Algorithms are developed to infer integrated cloud liquid water path (LWP) over the oceans from spaceborne and ground-based passive microwave measurements. These algorithms are built from simulated observations, which are calculated with a radiative transfer model applied to a set of about 10,000 atmospheric profiles obtained from the European Centre for Medium-Range Weather Forecasts forecast model. In this model the liquid water content is computed from a prognostic cloud scheme. A multilinear regression is applied to functions of simulated brightness temperatures (log linear form) and LWP to derive the algorithm coefficients. The retrieval accuracy based on the regression analysis including instrumental noise is 0.0257 and 0.0345 kg m−2 for the DMSP special sensor microwave imager (SSM/I) and the ERSl along-track scanning radiometer/microwave (ATSR/M), respectively, and 0.0308 kg m−2 for the ground-based radiometer. It is shown that the log linear form is adequate to transform the nonlinear problem into a quasi-linear problem for LWP below 0.8 kg m−2. The coherence of the global approach is verified through the validation of total precipitable water (TPW) algorithms developed in a way similar to LWP algorithms. The LWP retrievals from the algorithm for the ground-based radiometer are in good agreement with retrievals from airborne measurements performed in the vicinity of the radiometer. A coherence test is performed for ATSR/M, benefiting from the coincident infrared images obtained from an infrared radiometer (ATSR/IR) aboard the same platform to select clear-air areas. Regardless of the slight mean bias of the inferred LWP due to inaccurate calibration, there is no anomalous dependency upon latitude, i.e., upon high water vapor contents in the tropics and strong winds in the high latitudes. The results of the algorithm for SSM/I are compared with a Meteosat cloud classification. When the classification detects the ocean surface, the algorithm systematically retrieves contents close to zero. The retrievals for other classes (i.e., low stratiform clouds, medium clouds) are consistent with the Meteosat data; retrievals in the presence of low stratiform clouds appear more realistic than values provided by some already published algorithms. It is also shown that up to 0.8 kg m−2 the log linear regression approach has a quality of the same order as a variational method, which requires much more computation time.
If you can't find a tool you're looking for, please click the link at the top of the page to "Go to old article view". Alternatively, view our Knowledge Base articles for additional help. Your feedback is important to us, so please let us know if you have comments or ideas for improvement.