1. Water Vapor and Cloud Detection Validation for Aqua Using Raman Lidars and AERI David Whiteman, Belay Demoz NASA/Goddard Space Flight Center Zhien Wang, Chris Barnet, Wallace McMillan, Raymond Hoff, Igor Veselovskii, Felicita Russo University of Maryland, Baltimore County/JCET Gary J. Jedlovec NASA/Marshall Space Flight Center, GHCC Daniel H. DeSlover University of Wisconsin/CIMSS And thanks to John Braun, Chris Rocken, Teresa VanHove, Larry Miloshevich, June Wang – UCAR Barry Lesht – ANL Rich Ferrare – NASA/LaRC

2. Outline Overview of work performed to date NWS radiosonde conversion plans Raman Lidar introduction AFWEX water vapor measurements – 4 water vapor lidars, Vaisala radiosonde, SW Chilled mirror, in-situ sensors, GPS, MWR Raman Lidar calibration Lidar/retrieval comparisons – “To tune or not to tune” – Clear and cloudy water vapor and temperature profiles versus 5 retrieval schemes

3. Activity to Date and Outlook Sept – Nov, 2002 at GSFC – 26 nighttime clear and cloudy overpasses Scanning Raman Lidar water vapor, sonde T & P, GPS PW – Study various clear and cloudy retrievals of T & q Jan, 2003 and UMBC – 15 measurement sessions Scanning Raman Lidar, BBAERI, ALEX, ELF, sonde, GPS 15 Lidar/BBAERI measurements –7 AIRS overpasses –Others for study of cirrus particle size retrievals AERI vs lidar Dual sonde launches at GSFC Jan, 2004 deployment to UMBC planned MWR and AERI at GSFC to be running soon (Tsay, Code 913) – GPS vs MWR

4. NWS Radiosonde Conversion NWS announced that Sippican and Intermet will be supplying radiosonde packages for operational launches over next 3 years – Vaisala and Sippican used formerly – Previous comparisons have found significant differences Calibration Response at cold temperatures The new sondes will be phased in and transfer functions between the previous sondes (Vaisala or Sippican) implemented – Smooth through glitches Dual sonde (Intermet, Sippican) launches coming soon

5. Raman Lidar Laser transmitter (UV better) – excites Raman scattering in atmospheric species. Telescope receiver – wavelength selection optics separate the wavelengths Time gated data acquisition gives range information Measurements – Water vapor mixing ratio – Aerosol backscatter, extinction, depolarization – Liquid water mixing ratio – Many other things

6. Raman Lidar for water vapor measurements Advantages – Based on scattering physics-> direct measurement of mixing ratio – Purely vertical profiles – No time lag, sensor contamination issues – Stable calibration has been demonstrated – Temperature sensitivity of Raman scattering well known, is a small effect (<5%), and can be simulated (accepted Appl. Opt.) Disadvantages – UT measurements only at night – Raman cross section not well known Absolute calibration not difficult but will have uncertainties > 10%

7. Scanning Raman Lidar (SRL), Sippican Radiosonde (T+P), SuomiNet GPS GSFC site North-largely forested, some open fields South-suburban sprawl Patuxent River, Chesapeake Bay, Potomac River

8. ARM FIRE Water Vapor Experiment (2000) Goal: characterize UT water vapor technologies (4 WV lidar, sondes, passive, in-situ) Location: ARM CART SGP site northern OK

9. Example Water Vapor Profile Comparison CART Raman (CARL) and Scanning Raman (SRL) Lidar profiles correspond to 10 minute averages LASE profile corresponds to a 2 minute average centered over the SGP site. Courtesy of Richard Ferrare – NASA/LaRC

10. Summary of LASE Comparisons from AFWEX Courtesy of Richard Ferrare – NASA/LaRC

11. Vaisala RS-80H and RS-90 Time Lag Courtesy Larry Milosevich - UCAR

12. TPW Comparisons RS-80H and RS-90 vs MWR (SGP) RS-80H: ~2% dryer than MWR, ~5% standard deviation RS-90 : ~1% dryer than MWR, ~5% standard deviation Courtesy Barry Lesht, ANL

13. Assessment of Upper Troposphere Water Vapor Measurements using LASE LASE and Raman lidars in excellent average agreement (within ~2-3%) Corrections to Vaisala RS-80H radiosondes reduced sonde dry bias from 10% to less than 5% Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars Courtesy of Richard Ferrare – NASA/LaRC

14. Raman water vapor lidar calibration Comparison with respect to radiosonde profile – Normal radiosondes show variability and dry bias – Research radiosondes are promising Need large enough sample to study variability of calibration Comparison based on total column water – MWR scaling being performed at the SGP site – GPS provides a stable reference as well large volume average implies numerous comparisons required to assess calibration constant – Important to get the bottom part of the lidar profile correct ~5% of TPW can be in the bottom 250 meters SRL uses vertical and scanned lidar measurements along with 2 ground sensors for composite profile

15. SuomiNet GPS vs ARM CF MWR (WVIOP3) Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

16. SuomiNet GPS vs FSL GPS (WVIOP3) Plot courtesy John Braun, Chris Rocken, Teresa Van Hove, UCAR GPS Research Group

17. SRL Calibration constant determined from GPS and Sippican SuomiNet GPS (PW) Sippican radiosonde (profile comparison ~1-2 km) Sippican profile calibration ~6% moister, PW calibration ~15% moister

18. Chris Barnet’s retrieval “tweaks” Tune: T, E(n,m)=0 NoTune: T=0, E(n,m)=0 Covar: T=0, E(n,n)=(Obs-Calc(n)) 2 Covar_New: T=0, E(n,m) =(Obs-Calc(n)*Obs-Calc(m)) B50: T=0, E(n,n)=1/4 of Covar

19. Retrieval Tweaks.. Two versions of “NoTune” for Sept 13 “clear” case 2/3/03 2/21/03

20. More “clear” examples “NoTune” vs “NoTune2”

21. More “clear” examples “NoTune” vs “Notune2” - II

22. Some “Clear” Water Vapor Examples

23. Some “Cloudy” Water Vapor Examples

24. (a) (b)

25. The September 14 Case Cirrus cloud scattering ratio Quick look plot - uncalibrated A sharp increase in water vapor beneath cirrus cloud decks is common and can persist

26. SRL vs Aqua water vapor retrieval (mean of 17) ret dry ret wet

27. SRL vs Aqua water vapor retrieval (9 clear cases) ret dry ret wet

28. SRL vs Aqua water vapor retrieval (8 cloudy cases) ret dry ret wet

29. Sippican vs Aqua Temperature Retrieval (17 Cases) ret warm ret cold

30. Sippican vs Aqua Temperature Retrieval (9 clear cases) ret warm ret cold

31. Sippican vs Aqua Temperature Retrieval (8 cloudy cases) ret warm ret cold

32. (a) (b)

33. Summary of PW comparisons Total precipitable water comparisons Ret/SRL – All: 0.97 – 1.02, Clear: 0.91 – 0.95, Cloudy: 1.01 – 1.07 All retrievals show the following general trends – increase in moisture below 800-900 mb More rapid for cloudy cases than clear – clear conditions ~5 - 15% dry bias with respect to SRL above 900 mb – cloudy conditions generally good agreement (+2% to - 7%) with SRL above 900 mb wet bias (25 – 30%) between 270-460 mb due to missed structure below cloud layers “Tune” and “Covar_New” – performed similarly in water vapor comparisons Similar results can be obtained from damping as with tuning “No_Tune” – shows significant oscillations in clear profiles (+/- 30 – 50%), large positive bias near the surface in cloudy profiles (40-50%) “Covar” – showed the least positive bias (~25%) in the UT (270 – 460 mb) – Generally dryer than “Tune” by 1-7%

34. Summary of Temperature Comparisons Overall agreement of all retrievals within +/- 1K above 900 mb – NoTune2 up to 2K cold ~950 mb All retrievals show temperatures decreasing between 850 – 950 mb (~1K), then abrupt increase below 950 mb (~3K) Under both clear and cloudy conditions, warm bias between 250 – 600 mb of ~0.3 – 1.0K Significant difference between clear and cloudy retrievals between 600 – 900 mb – Clear retrievals ~0.5 – 1.0K colder “Tune” and “Covar_New” – similar performance with the smallest overall biases “NoTune” – Showed the largest deviations under most conditions “Covar” – Showed largest warm bias in UT (~0.7 – 1.1 K)

35. TPW and Temperature comparison results-II TPW: (retrieval/SRL) – 1) entire profile 2) UT (460 – 270 mb), 3) above 1 st km, 4) within 1 st km, Temp: mean (Sonde – Retrieval) for same layers Retrieval PW/SRL (986 – 51, 460-270, 904–51, 986–900 mb) Sonde-Retrieval (K) (986 – 51, 460-270, 904–51, 986–900 mb) All (Above 986, UT, Above 1 km, 1 st km) ClearCloudyAllClearCloudy Tune0.99, 1.16, 0.94, 1.06 0.94, 0.90, 0.88, 1.04 1.03, 1.32, 0.98, 1.08 -0.1, -0.2, -0.1, +0.0 0.1, -0.3, + 0.1, +0.2 -0.2, -0.2, -0.2, -0.1 NoTune0.97, 1.17, 0.89, 1.10 0.91, 0.95, 0.84, 1.03 1.02, 1.29, 0.93, 1.15 -0.1, -0.6, -0.1, +1.3 0.1,-0.8, + 0.0, +1.2 -0.1, -0.5, -0.2, +1.4 Covar0.98, 1.10, 0.94,1.04 0.91, 0.84, 0.87, 1.00 1.04, 1.25, 1.00, 1.07 -0.2, -0.9, -0.2 +0.0 0.0, -1.1, -0.1, +0.3 -0.3, -0.7, -0.4, -0.1 CovarNew0.97, 1.14, 0.92, 1.04 0.91, 0.89, 0.86, 1.01 1.01, 1.29, 0.97, 1.07 -0.1, -0.2, -0.1 +0.3 +0.1, -0.4, +0.1, +0.4 -0.2, -0.2, -0.2, +0.2 B501.02, 1.15, 0.97, 1.10 0.95, 0.95, 0.90, 1.03 1.07, 1.28, 1.02, 1.14 -0.2, -0.9 -0.2, +0.3 -0.1, -1.2, -0.1, +0.3 -0.3, -0.6, -0.3, +0.2

36. October 2, 2002 a “clear” case with a sharp increase

37. Use of MODIS to look for clouds and PW variability

39. Summary and Future Work Continue to work interactively in algorithm research – Clear and cloudy cases – Focus on UT and 900-1000 mb bias Assess AIRS cloud products – Lidar, MODIS Expand use of MODIS to study scene variability UMBC deployments – Analyze data from 2003 – Deploy in 2004 GSFC-based measurements – Dual (or more?) sonde launches – Compare GPS and MWR at GSFC – Further lidar measurements as needed

40. Revised Work Plan – July 2001 L+3 to L+5 – Perform validation measurements from mid-Atlantic region during Aqua overpasses Raman Lidar water vapor/aerosols, radiosonde, GPS 26 successful measurements out of 40 possible –Nighttime, angles > 40 degrees Years 2 and 3 – Deploy SRL to UMBC correlated measurements of SRL, BBAERI, ALEX, radiosonde, GPS Validation under presence of clouds Study particle size retrievals from ground based sensors

41. Temperature sensitivity of water vapor Raman scattering (accepted by Appl. Opt.) Simulated Raman water vapor spectrum (Avila et. al., 1999) Simulated temperature correction curves for SRL and CARL water vapor measurements

42. AFWEX (2000) Examples - I

43. Vaisala RS-80 H vs RS-90 Courtesy June Wang - NCAR

44. Vaisala RS-80H and RS-90 – RH Corrections Courtesy Larry Milosevich - UCAR Estimate of the residual dry bias in RS-90s is 5-8% RH.

45. Sippican PW versus SuomiNet GPS Both slope of line and offset indicate Sippican moister – Sippican mean is 15% moister than GPS

49. September 13

50. September 20

51. November 3

52. SA02 vs GOES (GSFC)

53. SA02 vs MODIS – Near IR (GSFC)

54. SA02 vs CIMEL (GSFC)

55. CIMEL vs MWR at SGP

56. Good Bad “UT bad, mid OK” “mid trop bad” “bad” “mid trop a little bad”

57. LASE vs. Vaisala RS-80H Radiosondes Uncorrected Vaisala sondes showed ~8-10% dry bias in upper troposphere Scaling Vaisala sondes to match MWR PWV did not reduce dry bias in upper troposphere Vaisala corrections for calibration and temp. dependent calibration reduced bias by 4-5% Additional correction (Miloshevich) for time lag of Vaisala sondes further reduced bias by 2-3%  Total of corrections to Vaisala radiosonde measurements reduced dry bias to less than 5% Courtesy of Rich Ferrare – NASA/LaRC

58. LASE vs. DC-8 In situ sensors, Chilled Mirror and Sippican sondes In upper troposphere… Chilled Mirror sondes about 8-10% drier than LASE and Raman lidars DC-8 diode laser hygrometer slightly (~3% drier) than LASE and Raman lidars DC-8 cryogenic frost point hygrometer 10-20% drier than LASE and Raman lidars Courtesy of Rich Ferrare – NASA/LaRC

59. LASE vs. CART Raman Lidar (CARL) Initial (uncorrected) CARL profiles were 5-7% wetter than LASE in upper trop. Correction for altitude dependence of CARL overlap reduced CARL values in upper trop. by ~4%  LASE and CARL measurements generally within 5% on average for all altitudes Courtesy of Rich Ferrare – NASA/LaRC

60. LASE vs. Scanning Raman Lidar (SRL) SRL profiles were ~3% wetter than LASE in upper trop. SRL slightly (~3-4%) drier than LASE below 5 km  LASE and SRL measurements generally within 5% on average for all altitudes Courtesy of Rich Ferrare – NASA/LaRC

61. AFWEX (2000) Examples - III

62. AFWEX(2000) Examples – IV

63. AFWEX – Profile Summaries 1)SRL/CARL agreement within +/- 5% from 1-10 km (no low channel in SRL). SRL tends toward being moister than CARL above 10km and below 2 km. 2)SRL/LASE shows moist bias of LASE in lowest 2 km, dry bias above 6 km. 3)SRL/Vaisala show increasing dry bias of sonde above 5 km

64. SRL vs Sippican Mean Comparison

65. SRL vs Sippican (Sept. 5)

66. SRL vs Sippican (Sept 6)

67. SRL vs Sippican (Sept 7)