database was constructed to have realistic statistics. One can conclude that, for midlatitudes, the best UTH retrieval strategy is to derive UTH with and without cloud/surface filter, and use these as error bounds for the true value.
The same strategy is likely to be also applicable to the tropics, but it is harder to prove this, since the radar-derived cases database is at present only available for midlatitudes. However, a first look at global AMSU UTH data reveals that in the tropics the difference between total-sky and clear-sky UTH is also less than 3 %RH. Thus, the total-sky and clear-sky UTH values are useful error bounds for microwave data. For IR data they are also error bounds, but further apart, and hence less useful.
Besides the cloud issue, it was shown that the proposed filter also removes surface contaminated data, which can occur in certain areas in the winter season.
The impact of surface contamination on UTH is comparable to the cloud impact, but slightly larger. Also, the impact can be a low or high bias, depending on the surface conditions. In areas and seasons where surface contamination occurs the data should only be used with caution.
6 UTH From Microwave Data
The importance of the water vapor and the publicly availability of as accurate as possible measurements of the upper tropospheric humidity (UTH) data were already mentioned elsewhere in this thesis. The current study I am going to describe in this chapter is the contribution to better understand the atmospheric processes linked to UTH and to fill a gap of the accurate long-term measure-ments of UTH.
As mentioned in Chapter 5, two frequency regions are used to obtain long-term UTH, one in the infrared (IR) around 6.3 to 6.7 µm and another one in the microwave region at 183.31 GHz. The IR measurements are available from the instruments on the geostationary (GEOS, METEOSAT) and the polar (HIRS) orbiting satellites. There are a number of studies conducted using these data [Soden and Bretherton, 1996;Held and Soden, 2006; Soden et al., 2005;Bates and Jackson, 2001;Spangenberg et al., 1997;Allan et al., 2003]. The microwave measurements are available from the SSM/T2 and AMSU-B instruments, which are both on board polar orbiting satellites.
Also the Microwave Limb Sounder (MLS) on board Upper Atmospheric Re-search Satellite (UARS) provides the UTH measurements [Read et al., 1995].
The UTH data are available at the certain pressure levels (316, 215, and 147 hPa). Because of the technical problems the instrument worked continu-ously for 2.3 years, after that only the intermittent measurements were avail-able. Nevertheless, there are a number of studies using this data [Sandor et al., 1998;Stone et al., 2000; Chen et al., 1998]. In July 15, 2004 a similar instru-ment was launched on board the Earth Observing System (EOS) Aura satellite [Cuddy et al., 2006]. There is a number of studies using these early data [Su et al., 2006;Liu et al., 2007]. Because of the limb viewing geometry these data provide a decent vertical structure of the water vapor profile.
Another recently available tropospheric humidity data are from the Atmo-spheric Infrared Sounder (AIRS) on board Aqua satellite launched on May 4, 2002 [Aumann et al., 2003]. As an example of the studies using these data, one can mention the recent work byGettelman et al.[2006]. There, the authors dis-cuss the climatology of UTH from the AIRS measurements and its implications for the climate.
Continuous data from the SSM/T2 are available since 1994. It also has a chan-nel at 183.31±1GHz. Therefore, the retrieval method developed byBuehler and
John[2005] is expected to work also for the SSM/T2 measurements. Moreover, there are measurements from the Microwave Humidity Sounder (MHS) on board NOAA-18 satellite. It is the same as the AMSU-B, but instead of the double-band channel at 183.31±7 GHz it has the single-double-band channel at 190.31 GHz.
The comparison study byKleespies and Watts[in press] showed that there are no differences in the results between the AMSU-B and the MHS sounding chan-nels. One exception, though, are above mentioned channels, where the AMSU-B 183.31±7 GHz brightness temperatures are slightly warmer than those of the MHS 190.31 GHz. The AMSU-B 183.31±7 GHz brightness temperatures are used to filter out the clouds [Buehler et al., 2007]. This filter can be easily adjusted to the MHS measurements for further studies.
Combining the continuous measurements from the SSM/T2, the AMSU-B, and the MHS instruments gives us a possibility to study UTH over a long period of time. Given that these instruments have very similar channels to retrieve UTH from, having large biases due to inter-satellite calibrations is not expected. For practical reasons, since the data were at hand, the AMSU-B measurements are used in the current study. However, the UTH studies using the SSM/T2 and the MHS measurements are planned for the near future [John et al., 2006b].
Rosenkranz [2001], Jimenez et al. [2005], and Houshangpour et al. [2005]
showed the possibility to retrieve the humidity from the A and AMSU-B measurements. However, to my best knowledge there is no published study on the long-term analysis of UTH from the AMSU-B measurements.
Here, the UTH data are not validated against other such data. There are sev-eral reasons for this. First of all, to my knowledge there are no true standard UTH data available to compare with. All available UTH data have the uncer-tainties of different kind of nature. In many cases it is very little known about the origin of those uncertainties. Therefore, it is expected to have differences in such validation study, and it is very difficult to explain those differences. Sec-ond, it is the lack of data to compare with. Moreover, conducting such study is not a focus of this thesis.
Instead, I rise a confidence in the quality of the retrieved UTH data by doing several exercises. These exercises are designed in such way that they examine the consistency of the properties of the UTH data with known meteorological observations, and with the properties of similar UTH data retrieved from dif-ferent sensors. These exercises are presented and discussed in Section 6.2. In subsection 6.2.1 the long-term UTH retrieved from the three satellites carrying the AMSU-B instrument will be discussed. Next, the differences between those satellites in the UTH space will be discussed. Then, the annual cycle of the global UTH distribution, the related zonal mean UTH, and the variation of UTH over a long period of time for the different latitude bands will be discussed.
This chapter will be continued with describing the methodology of the study
6.1 Methodology 55
and finished with a summary and the conclusions of this study.
6.1 Methodology
For this study the microwave data from the AMSU-B instrument (see page 13) are used. Time period of the data used varies with the instrument on different satellites. For 15 it is from January 2000 to February 2007, for NOAA-16 from October 2000 to February 2007, and for NOAA-17 from August 2002 to February 2007. The details of the origin of these data and the tools used to process them have been discussed in Section 4.1 of this thesis.
In the retrievals meant for climatology studies usual practice is to average the data over a certain period of time. However, John et al. [2006a] showed that for the UTH climatology studies using a median is more suitable than using an arithmetic mean. Also in the current study, to retrieve the monthly UTH a median is used.
In Buehler et al. [2007] we concluded that presence of clouds in the mi-crowave observations attributes to about 1 %RH of a bias. Moreover, screen-ing out the data which are classified as cloud contaminated introduces a slightly larger, about -2.5 %RH, bias. As one strategy in studying the UTH climatology we recommended to retrieve UTH once filtering clouds out and once leaving them in the data, then taking the value in the middle of two as a representative true UTH value. In the currently presented study this strategy is followed and the cloud filter discussed inBuehler et al.[2007] is used.
For practical reasons the data were gridded onto commonly used 1.5◦by 1.5◦ longitude-latitude grid. For example, this makes possible to compare the results to UTH retrieved from the ECMWF data. In the retrieval process the cloud filter was applied to the single measurement before it was converted to UTH, and then gridded. From now on, all results present UTH data in 1.5◦ by 1.5◦ longitude-latitude grid.
As a method to retrieve UTH from the microwave measurements, the method by Buehler and John [2005] was used. This simple method relates the mi-crowave measurements to UTH. In Buehler et al. [2007] we showed that this method suites very well to study the UTH climatology derived from the mi-crowave data. More information about this method was already given in the introductory part of Chapter 5.
To obtain a spacial coverage as good as possible, measurements from all AMSU-B viewing angles are used. The UTH retrieval method of Buehler and John[2005] provides the radiance to UTH relating coefficients for all AMSU-B viewing angles.
Since the atmosphere in the polar regions is much more drier and the ice cover
of Greenland is elevated, the signal from 183.31±1 GHz channel used to retrieve UTH is strongly contaminated by the surface. Therefore, the study is restricted to the latitudes between 60◦N and 60◦S.