L. Zenner 1, E. Fagiolini 2, F. Flechtner 3, T. Gruber 1, G. Schwarz 2, T. Trautmann 2, J. Wickert 3 1 Institut für Astronomische u. Physikalische Geodäsie,

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Presentation transcript:

L. Zenner 1, E. Fagiolini 2, F. Flechtner 3, T. Gruber 1, G. Schwarz 2, T. Trautmann 2, J. Wickert 3 1 Institut für Astronomische u. Physikalische Geodäsie, TU-München 2 Institut für Methodik der Fernerkundung, DLR, Oberpfaffenhofen 3 GeoForschungsZentrum Potsdam SPP Project: IDEAL-GRACE – Improved De-ALiasing for Gravity Field Modelling with GRACE The Impact of Uncertainties of Global Atmosphere Models on the Gravity Field Determination with GRACE

Motivation The De-Aliasing process seems to be one of the main remaining uncertainty source in the GRACE data analysis. Specifically atmospheric mass variations play a major role! Uncertainties in atmosphere models have direct impact on GRACE (c.p. fig: Degree-Variances of atmosphere signal is „above“ the error of GRACE)

`IDEAL – GRACE´ (Improved De-ALiasing for Gravity Field Modelling with GRACE) Project Goals Atmospheric De-Aliasing Process Geocenter L H H L P S Moving satellite  improved modelling of high frequency atmospheric and oceanic effects as well as improved GRACE gravity field time series by taking into account the uncertainties of these models. 1. Representative error measures for atmospheric and oceanic parameters shall be determined (comparison between different atmospheric models, radio occultation, remote sensing data, etc.). 2. The de-aliasing concept shall be improved by developing an error propagation for the de-aliasing.

Input – Parameter from ECMWF/NCEP: Temperature T, Specific Humidity S, Surface Pressure P s, Surface Geopot. Height Fundamental Formulas: Atmospheric De-Aliasing Concept - Vertical Integration - ==>

Input-Parameter Atmospheric De-Aliasing (I) Surface Specific Humidity [kg/kg] 01. December h Error Surface Specific Humidity [kg/kg]

Surface Temperature [K] Input-Parameter Atmospheric De-Aliasing (II) 01. December h Error Surface Temperature [K] Difference ECMWF – NCEP

Input-Parameter Atmospheric De-Aliasing (III) Surface Pressure [hPa] 01. December h Error Surface Pressure [hPa] Difference ECMWF – NCEP

Error „vertical integral“: Mathematical Model - Error Propagation (I) Law of error propagation (no covariances): Error of virtual temperature: Error of „half-level“ pressure: Error of geopot. height in „half-levels“:

 Error of the potential coefficients C nm, S nm due to uncertainties in the atmosphere model. Mathematical Model - Error Propagation (II) Full error propagation up to the „inner integral“ is realized. Simplified approach for the spherical analysis (error propagation for the spherical harmonic analyses not yet implemented)

Propagated Errors on potential coefficients Square root of degree variances in terms of geoid heights [mm] GRACE is SENSITIVE to Uncertainties in Atmosphere Models! Error in terms of geoid heights due to uncertainties in the atmosphere model (simplified approach!): „Error – Parameter“Global Mean [cm] all parameter1.8 surface pressure1.8 temperature1.1 e-3 geopotential height2.2 e-4 specific humidity4.1 e-5

Effects of the Uncertainties on the Geoid [cm] Propagated Error of all atmospheric input parameter (temperature, surface pressure, specific humidity, geopotential height) on the geoid. 01. December h Min1.73 cm Max1.94 cm Mean1.81 cm RMS cm

Conclusions Until now, the input parameters for atmospheric de-aliasing are regarded as error-free! BUT: Uncertainties in Atmospheric Models have significant impact on GRACE (for the long wavelengths up to degree 30) most significant: surface pressure almost negligible: temperature, specific humidity, geopotential height THEREFORE:  To get more reliable homogenous potential coefficient series, error propagation should be included in the standard GRACE – De-Aliasing process (AOD1B-Product).  Representative error measures for atmospheric and oceanic parameters have to be determined.

Thank you for your attention! Visit our poster: The Sensitivity of Satellite Gravity Field Determination to Uncertainties in Atmospheric Models E. Fagiolini, L. Zenner, F. Flechtner, T. Gruber, G. Schwarz, T. Trautmann, J. Wickert Poster Session B, Tuesday (Today!) 16/10 at 6 p.m