1. COST 723 UTLS Summerschool Cargese, Corsica, Oct. 3-15, 2005 Stefan A. Buehler Institute of Environmental Physics University of Bremen www.sat.uni-bremen.de OBS 13: Measuring Upper Tropospheric Humidity with Operational Microwave Satellite Sensors

2. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20052 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary Cited papers can be found at http://www.sat.uni-bremen.de

3. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20053 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

4. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20054 Earths Radiation Balance Outgoing Longwave Radiation OLR Incoming Shortwave Radiation Sun Earth

5. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20055 Earths Radiation Balance Wavelength [μm] λE λ [normalized] (Wallace und Hobbs, `Atmospheric Science', Academic Press, 1977.) Radiative equilibrium temperature: -18°C Global mean surface temperature: +15°C 34 K natural greenhouse effect

6. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20056 Clear-Sky OLR Spectrum A lot of the radiation comes from the UT Water vapor and CO 2 are the most important greenhouse gases

7. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20057 Jacobians [10 -14 W Hz -1 sr -1 m -2 ]

8. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20058 [10 -14 W Hz -1 sr -1 m -2 ]

9. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 20059 Important Altitude Range OLR is sensitive to changes of humidity in the upper troposphere, where it is difficult to measure. Sensitivity peak below TTL. [10 -14 W Hz -1 sr -1 m -2 ] MLS

10. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200510 15% change in humidity = double CO 2 - (for a tropical atmosphere) H 2 O is a stronger greenhouse gas than CO 2 Higher surface temperature = more evaporation  positive feedback. (Buehler et al., JQSRT, submitted 2005) Impact on Tropical OLR

11. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200511 The Water Vapor Feedback Convection and cyclones transport moisture into the UT (see lectures of Heini Wernli and Andrew Gettelman) Ascending air is dried by condensation processes High spatial and temporal variability Residence time of water substance ~10 days

12. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200512

13. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200513 Variability of Clear-Sky OLR Paradox: More humidity = more OLR! Simulated OLR [W/m 2 ] (Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005) Total water vapor [mm]

14. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200514 Variability of Clear-Sky OLR High temperature correlated with high humidity Positive temperature signal outweighs negative humidity signal Expected, otherwise runaway greenhouse effect Water vapor signal strongest in the tropics Simulated radiances agree with CERES OLR data (Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005) Radiosondes CERES Data Simulated OLR [W/m 2 ] Surface temperature [K]

15. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200515 Variability of Clear-Sky OLR (Buehler et al., Q. J. R. Meteorol. Soc., submitted 2005) No strong temperature variations in the tropics Temperature and Water Vapor variations are both important for clear-sky OLR Radiosondes CERES Data Simulated OLR [W/m 2 ] Delta OLR [W/m 2 ]

16. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200516 Climate GCMs indicate that the feedback is positive. A large part (about half) of the warming predicted by models for a CO 2 rise is due to the water vapor feedback (Held and Soden, Annu. Rev. Energy Environ., 2000). The UT is an important altitude region for this feedback, but humidity there is poorly known. Radiosonde measurements: Low spatial coverage Poor data quality in the UT Infrared satellite measurements: Good global coverage, but affected by clouds Clear sky bias Microwave satellite measurements (today) Radio occultation (Friday, OBS 16) Ice clouds play also an important role in the UT radiation balance (Friday, OBS 15)

17. Comparison: Radiosondes ↔ Infrared Satellite Data Big differences between the different data sets, for example: +/-15 %RH difference between IR satellite and radiosonde = 40% relative difference in humidity, as RH values are low in the UT. (Soden and Lanzante, JGR 1996) Problem: Large discrepancies, true climatology unknown (see e.g. SPARC UTLS H 2 O Assessment)

18. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200518 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

19. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200519 Microwave Satellite Data SSM-T2 since 1995 AMSU-B since 1999 Passive microwave instruments (measuring thermal radiation from the atmosphere) Less affected by cloud than infrared Well calibrated

20. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200520 AMSU-B Cross-track scanner 90 pixels per scan line Outermost pixels 49° off- nadir Swath with ≈ 2300 km Global coverage twice daily 16 km horizontal resolution (at nadir)

21. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200521 AMSU-B Channels (Details: John and Buehler, GRL, 31, L21108, doi:10.1029/2004GL021214) Water vapor Oxygen

22. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200522 AMSU-B Channels Water vapor Oxygen (Figure by Viju O. John)

23. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200523 AMSU-B Jacobians ARTS Simulation, Atmosphere: Midlatitude-Summer 20 19 18 19 20 (Figure by Viju O. John)

24. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200524 Jacobians depend on Atmospheric State (Figures by Viju O. John) Measurement not in TTL, but below Altitude where OLR is very sensitive to H2O changes

25. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200525 AMSU-B Data (Channel 18) Dry areas in the UT (NOAA 16, Channel 18, 15.6.2004. Figure: Oliver Lemke)

26. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200526 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

27. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200527 Retrieving humidity usually requires a priori, problematic for climate applications Humidity Assimilation can destroy information on absolute value due to the bias corrections applied (compare lecture by Francois Bouttier) Solution: Look for a humidity product that is related as closely as possible to the radiances

28. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200528 Method originally invented by Brian Soden for IR data. UTH = Jacobian-weighted relative humidity ≈ mean relative humidity between 500 and 200 hPa Simple relation: ln(UTH) = a + b T b Determine a and b by linear regression with training data set Details: Buehler and John, JGR, 2004 Regression UTH Retrieval

29. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200529 Coefficients independent of training data set Basically another unit for radiance Other humidity data must be processed in same way for comparison

30. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200530 AMSU UTH-Climatology (AMSU-B, Channel 18, NOAA 15, Winter 1999-2000. Figure by Mashrab Kuvatov) With deep apologies to Mark Baldwin for the weird color scale...

31. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200531 Walker Circulation during La Nina NOAA 15 AMSU-B UTH DJF 99-00 500-200 hPa HALOE, 82 hPa Gettelman et al, 2001, J. Clim

32. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200532 Comparison with an Infrared Climatology Infrared UTH (1981-1991), Soden and Bretherton, JGR, 101 (D5), 9333-9343, 1996 Microwave UTH (AMSU-B, NOAA 16, 2002), Mashrab Kuvatov

33. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200533 (Figures by Mashrab Kuvatov) UTH, AMSU-B, Channel 18, NOAA 16, 2002 Difference with and without cloud filter

34. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200534 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

35. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200535 Case Study for one selected Station

36. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200536 (Figure by Viju O. John)

37. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200537 Finding Matches Define target area (radius 50 km) Compare mean satellite value to radiosonde Take standard deviation σ 50km as measure of sampling error

38. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200538 Large variability in σ 50km Lowest values consistent with nominal radiometric noise

39. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200539 Sources of error: Radiometric noise of the AMSU measurement Sampling error due to atmospheric inhomogeneity Radiosonde measurement error in humidity and temperature RT model error AMSU calibration error χ 2 tests show that C 0 can be taken as a global constant with a value of 0.5K. Error Model

40. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200540 Results (Buehler et al., JGR, 109, D13103, doi:10.1029/2004JD004605, 2004) Non-unity radiance slope Possible reasons: RT model AMSU Radiosonde Increasing radiosonde dry bias under very dry conditions

41. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200541 Comparison for a Different Sensor Kem, Russia (64N, 34E) Goldbeater’s skin type sondes very large wet bias (expected from Soden and Lanzante, JGR, 1996 ) (Figure by Viju O. John)

42. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200542 Comparing different Radiosonde Stations 40 European stations Sonde data from BADC (John and Buehler, ACP, 2005)

43. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200543 Comparing different Radiosonde Stations General dry bias (expected for Vaisala sensor) Apparently erratic jumps can be understood by sensor and/or procedure changes for individual stations Information about stations not readily available Mystery: UK stations have less dry bias, although the are supposed to use similar sensors See also poster by T. Suortti (John and Buehler, ACP, 2005)

44. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200544 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

45. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200545 Supersaturation in UARS-MLS Data exponential drop-off

46. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200546 Gaussian T distr. Non-Gauss RH distr.

47. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200547 MLS Data Effect of 2K T uncertainty Some of the observed supersaturation can be due to temperature uncertainties (Buehler and Courcoux, GRL, 2003).

48. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200548 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

49. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200549 RTTOV Fast RT model Freely available from Eumetsat NWP SAF Already configured for most meteorological sensors Biases compared to more accurate ARTS model (see Poster by Nathalie Courcoux) Not used for the calculations in this lecture

50. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200550 Public Domain Program, developed together with Chalmers University, Göteborg and University of Edinburgh. Two branches: ARTS-1-0: Clear-sky ARTS-1-1: with cloud scattering

51. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200551 Radiative Transfer RT Workshop 2004 RT Workshop 2005 Core Developers (2005) Development and workshops since 1999.

52. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200552 ARTS Overview Freely available: http://www.sat.uni-bremen.de/arts/ Clear-sky (arts-1-0): Line spectra (HITRAN, JPL, GEISA) Continua (H 2 O, N 2, O 2, CO 2 ) Trivial RT Analytical Jacobians Cloudy-sky (arts-1-1): Two different algorithms for cloud scattering: Monte Carlo (MC) method Discrete Ordinate Iterative (DOIT) method

53. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200553 ARTS Properties All viewing geometries Spherical Polarized (up to 4 Stokes components) Validated against various other physical RT models from microwave to infrared Used as a reference to judge performance of RTTOV-8 (with scattering) within the NWP SAF

54. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200554 Overview Water vapor in the Earths radiation balance Operational meteorological microwave satellite instruments (AMSU-B) AMSU-B measurements of upper tropospheric water vapor Comparison with radiosonde measurements Temperature uncertainty and supersaturation The radiative transfer model ARTS Summary

55. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200555 Summary Upper tropospheric humidity (UTH) is an important parameter of the climate system. Better absolute measurements of the global UTH distribution are needed. Operational microwave sensors provide a new dataset that is independent of the IR satellite data. Advantage: Less affected by clouds. Disadvantage: So far short time series (since 1995 SSM T2, since 1999 AMSU-B). RT model required for work with satellite measurements.

56. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200556 How to Compare Satellite Data to other Data Find out which part of the satellite data is believed to be ok (check documentation and talk to others) Need enough matches to get statistics (a single in- situ measurement is useless for satellite validation) Set up error model, including sampling error (without error bars the comparison has no quantitative meaning) Comparison in radiance space can avoid problems due to use of a priori information for satellite retrieval (you can scale back the radiance differences to uncertainties in geophysical parameters at the end)

57. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200557 Outlook Very promising UT humidity data is now becoming available from MLS on Aura. Proposals for radio-occultation humidity measurements (my last lecture on Friday) Clouds play also a crucial role for the radiation balance (my first lecture on Friday)

58. Stefan Buehler, COST 723 UTLS Summerschool, Cargese, Oct. 3-15, 200558 Thanks for your attention. Questions?...