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Determining the Jacobians of the radiative transfer equation (RTE) is important to the qualities of the simultaneous retrieval of geophysical parameters from satellite radiance observations and the assimilation of radiance data into a numerical weather prediction system. Two linear forms of the RTE with analytic Jacobians are formulated. The first linear form has approximate analytic Jacobians, which involves some monochromatic approximation applied to a fast transmittance model. Unlike previous research, which lacks the transmittance Jacobian with respect to the atmospheric temperature profile, this form is complete in the sense that the transmittance Jacobians with respect to atmospheric temperature and absorbing constituent profiles are both present. The second linear form has exact analytic Jacobians derived consistently from the same fast transmittance model without using any monochromatic approximation. By numerical comparison between the two linear forms for the NOAA-12 High-Resolution Infrared Sounder, we show significant errors in the linear form with approximate analytic Jacobians. The relative absolute linearization error from the linear form with approximate analytic Jacobians is shown to be 2-4 orders of magnitude larger than that from the linear form with exact analytic Jacobians, even for the case of a 0.1% perturbation of the U.S. Standard Atmosphere. The errors unnecessarily complicate the ill-posed retrieval problem of atmospheric remote sensing and can be avoided if the correct linear form of the RTE with exact analytic Jacobians is adopted. OCIS codes: 010.1320, 010.1300, 100.3190, 120.0280, 300.6340.

A numerical model is developed for computation of the reflection of atmospheric-emitted IR radiance from a wind-roughened water body. The model assumes the Kirchhoff approximation for rough surface scattering. This allows application of the postulates of geometrical optics to determine the reflection of rays from an ensemble of wave facets. We performed the hemispherical integration with Gaussian quadrature by using an uplooking fast transmittance model. This calculation is simplified further through the concept of a reflection-diffusivity angle. The model compares favorably with observed radiance spectra obtained from the Marine-Atmospheric Emitted Radiance Interferometer during the Combined Sensor Program research cruise. [C] 2001 Optical Society of America OCIS codes: 010.4450, 120.0280, 120.5700, 240.6690, 290.5880.

A state-of-the-art numerical weather prediction (NWP) model, comprising weather research and forecast (WRF) model and analysis techniques, has been extensively exercised for weather prophecy all over the world. The WRF model, the soul role in NWP, constitutes dynamic solvers and elaborate physical components for conducting fluid behavior, all of which are sketched for both atmospheric research analyses and operational weather foretell. One salient physical ingredient in WRF is the surface layer simulation, which provides surface heat and moisture fluxes through calculation of surface friction velocities and exchange coefficients. The Monin–Obukhov–Janjic (MOJ) scheme is one popular surface layer option in WRF. This is one of the schemes in WRF we choose to expedite toward an end-to-end accelerated weather model. One advantageous aspect in WRF is the independence among grid points that facilitates programming implementations in parallel computation. We here present a parallel construction on the MOJ module with application of vectorization elements and efficient parallelization essentials furnished by Intel many integrated core (MIC) architecture. To achieve high computing performance, apart from the fundamental usage of Intel MIC architecture, this paper offers some new approaches related to code structure and art of optimization skills. At the end, in comparison with the original code separately executing on one CPU core and on one CPU socket (eight cores) with Intel Xeon E5-2670, the optimized MIC-based MOJ module running on Xeon Phi coprocessor 7120P ameliorates the computing performance by 9.6× and 1.5×, respectively. [ABSTRACT FROM PUBLISHER]