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Detailed Analysis of ECMWF Surface Pressure Data. E. Fagiolini 1 , T. Schmidt 1 , G. Schwarz 2 , L. Zenner 3 (1) GFZ Department 1, Potsdam, Germany (2) DLR IMF, Oberpfaffenhofen, Germany (3) TUM IAPG, Munich, Germany EGU 2012, Vienna, Austria. Summary of this Presentation.
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Detailed Analysis of ECMWF Surface Pressure Data E. Fagiolini1, T. Schmidt1, G. Schwarz2, L. Zenner3 (1) GFZ Department 1, Potsdam, Germany (2) DLR IMF, Oberpfaffenhofen, Germany (3) TUM IAPG, Munich, Germany EGU 2012, Vienna, Austria
Summary of this Presentation • Project Background • Selected Surface Pressure Data • Basic Data Characteristics • Spatial Neighbourhoods • Temporal Neighbourhoods • Outlook
Project Background • Priority Programme ”Mass transport and Mass distribution in the System Earth” of the German Research Foundation (DFG). • Project IDEAL-GRACE: ”Improved De-Aliasing for Gravity Field Modelling with GRACE”Partners: TUM IAPG, GFZ, DLR IMF, UHH IfM. • An improved gravity field modelling requires computation of atmospheric contributions (mainly surface pressure). • How good are available surface pressure data?
Selected Surface Pressure Data • We selected ECMWF surface pressure data from operational analysis,, global coverage, every 6 hours, N48 Gauss grid (192 columns, 96 lines), 29% land / 71% water. • ECMWF surface pressure = combination of predictive modelling and measurements. • Time period: 1 year from Sep. 1, 2007 to Aug. 31, 2008 366 days, every 6 hours => 1464 data sets. • Footprint per sample: about 200 * 200 km. • A few outliers had to be removed by interpolation.
Median Values of 00, 06, 12 and 18 Hour Data Small differences around Indonesia.
Pressure Profile Along +45 deg. N Europe Asia Pacific Ocean N. America Atlantic Ocean
Histogram of Global 1 Year Surface Pressure Levels Mountain areas
Contrast Enhanced Dynamic Pressure Range Equatorial Regions are quiet
Surface Pressure Time Series Data Horizontal axis: transect along -30°S Vertical axis: time (1 year, midnight data) Sept. Principal motion of the pressure areas. Febr. Aug.
Time Series Data Horizontal axis: transect along -60°S Vertical axis: time (1 year, midnight data) Pressure data Low pass data High pass data
Zero Meridian Transects versus Time (Contrast Enhanced) North Pole Equator South Pole Horizontal axis: time (1 year, midnight data) Vertical axis: transect along the zero meridian
Total Variation of 6 Hour Pressure Levels Longitude-dependent total variation: interpolated time step effects?
Contrast Enhanced Entropy of Pressure Levels The information content is not constant.
Histogram of East/West 1 Sample Differences Back-to-back Laplacians visible small fluctuations and noise.
Histogram of North/South 1 Sample Differences Broader the surface pressure differences are direction-dependent.
Histogram of East/West 2 Sample Differences Sum of 2 Gaussians two overlapping effects.
Histogram of North/South 2 Sample Differences Again, direction dependence.
Histogram of East/West 16 Sample Differences Single Gaussian we see different effects.
Histogram of North/South 16 Sample Differences the longitude dependence becomes smaller.
Pressure < 990 hPa above Water The east coast shores of the continents show rather low pressure values. Why?
Differences Between 6 h Time Steps above Land Single Gaussian very regular distributions above land.
Differences Between 6 h Time Steps above Water Double peak distribution above water. Due to the microwave instruments?
Distribution of 6 h Biases above Water at 06h The bias locations have changed. Is it a sensor effect, or is it physical reality?
Conclusion and Outlook • ECMWF data describe a large variety of atmospheric effects in the spatial and temporal domain and at various scales. • Outlier correction and removal of topographic background allows detailed studies of temporal phenomena (e.g., transects). • Many open questions. • A further topic to be discussed is the analysis of estimated errors provided by ECMWF (current activities).
