Search NASA⌕ Search

SEARCH · Search NASA

Results for “Geostationary Lightning Mapper”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Geostationary Lightning Mapper On-Orbit Sources of False Events

The first Geostationary Lightning Mapper (GLM) was launched aboard the GOES-R Spacecraft (now GOES-East) on November 19, 2016 and is now fully operational. GLM uses a high-speed camera and onboard video processing to detect the optical emission of lightning for the full disk observed from the geostationary orbit. During the Post Launch Test period the instrument and ground processing algorithms (GPAs) were tuned to optimize the tradeoff between detection efficiency and false event rate. False events, those not due to lightning, arise from a variety of sources. A primary function of the GPAs is to remove false events prior to assembling events into groups and flashes for use by weather forecasters and scientists. Effective discrimination of false events depends on understanding the phenomenology of the various sources of false events.

GOES-R↗
NASA NTRS2019

Utilizing the Geostationary Lightning Mapper for Lightning Safety

Outline: Collaborative Partners; What is the Geostationary Lightning Mapper (GLM)?; Initial observations (Density Products); Lightning safety with GLM; The 30-minute lightning hazard product; Goal - Basic understanding of and how to use the lightning hazard product.

Geostationary Lightning Mapper↗
NASA NTRS2010

The Geostationary Lightning Mapper (GLM) for the GOES-R Series Next Generation Operational Environmental Satellite Constellation

The next generation Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2015 is a follow on to the existing GOES system currently operating over the Western Hemisphere. The system will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. The system provides products including lightning, cloud properties, rainfall rate, volcanic ash, air quality, hurricane intensity, and fire/hot spot characterization. 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 spectral, spatial, and temporal resolution for the 16-channel Advanced Baseline Imager (ABI). The Geostationary Lightning Mapper (GLM), an optical transient detector will map total (in-cloud and cloud-to-ground) lightning flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the higher level algorithms and applications using the GLM alone and decision aids incorporating information from the ABI, ground-based weather radar, and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional lightning networks are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time total lightning mapping data are also being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate early on-orbit user readiness for this new capability.

Goodman, Steven J.↗
NASA NTRS2016

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on GOES-R Satellite

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 s) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

validation↗
NASA NTRS2016

Post Launch Calibration and Testing of the Geostationary Lightning Mapper on the GOES-R Satellite

The Geostationary Operational Environmental Satellite R (GOES-R) series is the planned next generation of operational weather satellites for the United States National Oceanic and Atmospheric Administration (NOAA). The National Aeronautics and Space Administration (NASA) is procuring the GOES-R spacecraft and instruments with the first launch of the GOES-R series planned for October 2016. Included in the GOES-R Instrument suite is the Geostationary Lightning Mapper (GLM). GLM is a single-channel, near-infrared optical detector that can sense extremely brief (800 microseconds) transient changes in the atmosphere, indicating the presence of lightning. GLM will measure total lightning activity continuously over the Americas and adjacent ocean regions with near-uniform spatial resolution of approximately 10 km. Due to its large CCD (1372x1300 pixels), high frame rate, sensitivity and onboard event filtering, GLM will require extensive post launch characterization and calibration. Daytime and nighttime images will be used to characterize both image quality criteria inherent to GLM as a space-based optic system (focus, stray light, crosstalk, solar glint) and programmable image processing criteria (dark offsets, gain, noise, linearity, dynamic range). In addition ground data filtering will be adjusted based on lightning-specific phenomenology (coherence) to isolate real from false transients with their own characteristics. These parameters will be updated, as needed, on orbit in an iterative process guided by pre-launch testing. This paper discusses the planned tests to be performed on GLM over the six-month Post Launch Test period to optimize and demonstrate GLM performance.

validation↗
NASA NTRS2018

On-orbit Validation of the Geolocation Accuracy of the GOES-16 Geostationary Lightning Mapper (GLM) Flashes Using Ground-based Laser Beacons

As part of the geolocation accuracy assessment of lightning flashes detected by the Geostationary Lightning Mapper (GLM) on the GOES-16 and GOES-17 satellites (Geostationary Operational Environmental Satellite), two satellite laser ranging stations employed laser beacon systems to generate transient light pulses that simulate natural lightning around 777.4 nm to validate the pre-launch spec of 5 km. The pulse width, repetition rate, wavelength, and power of the laser-pulses were configured to produce sufficient instrument response to be detected as synthetic lightning events by the GLM instrument. During the testing period from April 2017 to January 2018, the laser systems illuminated the GOES-16 satellite to observe diurnal variation of the GLM system response, with particular emphasis on geolocation accuracy. The final GOES-16 laser beacon tests, which used the latest updates of the geolocation algorithms implemented by the GOES-R Ground Segment, showed the offsets between the GLM geolocated location and the known laser locations were within 5 km.

Lightning↗
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 NTRS2018

Preliminary Assessment of Detection Efficiency for the Geostationary Lightning Mapper Using Intercomparisons with Ground-Based Systems

As part of the calibration/validation (cal/val) effort for the Geostationary Lightning Mapper (GLM) on GOES-16, we need to assess instrument performance (detection efficiency and accuracy). One major effort is to calculate the detection efficiency of GLM by comparing to multiple ground-based systems. These comparisons will be done pair-wise between GLM and each other source. A complication in this process is that the ground-based systems sense different properties of the lightning signal than does GLM (e.g., RF vs. optical). Also, each system has a different time and space resolution and accuracy. Preliminary results indicate that GLM is performing at or above its specification.

GL↗
NASA NTRS2012

Evaluation of NASA SPoRT's Pseudo-Geostationary Lightning Mapper Products in the 2011 Spring Program

NASA's Short-term Prediction Research and Transition (SPoRT) program is a contributing partner with the GOES-R Proving Ground (PG) preparing forecasters to understand and utilize the unique products that will be available in the GOES-R era. This presentation emphasizes SPoRT s actions to prepare the end user community for the Geostationary Lightning Mapper (GLM). This preparation is a collaborative effort with SPoRT's National Weather Service partners, the National Severe Storms Laboratory (NSSL), and the Hazardous Weather Testbed s Spring Program. SPoRT continues to use its effective paradigm of matching capabilities to forecast problems through collaborations with our end users and working with the developers at NSSL to create effective evaluations and visualizations. Furthermore, SPoRT continues to develop software plug-ins so that these products will be available to forecasters in their own decision support system, AWIPS and eventually AWIPS II. In 2009, the SPoRT program developed the original pseudo geostationary lightning mapper (PGLM) flash extent product to demonstrate what forecasters may see with GLM. The PGLM replaced the previous GLM product and serves as a stepping-stone until the AWG s official GLM proxy is ready. The PGLM algorithm is simple and can be applied to any ground-based total lightning network. For 2011, the PGLM used observations from four ground-based networks (North Alabama, Kennedy Space Center, Oklahoma, and Washington D.C.). While the PGLM is not a true proxy product, it is intended as a tool to train forecasters about total lightning as well as foster discussions on product visualizations and incorporating GLM-resolution data into forecast operations. The PGLM has been used in 2010 and 2011 and is likely to remain the primary lightning training tool for the GOES-R program for the near future. This presentation will emphasize the feedback received during the 2011 Spring Program. This will discuss several topics. Based on feedback from the 2010 Spring Program, SPoRT created two variant PGLM products, which NSSL produced locally and provided in real-time within AWIPS for 2011. The first is the flash initiation density (FID) product, which creates a gridded display showing the number of flashes that originated in each 8 8 km grid box. The second product is the maximum flash density (MFD). This shows the highest PGLM value for each grid point over a specific period of time, ranging from 30 to 120 minutes. In addition to the evaluation of these two new products, the evaluation of the PGLM itself will be covered. The presentation will conclude with forecaster feedback for additional improvements requested for future evaluations, such as within the 2012 Spring Program.

Stano, Geoffrey T.↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗
NASA NTRS2016

Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Navigation↗
NASA NTRS2016

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Image Registration↗
NASA NTRS2014

Lightning Jump Algorithm Development for the GOES·R Geostationary Lightning Mapper

Current work on the lightning jump algorithm to be used in GOES‐R Geostationary Lightning Mapper (GLM)'s data stream is multifaceted due to the intricate interplay between the storm tracking, GLM proxy data, and the performance of the lightning jump itself. This work outlines the progress of the last year, where analysis and performance of the lightning jump algorithm with automated storm tracking and GLM proxy data were assessed using over 700 storms from North Alabama. The cases analyzed coincide with previous semi‐objective work performed using total lightning mapping array (LMA) measurements in Schultz et al. (2011). Analysis shows that key components of the algorithm (flash rate and sigma thresholds) have the greatest influence on the performance of the algorithm when validating using severe storm reports. Automated objective analysis using the GLM proxy data has shown probability of detection (POD) values around 60% with false alarm rates (FAR) around 73% using similar methodology to Schultz et al. (2011). However, when applying verification methods similar to those employed by the National Weather Service, POD values increase slightly (69%) and FAR values decrease (63%). The relationship between storm tracking and lightning jump has also been tested in a real‐time framework at NSSL. This system includes fully automated tracking by radar alone, real‐time LMA and radar observations and the lightning jump. Results indicate that the POD is strong at 65%. However, the FAR is significantly higher than in Schultz et al. (2011) (50‐80% depending on various tracking/lightning jump parameters) when using storm reports for verification. Given known issues with Storm Data, the performance of the real‐time jump algorithm is also being tested with high density radar and surface observations from the NSSL Severe Hazards Analysis & Verification Experiment (SHAVE).

Schultz. E.↗
NASA NTRS2016

Automated Storm Tracking and the Lightning Jump Algorithm Using GOES-R Geostationary Lightning Mapper (GLM) Proxy Data

This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA), which are important elements in the transition of the LJA concept from a research to an operational based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid, VIL), and lightning information (flash rate density). Evaluations showed that the spatial scale of tracked features or storm clusters had a large impact on the lightning jump system performance, where increasing spatial scale size resulted in decreased dynamic range of the system's performance. This framework will also serve as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection (POD) and false alarm ratio (FAR) statistics. Of the algorithm parameters tested, sigma-level (metric of lightning jump strength) and flash rate threshold influenced the system's performance the most. Finally, verification methodologies are investigated. It is discovered that minor changes in verification methodology can dramatically impact the evaluation of the lightning jump system.

lightning jump↗
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↗