Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 CMSAF water vapour in comparison to radiosondes and CHAMP Ralf Lindau

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Water vapour from ATOVS Global daily fields of Layered Precipitable Water LPW are calculated by kriging method on 90 km resolution. Advantage of kriging : An error map is available for each field. Example: – 700 hPa

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Radiosondes 173 GUAN (GCOS Upper-Air Network) stations well distributed over the globe.

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 It‘s the resolution, stupid For any comparison of two data sets resolution is crucial. If resolutions are not equal, the often actually seriously presented conclusion would be: The low resolved observations underestimate high values and overestimate low values. ATOVS fields: Daily, 90 km Radiosondes: 4 times a day, point measurements Thus, average the radiosondes over 1 day. But still Radiosonde data include additionally the spatial variance of daily means within 90 x 90 km 2

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Variability of Water Vapour Famous paper: Lindau, R. and E. Ruprecht, 2000: SSM/I-derived total vapour content over the Baltic Sea compared to independent data, Met.Zeitschrift, 9, No.2, SSM/I-derived total vapour content over the Baltic Sea compared to independent data 1 day include 8 mm 2 of variance 90 by 90 km 2 include 3 mm 2 of variance The spatial variance of temporal means must be smaller than that of individuals (3 mm 2 ).

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 October mm + 17 % High correlation: r = 0.95 But wet bias of 3.82 mm 2

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Happy with correlation ? Correlation reduction due errors is: 1 - r=e 2 / (  2 + e 2 ) =7.5 mm 2 / 225 mm 2 =0.03 More than half of the scatter is explained by random errors in ATOVS and RS. But there is still the bias.... The error of ATOVS is explicitely calculated within kriging. The mean error variance for October 2004 and for those 173 gridboxes, where RS data is available is: e AT = 6.19 mm 2 The error variance of daily means from RS is calculated by: Internal variance / observ number: e RS = 7.65 mm 2

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Different layers mm + 21 % mm + 5 % 1000 – 850 hPa 700 – 500 hPa

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Time series of the bias The bias is not confined to October It persists through 10 month

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Discriminating GUAM data Ocean / LandHeight above sea or is it the ice surface in the Antarctica?

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Ocean, Height, Ice ? mm + 16 % mm + 9 % mm - 7 %

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Different First-guesses No GME NCEP Different first-guess (taken as basis for the retrieval) result in strong differences. Example: , hPa

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 ATOVS vs Radiosondes No GME NCEP mm - 3 % mm + 14 % mm + 27 % The corresponding bias vanishes for „No first guess“ and is doubled, if NCEP is used.

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar and what about Champ ? Example for 1 month of Champ data obs/month 1 obs/10 min Irregularly spreaded over the globe

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 ATOVS vs Champ 850 – 700 hPa 700 – 500 hPa mm - 15 % mm - 5 % mm + 2 %

Diplomanden-Doktoranden-Seminar Bonn, now Cologne – 8 Januar 2007 Conclusion ATOVS vs radiosondes -ATOVS has a wet bias compared to radiosondes. -Bias is decreasing with height. -It persists through all discrimination experiments. -First guesses play an important role for the retrieval. Champ vs ATOVS -Champ has dry bias compared to ATOVS -(thus, seems to agree with RS, but no direct comparison possible) -Bias is decresing with height.