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Steven M. Lazarus, Steven K. Krueger, and Gerald G. Mace

Abstract

Cloud amount statistics from three different sources were processed and compared. Surface observations from a National Centers for Environmental Prediction dataset were used. The data (Edited Cloud Report; ECR) consist of synoptic weather reports that have been edited to facilitate cloud analysis. Two stations near the Southern Great Plains (SGP) Cloud and Radiation Test Bed (CART) in north-central Oklahoma (Oklahoma City, Oklahoma and Wichita, Kansas) were selected. The ECR data span a 10-yr period from December 1981 to November 1991. The International Satellite Cloud Climatology Project (ISCCP) provided cloud amounts over the SGP CART for an 8-yr period (1983–91). Cloud amounts were also obtained from Micro Pulse Lidar (MPL) and Belfort Ceilometer (BLC) cloud-base height measurements made at the SGP CART over a 1-yr period. The annual and diurnal cycles of cloud amount as a function of cloud height and type were analyzed. The three datasets closely agree for total cloud amount. Good agreement was found in the ECR and MPL–BLC monthly low cloud amounts. With the exception of summer and midday in other seasons, the ISCCP low cloud amount estimates are generally 5%–10% less than the others. The ECR high cloud amount estimates are typically 10%–15% greater than those obtained from either the ISCCP or MPL–BLC datasets. The observed diurnal variations of altocumulus support the authors’ model results of radiatively induced circulations.

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David T. Myrick, John D. Horel, and Steven M. Lazarus

Abstract

The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster.

The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

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Bryan P. Holman, Steven M. Lazarus, and Michael E. Splitt

Abstract

A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.

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Kelvin K. Droegemeier, Steven M. Lazarus, and Robert Davies-Jones

Abstract

A three-dimensional numerical cloud model is used to investigate the influence of storm-relative environmental helicity (SREH) on convective storm structure and evolution, with a particular emphasis on the identification of ambient shear profiles that are conducive to the development of long-lived, strongly rotating storms. Eleven numerical simulations are made in which the depth and turning angle of the ambient vertical shear vector are varied systematically while maintaining a constant magnitude of the shear in the shear layer. In this manner, an attempt is made to isolate the effects of different environmental Felicities on storm morphology and show that the SREH and bulk Richardson number, rather than the mean shear in the low levels, determine the rotational characteristics and morphology of deep convection.

The results demonstrate that storms forming in environments characterized by large SREH are longer-lived than those in less helical surroundings. Further, it appears that the storm-relative winds in the layer 0–3 km must, on average, exceed 10 m s−1 over most of the lifetime of a convective event to obtain supercell storms. The correlation coefficient between vertical vorticityζ and vertical velocity w, which (according to linear theory of dry convection) should be proportional to the product of the normalized helicity density, NHD (i.e., relative helicity), and a function involving the storm-relative wind speed, has the largest peak values (in time) in those simulated storms exhibiting large SREH and strong storm-relative winds in the low levels. Even when the vorticity is predominantly streamwise in the storm-relative framework, giving a normalized helicity density near unity (as is the case in many of these simulations), significant updraft rotation and large w–ζcorrelation coefficients do not develop and persist unless the storm-relative winds are sufficiently strong.

The correlation coefficient between w and ζ based on linear theory is found to be a significantly better predictor of net updraft rotation than the bulk Richardson number (BRN) or the BRN shear, and slightly better than the 0-3-km SREH. Both the theoretical correlation coefficient and the SREH are based on the motion of the initial storm after its initially rapid growth. Linear theory also predicts correctly the relative locations of the buoyancy, vertical velocity, and vertical vorticity extrema within the storms after allowance is made for the effects of vertical advection. In predicting the maximum vertical vorticity both above and below 1.14 km, rather than the actual w and ζcorrelation, the 0–3-km SREH performs slightly worse than the BRN. The correlation coefficient, SREH, and BRN all do a credible job of predicting storm type. Thus, it is recommended that operational forecasters use the BRN to predict storm type because it is independent of storm motion, and the SREH to characterize the rotational properties of storms once their motions can be established.

Finally, the ability of the NHD to characterize storm type and rotational properties is examined. Computed using the storm-relative winds, the NHD shows little ability to predict storm rotation (i.e., maximum w-ζcorrelation and maximum vertical vorticity), because it neglects the magnitudes of the vorticity and storm-relative wind vectors. Histograms of the disturbance NHD show a distinct bias toward positive values near unity for supercell storms, indicating an extraction of helicity from the mean flow by the disturbance, and only a slight bias for multicell storms.

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Bryan P. Holman, Steven M. Lazarus, and Michael E. Splitt

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This paper presents a method to bias correct and downscale wind speed over water bodies that are unresolved by numerical weather prediction (NWP) models and analyses. The dependency of wind speeds over water bodies to fetch length is investigated as a predictor of model wind speed error. Because model bias is found to be related to the forecast wind direction, a statistical method that uses the forecast fetch to remove wind speed bias is developed and tested. The method estimates wind speed bias using recent forecast errors from similar stations (i.e., those with comparable fetch lengths). As a result, the bias correction is not tied to local observations but instead to locations with similar land–water characteristics. Thus, it can also be used to downscale wind fields over inland and coastal water bodies. The fetch method is compared to four reference bias correction methods using one year’s worth of wind speed output from three NWP analyses in Florida. The fetch method yields a bias error near zero and results in a reduction of the mean absolute error that is comparable to the reference methods. The fetch method is then used to bias correct and downscale a coarse analysis to 500-m grid spacing over a coastal estuary in central Florida.

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Katherine M. LaCasse, Michael E. Splitt, Steven M. Lazarus, and William M. Lapenta

Abstract

High- and low-resolution sea surface temperature (SST) analysis products are used to initialize the Weather Research and Forecasting (WRF) Model for May 2004 for short-term forecasts over Florida and surrounding waters. Initial and boundary conditions for the simulations were provided by a combination of observations, large-scale model output, and analysis products. The impact of using a 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) SST composite on subsequent evolution of the marine atmospheric boundary layer (MABL) is assessed through simulation comparisons and limited validation. Model results are presented for individual simulations, as well as for aggregates of easterly- and westerly-dominated low-level flows. The simulation comparisons show that the use of MODIS SST composites results in enhanced convergence zones, earlier and more intense horizontal convective rolls, and an increase in precipitation as well as a change in precipitation location. Validation of 10-m winds with buoys shows a slight improvement in wind speed. The most significant results of this study are that 1) vertical wind stress divergence and pressure gradient accelerations across the Florida Current region vary in importance as a function of flow direction and stability and 2) the warmer Florida Current in the MODIS product transports heat vertically and downwind of this heat source, modifying the thermal structure and the MABL wind field primarily through pressure gradient adjustments.

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Steven M. Lazarus, Carol M. Ciliberti, John D. Horel, and Keith A. Brewster

Abstract

Several mesoscale data analysis systems are reviewed, of which one is then adapted and applied to the complex terrain of northwest Utah and the western United States. The analysis system relies on the simple, but computationally efficient, successive correction methodology. Near-real-time three-dimensional mesoscale analyses are produced hourly over northwest Utah at 1-km horizontal resolution while analyses are produced every 15 min for surface fields over northwest Utah and the western United States. Surface analyses over the western United States are also generated at 0000 and 1200 UTC to help to initialize 36-h mesoscale model forecasts. Comparisons between the 1-km three-dimensional analyses and the background three-dimensional analysis provided by the National Centers for Environmental Prediction Rapid Update Cycle, version 2 (RUC-2), indicate that, where surface and upper-air observations are abundant, the local analysis adds information beyond that of simply interpolating the background (RUC-2) data to the high-resolution analysis grid.

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Steven M. Lazarus, Samuel T. Wilson, Michael E. Splitt, and Gary A. Zarillo

Abstract

A wind-wave forecast system, designed with the intention of generating unbiased ensemble wave forecasts for extreme wind events, is assessed. Wave hindcasts for 12 tropical cyclones (TCs) are forced using a wind analysis produced from a combination of the North American Regional Reanalysis (NARR) and a parametric wind model. The default drag parameterization is replaced by one that is more in line with recent studies where a cap at weak-to-moderate wind speeds is applied. Quadrant-based significant wave height (Hs) statistics are composited in a storm-relative reference frame and stratified by the radius of maximum wind, storm speed, and storm intensity. Improvements in Hs are gleaned from both downscaling the NARR winds and tuning the wave model. However, the paradigm whereby the drag coefficient depends solely on the wind speed is limiting. Results indicate that Hs is biased low in the right quadrants (for all statistical subcategories). Conversely, Hs is high biased in the left-rear quadrant even though the analysis wind field is underforecast there. At radii less than 100 nautical miles, the model peak wave direction is offset from the observed, with the model (buoy) peak more in line with (to the left of) the direction of the tropical cyclone motion. As a result, the predominant storm-relative wind direction, which is northwesterly in the left-rear quadrant, opposes that of the buoy peak wave direction, while the model peak is more crosswise with respect to the wind. This will likely reduce the magnitude of the wind stress in the model.

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Steven M. Lazarus, Samuel T. Wilson, Michael E. Splitt, and Gary A. Zarillo

Abstract

A computationally efficient method of producing tropical cyclone (TC) wind analyses is developed and tested, using a hindcast methodology, for 12 Gulf of Mexico storms. The analyses are created by blending synthetic data, generated from a simple parametric model constructed using extended best-track data and climatology, with a first-guess field obtained from the NCEP–NCAR North American Regional Reanalysis (NARR). Tests are performed whereby parameters in the wind analysis and vortex model are varied in an attempt to best represent the TC wind fields. A comparison between nonlinear and climatological estimates of the TC size parameter indicates that the former yields a much improved correlation with the best-track radius of maximum wind rm. The analysis, augmented by a pseudoerror term that controls the degree of blending between the NARR and parametric winds, is tuned using buoy observations to calculate wind speed root-mean-square deviation (RMSD), scatter index (SI), and bias. The bias is minimized when the parametric winds are confined to the inner-core region. Analysis wind statistics are stratified within a storm-relative reference frame and by radial distance from storm center, storm intensity, radius of maximum wind, and storm translation speed. The analysis decreases the bias and RMSD in all quadrants for both moderate and strong storms and is most improved for storms with an rm of less than 20 n mi. The largest SI reductions occur for strong storms and storms with an rm of less than 20 n mi. The NARR impacts the analysis bias: when the bias in the former is relatively large, it remains so in the latter.

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Michael E. Splitt, Steven M. Lazarus, Sarah Collins, Denis N. Botambekov, and William P. Roeder

Abstract

Probabilistic wind speed forecasts for tropical cyclones from Monte Carlo–type simulations are assessed within a theoretical framework for a simple unbiased Gaussian system that is based on feature size and location error that mimic tropical cyclone wind fields. Aspects of the wind speed probability data distribution, including maximum expected probability and forecast skill, are assessed. Wind speed probability distributions are shown to be well approximated by a bounded power-law distribution when the feature size is smaller than the location error and tends toward a U-shaped distribution as the location error becomes small. Forecast skill (i.e., true and Heidke skill scores) is shown to be highly dependent on the probability forecast data distribution. Forecasts from the National Hurricane Center (NHC) Wind Speed Probability Forecast Product are used to assess the applicability of the simple system in the interpretation and evaluation of a more advanced system.

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