A Lightning Prediction Index that Utilizes GPS Integrated Precipitable Water Vapor*

Mazany, Robert A.; Businger, Steven; Gutman, S. I.; Roeder, William P.

doi:10.1175/1520-0434(2002)017<1034:alpitu>2.0.co;2

Cited by 61 publications

(46 citation statements)

References 21 publications

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“…Higher frequency data assimilation drives the need for more timely observations within and upstream of the forecast area of concern. Data must also be available with minimal latency to be useful for other applications including diagnostic algorithms (Mazany et al 2002), data fusion (Keighton et al 2003), model verification, and general subjective forecasting.…”

Section: Data Acquisitionmentioning

confidence: 99%

Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

Gutman¹,

Sahm²,

Benjamin³

et al. 2004

Journal of the Meteorological Society of Japan

110

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Since 1994, the NOAA Research-Forecast Systems Laboratory (NOAA /FSL) has been evaluating the utility of ground-based Global Positioning System (GPS) remote sensing techniques for operational weather forecasting, climate monitoring, atmospheric research, and other applications such as satellite calibration and validation. Techniques have been developed to acquire, process, distribute GPS integrated precipitable water vapor (IPW) retrievals and ancillary surface meteorological observations every 30-minutes with less than 15 minute latency. Techniques to assimilate these observations into the research version of the Rapid Update Cycle (RUC) numerical weather prediction assimilation/model system running hourly at NOAA /FSL have been developed, and the impacts of these observations on shortrange weather forecast accuracy have been evaluated since 1998 using a 60-km version of the system. These assessments consist of data denial experiments (parallel runs with and without GPS water vapor observations) to determine the impact that GPS-derived integrated (or total column) precipitable water vapor (IPW) retrievals have on short-range moisture and precipitation forecasts. The experiments have been conducted over a portion of the central United States that, from a meteorological perspective, is one of the best-observed areas on Earth. While this greatly facilitates the impact assessments, it also presents a special challenge to a new observing system under evaluation, such as GPS-Met, since relatively few measurements have to ''compete'' with an enormous number of other (conventional and nonconventional) observations of similar and related parameters. Despite this, five years of experiments inCorresponding author: Seth I. Gutman, Chief, GPS-Met Observing Systems Branch, NOAA Forecast Systems Laboratory, 325 Broadway R/FS3, Boulder CO 80305-3328, USA E-mail: Seth.I.Gutman@noaa.gov ( 2004, Meteorological Society of Japan dicate more or less continuous improvements in 3-hour relative humidity forecasts at pressure levels below 500 hPa. The greatest skill is seen during the cold season when moisture changes are dominated by synoptic-scale weather systems. Perhaps the most significant result is that the impact in improved forecast skill from assimilation of GPS-IPW data has increased each year as the number of stations has increased, suggesting that further increases in the network density over the United States will result in further forecast improvement.

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Section: Data Acquisitionmentioning

confidence: 99%

Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

Gutman¹,

Sahm²,

Benjamin³

et al. 2004

Journal of the Meteorological Society of Japan

110

View full text Add to dashboard Cite

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“…The prediction of lightning within a storm has been done using radar data (Keighton et al 1991;Hondl and Eilts 1994;Gremillion and Orville 1999), and only recently with other techniques (Mazany et al 2002;Short et al 2004). Satellite and lightning data have been combined to estimate storm severity from cloud-to-ground flash rates (Goodman et al 1988;Roohr and Vonder Haar 1994), where it was suggested that the prediction of lightning may be possible using IR imagery, and would be valuable to forecasters of severe weather, aviation hazards, or forest fires.…”

Section: Introductionmentioning

confidence: 99%

The Definition of GOES Infrared Lightning Initiation Interest Fields

Harris

Mecikalski

MacKenzie

et al. 2010

Journal of Applied Meteorology and Climatology

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Within cumulus cloud fields that develop in conditionally unstable air masses, only a fraction of the cumuli may eventually develop into deep convection. Identifying which of these convective clouds is most likely to generate lightning often starts with little more than a qualitative visual satellite analysis. The goal of this study is to identify the observed satellite infrared (IR) signatures associated with growing cumulus clouds prior to the first lightning strike, or lightning initiation (LI). This study quantifies the behavior of 10 Geostationary Operational Environmental Satellite-12 (GOES-12) IR fields of interest in the 1 h in advance of LI. A total of 172 lightning-producing storms, which occurred during the 2009 convective season, are manually tracked and studied over four regions: northern Alabama, central Oklahoma, the Kennedy Space Center, and Washington, D.C. Four-dimensional and cloud-to-ground lightning array data provide a total cloud lightning picture (in-cloud, cloud-to-cloud, cloud-to-air, and cloud-to-ground) and thus precise LI points for each storm in both time and space. Statistical significance tests are conducted on observed trends for each of the 10 LI fields to determine the unique information each field provides in terms of behavior prior to LI. Eight out of 10 LI fields exhibited useful information at least 15 min in advance of LI, with 35 min being the average. Statistical tests on these eight fields are compared for separate large geographical areas. Median IR temperatures and 3.9-mm reflectance values are then determined for all 172 events as an outcome, which may be valuable when implementing a LI prediction algorithm into real-time satellite-based systems.

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“…The water molecule has an asymmetric distribution of charge that results in a permanent dipole moment. This unique structure results in a large latent heat associated with changes in the phase, making possible the phenomena of lightning (1). Specifically, lightning is a transient discharge of static electricity that serves to re-establish electrostatic equilibrium within a storm environment.…”

Section: Lightning In Central Floridamentioning

confidence: 99%

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

Kehrer

Graf

Roeder

2008

Weather and Forecasting

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Using meteorology data, focusing on precipitable water (PW), obtained during the [2000][2001][2002][2003] thunderstorm seasons in Central Florida, this paper will, one, assess the skill and accuracy measurements of the current Mazany forecasting tool and, two, provide additional forecasting tools that can be used in predicting lightning.Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) are located in east Central Florida. KSC and CCAFS process and launch manned (NASA Space Shuttle) and unmanned (NASA and Air Force Expendable Launch Vehicles) space vehicles. One of the biggest cost impacts is unplanned launch scrubs due to inclement weather conditions such as thunderstorms. Each launch delay/scrub costs over a quarter million dollars, and the need to land the Shuttle at another landing site and return to KSC costs approximately $ 1M. Given the amount of time lost and costs incurred, the ability to accurately forecast @redict) when lightning will occur can result in significant cost and time savings.All lightning prediction models were developed using binary logistic regression. Lightning is the dependent variable and is binary. The independent variables are the Precipitable Water (PW) value for a given time of the day, the change in PW up to 12 hours, the electric field mill value, and the K-index value.

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A Lightning Prediction Index that Utilizes GPS Integrated Precipitable Water Vapor*

Cited by 61 publications

References 21 publications

Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

Rapid Retrieval and Assimilation of Ground Based GPS Precipitable Water Observations at the NOAA Forecast Systems Laboratory: Impact on Weather Forecasts

The Definition of GOES Infrared Lightning Initiation Interest Fields

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

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