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April 2013 1 Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD) Evaluation of EURO4M Reanalysis data using Satellite.

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Presentation on theme: "April 2013 1 Jörg Trentmann, Uwe Pfeifroth, Jennifer Lenhardt, Richard Müller Deutscher Wetterdienst (DWD) Evaluation of EURO4M Reanalysis data using Satellite."— Presentation transcript:

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

2 April 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 General Concept

3 April 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 General Concept

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

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

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

7 April Cloud Fraction Cloud Fraction; Comparison with SYNOP SMHI Reanalysis EURO4M SatData 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 April Solar Radiation Surface Solar Radiation; July SMHI – SatDaten SMHI Reanalysis

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

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

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

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

13 April 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 April 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?

15 April 2013


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