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 One granule of CloudSat orbital track: 17:02:24 UTC June 5, 2007 A collocated GPS sounding: (72.98 o W, 43.79 o N) Reflectivity (dBz) of a deep convection system

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

9. CloudSat Cloud Types N GPSwet -N NCEP N GPSwet -N ECMWF

10. CloudSat-Measured Reflectivity 1 10 20 30 40 50 60 70 72 Single-Layer Profiles in the Four Month Period

11. GPS Wet Temperatures 1 10 20 30 40 50 60 70 72 Single-Layer Profiles in the Four Month Period Temperature ( o C)

12. 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) O Cloud-top height Thickness (km) Profile Number

13. T ECMWF – T GPSwet T NCEP – T GPSwet T GPSdry – T GPSwet Cloud-Top Temperature (all data) Cloud Top Height (km) Mean RMS

14. Refractivity at Cloud Top (all data) Mean and RMS N ECMWF – N GPSwet N NCEP – N GPSwet Cloud Top Height (km)

15. Temperature near the Cloud-Top (June 2007) Cloud top is indicated by solid horizontal (black) line. Cloud base is indicated by dotted line for those clouds whose thickness is < 2 km. T ECMWF – T GPSwet T NCEP – T GPSwet

16. Temperature near the Cloud-Top (all data) T ECMWF – T GPSwet T NCEP – T GPSwet Cloud top: 2-5 km Cloud top: 5-8 km Cloud top: 8-12 km Sounding Number

17. In-Cloud Temperatures (June 2007) Temperature decreases with height at different lapse rate

18. GPS Cloudy Retrieval Algorithm Assumption: Cloudy air is saturated. Atmospheric refractivity for cloudy air 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. dry termwet term GPS observation liquid water term

19. Cloud-Top Initial Conditions (i=1,2), : the variance of T, P of GPS wet retrieval  PP 2 2

20. A Flow Chart for GPS Cloudy Profile Retrieval Start at the cloud top: P 0, T 0, set m=0 m=m+1 Cloud base stop Yes No

21. Convergence of Temperature Solution Derived from GPS Refractivity is found withinusing an interval of 0.1 o C. T T m+1 GPSsat T cloud profile T m+1 GPSsat

22. T GPSsat -T GPSwet is small when the relative humidity is nearly 100% T GPSsat -T GPSwet is mostly less than 4 o C when the relative humidity >85% T GPSsat -T GPSwet > 4 o C appears when the relative humidity <85% Dependence of T GPSsat -T GPSwet on Relative Humidity T GPSsat -T GPSwet ( o C) ECMWF e/e s (%)

23. GPS Refractivity within Cloud Atmospheric refractivity for cloudy air Cloud occupies only a fraction of an analysis grid box. N clear =N dry +N wet N cloud =N dry +N sat and relative humidity parameter where

24. Mean Relative Humidity within Clouds RMSRMS MeanMean GPSwetECMWF NCEP Cloud-middle Height Relative Humidity (%)

25. Relationship between Relative Humidity and Liquid/Ice Water Content 19 Liquid water clouds67 Ice water clouds   =0.8 Liquid/Ice water content (g/m 3 ) 0.05 0.25 0.45 0.65 0.85 0.65 0.85 0.45 0.65 0.85 0.25 0.45 0.65 0.85 0.05 0.25 0.45 0.65 0.85  =0.5273*IWC+0.6849

26. Cloud Alignment Cloud Middle 0 1 -2 2 height (km) Cloud Top -3 -2 -4 -5 height (km) 0 Cloud Base 3 4 2 1 5 height (km) 0

27. In-cloud Temperature Differences Mean ( o C)Standard Deviation ( o C) 2 -2012 2 -2 -4 -6 6 4 2 height (km) -20130.60.811.4 0.811.21.6 2 -2 -4 -6 6 4 2 1 0 -2 2 0.911.20.8 00 0 0 1.2 1.4 1.1 0 0 T GPScloud - T GPSwet T  =0.85 - T GPSwet Cloud middle Cloud base Cloud top

28. T GPScloud - T ECMWF T GPScloud - T NCEP In-cloud Temperature Differences -0.500.5 1 1.5 -0.500.511.5 0.20.40.6 0.8 0.20.40.60.8 2 -2 -4 -6 6 4 2 2 -2 -4 -6 6 4 2 0 0 0 height (km) 0 0 0 Mean ( o C)Standard Deviation ( o C) -0.500.511.5 0.20.40.60.8 Cloud middle Cloud base Cloud top

29. Lapse Rates within Cloud Cloud middle Cloud base Cloud top 56781.20.42 56781.20.42 height (km) 2 -2 -4 -6 6 4 2 height (km) 0 0 0 2 -2 -4 -6 6 4 2 0 0 0 Mean ( o C)Standard Deviation ( o C) 56781.20.42  GPSwet  ECMWF  NCEP  GPScloud   =0.85

30. Summary 1.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.

31. Summary ECMWF temperature compares more favorably with GPS wet than NCEP Positive N-bias are found for cloudy soundings Negative N-bias for clear-sky conditions Major Findings: 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

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

33. 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)

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