Ground-based radiometric sensing of water vapour and temperature profiles
Yuei-An Liou and Chuang-: Lun Chang
Center for space and Remote Sensing Research
And Institute of Space Sciences
National Central University
Chung-Li, China
Taipei yueian@csrsr.ncuedu.tw
Abstract
A ground-based water vapor radiometer (WVR) operating at 23.8 and 31.4 GHz was utilized to observe water vapor and temperature profiles at the Taipei weather station from March 18 to 25, 1998. The profiles were inferred from WVR observed brightness temperatures of the sky through a statistical regression method whose coefficients are derived based on radiosonde soundings collected at the same site every March starting from the year 1998 to 1997. The regression method is evaluated through a self-consistent test by comparing its observation of water vapor and temperature profiles with those observed by radiosonde observations (ground truth). While the weighted root mean square error (RMSE) between the retrievals and ground truth is somewhat large about 1.5 g/m
3 for the water vapor, the RMSE decreases exponentially with altitude. In contrast, the RMSE for the temperature is on the range of 2.2 to 3 K. to improve the retrieval accuracy, surface observations of temperature, water vapor density, and pressure are used as constraints. We found the RMSE for both water vapor and temperature retrievals are significantly near the surface. Essentially, zero RMSEs are acquired at the surface for both variables.
With the constraints of surface meteorological measurements, the regression scheme is applied to derive water vapor and temperature profiles. Two extreme cases are chosen for the current study, one with an atmosphere of monotonically decreasing water vapor and temperature profile and the other with a non-monotonically decreasing (inversion) profile. While WVR does not capture a low-elevation inversion (high-frequency signals) in both temperature the profiles for the latter case, it does perform well in determining the profiles for the former case.
Introduction
Atmospheric water vapor and temperature profiles dominate the energy balance of the atmosphere. Their distributions are therefore important to better initialize and constrain numerical weather predictions models. The most typical way to measure the atmospheric profiles is by radiosonde soundings, which suffers from the cost of the advices and their limiting use to certain areas of land. Microwave radiometry represents an alternatives way to measure the profiles for all-weather conditions. This radiometric approach relies on the fact that the absorption lines of water vapor and oxygen locate in the microwave region (Solheim et al., 1998).
In this paper, we present observations of atmospheric profiles by a dual-channel, ground-based radiometric approach from a field campaign conducted at the Taipei weather station from March 18 to 25, 1998. this study is improved upon our previous study demonstrating measurements of total water vapor by the same data set (Liou, 1998). This paper is began with Radiative transfer that briefly addresses the fundamentals of radiometric sensing technique. The retrieval scheme and the 2-channel measurements of atmosphere profiles are subsequently presented.
Radiative transfer
Atmosphere profiles dominate microwave emissions of the atmosphere so that they can be retrieved from radiometric measurements. Atmospheric emissions are characterized by the Radiative transfer described as (Ulaby et, al., 1981).
where r is the position function, m, i, is the optical depth, Np and J is the source function, W/m
2-sr. Eq. (1) can be explained by the Kirchhoff's law, which states that under conditions of local thermodynamic equilibrium, thermal emission must be equal to absorption. For upward -looking radiometry, its solution can be written as
and K
e is the extinction coefficient of the atmosphere. In the microwave region, Eq. (2) by the Rayleigh-Jeans law can be rewritten as
where T
bg is the brightness temperature observed by radiometer, K, T
be represents cosmic brightness temperature (2.7 K), and T
a is the temperature of the atmospheric K. note that the extinction coefficients in Eq. (3) has been replaced by K
a in Eq. (4) because absorptions in liquid water cloud regions exceeds scattering by at least two orders of magnitude (Janssen, 1993).
Equation (4) states that atmospheric variables dominate brightness temperature through their influence on the absorption coefficient. In turn, the variables can be retrieved from the observed brightness temperatures through regression retrieval scheme.
