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Robert A. Mazany
,
Steven Businger
,
Seth I. Gutman
, and
William Roeder

Abstract

The primary weather forecast challenge at the Cape Canaveral Air Station and Kennedy Space Center is lightning. This paper describes a statistical approach that combines integrated precipitable water vapor (IPWV) data from a global positioning system (GPS) receiver site located at the Kennedy Space Center (KSC) with other meteorological data to develop a new GPS lightning index. The goal of this effort is to increase the forecasting skill and lead time for prediction of a first strike at the KSC. Statistical regression methods are used to identify predictors that contribute skill in forecasting a lightning event. Four predictors were identified out of a field of 23 predictors that were tested, determined using data from the 1999 summer thunderstorm season. They are maximum electric field mill values, GPS IPWV, 9-h change in IPWV, and K index. The GPS lightning index is a binary logistic regression model made up of coefficients multiplying the four predictors. When time series of the GPS lightning index are plotted, a common pattern emerges several hours prior to a lightning event. Whenever the GPS lightning index falls to 0.7 or below, lightning occurs within the next 12.5 h. An index threshold value of 0.7 was determined from the data for lightning prediction. Forecasting time constraints based on KSC weather notification requirements were incorporated into the verification. Forecast verification results obtained by using a contingency table revealed a 26.2% decrease from the KSC's previous-season false alarm rates for a nonindependent period and a 13.2% decrease in false alarm rates for an independent test season using the GPS lightning index. In addition, the index improved the KSC desired lead time by nearly 10%. Although the lightning strike window of 12 h is long, the GPS lightning index provides useful guidance to the forecaster in preparing lighting forecasts, when combined with other resources such as radar and satellite data. Future testing of the GPS lightning index and the prospect of using the logistic regression approach in forecasting related weather hazards are discussed.

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Steven Businger
,
Michael E. Adams
,
Steven E. Koch
, and
Michael L. Kaplan
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Steven Businger
,
Michael E. Adams
,
Steven E. Koch
, and
Michael L. Kaplan

Abstract

Mesoscale height and temperature fields can be extracted from the observed wind field by making use of the full divergence equation. Mass changes associated with irrotational ageostrophic motions are retained for a nearly complete description of the height field. Above the boundary layer, in the absence of friction, the divergence equation includes terms composed of the components of the wind and a Laplacian of the geopotential height field. Once the mass field is determined, the thermal structure is obtained through application of the hypsometric equation.

In this paper an error analysis of this divergence method is undertaken to estimate the potential magnitude of errors associated with random errors in the wind data. Previous applications of the divergence method have been refined in the following ways. (i) The domain over which the method is applied is expanded to encompass the entire STORM-FEST domain. (ii) Wind data from 23 profiler and 38 rawinsonde sites are combined in the analysis. (iii) Observed profiler and rawinsonde data are interpolated to grid points through a modified objective analysis, and (iv) the variation in elevation of the profiler sites is taken into account.

The results of the application of the divergence method to the combined wind data from profiler and rawinsonde sites show good agreement between the retrieved heights and temperatures and the observed values at rawinsonde sites. Standard deviations of the difference between the retrieved and observed data lie well within the precision of the rawinsonde instruments. The difference field shows features whose magnitude is significantly larger than the errors predicted by the error analysis, and these features are systematic rather than random in nature, suggesting that the retrieved fields are able to resolve mesoscale signatures not fully captured by the rawinsonde data alone.

The divergence method is also applied solely to the profiler data to demonstrate the potential of the divergence method to provide mass and thermal fields on a routine basis at synoptic times when operational rawinsonde data are not available. A comparison of the heights derived from the profiler winds with those independently measured by rawinsondes indicates that valuable information on the evolution of atmospheric height and temperature fields can be retrieved between conventional rawinsonde release times through application of the divergence method. The implications of the results for applications of the method in weather analysis and in numerical weather prediction are discussed.

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Steven R. Chiswell
,
Steven Businger
,
Michael Bevis
,
Fredrick Solheim
,
Christian Rocken
, and
Randolph Ware

Abstract

Water vapor radiometer (WVR) retrieval algorithms require a priori information on atmospheric conditions along the line of sight of the radiometer in order to derive opacities from observed brightness temperatures. This paper's focus is the mean radiating temperature of the atmosphere (T mr), which is utilized in these algorithms to relate WVR measurements to integrated water vapor. Current methods for specifying T mr rely on the climatology of the WVR site-for example, a seasonal average-or information from nearby soundings to specify T mr. However, values of T mr, calculated from radiosonde data, not only vary according to site and season but also exhibit large fluctuations in response to local weather conditions. By utilizing output from numerical weather prediction (NWP) models, T mr can be accurately prescribed for an arbitrary WVR site at a specific time. Temporal variations in local weather conditions can he resolved by NWP models on timescales shorter than standard radiosonde soundings.

Currently used methods for obtaining T mr are reviewed. Values of T mr obtained from current methods and this new approach are compared to those obtained from in situ radiosonde soundings. The improvement of the T mr calculation using available model forecast data rather than climatological values yields a corresponding improvement of comparable magnitude in the retrieval of atmospheric opacity. Use of forecast model data relieves a WVR site of its dependency on local climatology or the necessity of a nearby sounding, allowing more accurate retrieval of observed conditions and increased flexibility in choosing site location. Furthermore, it is found that the calculation of precipitable water by means of atmospheric opacities does not require time-dependent tuning parameters when model data are used. These results were obtained using an archived subset of the full nested grid model output. The added horizontal and vertical resolution of operational data should further improve this approach.

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Paolo Antonelli
,
Tiziana Cherubini
,
Steven Businger
,
Siebren de Haan
,
Paolo Scaccia
, and
Jean-Luc Moncet

Abstract

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini’s approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model’s Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.

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Gerald Geernaert
,
Steven Businger
,
Christopher Jeffery
,
Thomas Dunn
,
Russ Elsberry
, and
Don MacGorman

Abstract

No Abstract available.

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Malte F. Stuecker
,
Christina Karamperidou
,
Alison D. Nugent
,
Giuseppe Torri
,
Sloan Coats
, and
Steven Businger
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Steven Businger
,
M. Puakea Nogelmeier
,
Pauline W. U. Chinn
, and
Thomas Schroeder

Abstract

High literacy rates among Native Hawaiians in the nineteenth century and publication of more than 100 Hawaiian-language newspapers from 1834 to 1948 produced the largest archive of indigenous writing in the Western Hemisphere. These newspapers extend our knowledge of historical environmental events and natural disasters back into the early nineteenth century and deeper into precontact times. Articles reporting observations of meteorological events allowed the authors to reconstruct the track and intensity of an 1871 hurricane that brought devastation to the islands of Hawaii and Maui and to discern historical patterns of droughts and floods in Hawaii. These findings illustrate the value of Hawaiian-language newspapers as resources for science research and science education.

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Steven Businger
,
Roy Huff
,
Andre Pattantyus
,
Keith Horton
,
A. Jeff Sutton
,
Tamar Elias
, and
Tiziana Cherubini

Abstract

Emissions from Kīlauea volcano, known locally as “vog” for volcanic smog, pose significant environmental and health risks to the Hawaiian community. The Vog Measurement and Prediction (VMAP) project was conceived to help mitigate the negative impacts of Kīlauea’s emissions. To date, the VMAP project has achieved the following milestones: i) created a custom application of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, hereafter Vog model) to produce statewide forecasts of the concentration and dispersion of sulfur dioxide (SO2) and sulfate aerosol from Kīlauea volcano; ii) developed an ultraviolet (UV) spectrometer array to provide near-real-time volcanic gas emission rate measurements for use as input into the Vog model; iii) developed and deployed a stationary array of ambient SO2 and meteorological sensors to record the spatial characteristics of Kīlauea’s gas plume in high temporal and spatial resolution for model verification; and iv) developed web-based tools to facilitate the dissemination of observations and model forecasts to provide guidance for safety officials and the public, and to raise awareness of the potential hazards of volcanic emissions to respiratory health, agriculture, and general aviation.

Wind fields and thermodynamic data from the Weather Research and Forecasting (WRF) Model provide input to the Vog model, with a statewide grid spacing of 3 km and a 1-km grid covering the island of Hawaii. Validation of the Vog model forecasts is accomplished with reference to data from Hawaii State Department of Health ground-based air quality monitors. VMAP results show that this approach can provide useful guidance for the people of Hawaii.

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Steven Businger
,
Thomas Birchard Jr.
,
Kevin Kodama
,
Paul A. Jendrowski
, and
Jian-Jian Wang

Abstract

On 2 November 1995 a kona low formed to the northwest of Hawaii. During the following 48 h a series of convective rainbands developed on the southeastern side of the low as it slowly moved eastward. On the afternoon of 3 November 1995 Hawaiian standard time (HST), a bow-echo signature was identified in the reflectivity observations from the recently installed WSR-88D located on the south shore of Kauai, and led to the first severe thunderstorm warning ever issued by the National Weather Service Forecast Office in Honolulu, Hawaii. Subsequent to the warning, winds of 40 m s−1 (80 kt) were observed at Nawiliwili Harbor on the southeast side of Kauai. The goals of this paper were to (i) document, within the constraints of the observational data, the synoptic and mesoscale environment associated with the formation of the bow echo and severe weather in Hawaii and contrast them with investigations of similar phenomena in the midlatitudes and Tropics, and (ii) provide a discussion of the implications of the availability of data from the new WSR-88D radars in Hawaii to operational forecasting of severe weather in the central Pacific.

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