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At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

NASA NTRS1999

Initial Comparison of the Lightning Imaging Sensor (LIS) with Lightning Detection and Ranging (LDAR)

The mapping of the lightning optical pulses detected by the Lightning Imaging Sensor (LIS) is compared with the radiation sources by Lightning Detection and Ranging (LDAR) and the National Lightning Detection Network (NLDN) for three thunderstorms observed during and overpasses on 15 August 1998. The comparison involves 122 flashes including 42 ground and 80 cloud flashes. For ground flash, the LIS recorded the subsequent strokes and changes inside the cloud. For cloud flashes, LIS recorded those with higher sources in altitude and larger number of sources. The discrepancies between the LIS and LDAR flash locations are about 4.3 km for cloud flashes and 12.2 km for ground flashes. The reason for these differences remain a mystery.

Ushio, Tomoo↗

NASA NTRS2009

An Algorithm for Obtaining the Distribution of 1-Meter Lightning Channel Segment Altitudes for Application in Lightning NOx Production Estimation

An algorithm has been developed to estimate the altitude distribution of one-meter lightning channel segments. The algorithm is required as part of a broader objective that involves improving the lightning NOx emission inventories of both regional air quality and global chemistry/climate models. The algorithm was tested and applied to VHF signals detected by the North Alabama Lightning Mapping Array (NALMA). The accuracy of the algorithm was characterized by comparing algorithm output to the plots of individual discharges whose lengths were computed by hand; VHF source amplitude thresholding and smoothing were applied to optimize results. Several thousands of lightning flashes within 120 km of the NALMA network centroid were gathered from all four seasons, and were analyzed by the algorithm. The mean, standard deviation, and median statistics were obtained for all the flashes, the ground flashes, and the cloud flashes. One-meter channel segment altitude distributions were also obtained for the different seasons.

Peterson, Harold↗

NASA NTRS2014

The GOES-R Geostationary Lightning Mapper (GLM) and the Global Observing System for Total Lightning

for the existing GOES system currently operating over the Western Hemisphere. New and improved instrument technology will support expanded detection of environmental phenomena, resulting in more timely and accurate forecasts and warnings. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved temporal, spatial, and spectral resolution for the next generation Advanced Baseline Imager (ABI). The GLM will map total lightning continuously day and night with near-uniform spatial resolution of 8 km with a product latency of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency among a number of potential applications. The GLM will help address the National Weather Service requirement for total lightning observations globally to support warning decision-making and forecast services. Science and application development along with pre-operational product demonstrations and evaluations at NWS national centers, forecast offices, and NOAA testbeds will prepare the forecasters to use GLM as soon as possible after the planned launch and check-out of GOES-R in 2016. New applications will use GLM alone, in combination with the ABI, or integrated (fused) with other available tools (weather radar and ground strike networks, nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing more timely and accurate forecasts and warnings.

Goodman, Steven J.↗

NASA NTRS2015

Global Lightning Climatology from the Tropical Rainfall Measuring Mission (TRMM), Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD)

The Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) has been collecting observations of total lightning in the global tropics and subtropics (roughly 38 deg S - 38 deg N) since December 1997. A similar instrument, the Optical Transient Detector, operated from 1995-2000 on another low earth orbit satellite that also saw high latitudes. Lightning data from these instruments have been used to create gridded climatologies and time series of lightning flash rate. These include a 0.5 deg resolution global annual climatology, and lower resolution products describing the annual cycle and the diurnal cycle. These products are updated annually. Results from the update through 2013 will be shown at the conference. The gridded products are publicly available for download. Descriptions of how each product can be used will be discussed, including strengths, weaknesses, and caveats about the smoothing and sampling used in various products.

Cecil, Daniel J.↗

DOE OSTI2019

Local and statistical maps of lightning-generated wave power density estimated at the Van Allen Probes footprints from the World-Wide Lightning Location Network database

We introduce a new method that uses the World-Wide Lightning Location Network (WWLLN) to estimate both the local and the drift lightning power density at the Van Allen Probes footprints during 4.3 years (~2x10 8 strokes.). The ratio of the drift power density to the local power density defines a time-resolved WWLLN-based model of lightning-generated wave (LGW) power density ratio, $\mathcal{R_{WWLLN}}$. $\mathcal{R_{WWLLN}}$ is computed every ~34s. This ratio multiplied by the time-resolved LGW intensity measured by the Probes allows direct computation of pitch angle diffusion coefficients used in radiation belts codes. According to Statistical analysis, the median power density ratio is $\mathcal{\hat{R}_{WWLLN}}$ = 0.3 - 4 over the Americas. Elsewhere, $\mathcal{\hat{R}_{WWLLN}}$ > 1 in general. Over oceans, $\mathcal{\hat{R}_{WWLLN}}$ is larger than ~10 and varies with season (x2.5 from winter to summer). The yearly median $\mathcal{\hat{R}_{WWLLN}}$ decays as $\mathcal{\hat{R}_{WWLLN}}$~9.9/L 0.91 . The strong geographical and temporal variation should be kept in assessing effects in space. $\mathcal{R_{WWLLN}}$> 1 suggests significant LGW effects in the inner belt.

58 GEOSCIENCES↗

NASA NTRS2017

An Assessment of Land Surface and Lightning Characteristics Associated with Lightning-Initiated Wildfires

Can we use modeled information of the land surface and characteristics of lightning beyond flash occurrence to increase the identification and prediction of wildfires? Combine observed cloud-to-ground (CG) flashes with real-time land surface model output, and Compare data with areas where lightning did not start a wildfire to determine what land surface conditions and lightning characteristics were responsible for causing wildfires. Statistical differences between suspected fire-starters and non-fire-starters were peak-current dependent 0-10 cm Volumetric and Relative Soil Moisture comparisons were statistically dependent to at least the p = 0.05 independence level for both polarity flash types Suspected fire-starters typically occurred in areas of lower soil moisture than non-fire-starters. GVF value comparisons were only found to be statistically dependent for -CG flashes. However, random sampling of the -CG non-fire starter dataset revealed that this relationship may not always hold.

wildfire↗

NASA NTRS1982

Correlation of satellite lightning observations with ground-based lightning experiments in Florida, Texas and Oklahoma

Satellite observations of lightning were correlated with ground-based measurements of lightning from data bases obtained at three separate sites. The percentage of ground-based observations of lightning that would be seen by an orbiting satellite was determined.

Edgar, B. C.↗

NASA NTRS2004

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Boccippio, Dennis↗

NASA NTRS2011

Assessing the Lifetime Performance of the Lightning Imaging Sensor (LIS): Implications for the Geostationary Lightning Mapper (GLM)

The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth s Tropics for 13 years. This study examines the performance of the LIS throughout its time in orbit. Application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) was performed on the LIS background pixels to assess the stability of the LIS instrument. The DCCT analysis indicates that the maximum deviation of the monthly mean radiance is within 2% of the overall mean, indicating stable performance over the period. In addition, an examination of the number of flashes detected over time similarly shows no significant trend (after adjusting for the orbit boost that occurred in August 2001). These and other results indicate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of the Geostationary Lightning Mapper (GLM) onboard the next generation Geostationary Operational Environmental Satellite-R (GOES-R). Since GLM is based on LIS design heritage, the LIS results indicate that GLM may also experience stable performance over its lifetime.

Buechler, D. E.↗

NASA NTRS2012

Correlated Lightning Mapping Array (LMA) and Radar Observations of the Initial Stages of Florida Triggered Lightning Discharges

We characterize the geometrical and electrical characteristics of the initial stages of nine Florida triggered lightning discharges using a Lightning Mapping Array (LMA), a C-band SMART radar, and measured channel-base currents. We determine initial channel and subsequent branch lengths, average initial channel and branch propagation speeds, and channel-base current at the time of each branch initiation. The channel-base current is found to not change significantly when branching occurs, an unexpected result. The initial stage of Florida triggered lightning typically transitions from vertical to horizontal propagation at altitudes of 3-6 km, near the typical 0 C level of 4-5 km and several kilometers below the expected center of the negative cloud-charge region at 7-8 km. The data presented potentially provide information on thunderstorm electrical and hydrometeor structure and discharge propagation physics. LMA source locations were obtained from VHF sources of positive impulsive currents as small as 10 A, in contrast to expectations found in the literature.

Hill, J. D.↗

NASA NTRS2007

Comparison of the KSC-ER Cloud-to-Ground Lightning Surveillance System (CGLSS) and the U.S. National Lightning Detection Network(TradeMark)(NLDN)

The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground lightning detection networks, CGLSS and NLDN, during ground and launch operations at the KSC-ER. For these applications, it is very important to understand the location accuracy and detection efficiency of each network near the KSC-ER. If a cloud-to-ground (CG) lightning strike is missed or mis-located by even a small amount, the result could have significant safety implications, require expensive retests, or create unnecessary delays or scrubs in launches. Therefore, it is important to understand the performance of each lightning detection system in considerable detail. To evaluate recent upgrades in the CGLSS sensors in 2000 and the entire NLDN in 2002- 2003, we have compared. measurements provided by these independent networks in the summers of 2005 and 2006. Our analyses have focused on the fraction of first strokes reported individually and in-common by each network (flash detection efficiency), the spatial separation between the strike points reported by both networks (relative location accuracy), and the values of the estimated peak current, Ip, reported by each network. The results within 100 km of the KSC-ER show that the networks produce very similar values of Ip (except for a small scaling difference) and that the relative location accuracy is consistent with model estimates that give median values of 200-300m for the CGLSS and 600-700m for the NLDN in the region of the KSC-ER. Because of differences in the network geometries and sensor gains, the NLDN does not report 10-20% of the flashes that have a low Ip (2 kA < |Ip| < 16 kA), both networks report 99 % of the flashes that have intermediate values of Ip (16< |Ip| < 50 kA), and the CGLSS fails to report 20-30% of the high-current events (|Ip| >=0 kA).

Ward, Jennifer G.↗

NASA NTRS2013

The Use of the Deep Convective Cloud Technique (DCCT) to Monitor On-Orbit Performance of the Geostationary Lightning Mapper (GLM): Use of Lightning Imaging Sensor (LIS) Data as Proxy

The Geostationary Lightning Mapper (GLM) on the next generation Geostationary Operational Environmental Satellite-R (GOES-R) will not have onboard calibration capability to monitor its performance. The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth's Tropics since 1997. The GLM design is based on LIS heritage, making it a good proxy dataset. This study examines the performance of LIS throughout its time in orbit. This was accomplished through application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) to LIS background pixel radiance data. The DCCT identifies deep convective clouds by their cold Infrared (IR) brightness temperatures and using them as invariant targets in the solar reflective portion of the solar spectrum. The GLM and LIS operate in the near-IR at a wavelength of 777.4 nm. In the present study the IR data is obtained from the Visible Infrared Sensor (VIRS) which is collocated with LIS onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The DCCT is applied to LIS observations for July and August of each year from 1998-2010. The resulting distributions of LIS background DCC pixel radiance for each July August are very similar, indicating stable performance. The mean radiance of the DCCT analysis does not show a long term trend and the maximum deviation of the July August mean radiance for each year is within 0.7% of the overall mean. These results demonstrate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of GLM, with cold clouds identified using IR data from the Advanced Baseline Imager (ABI) which will also be located on GOES-R. Since GLM is based on LIS design heritage, the LIS results indicate that GLM should also experience stable performance over its lifetime.

Buechler, Dennis E.↗

NASA NTRS2013

Investigating the Use of Deep Convective Clouds (DCCT) to Monitor On-orbit Performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging Sensor (LIS) Measurements

There is a need to monitor the on-orbit performance of the Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite R (GOES-R) for changes in instrument calibration that will affect GLM's lightning detection efficiency. GLM has no onboard calibration so GLM background radiance observations (available every 2.5 min) of Deep Convective Clouds (DCCs) are investigated as invariant targets to monitor GLM performance. Observations from the Lightning Imaging Sensor (LIS) and the Visible and Infrared Scanner (VIRS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite are used as proxy datasets for GLM and ABI 11 m measurements.

Buechler, Dennis E.↗

NASA NTRS2018

An Analysis of the Lightning Jump Algorithm Using Geostationary Lightning Mapper Flashes

This project aims to implement the two-sigma lightning jump algorithm (LJA) developed using Lightning Mapping Arrays (LMAs), with GOES-16 Geostationary Lightning Mapper (GLM) flashes, evaluate its performance, and identify any needed adjustments to the algorithm to optimize operational skill. The GLM is projected to have lower detection efficiency (DE) (70-90 percent) than operational LMAs (95-99 percent). The reduced GLM DE coupled with the coarser spatial resolution of the GLM could have impacts on flash rates and trends that could affect the LJA in various ways. Deep dives are conducted on four separate cases. Three of four cases show LMAs seeing two to three times as many flashes as the GLM. Only fifteen of twenty five GLM jumps saw increases in radar intensity while fourteen of nineteen LMA jumps did. These results suggest a larger sample sized study must be conducted to determine how to implement the LJA with the GLM.

Curtis, Nathan↗

DOE OSTI2020

Synergetic Use of the WSR-88D Radars, GOES-R Satellites, and Lightning Networks to Study Microphysical Characteristics of Hurricanes

Abstract This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.

Meteorology & Atmospheric Sciences↗

DOE OSTI2024

Lightning Climatology Derived from World Wide Lightning Location Network (WWLLN) for US East Coast and European North Sea

This dataset contains gridded (1x1 km) data of lightning stroke observations during 2020, 2021, and 2022 over the US East Coast and European North Sea. Data processing involved intersecting each of the ~26 million lightning strokes over the US East Coast and ~365 thousand strokes over the North Sea within a corresponding 1x1 km grid, resulting in each grid reporting a 'strokes per square kilometer' and 'energy per square kilometer'. For the US, these results are presented as annual averages for 2020, 2021, and 2022, while for the North Sea results are presented as a 3-year annual average.

17 WIND ENERGY↗

DOE OSTI2024

Lightning Climatology Derived from World Wide Lightning Location Network (WWLLN) for US East Coast and European North Sea

17 WIND ENERGY↗

NASA NTRS1991

Lightning current rate of rise in the new lightning flash model for the space shuttle program

The recently baselined Space Shuttle lightning effects flash model is discussed. The discussion is limited to the current rate(s) of rise. A review of some of the recent data is presented, as well as a discussion of how interaction processes affect the induced voltages and currents which arise from a lightning strike. It is concluded that the multiple pulse components of the new flash model present a more severe threat to complex avionic systems, than is presented by a single pulse having an extremely fast rate of rise.

Bankston, Nathaniel G.↗