ABSTRACT Using stringent criteria pertaining to rain-cloud optical thickness and horizontal extent, 3203 multichannel microwave observations of heavy, widespread tropical precipitation over ocean were selected from 9 months of global Special Sensor Microwave Imager (SSM/I) data. These observations subsequently were found to be associated almost exclusively with stratiform rain areas in tropical cyclones. Because of the restrictions on optical thickness and spatial extent, the mean multichannel microwave brightness temperatures and their interchannel covariances are presumed to be determined primarily by the vertical microphysical structure of the rain clouds. The distribution of the above observations in seven-dimensional channel space is characterized concisely using principal component analysis. It is found that only three independent variables are sufficient to explain 97% of the variance in the correlation matrix. This result suggests that the radiometrically important microphysical properties of these rain clouds are strongly interdependent. The most significant eigenvector of the observation correlation matrix corresponds to variable scattering at high frequencies by ice aloft. Its spectral dependence is accurately given by 1.76 , where is the microwave frequency. This empirical result constrains the effective mean sizes of ice particles responsible for observed passive microwave scattering in rain clouds and provides a plausible empirical basis for accurately predicting the magnitude of scattering effects by ice at non-SSM/I microwave frequencies. There are also qualitative indications that this mode of brightness temperature variability is poorly correlated with surface rain rate in this study sample. The empirical results presented herein are expected to be of value for the validation and improvement of microphysical assumptions and optical parameterizations in forward microwave radiative transfer models. Companion papers describe the actual retrieval of effective rain-cloud microphysical properties from the observed multichannel radiances.

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm’s poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scat-tering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme’s Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm’s operational version have been

Aircraft and ground-based radar data from the Tropical Ocean and Global Atmosphere Coupled Ocean– Atmosphere Response Experiment show that convective systems are not always vertical. Instead, many are tilted from vertical. Satellite passive microwave radiometers observe the atmosphere at an oblique angle. For example, the Special Sensor Microwave Imager on Defense Meteorological Satellite Program satellites and the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) on the TRMM satellite view at an incident angle of about 508. Thus, the brightness temperature measured from one direction of tilt may be different than that viewed from the opposite direction because of the different optical path. This paper presents an investigation of passive microwave brightness temperatures upwelling from tilted convective systems. To account for the effect of tilt, a 3D backward Monte Carlo radiative transfer model has been applied to a simple tilted cloud model and a dynamically evolving cloud model to derive the brightness temperature. The radiative transfer results indicate that brightness temperature varies when the viewing angle changes because of the different optical path. The tilt increases the displacements between the high 19-GHz brightness temperature (Tb19) due to liquid emission from the lower level of cloud and the low 85-GHz brightness temperature (Tb85) due to ice scattering from the upper level of cloud. As the resolution degrades, the difference of brightness

While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncer-tainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world’s oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25 % uncertainty that cannot be detected by a radiometer alone. Of this value, 20 % is attributed to changes in cloud morphology and microphysics, while 5 % is a result of changes in the rain/no-rain thresholds. All but 2%–3 % of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined. 1.

doi:10.5194/amt-7-1839-2014 © Author(s) 2014. CC Attribution 3.0 License. Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies

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Turkey is a mountainous country which includes 4 different climate sturucture and due to these special features, an enhanced Bayesian Algorithm is employed in the processing schedula of the project. This algorithm is developed based on Cloud-Radiation Database (CDR) from 14 rainy cases by Dr. Alberto MUGNAI and his team (Dr. Daniele CASELLA,

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