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Assessment of precipitation characteristics in the South American Monsoon System from different data sets Charles Jones 2 Leila M. V. Carvalho 1,2 , Adolfo N. D. Posadas 3,4 , Roberto Quiroz 3,4 , Bodo Bookhagen 1,2 and Brant Liebmann 5

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  1. Assessment of precipitation characteristics in the South American Monsoon System from different data sets Charles Jones2 Leila M. V. Carvalho1,2, Adolfo N. D. Posadas3,4, Roberto Quiroz3,4, Bodo Bookhagen1,2and Brant Liebmann5 1Department of Geography, University of California, Santa Barbara, CA 2Earth Research Institute, University of California, Santa Barbara, CA 3 International Potato Center (CIP), Lima, Peru 4 Department of Physics, Universidad Nacional Mayor San Marcos, Lima, Peru 5 CIRES Climate Diagnostics Center, Boulder, CO, USA

  2. Outline • Objective: compare statistical properties of precipitation in the South American Monsoon System in different data sets (sensors and spatial resolution) • Statistical downscaling of precipitation

  3. Data sets • ESRL (B. Liebmann)*: simple grid point averaging of station data; 2.5 latitude/longitude • GPCP (Global Precipitation Climatology Project)*: (infrared) satellite/station merged; 1.0 latitude/longitude • CPCu (Climate Prediction Center unified precipitation): optimal interpolation (OI) of station data; 0.5 latitude/longitude • CFSR (Climate Forecast System Reanalysis): latest NCEP reanalysis; first-guess; 0.5 latitude/longitude • MERRA (Modern Era Retrospective-analysis for Research and Applications): latest NASA reanalysis; first-guess; 0.5 latitude x ~0.3 longitude • TRMM (Tropical Rainfall measuring Mission): microwave satellite; 0.25 latitude/longitude • Daily averages 1 January 1998 – 31 December 2008 • *Some missing data

  4. Analysis of SAMS • Empirical Orthogonal Function (EOF) analysis: • Precipitation 1 Jan – 31 Dec 1998-2008 • Remove long-term mean anomalies • First and Second EOFs and Principal components • SAMS Onset/demise: smooth PC1 (10 x w/15 day mav) Onset Demise Duration

  5. First EOF Correlation between PC1 and precipitation anomalies

  6. Second EOF Correlation between PC1 and precipitation anomalies

  7. SAMS

  8. SAMS Seasonal amplitude Integral of positive anomalies of PC1 from onset to demise

  9. SAMS Seasonal amplitude

  10. SAMS Anomaly correlation • remove annual cycle from PC1 and PC2 • compute anomaly correlation • (1 Nov – 31 Mar)

  11. SAMS Power spectrum of PC1 normalized by seasonal variance

  12. SAMS Power spectrum of PC2 normalized by seasonal variance

  13. SAMS Mean Precipitation (1 Nov – 31 Mar)

  14. SAMS • Frequency distribution of precipitation • 1 Nov – 31 mar 1998-2008 • Precipitation  0.1 mm day-1 • Fit gamma frequency distribution: • shape () and scale () parameters • 25th and 75th percentiles

  15. Examples of  and 

  16. SAMS Gamma frequency distribution: 

  17. SAMS Gamma frequency distribution: 

  18. SAMS Gamma frequency distribution: 75th percentile

  19. SAMS Gamma frequency distribution: 25th percentile

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