Abstract
Abstract. Microwave radiometers are widely used for the retrieval of liquid water path (LWP) and integrated water vapor (IWV) in the context of cloud and precipitation studies. This paper presents a new site-independent retrieval algorithm for LWP and IWV, relying on a single-frequency 89 GHz ground-based radiometer. A statistical approach is used based on a neural network, which is trained and tested on a synthetic dataset constructed from radiosonde profiles worldwide. In addition to 89 GHz brightness temperature, the input features include surface measurements of temperature, pressure, and humidity, as well as geographical information and, when available, estimates of IWV and LWP from reanalysis data. An analysis of the algorithm is presented to assess its accuracy, the impact of the various input features, its sensitivity to radiometer calibration, and its stability across geographical locations. While 89 GHz brightness temperature is crucial to LWP retrieval, it only moderately contributes to IWV estimation, which is more constrained by the additional input features. The algorithm is shown to be quite robust, although its accuracy is inevitably lower than that obtained with state-of-the-art multi-channel radiometers, with a relative error of 18 % for LWP (in cloudy cases with LWP >30 g m−2) and 6.5 % for IWV. The highest accuracy is obtained in midlatitude environments with a moderately moist climate, which are more represented in the training dataset. The new method is then implemented and evaluated on real data that were collected during a field deployment in Switzerland and during the ICE-POP 2018 campaign in South Korea.
Highlights
Clouds play a key, though complex, role in the atmosphere’s radiative balance and global circulation (Hartmann and Short, 1980; Slingo, 1990; Hartmann et al, 1992; Wang and Rossow, 1998; Stephens, 2005; Mace et al, 2006; McFarlane et al, 2008), and cloud studies have been propelled to the forefront of climate research
The first category consists of TB and higherorder polynomials and is expected to have the greatest importance in the retrieval of liquid water path (LWP), while the other categories would likely be more correlated with integrated water vapor (IWV)
In panels (c) and (d), the target variables IWV and LWP, respectively, are binned into intervals in which the root mean square error (RMSE) is calculated. This illustrates the behavior of the algorithm across the entire range of values rather than summarizing the performance with a single metric such as total RMSE, which can conceal specific behaviors related to the distribution of the target variable in the dataset
Summary
Though complex, role in the atmosphere’s radiative balance and global circulation (Hartmann and Short, 1980; Slingo, 1990; Hartmann et al, 1992; Wang and Rossow, 1998; Stephens, 2005; Mace et al, 2006; McFarlane et al, 2008), and cloud studies have been propelled to the forefront of climate research. One of the core challenges is the monitoring, quantification, and modeling of cloud liquid water, which has a significant contribution to radiative processes on a global scale. In this perspective, highly accurate methods were developed to retrieve liquid water path (LWP) and integrated water vapor (IWV) from microwave radiometer measurements, relying on the fact that water in its liquid and vapor phases is the main atmospheric contributor to brightness temperatures in millimeter wavelengths outside the oxygen window. On a different note, quantifying cloud liquid water content is relevant to the field of snowfall studies. Improving the monitoring of cloud liquid water processes is valuable to climatological, meteorological, and hydrological applications
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