1 / 25

Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data

Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data. Xiaolei Zou, Li Lin Florida State University Rick Anthes, Bill Kuo, UCAR. Fourth FORMOSAT-3/COSMIC Data Users Workshop 27-29 October 2009: Boulder, Colorado, U. S. A. Outline. Motivations

lemuel
Download Presentation

Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Vertical Structure of the Atmosphere within Clouds Revealed by COSMIC Data Xiaolei Zou, Li Lin Florida State University Rick Anthes, Bill Kuo, UCAR Fourth FORMOSAT-3/COSMIC Data Users Workshop 27-29 October 2009: Boulder, Colorado, U. S. A.

  2. Outline • Motivations • A Brief Description of GPS RO & CloudSat Data • Comparisons between GPS ROs and ECMWF&NCEP Analyses at Cloud Top and within Clouds • Development of a New Algorithm for GPS Cloudy-Profile Retrieval & Comparison with Standard GPS Retrieval • Summary and Future Work

  3. Motivations GPS RO data are globally available, not affected by clouds, and of high vertical resolution, making them ideally suitable for studying the environment of clouds. This study uses GPS RO data to examine the observed vertical structures of the atmosphere within and outside clouds and compare them with large-scale analyses.

  4. CloudSat Instrument:94-GHz profiling radar Launch time: April 28, 2006 One orbital time: ~1.5 hours Along-track resolution: ~1.1 km Track width: ~1.4 km reflectivity liquid/ice water content Observed variables: cloud top height cloud base height cloud types

  5. A CloudSat Orbital Track and a Collocated GPS RO Reflectivity (dBz) of a deep convection system One granule of CloudSat orbital track: 17:02:24 UTC June 5, 2007 A collocatedGPS sounding: (72.98oW, 43.79oN)

  6. Data Selection • Time periods of data search: • (1) June-September 2006, June 2007 • (2) September 2007 to August 2008 • Collocation of CloudSat and COSMIC data: • Time difference< 0.5 hour • Spatial distance < 30 km • Cloud top >2 km

  7. Collocated Cloudy and Clear-Sky Sounding Numbers Four-month period: Total cloudy profiles: 147 Total clear-sky profiles: 86

  8. clear-sky NCEP cloudy Mean/RMS of Fractional N Differences ECMWF clear-sky Four-month period cloudy mean RMS

  9. CloudSat Cloud Types NGPSwet-NECMWF NGPSwet-NNCEP

  10. Cloud-Top Temperature (data in June 2007) NCEP analysis is warmer than both GPS and ECMWF ECMWF compares more favorably with GPS than NCEP GPS dry retrieval is several degrees colder than other data for low cloud (z<5km) OCloud-top height Thickness (km) Profile Number

  11. Cloud-Top Temperature (all data) Cloud Top Height (km) Cloud Top Height (km) TECMWF– TGPSwet TNCEP – TGPSwet TGPSdry – TGPSwet Mean RMS Cloud Top Height (km) Cloud Top Height (km)

  12. Refractivity at Cloud Top (all data) Cloud Top Height (km) Cloud Top Height (km) NECMWF– NGPSwet NNCEP– NGPSwet Cloud Top Height (km) Mean and RMS

  13. Temperature near the Cloud-Top (all data) TECMWF– TGPSwet TNCEP – TGPSwet Cloud top: 2-5 km Cloud top: 5-8 km Cloud top: 8-12 km Sounding Number

  14. GPS Cloudy Retrieval Algorithm Assumption: Cloudy air is saturated. Atmospheric refractivity for cloudy air GPS observation dry term wet term liquid water term Hydrostatic equation: We have two equations for two unknown variables T and P. In-cloud profiles of T and p can be uniquely determined from GPS ROs given initial conditions at the cloud top.

  15. Dependence of TGPSsat-TGPSwet on Relative Humidity ECMWF e/es(%) TGPSsat-TGPSwet(oC) • TGPSsat-TGPSwet is small when the relative humidity is nearly 100% • TGPSsat-TGPSwet is mostly less than 4oC when the relative humidity >85% • TGPSsat-TGPSwet > 4oC appears when the relative humidity <85%

  16. GPS Refractivity within Cloud Cloud occupies only a fraction of an analysis grid box. Atmospheric refractivity for cloudy air relative humidity parameter where Nclear=Ndry+Nwet Ncloud=Ndry+Nsat and

  17. Mean Relative Humidity within Clouds GPSwet ECMWF Cloud-middle Height Cloud-middle Height Relative Humidity (%) Relative Humidity (%) NCEP Mean Cloud-middle Height Cloud-middle Height RMS Relative Humidity (%) Relative Humidity (%)

  18. In-cloud Temperature Differences Cloud middle 2 2 height (km) 0 0 -2 -2 Cloud top -2 -1 0 1 2 3 0.6 0.8 1 1.2 1.4 0 0 -2 -2 height (km) -4 -4 -6 -6 Cloud base -2 -1 0 1 2 0.8 1 1.2 1.4 1.6 6 6 4 4 height (km) 2 2 0 0 TGPScloud - TGPSwet -2 -1 0 0.8 0.9 1 1.1 1.2 1 2 Mean (oC) Standard Deviation (oC) Ta=0.85 - TGPSwet

  19. In-cloud Temperature Differences Cloud middle 2 2 0 0 height (km) -2 -2 -1 -0.5 0 0.5 1.5 1 0.8 Cloud top 0.2 0.4 0.6 0 0 -2 -2 height (km) -4 -4 -6 -6 0.2 0.4 0.6 0.8 -1 -0.5 0 0.5 1 1.5 Cloud base 6 6 4 4 height (km) 2 2 TGPScloud - TECMWF 0 0 0.2 0.4 0.6 0.8 -1 -0.5 0 0.5 1 1.5 TGPScloud - TNCEP Mean (oC) Standard Deviation (oC)

  20. Lapse Rates within Cloud Cloud middle GPSwet ECMWF NCEP GPScloud a=0.85 2 2 0 0 height (km) -2 -2 5 6 7 8 0.4 1.2 2 Cloud top 0 0 -2 -2 height (km) -4 -4 -6 -6 Cloud base 5 6 7 8 0.4 1.2 2 6 6 4 4 height (km) 2 2 0 0 5 6 7 8 0.4 1.2 2 Mean (oC) Standard Deviation (oC)

  21. Summary A new cloudy retrieval algorithm is developed. 2. GPS ROs are compared with large-scale analysis separately in cloudy and clear-sky environment for the first time. 3. CloudSAT data are combined with GPS RO data for studying clouds.

  22. Summary Major Findings: • Positive N-bias are found for cloudy soundings Negative N-bias for clear-sky conditions • ECMWF temperature compares more favorably with GPS wet than NCEP • Cloudy-algorithm retrieved temperature is warmer than • GPS wet retrieval in the middle of cloud and slightly colder • near cloud top and cloud base, resulting a lapse rate that • increases with height above cloud middle

  23. Future Work 1. Algorithm Adaptation • Use cloud-top pressure (height) provided by IR 2. Validation of Cloudy Retrieval • In-cloud profile retrieval with dropsonde data (average) 3. Extended Period • Investigation global thermodynamic characteristics based on cloud types

  24. More details: Lin, L., X. Zou, R. Anthes, and Y.-H. Kuo, 2009: COSMIC GPS radio occultation temperature profiles in clouds, Mon. Wea. Rev., (accepted for publication last week)

  25. Future Work 1. Algorithm Adaptation • Use cloud-top pressure (height) provided by IR 2. Validation of Cloudy Retrieval 3. Extended Period • Investigation global thermodynamic characteristics based on cloud types More Ideas?

More Related