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Overview

Validation of Mid- and High-Latitude Ocean Precipitation Retrievals from AMSR-E Grant W. Petty Longtao Wu University of Wisconsin-Madison. Overview. A variety of quasi-standard global precipitation products are now being routinely produced from AMSR-E data.

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Overview

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  1. Validation of Mid- and High-Latitude Ocean Precipitation Retrievals fromAMSR-EGrant W. PettyLongtao WuUniversity of Wisconsin-Madison

  2. Overview • A variety of quasi-standard global precipitation products are now being routinely produced from AMSR-E data. • e.g., GPROF (US), Wilheit (US), Liu (JAXA), Petty (JAXA), Wentz (US/RSS) • High-latitude precipitation, especially snowfall, presents the greatest challenge: • Greatest disagreement between different algorithms • No direct validation to date • Selected island weather stations at high latitudes show promise for validation of ocean retrievals but require careful assessment for measurement biases.

  3. Climatology of Fractional Time Precipitating (DJF)

  4. Climatology of snow occurrence – Northern Hemis. Winter

  5. Mid- and high-latitude islands with existing WMO stations – neglected resource?

  6. WMO stations on mid- and high-latitude islands • Unlike Morrissey's tropical atoll stations, these islands are neither very small nor very flat. • Issues with respect to use as validation for ocean precipitation: • Accuracy of measurements of local precipitation • Undercatch of snowfall, especially when windy • Representativeness with respect to surrounding open ocean • Orographic enhancement or rain shadows • Frictionally induced convergence • Differential heating

  7. Mid- and high-latitude islands with existing WMO stations – neglected resource? Thorshavn Jan Mayen Nikol’skaya Marion Isl.

  8. Comparison details • Monthly precipitation from a variety of quasi-standard AMSR-E algorithms (latest publicly available version) • averaged over 2.5 degree box, excluding land and coast. • 36 months (Jan. 2003 - Dec. 2005) • Initially: uncorrected gauge totals as reported in routine synoptic reports • No algorithm evaluated has been tuned to these island reports.

  9. Validation Statistics • Correlation coefficient • Measures tendency of algorithm to match the shape of the time series from the gauge, without regard to possible biases • Poor correlation indicates algorithm and gauges are not “seeing the same thing” -- potentially hard to correct • Good correlation hard to achieve by chance (N=36) –> strong circumstantial evidence that both retrieval and validation data are fundamentally sound, apart from possible biases • Ratio of the means • Reveals systematic biases in either algorithm or station data • Biases may or may not be easily correctable, depending on source • RMS difference • small RMS difference requires both good correlation and small bias • good measure of overall agreement, but poor diagnostic statistic

  10. Jan Mayen

  11. Jan Mayen comparisons

  12. Jan Mayen comparisons NObs Ratio Corr RMS Petty 36 1.38 0.81 33.85 GPROF 36 0.05 0.38 64.32 Liu 36 0.18 0.54 55.53 (Note: Daqing Yang's gauge correction, which is NOT applied above, increases Jan Mayen's annual precip by 14%, with most of the increase occurring during winter months)

  13. Thorshavn (Faeroe Isl.)

  14. NObs Ratio Corr RMS Petty 36 0.81 0.75 41.94 GPROF 36 0.31 0.59 91.98 Liu 36 0.29 0.63 92.14 (Note: Daqing Yang's gauge correction, which is NOT applied above, increases Thorshavn's annual precip by 6%, with most of the increase occurring during winter months)

  15. Bering Island

  16. Nikol'skoe, Bering Isl.

  17. Nikol'skoe comparisons

  18. NObs Ratio Corr RMS Petty 36 1.38 0.47 37.88 GPROF 36 0.26 0.08 50.30 Liu 36 0.46 0.22 46.38 (Note: Daqing Yang's gauge correction, which is NOT applied above, increases Nikol'skoe's annual precip by 35%, with most of the increase occurring during winter months)

  19. NObs Ratio Corr RMS Petty 22 1.120.2150.82 GPROF 22 0.41 0.13 104.88 Liu 22 0.58 0.06 89.98 Wilheit 22 0.62 0.01 77.10 Note very poor correlations with all algorithms!

  20. A closer look at Marion Island Note position of meteorological station in the lee of a large mountain – likely rain shadow! Measured precipitation will probably be biased relative to the open ocean, as well as varying strongly with direction of the prevailin wind. Conclusion: Marion Isl. Is probably NOT a suitable validation site for AMSR-E precipitation!

  21. Summary of Raw Results – Correlation Coeff. Lat MaxEl Petty GPROF Liu Wilheit JAN MAYEN 70.93 638 0.81 0.38 0.54 --- THORSHAVN 62.02 736 0.75 0.59 0.63 --- ST PAUL ISL. 57.17 173 0.75 0.77 0.61 --- NIKOL'SKOE 55.21 510 0.47 0.08 0.22 --- HORTA 38.52 2260 0.84 0.87 0.85 0.82 PONTA DELGADA 37.75 868 0.86 0.77 0.84 0.70 SANTA MARIA 36.97 575 0.81 0.85 0.80 0.77 LORD HOWE ISL -31.53 103 0.65 0.61 0.61 0.41 ISL. JUAN FER -33.62 857 0.84 0.84 0.75 0.70 GOUGH ISL -40.35 861 0.54 0.55 0.28 0.58 CHATHAM ISLAN -43.95 271 0.63 0.68 0.50 0.45 MARION ISL -46.88 1171 0.21 0.13 0.06 0.01 PORT-AUX-FRAN -49.35 878 0.40 0.44 0.34 0.02 FALKLAND ISL. -51.82 433 0.71 0.64 0.56 0.17 MACQUARIE ISL -54.48 360 0.68 0.55 0.63 0.32

  22. Summary of Raw Results – Ratios of Means Lat MaxEl Petty GPROF Liu Wilheit JAN MAYEN 70.93 638 1.38 0.05 0.18 --- THORSHAVN 62.02 736 0.81 0.31 0.29 --- ST PAUL ISL. 57.17 173 2.32 0.35 0.73 --- NIKOL'SKOE 55.21 510 1.38 0.26 0.46 --- HORTA 38.52 2260 0.91 0.51 0.64 0.73 PONTA DELGADA 37.75 868 0.64 0.38 0.52 0.57 SANTA MARIA 36.97 575 1.12 0.65 0.86 1.02 LORD HOWE ISL -31.53 103 0.93 0.57 0.75 0.88 ISL. JUAN FER -33.62 857 0.32 0.19 0.16 0.29 GOUGH ISL -40.35 861 0.54 0.27 0.35 0.35 CHATHAM ISLAN -43.95 271 1.01 0.48 0.59 1.04 MARION ISL -46.88 1171 1.12 0.41 0.58 0.62 PORT-AUX-FRAN -49.35 878 2.43 0.79 1.15 1.97 FALKLAND ISL. -51.82 433 0.73 0.26 0.35 1.13 MACQUARIE ISL -54.48 360 1.52 0.43 0.41 1.00

  23. Summary of Raw Results – RMS Difference (mm/month) Lat MaxEl Petty GPROF Liu Wilheit JAN MAYEN 70.93 638 34. 64. 56. --- THORSHAVN 62.02 736 42. 92. 92. --- ST PAUL ISL. 57.17 173 66. 36. 33. --- NIKOL'SKOE 55.21 510 38. 50. 46. --- HORTA 38.52 2260 34. 59. 45. 43. PONTA DELGADA 37.75 868 45. 71. 54. 57. SANTA MARIA 36.97 575 40. 46. 41. 41. LORD HOWE ISL -31.53 103 53. 74. 65. 62. ISL. JUAN FER -33.62 857 80. 97. 97. 91. GOUGH ISL -40.35 861 144. 214. 197. 192. CHATHAM ISLAN -43.95 271 45. 49. 57. 44. MARION ISL -46.88 1171 51. 105. 90. 77. PORT-AUX-FRAN -49.35 878 66. 20. 28. 54. FALKLAND ISL. -51.82 433 21. 40. 38. 38. MACQUARIE ISL -54.48 360 57. 56. 58. 53.

  24. Hot off the press: Wentz algo.(2003 only) Petty GPROF Wentz Petty GPROF Wentz Corr. Ratio Corr. Ratio Petty GPROF Wentz Petty GPROF Wentz Corr. Ratio Corr. Ratio

  25. Hotter off the press: Corrected gauge values from Daqing Yang • Accounts for • Gauge type and height above ground • Precipitation phase • Wind speed at time of precipitation • Temperature

  26. WMO ID Name Lat Lon 01001 JAN MAYEN 70.93 -8.67 NObs Ratio Ratio_PC Corr Corr_PC RMS RMS_PC Petty 26 1.46 1.30 0.79 0.81 37.07 32.11 GPROF 26 0.04 0.04 0.54 0.52 61.70 69.73 Liu 26 0.16 0.14 0.49 0.44 54.64 62.83 06011 THORSHAVN 62.02 -6.77 NObs Ratio Ratio_PC Corr Corr_PC RMS RMS_PC Petty 26 0.83 0.78 0.81 0.81 34.76 41.00 GPROF 26 0.29 0.28 0.51 0.51 89.12 97.14 Liu 26 0.24 0.22 0.55 0.55 93.52 101.33 70308 ST PAUL ISL. 57.17 -170.22 NObs Ratio Ratio_PC Corr Corr_PC RMS RMS_PC Petty 26 2.36 1.66 0.68 0.56 67.31 58.41 GPROF 26 0.33 0.23 0.65 0.43 35.83 57.75 Liu 26 0.76 0.54 0.49 0.30 34.58 52.63

  27. Conclusions • Despite two decades of ocean precipitation retrievals from SSM/I, TMI, and AMSR-E and multiple algorithm intercomparisons, this analysis represents the first known direct validation of mid- and high-latitude amounts. • Except for Petty algorithm, apparent tendency by standard algorithms to underestimate and/or miss precipitation at high latitudes, especially snowfall. • For greater confidence in the detailed validation results, we urgently need • Careful assessment of station biases relative to surrounding ocean (e.g., importance of local orographic suppression or enhancement of precip) • Correction of gauge biases due to wind+snow, etc., using methodology of Yang et al. applied to S.H. stations.

  28. Ongoing Work • Acquire high resolution topography of all islands (not as easy as it sounds!) • Run mesoscale model simulations of precipitation events to assess orographic influences. • Funding permitting: Site visits to assess gauge siting, maintenance, etc. Also, may place additional recording gauges to assess variability.

  29. Passive microwave methods over ocean • Emission/attenuation based methods (10-37 Ghz) • Viable when reasonably deep rain layer present (not always true!) • Quasi-direct when effective rain layer depth can be estimated. • Complementary spatial and intensity information from different frequencies. • Use of normalized polarization (e.g., Petty, GPROF) greatly reduces ambiguities due to scattering and background variability. • Scattering based methods (89 Ghz) viable • Viable when ice-phase precipitation present in column (not always true!) • Indirect (statistical) relationship between ice scattering aloft and surface precipitation rate; also, scattering signature may be weak. • Can complement emission-based retrievals when liquid phase is present. • Use of polarization (e.g., Spencer, Petty, GPROF) allows separation of cold strongly polarized ocean from cold weakly polarized precipitation.

  30. Example over the Baltic Sea (Bennartz et al.)

  31. Passive and active MW aircraft observations of shallow convective snowfall – Wakasa Bay

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