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Scott J. Richardson, Michael E. Splitt, and Barry M. Lesht

Abstract

This work describes in situ moisture sensor comparisons that were performed in conjunction with the first Water Vapor Intensive Observation Period (IOP) conducted at the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site during September of 1996. Two Raman lidars, two Atmospheric Emitted Radiance Interferometers, (AERIs), and a suite of 13 microwave radiometers were assembled at the CART site during the IOP, and in situ measurements were used for calibration and verification. In addition, this work was meant to help assess the current observing strategy in an effort to make improvements to the routine continuous measurements. To accomplish these goals, verification of the in situ measurements was required. Therefore, a laboratory intercomparison of the in situ moisture sensors (nine capacitive chip relative humidity sensors and four chilled mirror sensors) was performed at the Oklahoma Mesonet temperature and relative humidity testing and calibration facility. Tests were conducted both before and after the instruments were used in the IOP, making it possible to detect instrument problems prior to the IOP and to determine if instrument failure or drift occurred during the IOP.

Preliminary results comparing in situ moisture measurements with remotely sensed atmospheric moisture will be presented and additional applications will be discussed.

As a consequence of this work, modifications were made to the ARM CART calibration procedures, and there are now redundant temperature and relative humidity measurements so that sensor drift or calibration errors may be detected. These modifications to the observation and calibration strategy led to improvements in the continuous routine measurements at the ARM CART site.

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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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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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J. Michalsky, E. Dutton, M. Rubes, D. Nelson, T. Stoffel, M. Wesley, M. Splitt, and J. DeLuisi

Abstract

Although most measurements of total downwelling shortwave irradiance are made with pyranometers, the World Climate Research Program’s Baseline Surface Radiation Network has recommended the use of the summation of shortwave components in which the direct normal irradiance is measured and multiplied by the cosine of the solar zenith angle and then added to the diffuse horizontal irradiance measured by a pyranometer that is shaded from direct solar radiation by a disk. The nonideal angular response of most pyranometers limits their accuracy to about 3%, or 20–30 W m−2, for instantaneous clear-sky measurements. An intensive study of 21 separate measurements of total horizontal irradiance was conducted during extreme winter conditions of low sun and cold temperatures over 12 days at the National Oceanic and Atmospheric Administration’s Climate Monitoring and Diagnostics Laboratory. The experiment showed that the component sum methodology could lower the uncertainty by a factor of 2 or 3. A clear demonstration of this improvement was realized in a separate experiment conducted at the Atmospheric Radiation Measurement Southern Great Plains Cloud and Radiation Testbed site during April 1996. Four independent measurements of downwelling shortwave irradiance using the component sum technique showed typical differences at solar noon of about 10 W m−2. The mean of these summed measurements at solar noon was lower than the mean of the most-well-calibrated pyranometer measurements, acquired simultaneously, by about 30 W m−2, which is consistent with the typical angular response of many pyranometers.

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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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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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Steven M. Lazarus, Jason Chiappa, Hadley Besing, Michael E. Splitt, and Jeremy A. Riousset

Abstract

The meteorological characteristics associated with thunderstorm-top turbulence and tropical cyclone (TC) gigantic jets (GJs) are investigated. Using reanalysis data and observations, the large-scale environment and storm-top structure of three GJ-producing TCs are compared to three non-GJ oceanic thunderstorms observed via low-light camera. Evidence of gravity wave (GW) breaking is manifest in the IR satellite images with cold ring and enhanced-V signatures prevalent in TCs Hilda and Harvey and embedded warm spots in the Dorian and null storms. Statistics from an additional six less prodigious GJ environments are also included as a baseline. Distinguishing features of the TC GJ environment include higher tropopause, colder brightness temperatures, more stable lower stratosphere/distinct tropopause, and reduced tropopause penetration. These factors support enhanced GW breaking near the cloud top (overshoot). The advantage of a higher tropopause is that both electrical conductivity and GW breaking increase with altitude and thus act in tandem to promote charge dilution by increasing the rate at which the screening layer forms as well as enhancing the storm-top mixing. The roles of the upper-level ambient flow and shear are less certain. Environments with significant upper-tropospheric shear may compensate for a lower tropopause by reducing the height of the critical layer which would also promote more intense GW breaking and turbulence near the cloud top.

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Michael E. Splitt, Jaclyn A. Shafer, Steven M. Lazarus, and William P. Roeder

Abstract

A tropical cyclone (TC) wind speed probability forecast product developed at the Cooperative Institute for Research in the Atmosphere (CIRA) and adopted by the National Hurricane Center (NHC) is evaluated for U.S. land-threatening and landfalling events over four hurricane seasons from 2004 to 2007. A key element of this work is the discernment of risk associated with the interval forecast probabilities for the three wind speed categories (i.e., 34, 50, and 64 kt, where 1 kt = 0.52 m s−1). A quantitative assessment of the interval probabilities (0–12, 12–24, 24–36, 36–48, 48–72, 72–96, and 96–120 h) is conducted by converting them into binary (yes–no) forecasts using decision thresholds that are selected using the true skill statistic (TSS) and the Heidke skill score (HSS). The NHC product performs well as both the HSS and TSS demonstrate skill out to the 48–72- and 72–120-h intervals, respectively. Overall, reliability diagrams and bias scores indicate that the NHC product has a tendency to overforecast event likelihood for cases where the forecast probabilities exceed 60%. Specifically, the NHC product tends to overforecast for the 34-kt category but underforecasts for the 64-kt category, especially at later forecast intervals. Results for the 50-kt category are mixed but also exhibit a tendency to underforecast during the latter intervals. Decision thresholds range from 1% to 55% depending on the selection method, wind speed category, and time interval. Given that the average forecast probabilities decrease with forecast hour, small forecast probabilities may be meaningful. The HSS is recommended over the TSS for decision threshold selection because the use of the TSS introduces significant bias and the HSS is less sensitive to filtering of correct negatives.

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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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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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