RADIOSONDE TEMPERATURE BIAS ESTIMATION USING A VARIATIONAL APPROACH

1. RADIOSONDE TEMPERATURE BIAS ESTIMATION USING A VARIATIONAL APPROACH Marco Milan Vienna 19/04/2012

2. MOTIVATION • Radiosondes data often used as reference for other data • Radiosondes data, especially upper air data, are not unbiased • Different radiosonde type have, generally, different bias • Different location for the same radiosonde leads to different bias • All long-period stations have shiftsleading to different bias • A bias adjustment, which take into account all this problems, is needed • Without bias adjustment, temperature trend are not believable

3. OUTLINE • Previous works: • Radiosondeadjustment during ERA-INTERIM • Current approach • Variational Bias correction • Type or predictors • Grouping • Preliminary results • Conclusions

4. ERA-Interim adjustment • Previous homogenization of radiosonde temperature dataset • Adjustment of annual mean bias • Use of RAOBCORE (Haimberger et al. 2007, 2008) • Based on time series of individual station • Detection of shifts in background departure • Adjustment can change temperature trends • Adjustment of daily/seasonal bias • Method based on solar elevation adjustment (Andrae et al. 2004) • Based on station groups • Four classes of solar elevation • Corrections calculated from the statistics of background departures over the previous 12 months

5. ERA-Interim adjustment • Russian radiosonde, 12 UTC, 200hPa • Start from 1979, when satellite data are available • First guess departure, using uncorrected radiosondes • Bias correction to apply • Bias correction only until 2008, no applied any more • After 2008 less departure but still existing, a bias correction is needed • Limited departure probably due tochanges on radiosondes dataset in the Russian federation

6. Variational Bias correction • The observations are considered biased, a linear predictor model is used as observation operator in the 4DVAR equations: • Introduction of a “bias term” in the variational cost function • With xband bb a priori estimations of model state and bias control parameters • A weak constraint (large Bb) allows the parameter estimates to respond more quickly to the latest observation. • The adjustment of the radiosondes depends on the resulting fit of the analysis to all other OBS, given the Background from the model.

7. Variational Bias correction • Bias in observation can change during the time • Seasonal and daily variations in bias exist • The Bias model : Must be choose according with observations and physical origins of the bias. • VarBC can be applied in the period where RAOBCORE detects a shift • We assume the model unbiased, the presence of model bias attributes a wrong bias to the observations where there are not enough observation to correct the analysis

8. Radiosonde temperature biascorrection • First results using only a constant bias parameter • Pressure, for every class (group) j: First approach, good for US and Japanese radiosonde • Solar elevation • The equation can be formulate also for classes of solar elevation, grouping stations with similar solar elevation.

9. First guessdeparturenight • Analysis of July 2011 • Results divided per station type • Largedifferences between different station type • Average of first guess departure • Control run without variational bias correction • Same analysis applying a basic variational bias correction (only b0) • No significant differences are visible with and without VarBC 1 2

10. RMS firstguessdeparturenight • The negative departures do not counteract the positive departures • RMS give more weight to the bigger first guess departure • The Russian stations has larger RMS in the upper levels and near 200 hPa • No significant differences are visible with and without VarBC 1 2

11. First guessdeparture solar elevation > 22.5° • Different behaviour between station during the night and with high solar elevation • Station in USA and Japan have larger positive departure in the upper levels • No significant differences are visible with and without VarBC 1 2

12. RMS firstguessdeparture solar elevation > 22.5° • Large RMS in the upper levels • About 0.5K smaller for USA • For Japan RMS in the upper levels different than during the night • Russia has still problems around 200 hPa • No significant differences are visible with and without VarBC 1 2

13. Bias correctionat 20 hPa NIGHT • Time series for bias correction at 20hPa • Generally very small bias corrections (as aspected). • For higher solar elevation larger bias corrections • Radiosondes with larger first-guess departure (Russia) have also the higher bias correction • The bias correction are in the “right” direction but the amount is too low SOLAR ELEVATION > 22.5°

14. First-guessdeparture biascorrection <T>=0.389 K • Station 23921, Russia • Vertical averaged first guess departure quite constant positive • We aspire a bias correction which converge to a positive value of about 0.4K • The bias correction for this station increase until a value around 0.02K and then decrease (negative fg-departure) • The bias correction is in the “right” direction but the amount is too low • B too large • We do not exclude the occurrence of computational problems

15. CONCLUSIONS AND OUTLOOK • Bias correction for temperature for radiosondes is far away from have a final solution. • Different approach as in ERA-CLIM • Use a “physical approach” (function of predictors) taking into account grouping of radiosondes • VarBC can be applied where RAOBCORE detects the shifts • First results, too low Bias correction but in the right directions • Different predictors and functions for different groups have to be tested (work in doing)

16. THANK YOU FOR YOUR ATTENTION

17. First-guessdeparture biascorrection <T>=0.137K • Station 27730, Russia • Vertical averaged first guess departure change from positive to negative • The negative bias corrections could counteract the positive • The bias corrections could be too slow