Tomislava Vukicevic

Sensitivity Analysis Using an Adjoint of the PSU-NCAR Mesoseale Model

Abstract An adjoint of the Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) Mesoscale Model has been developed for use in sensitivity analysis following Cacuci. Sensitivity analysis is defined as the determination of the potential impact on some quantitative measure of a forecast aspect due to arbitrary perturbations of the model dynamic fields at earlier times. Input to the adjoint operator is the gradient of the forecast-aspect measure with respect to the model fields at the verification time, and output is the corresponding gradients defined at earlier times. The adjoint is exactly determined from a tangent linear model, which is itself an approximation to the dry nonlinear model. This approximation is shown to be accurate even when evaluated with regard to the moist nonlinear model for periods up to 36 h, although this accuracy is necessarily case and perturbation dependent. The mathematics describing the scheme are applied to the model in its spatially and temporally ...

The Influence of Artificial and Physical Factors upon Predictability Estimates Using a Complex Limited-Area Model

Abstract Recently, optimistic reports have appeared indicating that mesoscale circulations are more predictable than synoptic scale circulations. These have been based on studies using limited-area meso-α-scale forecast models. Warnings have also appeared suggesting that these results are party artifacts of the experimental and model designs, particularly strong diffusion and an “error sweeping effect” of lateral boundaries. We demonstrate that an additionally important effect of the lateral boundaries is to restrict the scales at which errors can grow: if the domain is sufficiently large, forecast differences grow with time, but only at large scales. Our results show a strong sensitivity to synoptic situation and selection of an initial perturbation. Experiments with and without topography reveal that predictability is enhanced due to systematic topographic forcing. Detailed scale analysis of forecast differences and comparison with global model results indicate that the predictability using limited-area...

An All-Weather Observational Operator for Radiance Data Assimilation with Mesoscale Forecast Models

Abstract Assimilating satellite radiance data under all weather conditions remains an outstanding problem in numerical weather prediction. This study develops an observational operator for use in radiance assimilation under both clear and cloudy conditions specifically for mesoscale models containing explicit microphysics. It is part of a larger research effort to build a 4D variational radiance assimilation system for optimal use of satellite data. The operator is suitable for radiance calculations at visible/infrared wavelengths and is adaptable to the different spectral characteristics of many types of narrowband satellite sensors. The new operator makes use of a gas extinction model and fast, multiple-scattering radiative transfer models, and relies on physical approximations for deriving cloud optical properties. One property, the asymmetry factor, is estimated through a new application of anomalous diffraction theory. A test of the observational operators ability to estimate cloudy radiances was pe...

NOAA'S Hurricane Intensity Forecasting Experiment: A Progress Report

An update of the progress achieved as part of the NOAA Intensity Forecasting Experiment (IFEX) is provided. Included is a brief summary of the noteworthy aircraft missions flown in the years since 2005, the first year IFEX flights occurred, as well as a description of the research and development activities that directly address the three primary IFEX goals: 1) collect observations that span the tropical cyclone (TC) life cycle in a variety of environments for model initialization and evaluation; 2) develop and refine measurement strategies and technologies that provide improved real-time monitoring of TC intensity, structure, and environment; and 3) improve the understanding of physical processes important in intensity change for a TC at all stages of its life cycle. Such activities include the real-time analysis and transmission of Doppler radar measurements; numerical model and data assimilation advancements; characterization of tropical cyclone composite structure across multiple scales, from vortex s...

Nonlinear and Linear Evolution of Initial Forecast Errors

Abstract The hypothesis that the short-time evolution of forecast errors originating from initial data uncertainties can be approximated by linear model solutions is investigated using a realistic prognostic model. A tangent linear limited-area model based on a state of the art mesoscale numerical forecast model is developed. The linearization is performed with respect to a temporally and spatially varying basic state. The basic state fields are produced by the nonlinear model using observed data. The tangent model solutions and the error fields based on the nonlinear integrations are compared. The results demonstrate that the initial error evolution is well represented by the tangent model for periods of 1–1.5 days duration. The linear model solutions based on the time-independent basic state are also good approximations of the real-error evolutions, providing the prognostic fields are not changing rapidly in time. The application of the linear model for estimating appropriate initial perturbation for th...

Mesoscale Cloud State Estimation from Visible and Infrared Satellite Radiances

Abstract This study focuses on cloudy atmosphere state estimation from high-resolution visible and infrared satellite remote sensing measurements and a mesoscale model with explicit cloud prediction. The cloud state is defined as 3D spatially distributed hydrometeors characterized with microphysical properties: mixing ratio, number concentration, and size distribution. The Geostationary Operational Environmental Satellite-9 (GOES-9) imager visible and infrared measurements were used in a new four-dimensional variational data assimilation (4DVAR) mesoscale algorithm for a warm continental stratus cloud system case to test the impact of these observations on the cloud simulation. The new data assimilation algorithm includes the Regional Atmospheric Modeling System (RAMS) with explicit cloud state prediction, the associated adjoint system, and an observational operator for forward and adjoint integrations of the GOES radiances. The results show positive impact of GOES imager measurements on the 3D cloud shor...

Cloud-Resolving Satellite Data Assimilation: Information Content of IR Window Observations and Uncertainties in Estimation

Abstract This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model firs...

CIRA/CSU Four-Dimensional Variational Data Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for High-Resolution Data: The Impact of Airborne Doppler Radar Observations in an OSSE

AbstractWithin the National Oceanic and Atmospheric Administration, the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory has developed the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) to assimilate hurricane inner-core observations for high-resolution vortex initialization. HEDAS is based on a serial implementation of the square root ensemble Kalman filter. HWRF is configured with a horizontal grid spacing of km on the outer/inner domains. In this preliminary study, airborne Doppler radar radial wind observations are simulated from a higher-resolution km version of the same model with other modifications that resulted in appreciable model error.A 24-h nature run simulation of Hurricane Paloma was initialized at 1200 UTC 7 November 2008 and produced a realistic, category-2-strength hurricane vortex. The impact of assimilating Doppler wind observations is assessed in observation space as well as in model space. It is obser...

Observed Hurricane Wind Speed Asymmetries and Relationships to Motion and Environmental Shear

AbstractWavenumber-1 wind speed asymmetries in 35 hurricanes are quantified in terms of their amplitude and phase, based on aircraft observations from 128 individual flights between 1998 and 2011. The impacts of motion and 850–200-mb environmental vertical shear are examined separately to estimate the resulting asymmetric structures at the sea surface and standard 700-mb reconnaissance flight level. The surface asymmetry amplitude is on average around 50% smaller than found at flight level, and while the asymmetry amplitude grows in proportion to storm translation speed at the flight level, no significant growth at the surface is observed, contrary to conventional assumption. However, a significant upwind storm-motion-relative phase rotation is found at the surface as translation speed increases, while the flight-level phase remains fairly constant. After removing the estimated impact of storm motion on the asymmetry, a significant residual shear direction-relative asymmetry is found, particularly at the ...