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| Poster Session 1 |
Ground-based radiometric sensing of water vapour and temperature profiles
Multiple Regression Retrieval Scheme
To derive multiple regression coefficients, 10-years radiosonde soundings collected at the Taipei weather station, Central Weather Bureau (CWB), every March from the year 1998 to 1997 were used. The use of monthly climatological data is intended to reduce the influence of seasonal variation on the inversions of atmospheric variables. Atmospheric brightness temperatures are determined by using Eq. (4) whose water vapor, liquid water and oxygen absorption characteristics are characterized by the Liebe model (Liebe and Layton, 1987). The amount of cloud liquid water is derived with an adiabatic assumption when relative humidity is higher than 98% (Liou, 1998).
Since water vapor and temperature are of two key parameters to determine the absorption characteristics of the atmosphere, their profiles can be determined by the radiometric observations of the atmosphere. That is,
where
X(j) represent water vapor density or temperature at the jth layer of the atmosphere that is divided into n layers, i.e.. j=1,2,3….,n
Aij are multiple regression coefficients, where the subscript I=1,2,3…, m represents the number of the WVR channels, i.e., 23, 8, or 31.4 GHz (m=20)
Tbi are observed brightness temperature of the atmosphere, K.
For self-consistency, the regression coefficients obtained from the 10-year radiosonde data are used to infer atmospheric profiles from brightness temperatures at the two frequencies of interest. The retrievals are compared with those measured by radiosonde soundings (ground truth). Fig.1 shows climatological profiles of (a) water vapor density and (b) temperature, and RMSE's in (c) water vapor density (d) temperature between retrievals and ground truth based on radiosonde data collected every march from the year 1998 to 1997. The weighted RMSE's appear large, 1.50 g/m3 for water vapor density, and 2.9 K for temperature. Absolute values of the variables are used ad the weights to compute the weighted RMSE's is greatly influenced by the mismatching near the surface. Hence, surface meteorological measurements are incorporated constraints to improve the retrievals. Eq. (5) can then be written as
Where
To represents surface temperature, K
Po is surface pressure, mb
VDo is surface water vapor density, g/m3.
Figure 2 shows RMSE's between WVR observations with constraints of surface meteorology and radiodonde measurements for (a) water vapor density, and (b) temperature. A comparison between Fig. 1 and Fig. 2 demonstrates that surface meteorology constraints have improved the retrievals. The improvement appear more significant near surface, essentially zero RMSEs for both variables. The corresponding weighted RMSEs are reduced to 0.83 g/m3 for water vapor density, and to 1.62 K for temperature.
Observed Atmospheric Profiles from Radiosondes and WVR
Two extreme atmospheric profiles are chosen for the current study to examine the performance of the 2-channel WVR approach's capability in measuring atmospheric profiles. One of them has an atmosphere with monotonically decreasing water vapor and temperature profiles and the other with an inversion profile.
Figure 3 compares WVR and radiosonde observed (a) water vapor density and (b) temperature profiles collected at 03/19/1998 OOUTZ when water vapor density and temperature monotonically decreases with altitude. The differences between WVR and radiosonde observations in water vapor density and temperature are given in Fig. 3 (c) and (d), respectively. Generally, speaking, WVR reasonably captures variations of atmospheric profiles without extra information of surface meteorology. Nevertheless, its observations tend to worsen closed to the surface, by deviating from radiosonde observations by 3 g.m3 for water vapor density and by 2 to 5 K for temperature. With surface meteorological measurements, the retrievals are much improved near the surface for both water vapor density and temperature although they are somewhat enlarged a little bit near 3 km height for the water vapor density.
Figure 4 shows WVR and radiosonde observed (a ) water vapor density and (b) temperature profiles collected at 03/19/1998 12UTZ when inversion in water vapor density and temperature occur at low elevation. The differences between WVR and radiosonde observations in water vapor density and temperature are given in Fig. 4 (c) and (d), respectively. Similar to the precious case (Fig. 3), WVR indeed captures the general trend of the variations in the atmospheric profiles without surface meteorology. However, Fig. 4(a) and (c) indicate that a 2-channel WVR approach can not resolve fast-varying water vapor density profile. Moreover, the 2-channel approach is not able to improve its retrieval of water vapor density in the regions of fast-varying profile even though the near-surface profile is improved with more magnificent because the inversion in temperature is relatively weak compared to that in water vapor density.
Conclusion
We investigate the use of a ground-based WVR operating at 23.8 and 31.4 GHz in measuring water vapor and temperature profiles. Field data of WVR, radiosonde, and surface meteorological observations at the Taipei weather station were collected from March 18 to 25, 1998. The WVR observations are used to infer water vapor and temperature profiles with and without surface meteorological measurements as constraints. Two extreme atmospheric profiles are chosen for the study, one with an atmosphere of monotonically decreasing water vapor and temperature profiles and the other with a non-monotonically decreasing profile. We show that the 2-channel WVR approach captures the general trend of atmospheric variations in water vapor and temperature except that it does not resolve the high-frequency signals in the region of inversion due to the limited number of WVR channels. In addition, the WVR observations can be significantly improved near the surface if the surface meteorological measurements are incorporated in the retrieving process.
Acknowledgements:
The authors appreciate much the National Space Program Office grant NSC87-NSPO(A)-PC-FA07-05. They also thank Radiometrics Corporation fir the loan of WVR and CWB for providing a space to conduct the WVR experiment.
References
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Liebe, H.J., and D. H. Layton, 1987: Millimeter-wave Properties of the Atmospheric. National Telecommunication and Information Administration, Boulder, CO, USA, 470pp.
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Liou, Y, A., 1998: Observed spatial variation in perceptible water vapor by a ground-based, dual-channel radiometer. 19th Asian Conference on Remote Sensing, Manila, Nov. 16-20.
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Solheim, F., J.R. Godwin, E.R. Westwater, Y. Han, S.J. Keihm, K. Marsh, and R. Ware, 1998: Radiometric profiling of temperature, water vapor and cloud liquid water using various inversion methods. Radio Sci., 33, 393-404.
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Ulaby, F. T., R.K. Moore, and A.K. Fung., 1981: Microwave Remote Sensing: Active and Passive, Vol. 1, Artehc House Inc., Norwood.
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