
  8. The TRMM Large-Scale Biosphere-Atmosphere Experiment in Amazonia (TRMM LBA)</li></li></ul><li>Zhou et al. (2007)<br /><ul><li>used the GCE model to simulate China Sea Monsoon and compared their simulated cloud products with TRMM retrieval products</li></ul>Lang et al. (2007) , Han et al. (2010)<br /><ul><li>Land et al. (2007) compared the calculated TBs and simulated reflectivities from cloud-radiative simulations (GCE model) of TRMM LBA domain with the direct observations of TRMM TMI and PR
  9. Han et al. (2010) also evaluated five cloud microphysical schemes in the MM5 using observations of TRMM TMI and PR</li></ul>Grecu and Olson (2006)<br /><ul><li>constructed a-priori database from observation of TRMM PR and TMI only to reduce forward error related to cloud and radiative transfer calculations, and compared their retrieval results to products from GPROF version-6 operational algorithm</li></ul>Many studies pointed out that CRMs (mainly GCE model) tend to produce excessive ice particles above freezing level and it may bring wrong retrieval results in microwave remote sensing of precipitation.<br />
  10. Methodology<br />Different Cloud <br />Microphysics <br />PLIN<br />TRMM Observation of Typhoon Sudal<br />WSM6<br />TyphoonJangmi Simulations with WRF model (V3.1)<br />36532<br />36522<br />Goddard<br />Thompson<br />WDM6<br />Morrison<br />Parametric rainfall algorithm<br /><ul><li>Shin and Kummerow (2003)
  11. Masunaga and Kummerow (2005)
  12. Kummerow et al. (2011)</li></ul>Six kinds of a-priori rainfall databases !<br />
  13. Prognostic variable of Single-moment scheme<br />Ty JangmiSimulation with WRF model<br />Single-Moment<br />PLIN<br />WSM6<br /> + Ns, Ng, Nr<br />Goddard<br /> + Nccn, Nc, Nr<br />Thompson<br /> + Ns, Ng, (Nc, Nr)<br />WDM6<br />Double-Moment<br />Morrison<br /><ul><li>Single moment schemes have differences in their cold rain processes (ice initiation, sedimentation property of solid particles).
  14. The microphysical processes related to ice-phase in the WDM6 are identical to the WSM6 scheme.
  15. WDM6 is double moment scheme for (only) warm rain processes and it predicts a cloud condensation nuclei (CCN) number concentration.</li></li></ul><li>Typhoon Jangmi Simulation with Six different Microphysics schemes in the WRF Model<br /><ul><li>Similar distributions of rain and cloud water compared to WSM6
  16. Reduction of snow near and above the melting layer
  17. More rain water and more ice particle than WSM6
  18. Increased rain water below 5 km altitude
  19. Similar distribution of ice particle compared to WSM6
  20. Much more snow
  21. Less rain water
  22. More snow
  23. Less rain water</li></li></ul><li>Impacts of microphysics on a-priori databases<br /><ul><li>Correctness of simulated DBs</li></ul>PLIN <br />Modified Radiative Indices<br />Petty (1994)<br />Biggerstaff and Seo (2010)<br />WSM6 <br />GCE <br />THOM <br /><ul><li>For the emission indices, TBs agree well. (The biases at 10 GHz channel from six databases are quite small, especially when the WSM6 and WDM6 schemes are used.)
  24. The simulated and observed databases show relatively large discrepancy at 85 GHz scattering index (Sm).</li></ul>WDM6 <br />Simulated Indices<br />MORR <br />SM85<br />PM37<br />PM10<br />PM19<br />PM85<br />Observed Indices<br />
  25. <ul><li>Representativeness of simulated DBs</li></ul>First EOF vector of Radiance indices<br /><ul><li>Observed database shows a positive variation for attenuation indices and negative variation for the scattering index
  26. Simulated DBs generally follow the pattern of the Obs. DB. (smaller variability in 10, 19, and 37 GHz attenuation indices. Larger variability in 85 GHz attenuation index). </li></ul>/ PLIN /<br />/ WSM6, WDM6 /<br />Difference between Obs. and Simulated DBs<br />/ GCE, THOM, MORR /<br />
  27. Impacts of microphysics on rainfall retrievals<br />Orbit : 36537<br />Retrieved rainfall distributions for Ty Sudal<br />PR 2A25<br />TMI 2A12<br />
  28. Scatter plots of PR vs retrieved rain rates for Ty Sudal<br />Retrieved rainfall<br />PR rainfall<br />
  29. Retrieval statistics for different rain types (convective vsstratiform)<br />PR 2A23<br />Convective<br />Yellow : Convective<br />Blue : Stratiform<br />Stratiform<br />
  30. Comparison of averaged hydrometeor amounts<br />In the databases<br />PLIN ~ Too much graupel<br />In the retrieval s<br />THOM ~ Too much snow<br />WDM6 ~ Increased rain water and reduced cloud water<br />
  31. Conclusions<br />PLIN<br /><ul><li>A-priori databases with six microphysics schemes are built by the WRF model V3.1 and TRMM PR observations and the impacts of the different microphysics on rainfall estimations are evaluated under the frame of parametric rainfall algorithm for extreme rain events (Typhoons).
  32. Major difference in six microphysics schemes exists in their cold rain processes (ice initiation, sedimentation property of solid particles).
  33. PLIN and THOM schemes produce too much graupel and snow, respectively, while the ice processes seem to be comparable to those from WSM6 and WDM6.
  34. This study suggests that uncertainties associated with cloud microphysics affect significantly PMW rainfall measurements (at least for extreme events).  Both intensity and distribution of retrieved rainfalls are better represented by the WDM6, WSM6 and Goddard microphysics-based DBs.</li></ul>WSM6<br />Goddard<br />Thompson<br />WDM6<br />Morrison<br />

×