The capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study. In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the levels of cloud variability from the ensemble forecast in single- and dual-resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west coasts of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1–5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of 12 May 2016.
A linearized prognostic cloud scheme has been developed to accompany the linearized convection scheme recently implemented in NASA’s Goddard Earth Observing System data assimilation tools. The linearization, developed from the nonlinear cloud scheme, treats cloud variables prognostically so they are subject to linearized advection, diffusion, generation, and evaporation. Four linearized cloud variables are modeled, the ice and water phases of clouds generated by large-scale condensation and, separately, by detraining convection. For each species the scheme models their sources, sublimation, evaporation, and autoconversion. Large-scale, anvil and convective species of precipitation are modeled and evaporated. The cloud scheme exhibits linearity and realistic perturbation growth, except around the generation of clouds through large-scale condensation. Discontinuities and steep gradients are widely used here and severe problems occur in the calculation of cloud fraction. For data assimilation applications this poor behavior is controlled by replacing this part of the scheme with a perturbation model. For observation impacts, where efficiency is less of a concern, a filtering is developed that examines the Jacobian. The replacement scheme is only invoked if Jacobian elements or eigenvalues violate a series of tuned constants. The linearized prognostic cloud scheme is tested by comparing the linear and nonlinear perturbation trajectories for 6-, 12-, and 24-h forecast times. The tangent linear model performs well and perturbations of clouds are well captured for the lead times of interest.
The spatial spreading of infinitesimal disturbances superposed on a turbulent baroclinic jet is explored. This configuration is representative of analysis errors in an idealized midlatitude storm track and the insight gained may be helpful to understand the spreading of forecast errors in numerical weather prediction models.This problem is explored through numerical experiments of a turbulent baroclinic jet that is perturbed with spatially localized disturbances. Solutions from a quasigeostrophic model for the disturbance fields are compared with those for a passive tracer to determine whether disturbances propagate faster than the basic-state flow. Results show that the disturbance spreading rate is sensitive to the structure of the initial disturbance. Disturbances that are localized in potential vorticity (PV) have far-field winds that allow the disturbance to travel downstream faster than disturbances that are initially localized in geopotential, which have no far-field wind. Near the jet, the spread of the disturbance field is observed to exceed the tracer field for PV-localized disturbances, but not for the geopotential-localized disturbances. Spreading rates faster than the flow for geopotential-localized disturbances are found to occur only for disturbances located off the jet axis.These results are compared with those for zonal and time-independent jets to qualitatively assess the effects of transience and nonlinearity. This comparison suggests that the average properties of localized perturbations to the turbulent jet can be decomposed into a superposition of dynamics associated with a timeindependent parallel flow plus a ''diffusion'' process.
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