2 Calculating Biases from Radiosonde Intercomparison Experiments

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1 RSHOM-NOTIZ Nr. 157 Concern: Experimental Estimated Biases of Radiosondes and RAOBCORE Comparison Date: November 28, 2005 Author: S. Sperka Pages: 7 1 Introduction Radiosondes have been launched from weatherstations all over the world since the 1940 s, and provided upper air data for operational weatherforecast and other applications like climate research, verification of satellite measurements and model verification. For climate research it is necessary to look at time series, and radiosondes are the only source of upper air observation prior to the 1970 s. These time series can be affected by a change of the radiosonde used in operational observation, due to the fact that different measurement systems lead to different results. To work out these biases in reality the WMO organized Radiosonde Intercomparison Experiments, where different radiosondes were launched at the same time to work out the abberation between them. In this manuscript the author is comparing biases, calculated from Radiosonde Intercomparison experiments, with corrections obtained from the project checking the temporal homogeneity of ERA-40 observations and analysis using analysis feedback data by Haimberger. That means the only parameter that is available for comparison is the temperatur in different standard pressure levels. 2 Calculating Biases from Radiosonde Intercomparison Experiments The main reason for errors in temperatur measurements is the fact, that the different radiation protection devices that were used are not from the same quality. Radiation protection has got to prevent the temperature sensor from solar radiation (which would cause a temperatur too high) as well as from infrared cooling (which would cause a temperatur too low). Cooling from evaporating water when emerging from cloud layers is another effect that causes different results in temperatur measurements, but this errors are smaller and only minor effects. In the WMO Radiosonde Intercomparison Experiments up to five common radiosonde types were flown in simultaneous operation, suspended about 40 m below a single ballon. The flight data obtained from these experiments is the base for calculating the systematic errors between the sondes. To obtain a value for the systematic errors between two radiosondes, it is importend to compare only flights were both systems took part and successfully delivered flight data. For every flight that fulfilled these conditions the temperatur differences in standard pressure levels were calculated and in the end averaged over the number of flights. Picture 2.1 shows the mean difference between Vaisala RS80 and four other common Radiosondes, that took part in the WMO Radiosonde Intercomparison Experiment Phase II, carried out on Wallops Island (USA) in The number of flights available for estimating the differences was between 20 and 80, depending on the type of radiosonde, how often it was flown and how successful those flights were. 1

2 Figure 2.1: Mean difference to Vaisala RS80 calculated from WMO Radiosonde Intercomparison Experiment Phase II 3 Standard Deviation and Spreading of Measurement Data In picture 2.1 all available flights were used to estimate the mean difference, without considering the influence of sunelevation at the time the flight took place. This leads to a very wide distribution, easy to see in picture 3.2. To work out the effects of the sun on the bias, the sunelevation was calculated for every flight. This gives an easy tool to work out the importance of a good radiation protection. Picture 3.3 shows the day and night abberation between the models VIZ 1392 and Vaisala RS80. For this picture the VIZ-Temperatures were substracted from Vaisala-Temperatures. Positiv abberation at nighttime means that VIZ-Temperatures are to cold, maybe effected by infrared cooling, whereas negative abberations during daytime hints a bigger solar radiation effect on the VIZ sonde. These differences are distributed a lot sharper easy to see in picture 3.4. Derived from this examination, it can be said that the sunelevation has got a major influence on radiosonde temperatur data and their correction. It was not allways possible to consider this factor in the following comparisons, but done whenever makeable and often leading to better results. 4 RAOBCORE RAdiosonde OBservation COrrection using REanalysis(Haimberger 2005) is an attempt to use innovation statistics of a data asimilation system to correct climatological time series. In this project background-observation differences, available from projects like ERA-40, are used to detect and correct artificial breaks in radiosonde time series. The adjustements,derived from this 2

3 Figure 3.2: Distribution of abberations between Graw M60 and Vaisala RS80 in 30hPa. Figure 3.3: Daytime difference(sunelevation more than 0 ) shown with dotted line, nighttime difference (sunelevation less than -0 ) solid line, horizontal lines are standard deviations of difference. 3

4 Figure 3.4: Distribution of daytime-abberations between VIZ-1392 and Vaisala RS80 in 70 hpa. procedure, are available for every month between 1957 and 1997 respectively for 12 and 00 UTC, and are getting compared to the biases calculated above. 5 Comparison Between RAOBCORE Corrections and Estimated Biases The first attempt to compare RABCORE adjustements and estimated systematic errors was to examine selected stations, that went threw a documented change of observation systems. For picture 4.5 the sample of flights used to calculate the bias, had to be limited to those who had a sunelevation similar to the 00 UTC flight in darwin. To realize this, without cutting off to much from the sample and not having a decent amount of flights available to build the bias, it seemed to be appropriate to use flights that were within the interval of the darwin sunelevation +/-15. Although the correction is fairly bigger than the calculated difference, the two graphs are related. This could be shown for a few stations, but there were also cases where RAOBCORE adjustments looked different from the calculated abberations. The main problem while looking for stations that had a well documented change of observation systems was, that the swap went from old to new system, and the WMO Radiosonde Intercomparison Experiments were comparing systems which were common at the time they took place, with Vaisala RS80 and VIZ-1392 being the exceptions. These two Radiosondes took part in every experiment, so they can be used to link Radiosondes and calculat a bias, even if the radiosondes were not flying together in one experiment. To do so the abberations to the link sonde were worked out and put together. This happened for picture 5.6, where the change went from Graw M60 (took part in WMO experiment Phase2) to Graw 78C (took part in Phase1). As before, only flights that had resembling sunelevations to the 12 UTC flight in Munich were used to calculat the abberations. Although the graphs have not got a 4

5 Figure 4.5: Comparison for a change from Philips MK-III to Vaisala RS80, 00 UTC correction. great similarity, they both show positiv values in the stratosphere, between 70 and 20 hpa. The second attempt to compare RAOBCORE adjustements was to compare all stations, that had a documented change of some kind, to the biases calculated from comparison experiments. This was fairly easy for the change from Philips Mk-III to Vaisala RS80 radiosondes, because a lot of stations in Australia did have this change around After making sure that only RAOB- CORE adjustements from Australia were about to be compared, it was even possible to consider the effect of sun-elevation. The sun has allready been rising in australia for the observation time 00 UTC, so it seemed to be appropriate to use only flights that took place when the sunelevation was greater than 5 for comparison. The outcome is shown in picture 5.7. The adjustements seem to correct a lot more than necessary, knowing the radiosonde bias, but there s an undeniable resembling. Other changes examinated did not show such an analogy, for example the change from Meisei RS2-80 to Meisei RS2-90, shown in picture 5.8. This change took place in Japan in the 1990 s, and it is interesting to see that the calculated bias from the WMO Experiments has got a rather small standard deviation. RAOBCORE corrections for this change neither resemble themselves nor do they show a clear analogy to the estimated difference. A lot of observation system changes are not well documented, limiting the adjustements to compare. For the change from Graw M60 to Vaisala RS80 only two stations could be found that provided enough metadata to take them in consideration, see picture Summary The lack of metadata documenting changes and radiosonde comparisons between old and new models, make it hard to effectively compare RAOBCORE adjustements to calculated biases. For the author conclusion of this article would be that radiosonde temperature data is very sensibel 5

6 Figure 5.6: Comparison for a change from Graw M60 to a Graw 78 radiosonde in munich Figure 5.7: Change from Philips Mk-III to Vaisala RS80 thick red line, RAOBCORE adjustements thin blue lines 6

7 Figure 5.8: Change from Meisei RS2-80 to Meisei RS2-90 thick red line, RAOBCORE adjustements thin blue lines pertaining to sunelevation. This means radiosonde data should be corrected differently for day and night, depending on the position of the sun. References 7

8 Figure 5.9: Change from Graw M60 to Vaisala RS80 thick red line, RAOBCORE adjustements thin blue lines 8