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Yudong Tian NASA/GSFC & ESSIC, Univ. Maryland, Maryland, USA Christa Peters-Lidard

Quantifying Uncertainties in Satellite-based Global Precipitation Measurements. Yudong Tian NASA/GSFC & ESSIC, Univ. Maryland, Maryland, USA Christa Peters-Lidard NASA/GSFC, Maryland, USA.

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Yudong Tian NASA/GSFC & ESSIC, Univ. Maryland, Maryland, USA Christa Peters-Lidard

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  1. Quantifying Uncertainties in Satellite-based Global Precipitation Measurements Yudong Tian NASA/GSFC & ESSIC, Univ. Maryland, Maryland, USA Christa Peters-Lidard NASA/GSFC, Maryland, USA Special thanks: John Eylander, Robert Adler, George Huffman, Robert Joyce, Takuji Kubota, Tomoo Ushio, F. Joe Turk, Kuo-lin Hsu, Pingping Xie, Mingyue Chen and many others. IPWG-V, Hamburg, Germany, 11-15 October, 2010

  2. Introduction • Satellite remote-sensing is critical for global-scale precipitation measurements. • Satellite sensors can cover vast, diverse areas of the globe • A global perspective of uncertainties is needed

  3. Questions to address: Globally, how much uncertainty is in current satellite-based precipitation measurements? How does the uncertainty depend on regions, land surface types, seasons, precipitation regimes, etc.? Are these dependencies systematic?

  4. Methodology un·cer·tain·tyn. range of disagreement among independent measurements of the same physical quantity • We use the measurement spread from an ensemble of independent datasets to estimate uncertainties • It does not require “ground-truth”

  5. Six high-resolution datasets formed the ensemble

  6. Caveats: • Ensemble members are different but not independent • Ensemble size is not large • Outlier can spoil results (we removed one outlier at each time/grid) • Consequently, • Uncertainties will be underestimated • Include contribution from both systematic and random errors • Do not require “ground-truth,” but also can not determine systematic errors • Can not tell if one member is better than others

  7. Results: Mean state of the ensemble Global patterns of uncertainties Factors affecting the uncertainties

  8. Ensemble mean of global precipitation for winter (DJF) and summer (JJA)

  9. Uncertainties over the globe: winter vs. summer

  10. Uncertainties over the globe: spring vs. autumn

  11. Uncertainties are affected by surface type, season and region • Higher uncertainties over winter, NH land

  12. Different regions show different seasonal variations • May be related to change of precipitation regimes (stratiform vs. convective)

  13. Inferring contribution from systematic errors: comparing ensemble members with gauge analysis over U. S.

  14. Summary • An ensemble of 6 global satellite-based precipitation datasets was used to estimate measurement uncertainties. • 2. Lower uncertainties (20-50%) over ocean and over tropical land. • 3. Higher uncertainties (60-100%) over higher latitude land surfaces (Europe, N. America): • -- cold seasons worse than warm seasons • -- bad over complex terrains • 4. Strong seasonal dependence for Europe and N. America

  15. Quantifying the challenges for satellite-based retrievals • Higher latitudes, especially land surfaces • Areas with light rain or snow • Complex terrain • Coastlines

  16. Spread within ensemble as function of mean rain rate: • Spread among the ensemble members increases with mean rain rate • Lower at higher rain rate: better agreement for stronger events, relatively

  17. Look under the hood: TRMM Multi-satellite Precipitation Analysis (TMPA; Huffman et al. 2007)

  18. Each dataset was produced by merging retrievals from various sensors

  19. Introduction • Challenges in quantifying uncertainties in satellite data (Rs): • Ground truth (R0) unknown • Lack of reference data (Rr) • Lack of uncertainty estimates in reference data themselves • var(Rs-R0) = var(Rs-Rr) - var (Rr – R0)

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