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Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD)

Evaluation of EURO4M Reanalysis data using Satellite Data. Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD). General Concept. Compare monthly means from reanalysis with gridded data

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Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD)

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  1. Evaluation of EURO4M Reanalysis data using Satellite Data Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD)

  2. General Concept • Compare monthly means from reanalysis with gridded data • So far, we used SMHI Reanalysis (1990 to 1995) (+ ERA-I for comparison) • Calculate spatial differences, distributions, correlations • Compare Anomalies, trends…. • Cloud Fraction • Surface Solar Radiation • Precipitation • Surface Albedo • Integrated Water Vapor

  3. General Concept • 6-hourly SMHI reanalysis data temporally averaged to monthly means on the 0.2 deg rotated grid (SMHI, 1990 to 1995); for radiation + precipitation the 24 h minus 12 h forecasted accumulation was used • Regridding of all data sets (sat, reanalysis) to a common regular lon-lat grid (conservative remapping); spatial resolution 0.2 or 0.5 deg (determined by the lowest-resolution data set) • Generation of one file with all available monthly means and the corresponding differences • Preparation of standardized figures for comparison

  4. Cloud Fraction Cloud Fraction; July 1994 SMHI SMHI – EURO4M SMHI – ERA-I Sat ERA-I- EURO4M ERA-I

  5. Cloud Fraction Cloud Fraction; July (mean, 1990 – 1995) SMHI – SatDaten SMHI Reanalysis

  6. Cloud Fraction • SMHI Reanalysis underestimates cloud fraction in the Mediterranean • Overestimation in January along the Norwegian Coast Cloud Fraction; January SMHI Reanalysis SMHI – SatData

  7. Cloud Fraction Cloud Fraction; Comparison with SYNOP EURO4M SatData SMHI Reanalysis • SMHI Reanalysis fits perfectly with SYNOP Cloud Fraction, satellite data overestimates SYNOP • Cloud Fraction is a tricky parameter for comparison, because of different definitions etc

  8. Solar Radiation Surface Solar Radiation; July SMHI Reanalysis SMHI – SatDaten

  9. Surface Solar Radiation; July SMHI Reanalyse ERA-Interim ERA-I – SatDaten

  10. Solar Radiation; Mean SMHI Reanalysis ERA-Interim • SMHI Reanalysis overestimates surface solar radiation • ERA-I compares better with Satellite Data • Interannual Variability captured by Reanalyses ERA – Sat-Daten SMHI – Sat-Daten

  11. Precipitation, Mean SMHI Reanalysis SMHI – EURO4M DataSet ERA – EURO4M DataSet

  12. Precipitation, July SMHI Reanalysis SMHI – EURO4M DataSet • Reanalyses overestimate precipitation • Interannual variability captured by Reanalyses ERA – EURO4M DataSet 12

  13. Conclusions • SMHI reanalysis compares well with SYNOP cloud fraction; underestimates satellite-derived cloud fraction in the Mediterranean • Satellite-derived Cloud Fraction not well suited for evaluation; different definitions, viewing geometries etc. • Surface Solar Radiation overestimated in SMHI Reanalysis (ERA is doing better); clouds too thin, wrong timing?? • Year-to-year variability captured • Too much precipitation in SMHI (and ERA-I) reanalysis, especially in mountainous regions, e.g. Alps

  14. Next Steps • Assess the surface albedo and integrated water vapor • Develop additional quality measures / metrics, e.g., for the ability of the reanalysis to quantify anomalies / trends etc. • How to perform the evaluation in the “space of the user”? What means “good enough”? • How to communicate the results to possible users of reanalysis data? What are the consequences of these results? 14

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