ALBERT

Beschreibung: A neural network (NN) has been developed in order to retrieve the cloud liquid water path (LWP) over the oceans from Special Sensor Microwave/Imager (SSM/I) data. The retrieval with NNs depends crucially on the SSM/I channels used as input and the number of hidden neurons—that is, the NN architecture. Three different combinations of the seven SSM/I channels have been tested. For all three methods an NN with five hidden neurons yields the best results. The NN-based LWP algorithms for SSM/I observations are intercompared with a standard regression algorithm. The calibration and validation of the retrieval algorithms are based on 2060 radiosonde observations over the global ocean. For each radiosonde profile the LWP is parameterized and the brightness temperatures (Tb’s) are simulated using a radiative transfer model. The best LWP algorithm (all SSM/I channels except T85V) shows a theoretical error of 0.009 kg m−2 for LWPs up to 2.8 kg m−2 and theoretical “clear-sky noise” (0.002 kg m−2), which has been reduced relative to the regression algorithm (0.031 kg m−2). Additionally, this new algorithm avoids the estimate of negative LWPs. An indirect validation and intercomparison is presented that is based upon SSM/I measurements (F-10) under clear-sky conditions, classified with independent IR-Meteosat data. The NN-based algorithms outperform the regression algorithm. The best LWP algorithm shows a clear-sky standard deviation of 0.006 kg m−2, a bias of 0.001 kg m−2, nonnegative LWPs, and no correlation with total precipitable water. The estimated accuracy for SSM/I observations and two of the proposed new LWP algorithms is 0.023 kg m−2 for LWP ⩽ 0.5 kg m−2.

Beschreibung: A three-dimensional Monte Carlo transfer model for polarized radiation is developed and used to study three-dimensional (3-D) effects of raining clouds on the microwave brightness temperature. The backward method is combined with the forward method to treat polarization correctly within the cloud. In comparison with horizontally homogeneous clouds, two effects are observed: First, brightness temperatures from clouds are reduced in the 3-D case due to net leakage of radiation from the sidewalls of the cloud. Second, radiation which is emitted by the warm cloud and then reflected from the water surface increases the brightness temperatures of the cloud-free areas in the vicinity of the cloud. Both effects compete with each other, leading to either lower or higher overall brightness temperatures, depending on the geometry of the cloud, the satellite viewing angle, the coverage, and the position of the cloud within the field of view (FOV) of the satellite. At 37 GHz, for example, up to 10 K differences can occur for a cloud of 50% coverage. Finite homogeneous raining clouds matching the size of the FOV of the satellite show a similar relationship between rain rates and brightness temperatures (TB) as horizontally infinite clouds. Namely, an increase of TB with increasing rain rates at low rain rates, due to emission effects, is followed by a decrease due to temperature and scattering effects. For small horizontal cloud diameter, however, the 3-D brightness temperatures may show a second maximum due to the decrease of the leakage effect with increasing rain rates. At nadir, 3-D brightness temperatures are always lower than the 1-D values with differences up to 20 K for a cloud of 5-km vertical extent and a base of 1 × 1 km. To quantify the 3-D effects for more realistic cloud structures, we used results of a three-dimensional dynamic cloud model as input for the radiative transfer codes. The same 3-D effects are obtained, but the differences between 1-D and 3-D modeling are smaller. In general, most of the differences between the 1-D and 3-D results for off-nadir view angles are pure geometry effects, which can be accounted for in part by a modified 1-D model.

Beschreibung: A neural network is used to calculate the longwave net radiation (Lnet) at the sea surface from measurements of the Special Sensor Microwave/Imager (SSM/I). The neural network applied in this study is able to account largely for the nonlinearity between Lnet and the satellite-measured brightness temperatures (TB). The algorithm can be applied for instantaneous measurements over oceanic regions with the area extent of satellite passive microwave observations (30–60 km in diameter). Comparing with a linear regression method the neural network reduces the standard error for Lnet from 17 to 5 W m−2 when applied to model results. For clear-sky cases, a good agreement with an error of less than 5 W m−2 for Lnet between calculations from SSM/I observations and pyrgeometer measurements on the German research vessel Poseidon during the International Cirrus Experiment (ICE) 1989 is obtained. For cloudy cases, the comparison is problematic due to the inhomogenities of clouds and the low and different spatial resolutions of the SSM/I data. Global monthly mean values of Lnet for October 1989 are computed and compared to other sources. Differences are observed among the climatological values from previous studies by H.-J. Isemer and L. Hasse, the climatological values from R. Lindau and L. Hasse, the values of W. L. Darnell et al., and results from this study. Some structures of Lnet are similar for results from W. L. Darnell et al. and the present authors. The differences between both results are generally less than 15 W m−2. Over the North Atlantic Ocean the authors found a poleward increase for Lnet, which is contrary to the results of H.-J. Isemer and L. Hasse.