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1. Introduction Satellite observations of the atmosphere, land, and oceans are now a major component of the environmental observing system, since they provide critically important information to better understand and forecast short-term as well as climatic changes in weather. Through data assimilation techniques, the satellite observations as well as other sources of atmospheric and oceanic data, sampled at different times, intervals, and locations can be combined into a unified and consistent
1. Introduction Satellite observations of the atmosphere, land, and oceans are now a major component of the environmental observing system, since they provide critically important information to better understand and forecast short-term as well as climatic changes in weather. Through data assimilation techniques, the satellite observations as well as other sources of atmospheric and oceanic data, sampled at different times, intervals, and locations can be combined into a unified and consistent
sounding channels in addition to MW window channels for retrieving profiles of precipitation over both water and land. Emission methods applied to optically thin media are commonly formulated using very simplistic expressions of radiative transfer often posed in terms of the transfer through a single layer in the form where τ is the optical depth determined by absorption, I below is the radiance of the surface and/or atmosphere below the cloud layer, and μ is cosine of the view angle. Here B ( T
sounding channels in addition to MW window channels for retrieving profiles of precipitation over both water and land. Emission methods applied to optically thin media are commonly formulated using very simplistic expressions of radiative transfer often posed in terms of the transfer through a single layer in the form where τ is the optical depth determined by absorption, I below is the radiance of the surface and/or atmosphere below the cloud layer, and μ is cosine of the view angle. Here B ( T
. I: Theory. J. Quant. Spectrosc. Radiat. Transfer , 47 , 19 – 33 . Weng , F. , and Q. Liu , 2003 : Satellite data assimilation in numerical weather prediction models. Part I: Forward radiative transfer and Jacobian modeling in cloudy atmospheres. J. Atmos. Sci. , 60 , 2633 – 2646 . Weng , F. , B. Yan , and N. C. Grody , 2001 : A microwave land emissivity model. J. Geophys. Res. , 106 , 20115 – 20123 . Weng , F. , L. Zhao , R. Ferraro , G. Poe , X. Li , and
. I: Theory. J. Quant. Spectrosc. Radiat. Transfer , 47 , 19 – 33 . Weng , F. , and Q. Liu , 2003 : Satellite data assimilation in numerical weather prediction models. Part I: Forward radiative transfer and Jacobian modeling in cloudy atmospheres. J. Atmos. Sci. , 60 , 2633 – 2646 . Weng , F. , B. Yan , and N. C. Grody , 2001 : A microwave land emissivity model. J. Geophys. Res. , 106 , 20115 – 20123 . Weng , F. , L. Zhao , R. Ferraro , G. Poe , X. Li , and
deep convection over land. It is also essential to improve our knowledge and thus our representation of entrainment/detrainment in convective clouds, of the coupling between convective and stratiform clouds, and of the interaction between precipitation and land/sea surface processes (heat fluxes, orography, triggering of convection). In the context of variational data assimilation, the two major questions are: 1) whether it is useful to include moist processes in the assimilation process itself
deep convection over land. It is also essential to improve our knowledge and thus our representation of entrainment/detrainment in convective clouds, of the coupling between convective and stratiform clouds, and of the interaction between precipitation and land/sea surface processes (heat fluxes, orography, triggering of convection). In the context of variational data assimilation, the two major questions are: 1) whether it is useful to include moist processes in the assimilation process itself
1. Introduction It is widely recognized that clouds play an essential role in moderating climate and are therefore an important feature to accurately model in GCMs. This is not a simple task, owing to the mismatch in scales between the typical GCM grid box (∼100 km) and the smaller scales at which clouds form and evolve and due to the complexity of cloud microphysical processes and their interaction with cloud dynamics and radiative transfer. As a result, clouds continue to represent a major
1. Introduction It is widely recognized that clouds play an essential role in moderating climate and are therefore an important feature to accurately model in GCMs. This is not a simple task, owing to the mismatch in scales between the typical GCM grid box (∼100 km) and the smaller scales at which clouds form and evolve and due to the complexity of cloud microphysical processes and their interaction with cloud dynamics and radiative transfer. As a result, clouds continue to represent a major
