Retrieval of upper tropospheric humidity data from microwave measurements

Abstract

The current thesis shows three sub-studies in order to analyze the upper tropospheric humidity (UTH) from microwave measurements. First, a simple method of comparing the symmetric measurements across the scan-line revealed that the asymmetry errors in the AMSU-B Channel 18 for NOAA-16 and 17 are within the instrument noise temperatures. However, for the same AMSU-B channel, but on NOAA-15 for the recent data the asymmetry errors exceed the instrument noise temperature. The asymmetry errors for Channel 18 are less than 1.90, -0.53, and 0.49 K for NOAA-15, 16, and 17, respectively.Fairly large asymmetry errors were found in the AMSU-B Channel 17, 19, and 20 on NOAA-15. This seems to be related to the known radio frequency interference (RFI) problem for this instrument. If the appropriate set of the recent RFI correction coefficients are used, the errors due to asymmetry are significantly reduced.Second, this thesis also presents a cloud filtering method for the UTH measurements at 183.31 Ã ± 1.00 GHz. This method combines two known cloud detection techniques. Namely, it utilizes the difference between the brightness temperatures at 183.31 Ã ± 7.00 and 183.31 Ã ± 1.00 GHz, and a threshold for the brightness temperature at 183.31 Ã ± 1.00 GHz. The robustness of this cloud filter is demonstrated by a mid-latitudes winter case-study.Studies on possible cloud effects on the microwave UTH climatology also presented. Using cloud contaminated measurements will overestimate the derived UTH, and not using such measurements will underestimate UTH, since clouds are associated with high humidity. These biases due to clouds are estimated and compared. The simulations of a cloud event and cloud database with a reasonable statistics for the midlatitude conditions are used.The consistent result is that both cloud wet bias (0.8 %RH) and cloud filtering dry bias (-2.4 %RH) are modest for microwave data, where the numbers are from the cloud database analysis. For the case study the biases are slightly higher, -3 % and 2 %, respectively, because the studied case was a particularly strong ice cloud event. This indicates that for microwave data the cloud-filtered UTH and unfiltered UTH can be taken as error bounds for errors due to clouds. This is not possible for the more traditional infrared data, since the radiative effect of clouds is much stronger there.For practical reasons, since the data only for the midlatitude atmosphere were at hand, the effects of the cloud filter only on the midlatitude data are discussed. However, those effects are expected to be valid for subtropical and tropical data, too.Finally, the studies on UTH retrieved from 183.31 Ã ± 1.00 GHz radiances of the three operational microwave sounders are presented. The long-term UTH climatology data show similar patterns and features known from the meteorology and other available UTH data. Most of the high humidity values were found around the intertropical convergence zone (ITCZ). The inter-satellite calibration is investigated by comparing different sensors in the UTH space.A study of the zonal averaged UTH showed that there are two maxima around the ITCZ in the annual UTH cycle. They coincide with the shift of the Indian Monsoon system around the ITCZ. A seasonal variation in UTH data for different latitude bands is also studied. It shows thatthe Northern hemisphere is more humid than its Southern counterpart. Also, the seasonal cycle of UTH is more pronounced in the Northern than in the Southern hemisphere.A trend analysis is conducted by using a simple linear fit method. It revealed that there are visible trends in UTH on a regional scale. However, the trend values are contradictive. Also a time period of the available data is relatively short. Therefore, it is yet too early to make conclusive analysis of the UTH trends.