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Thomas Nehrkorn

Abstract

Prefrontal squall lines are mesoscale convective systems that often cannot be linked to any preexisting organizing mechanism. This suggests the possibility that they are manifestations of a mesoscale instability involving the interaction between convective and larger scales. To investigate this hypothesis, a wave-CISK model is developed for two-dimensional disturbances in a baroclinic basic state with constant vertical wind shear. The governing equations are the linearized Boussinesq equations for an inviscid and hydrostatic fluid on an f-plane. The model domain is infinite in the horizontal and consists of two layers in the vertical representing the troposphere and the stratosphere. The model stratosphere has a larger static stability and no wind shear. The convective heating is confined to the troposphere. Normal mode solutions are assumed, and the convective heating is parameterized in the standard simple fashion its vertical structure is specified, and it is set proportional to the low-level vertical velocity. The model allows for arbitrary orientations of the disturbance axis in the horizontal plane.

Results show the existence of two modes: large scale Eady modes, which are amplified slightly by heating, and smaller scale wave-USK model The wave-OSK modes have their maximum growth rates near the symmetric axis, i.e., with disturbance axes approximately parallel to the shear vector. For heating amplitudes that are not unrealistically large, wavelengths of maximum growth are finite and on the mesoscale (on the order of 500 km). Sensitivity experiments for these wave-CISK modes show that the value of the maximum growth rates and the wavelength of maximum growth, are not very sensitive to the form of the vertical heating profile, while other characteristics of the fastest growing mode are. In particular, the orientation of the disturbance axis depends on the heating profile: for maximum heating in the middle troposphere, the disturbance axis is rotated 20°–30° clockwise from the symmetric axis, implying upshear propagation, while for higher levels of maximum heating the disturbance is more nearly aligned with the shear vector. If low level cooling is included in the heating profile, disturbance axes are rotated counterclockwise.

Comparisons with observations of squall lines in the atmosphere show some aspects of the solution, such as its vertical structure, to be in qualitative agreement. The orientation angle of the fastest growing mode, however, is near to observed values only if beating profiles with heating maxima at upper levels and cooling at lower levels are used. Predicted phase speeds at observed orientation angles are then too high by a factor of two to five. Reasons for this failure of the wave-CISK theory are discussed.

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Thomas Nehrkorn

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An application of optimum interpolation (OI) is described for quality control and combination of wind profiler data with other observations of wind profiles. Data from three separate wind sensors at the National Aeronautics and Space Administration’s Kennedy Space Center are used: conventional rawinsondes, precision wind sounding balloons (jimspheres), and a vertically pointing 50-MHz Doppler radar wind profiler. Collocation statistics of the three sensors are presented, along with analysis and quality control results from selected case studies. The results show the utility of the OI technique for the integration and quality control of disparate wind profile data. The proper choice of the vertical correlation length of the observation error was found to be crucial for a proper weighting of the different data sources. The performance of the OI quality control algorithm was improved if an accurate background field was used for the analysis.

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Thomas Nehrkorn

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No abstract available.

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Thomas Nehrkorn and Marina Živković

Abstract

The performance of several schemes for diagnosing cloud cover from forecast model output was tested using a global numerical weather prediction model and the operational USAF RTNEPH (real-time nephanalysis) cloud analysis. In the present study, schemes were developed from cloud cover statistics stratified by synoptic weather regime. The synoptic regime were defined in terms of vertical profiles of temperature, winds, and moisture. The meteorological significance of these regimes was illustrated by relating them to synoptic features. The simplest scheme (AVG) assigned the average cloud cover to each of the regimes; a variant of the cloud curve algorithm (CCA) technique was developed in which separate cloud-RH curves were derived for each regime by a mapping of the cumulative frequency distribution of RH and cloud cover. Their performance was compared against a number of other diagnostic schemes, including a multiple linear regression method that used global regression equations for cloud cover from a large number of atmospheric and geographic predictors; a version of the Slingo scheme; and simple persistence. Results indicate that the schemes with the lowest rms errors (AVG, and the regression scheme) also had highly unrealistic frequency distributions, with too few points that were close to either clear or overcast values. Persistence was found to provide competitive or superior forecasts out to 24–36 h. The applicability of these results to improved model and cloud observations is discussed.

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Thomas M. Hamill and Thomas Nehrkorn

Abstract

This paper describes a cloud forecast technique using lag cross correlations. Cloud motion vectors are retrieved at a subset of points through multiple applications of a cross-correlation analysis. An area in the first of two sequential frames of satellite data is correlated with surrounding areas in the second frame to find the one surrounding area best correlated. The location difference of the areas defines the displacement vector. An objective analysis is used to define displacements at every satellite pixel throughout the domain and smooth the local inconsistencies. Using these displacements, forecasts are then produced with a backward trajectory technique. This scheme was tested using two IR satellite images of the same scene a half-hour apart and found to generate realistic, high-quality forecast IR pixel images. Results demonstrate improvements over persistence and movable persistence for forecasts of a few hours’ length. The technique is visually appealing, since forecasts are created in pixel images of the same form and resolution as the initializing satellite data, permitting animation. It is also computationally inexpensive.

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Thomas Nehrkorn and Ross N. Hoffman

Abstract

A feature-based statistical method is investigated as a method of generating pseudoensembles of numerical weather prediction forecasts. The goal is to enhance or dress a single dynamical forecast or an ensemble of dynamical forecasts with many realistic perturbations so as to represent better the forecast uncertainty. The feature calibration and alignment method (FCA) is used to characterize forecast differences and to generate the additional ensemble members. FCA is unique in decomposing forecast errors or differences into phase, bias, and residual error or difference components. In a pilot study using 500-hPa geopotential height data, pseudoensembles of weather forecasts are generated from one deterministic forecast and perturbations obtained by randomly sampling FCA displacements based on a priori statistics and applying these displacements to the original deterministic forecast. Comparison with actual dynamical ensembles of 500-hPa geopotential height generated by ECMWF show that important features of the dynamical ensemble, such as the spatial patterns of the ensemble mean and variance, can be approximated by the FCA pseudoensemble. Ensemble verification statistics are presented for the dynamic and FCA ensemble and compared with those of simpler statistically based pseudoensembles. Some limitations of the FCA ensembles are noted, and mitigation approaches are discussed, with a view toward applying the method to mesoscale forecasts for dispersion modeling.

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Ross N. Hoffman and Thomas Nehrkorn

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Retrieving information from remotely sensed data and analyzing the resulting geophysical parameters on a regular grid may be combined using a variational analysis method. This approach is applicable to the problem of retrieving temperature and cloud parameters within a three-dimensional volume from observations of infrared radiances at several frequencies and locations. The feasibility of such a three-dimensional retrieval method is demonstrated using simulated HIRS2 data. The method is successful in fitting the radiance data with a three-dimensional temperature representation. When clouds are included in the problem the results are sensitive to the initial estimates of the cloud parameters.

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Thomas Nehrkorn and Ross N. Hoffman

Abstract

The inference of profiles of relative humidity from cloud data was investigated in a collocation study of 3DNEPH and radiosonde data over North America. Regression equations were developed for the first two EOFs of relative humidity, using vertically compacted and horizontally averaged 3DNEPH cloud cover values as predictors. The regression equations were found to have smaller errors than existing level-to-level cloud to humidity conversion techniques. However, no attempt was made to tune the existing methods for optimal performance.

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Thomas Nehrkorn, Bryan K. Woods, Ross N. Hoffman, and Thomas Auligné

Abstract

The Feature Calibration and Alignment technique (FCA) has been developed to characterize errors that a human would ascribe to a change in the position or intensity of a coherent feature, such as a hurricane. Here the feature alignment part of FCA is implemented in the Weather Research and Forecasting Data Assimilation system (WRFDA) to correct position errors in background fields and tested in simulation for the case of Hurricane Katrina (2005). The displacement vectors determined by feature alignment can be used to explain part of the background error and make the residual background errors smaller and more Gaussian. Here a set of 2D displacement vectors to improve the alignment of features in the forecast and observations is determined by solving the usual variational data assimilation problem—simultaneously minimizing the misfit to observations and a constraint on the displacements. This latter constraint is currently implemented by hijacking the usual background term for the midlevel u- and υ-wind components. The full model fields are then aligned using a procedure that minimizes dynamical imbalances by displacing only conserved or quasi-conserved quantities. Simulation experiments show the effectiveness of these procedures in correcting gross position errors and improving short-term forecasts. Compared to earlier experiments, even this initial implementation of feature alignment produces improved short-term forecasts. Adding the calculation of displacements to WRFDA advances the key contribution of FCA toward mainstream implementation since all observations with a corresponding observation operator may be used and the existing methodology for estimating the background error covariances may be used to refine the displacement error covariances.

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Thomas Nehrkorn, Bryan Woods, Thomas Auligné, and Ross N. Hoffman

Abstract

Alignment errors [i.e., cases where coherent structures (“features”) of clouds or precipitation in the background have position errors] can lead to large and non-Gaussian background errors. Assimilation of cloud-affected radiances using additive increments derived by variational and/or ensemble methods can be problematic in these situations. To address this problem, the Feature Calibration and Alignment technique (FCA) is used here for correcting position errors by displacing background fields. A set of two-dimensional displacement vectors is applied to forecast fields to improve the alignment of features in the forecast and observations. These displacement vectors are obtained by a nonlinear minimization of a cost function that measures the misfit to observations, along with a number of additional constraints (e.g., smoothness and nondivergence of the displacement vectors) to prevent unphysical solutions. The method was applied in an idealized case using Weather Research and Forecasting Model (WRF) forecast fields for Hurricane Katrina. Application of the displacement vectors to the three-dimensional WRF fields resulted in improved predicted hurricane positions in subsequent forecasts. When applied to a set of high-resolution forecasts of deep moist convection over the central United States, displacements are able to efficiently characterize part of the ensemble spread. To test its application as an analysis preprocessor, FCA was applied to a real-data case of cloud-affected radiances of one of the Atmospheric Infrared Sounder (AIRS) channels. The displaced background resulted in an improved fit to the AIRS observations in all cloud-sensitive channels.

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