GLM is coming: The instrument

The first beta-release data of the Geostationary Lightning Mapper (GLM) instrument will be out this week. (Update as of 12 June 2017:  GLM beta release has been delayed until July.)  As we get closer to having real-time GLM observations, here is a quick post about the GLM instrument itself.

glm_instrument

Figure 1:  An artist’s image of the GOES-16 satellite with the Geostationary Lightning Mapper (GLM) shown as the zoom out in the upper right.

In the post describing the origin of the GLM (here), it was discussed how the GLM is not the first space-based instrument to observe lightning.  However, it is the first lightning sensor available in geostationary orbit.  Conceptually, the GLM can be thought of as a very large digital camera.  Each pixel of the camera is identifying optical brightness difference from cloud top.  Each pixel is monitoring if any light is observed and if the light observed exceeds a background threshold.  This check is occurring every 2 ms and these observations become the basic GLM “event” observations.  The background field and threshold criteria are designed to reduce false alarms.  The placement of the charge couple device, or CCD pixels, on the instrument designed to help with the instrument’s spatial resolution.  The instrument’s CCD pixels vary in size to help account for the increasing parallax the closer to the edge of the field of view the observations get.  This allows the resolution of the GLM to go from 8 km directly beneath the satellite to only 14 km at the edge of the field of view.

The actual field of view for GLM is shown in Figure 2 for both the GOES-East (eventual location of GOES-16) and -West (future position of GOES-17) positions.  The underlying, normalized annual lightning flash rate comes from observations made by the Optical Transient Detector and Lightning Imaging Sensor from 1995-2005.  Currently, the GLM is in the GOES-16 check-out location (Figure 3).  The total field of view will range from 52 degrees north and south.  Additionally, the GLM does observe total lightning, or the combination of intra-cloud and cloud-to-ground observations.  However, the GLM will not distinguish between the two.  Still, observing total lightning, particularly over such a large domain will aid in warning decision support, lightning safety, as well as situational awareness in data sparse regions.  This will be helpful for detecting flash flooding (noting where is convection) in the inter-mountain west, convection monitoring for aviation, as well as opening up new avenues of research for tropical cyclone forecasting.  Lastly, the GLM was designed to be able to detect 70% of total flashes over the entire field of view over 24 hours.  The false alarm rate was designed to be less than 5%.  Recently, a calibration and validation field campaign had been underway to investigate the GOES-16 instruments.  Early indications are that the GLM will likely exceed the design specifications.  Exact values will be provided later after the field data has been analyzed.

glm_fov_E_and_W

Figure 2:  The field of view for GLM in the GOES-East and -West position.  The normalized, annual lightning flash rate shown is derived from 10 years of Optical Transient Detector and Lightning Imaging Sensor, low-Earth orbiting instrument observations.

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Figure 3:  Same as Figure 2, but showing the current GLM field of view through November 2017.

Subsequent posts will start to focus on actual GLM observations once they are made available.

GLM is coming: Preparations

Creating demonstration data and products to train forecasters for GLM has presented unique challenges.  Aside from the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS), no other space-based platform had similar capabilities to the GLM.  Furthermore, the OTD and LIS were low-Earth orbiting instruments and would only view a small portion of the Earth for no more than a couple minutes at a time.  This prevented their use as a demonstration data set as forecasters would need to see how total lightning (the combination of intra-cloud and cloud-to-ground) evolved with time.  That put the focus on ground networks, which could observe the entire life cycle of a storm.  The ground networks, unfortunately, lacked the ability to observe total lightning (or the capability was not yet available).  The exception was the ground-based lightning mapping array.

The lightning mapping array (LMA) was developed by New Mexico Tech and evolved out of earlier systems, some of which were tested at Kennedy Space Center.  By operating in the very high frequency end of the electromagnetic spectrum (~80 GHz), the LMAs could observe the entire lightning channel within a cloud.  Primarily designed for lightning research, these would become instrumental in the training activities for the GOES-R Proving Ground and the GLM.  This is because the LMAs were capable of observing total lightning.  Their accuracy was extremely good and they have been used for ground verification for OTD and LIS and will do so again for GLM.  Their primary disadvantage is a very short range; generally no more than 200 km from the center of the network.  Figure 1 shows the physical relationship of total lightning to a storm updraft as well as the lightning jump concept.

total_lightning_physical_reasoning

Figure 1:  The top two panels show the total lightning density (left) and radar reflectivity at ~20 thousand feet (right) and 1442 UTC.  The radar elevation corresponds to the mixed phase region.  Total lightning is produced by a strong, voluminous updraft extending into the mixed phase region.  This is a non-linear relationship so the strongest updrafts will produce the most lightning.  This physical connection can be harnessed by forecasters as shown in the lower left (total lightning density at 1450 UTC) and right (radar reflectivity at 20 thousand feet at 1452 UTC).  The total lightning shows a “bull’s eye” feature indicating rapid intensification, or lightning jump.  This preceded the radar update at 1452 UTC showing the updraft now extending into the mixed phase region.  This allowed the total lightning to provide additional information on the intensification of this storm and the rapid increase indicates that severe weather is imminent.  Also, the total lightning information shows the spatial extent of the lightning that can be used for safety applications.

The Melbourne, Florida forecast office was the first office to use total lightning data from a local lightning detection and ranging network (very similar to an LMA) in the late 1990s.  This was a combined effort by the forecast office, Massachusetts Institute of Technology (MIT), MIT Lincoln Lab, and NASA Marshall Space Flight Center.  The data became extremely popular with the office and the Lightning Imaging Sensor Demonstration and Display (LISDAD) system was instrumental in investigating the uses of total lightning in real-time.

In 2002, NASA’s Marshall Space Flight Center had installed the research oriented North Alabama Lightning Mapping Array (NALMA).  By March 2003, the NASA SPoRT team, in collaboration with the Huntsville weather forecast office, had made NALMA data available in the National Weather Service display system; AWIPS.  The Huntsville forecast office would then go on to issue its first warning using total lightning data that May.  NASA SPoRT would extend collaborations with a handful of other forecast offices using NALMA as well as the NASA owned Washington D.C. LMA in the late 2000s.

By 2008, the GOES-R Proving Ground was accelerating its efforts with training and hands-on activities, such as the Hazardous Weather Testbed in Norman, Oklahoma.  This required a demonstration product for GLM that could be used in real-time.  The Marshall Space Flight Center had developed the GLM proxy that was derived from NALMA to test data processing algorithms for the GLM.  The drawback was that it could not be run in real-time.  However, in 2009 NASA SPoRT produced the pseudo-GLM (PGLM) product (Figure 2).  It was not an exact replica of future GLM observations, but it represented a reasonable facsimile that allowed for hands-on training that let forecasters better learn about total lightning and its relation to storms intensity and severe weather.

pglm_example

Figure 2:  An example of the pseudo-GLM flash extent density product derived from the Washington D.C. lightning mapping array during the derecho event of 2012.  The radar reflectivity (right) shows a strong line of storms approaching Washington D.C.  The greatest reflectivities (and likely strongest storms) are towards the northeast.  The pseudo-GLM (left) shows that it has update sooner than the radar, but also emphasizes the northeastern end of the line.  In fact, over 110 flashes are observed in two minutes at one location, highlighting the strongest overall storm.  The convection to the southwest is weaker as evidenced by the lack of pseudo-GLM observations.

NASA SPoRT, thanks to funding via the GOES-R visiting scientist program, was able to reach out to each of the other LMAs that were in operation across the country (and one in Canada!).  By 2014, almost a dozen LMAs were collaborating.  This allowed for the PGLM to be produced for numerous locations as well as expand partnerships to over a dozen forecast offices, three center weather service units, and the Aviation Weather Center.  Figure 3 shows the approximate domain and collaborating organization for each available LMA.  Combined, the collaborations between the forecasters, LMA owners, and the testbeds allowed for a wide variety of feedback discussing operational uses and visualization concepts.  Much of this has directly supported my own efforts as the GLM satellite liaison for the ongoing work in the Satellite Foundational Course for GOES-R, preparing for operational applications training, and the 2017 summer assessment.

lma_collaborators

Figure 3:  The approximate domain of the 11 collaborating lightning mapping arrays that have been used as part of the GLM preparations for the GOES-R Proving Ground.  The numbers correspond to the list at top showing the owners of the collaborative network.

Next up in the “GLM is coming” series is a post describing the GLM instrument itself as we await in initial release of GLM data.

GLM is coming: The origin of the GLM

The Geostationary Lightning Mapper (GLM) successfully launched aboard GOES-R (now GOES-16) on November 19, 2016.  Now we are a week away from the initial preliminary, beta data observations being made available.  This is an exciting time, especially with some of the early public release imager from the GLM available on the GOES-R multimedia page (http://www.goes-r.gov/multimedia/goes-16DataAndImagery.html).  In advance of next week’s milestone here is some of the history that has led to the development of the GLM.

One of the earliest satellite-based instruments specifically designed for lightning observations was the Optical Transient Detector (OTD).  Figure 1 (below) shows the annual flash frequency for 1995 to 2000. This was developed by NASA’s Marshall Space Flight Center in Huntsville, Alabama.  Amazingly, the OTD was built in nine months.  Launched on April 3, 1995 the OTD was placed in a near polar orbit allowing it to monitor lightning over much of the Earth during both the day and night.  However, the OTD only provides a few minutes a day for any given location.  This prevented the OTD from studying local weather activities, but allowed the OTD to study global lightning patterns and their evolution.  The OTD also launched at a time when the awareness of the important role lightning played in the Earth’s atmosphere was becoming better understood and that lightning was likely an indicator of the strength of convective storms.  OTD efforts would contribute to the discovery of lightning as an indicator of potential severe weather, what we now call lightning jumps.  Additionally, OTD discovered that the global flash rate is approximately 40 flashes per second.  Ultimately, the OTD’s contributions reinforced the need for lightning observations from geosynchronous orbit, which would ultimately lead to the development and launch of the GLM.

OTD_images

Figure 1:  Annual flash frequency from 1995 to 2000 from Christian et al. (2003).

Given its short production time, the OTD served as a production prototype for a more robust, low-Earth orbiting lightning sensor.  This new instrument was the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measuring Mission (TRMM).  The LIS was designed by scientists at the University of Alabama in Huntsville as well as NASA’s Marshall Space Flight Center.  Launched in 1997, LIS, and the TRMM satellite as a whole, far exceeded their projected service life and provided 17 years of continuous observations.  Unlike the OTD, the LIS was on an orbit that focused on the tropical regions of Earth.  However, LIS had superior detection abilities for both day and night.  Figure 2 (below) shows the lightning activity in the LIS field of view for 2012.  Once operational, the LIS has provided significant contributions to investigating convective and precipitation processes.  The long operational life of LIS has also helped identify most lightning active regions on Earth, such as Lake Maracaibo, Venezuela with 232 flashes per square kilometer per year!  Like the OTD, LIS reinforced the importance of a geostationary platform where storm morphology can be monitored continuously.  Many concepts in the design of the LIS have been used in the GLM instrument.

lis_example

Figure 2:  Lightning Imaging Sensor observations of lightning across the instrument’s field of view for 2012.  Image courtesy of NASA’s Marshall Space Flight Center.

Stay tuned for the next “GLM is coming” blog post that will focus on the efforts to prepare for the Geostationary Lightning Mapper.

Detecting tornado tracks using Synthetic Aperture Radar (SAR) imagery

NASA SPoRT has been working to support the NWS’s use of the Damage Assessment Toolkit (DAT) by integrating multiple satellite datasets into the DAT framework to assist in damage surveys.  Imagery from MODIS, VIIRS, and Landsat 8 are available daily within the application while imagery from higher resolution satellites, such as Terra ASTER and other high-resolution commercial imagery are facilitated by our partnership with the USGS’s Hazard Data Distribution System (HDDSexplorer.usgs.gov). One new area being explored is the application of Synthetic Aperture Radar (SAR) imagery to detect tornado damage.

ClearkLake_WI_SAR_Tornado.gif

Zoomed-in section of the SAR change detection RGB generated from Sentinel-1B imagery from May 10 and May 22, 2017.  The damage indicators show preliminary track information as of June 2, 2017 and are not considered final.

On the evening of May 16th, 2017, a supercell tracked across Wisconsin producing a strong tornado. The resulting 83-mile long track tornado produced EF-3 damage.  Shown in the image above is a prototype change detection RGB using data from Sentinel-1(A/B), a European Space Agency (ESA) satellite with a SAR instrument on board. Unlike optical sensors, which observe surface reflectance and temperature, SAR instruments measure backscatter from the surface, allowing the instrument to be used at all times of the day and in any sky conditions. SPoRT has been working with the Alaska Satellite Facility, NASA’s SAR Distributed Active Archive Center (DAAC) to receive these products for evaluation and put them in the DAT to help with the identifying of damage tracks, especially in areas where damage surveys can be more challenging (i.e. forested areas, poor road network).  The RGB takes advantage of the dual polarization from the sensor, assigning the VV and VH corrected polarization from the post-event granule to the red and green channels of the RGB, respectively.  The blue channel is a difference image of the VH polarization (same as what is used in the green channel) from the before and after granules.  The resulting RGB will show any changes between the two granules in a aqua/periwinkle/purple-color.  Although the RGB will show all change between the granules over the ~12-day period (i.e. agricultural growth), tornado tracks tend to be linear, making it a possible to discern/identify the damage track.  Without the hindrance of clouds that constantly plague damage detection in optical imagery, SAR imagery offers another tool to operational forecasters for use during damage surveys.  The team is also working on other change or anomaly detection techniques to facilitate mapping of tornado and severe weather damage.

GOES-16 ABI and GOES-R CI aid IDSS over the weekend

Once again, NWS Huntsville provided impact-based decision support services (IDSS) for the Panoply Arts Festival in downtown Huntsville.  Since it occurs in late April every year, Panoply has a long history of coping with challenging weather conditions, and NWS Huntsville has staffed the event every year to help with those challenges.  This year was no exception.

 

Saturday was a summer-like day, with the main forecast challenge being convective initiation from a field of cumulus clouds.  The UAH-developed GOES-R Convective Initiation algorithm output was helpful with this process as it correctly forecast low probabilities for much of the day.

 

We also decided to look at GOES-16 ABI data to see if it added any value.  In addition to monitoring the low (7.3um) and mid-level (6.9um) water vapor channels on a larger scale, the Red Visible (0.64 micron) was most beneficial.  A mesoscale domain sector was in place over the region at the time, enabling forecasters to easily look for growing cumulus clouds (though there were not many of these).  (Apologies for the quick and small screen captures!)

GOES-16 ABI 0.64um imagery – valid 29 April 2017 1950 UTC

During the mid-afternoon, forecasters staffing the emergency operations center noticed an interesting trend in the visible imagery: areas to the south that were shrouded by thicker cirrus were seeing clearly-suppressed cumulus development, and the cumulus clouds were developing again once the cirrus had passed by. This almost created a “moving shadow” effect.

GOES-16 ABI 0.64um imagery – valid 29 April 2017 2013 UTC

GOES-16 ABI 0.64um imagery – valid 29 April 2017 2029 UTC

The forecasters were able to use this to determine that convective initiation–and thus impacts to Panoply and downtown Huntsville–were very unlikely, since the cirrus clouds were moving into the area.
There is a great deal of promise for IDSS using the new GOES-16 data, particularly once the Geostationary Lightning Mapper begins flowing on a preliminary basis.
Note The GOES-16 data posted on this page are preliminary, non-operational data and are undergoing testing. Users bear all responsibility for inspecting the data prior to use and for the manner in which the data are utilized.

Lightning Jump in the North Alabama Lightning Mapping Array

It’s a busy day in North Alabama with NASA and NOAA aircraft in the region supporting a field campaign for GOES-16.  Another instrument supporting activities is the North Alabama Lightning Mapping Array (NALMA), which observes total lightning (both intra-cloud and cloud-to-ground).  SPoRT has been providing NALMA data to local forecast offices for 14 years and has used these data to serve as a proxy for the Geostationary Lightning Mapper on GOES-16 as part of the GOES-R Proving Ground.  The images below show the total lightning activity across southern Tennessee and northern Alabama at 2138 and 2152 UTC on 22 April 2017.  The main storm of interest is right along the Alabama-Tennessee border, just north of Huntsville, Alabama.  The maximum number of flashes per 2 square kilometers in two minutes is about 50 flashes at 2138.  In 14 minutes, that has jumped to nearly 150 flashes over two minutes highlighting a lightning jump.   A long flash extending to the south towards Huntsville is also seen.  This storm already had a severe thunderstorm warning active and the jump here indicates that the storm will maintain it’s intensity.  The weather community will look forward to the Geostationary Lightning Mapper observations when they a made available in the next few months.

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Total lightning observations from the North Alabama Lightning Mapping Array at 2138 UTC on 22 April 2017.

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Total lightning observations from the North Alabama Lightning Mapping Array at 2152 UTC on 22 April 2017.

Dust RGB analyzes “dryline” for 3/23/17

Dust RGB analyzes “dryline” for 3/23/17

 

The Dust RGB, originally from EUMETSAT and a capability of GOES-R/ABI, can be helpful in identifying features other than dust, including drylines. A dryline represents a sharp boundary at the surface between a dry air mass and moist air mass where there is a sudden change in dew point temperatures. In this event from 3/23/17, a dryline in eastern New Mexico and west Texas is distinguishable via the Dust RGB imagery animation from GOES-16 (Fig. 1), while a large dust plume (magenta) is impacting areas further west. Note that the visible imagery (Fig. 2) shows clouds forming along the dryline, but these clouds drift downwind toward the northeast as they mature, away from the dryline itself, making it difficult to monitor the dryline position.  However, the dryline position can easily be seen via the color difference of the Dust RGB across the boundary of dry and moist air, and in fact, the dryline appears fairly stationary or moves in a slight westward direction, opposite of the cloud motion.  In situ observations (Fig. 3) are a primary tool for monitoring the dryline location, but the advantage of satellite imagery is an increased spatial and temporal resolution for forecasters.

Dust_SENM_2022to2322_loop

Figure 1. GOES-16 Dust RGB valid from 2022 to 2322 UTC, on 23 March 2017 centered on extreme western Texas.  Dryline seen in color difference of cloud-free area in eastern New Mexico and west Texas while dust plume is in magenta shades.

Vis064_SENM_2027to2322_loop

Figure 2. GOES-16 Visible (0.64u) channel valid from 2027 to 2322 UTC on 23 March 2017 as in Figure 1.

For the above and subsequent images/animations: NOAA’s GOES-16 satellite has not been declared operational and its data are preliminary and undergoing testing. Users receiving these data through any dissemination means  (including, but not limited to, PDA and GRB) assume all risk related to their use of GOES-16 data and NOAA disclaims any and all warranties, whether express or implied, including (without limitation) any implied warranties of merchantability or fitness for a particular purpose.

UCAR_RAP_METAR2143Z

Figure 3. METAR station plot of surface observations at 2143 UTC on 23 March 2017 centered over New Mexico.

The ability to identify drylines using the Dust RGB gives the forecaster the capability to analyze these boundaries in ways not seen before. In the Dust RGB (Fig. 4), the surface area on the dry side is seen as a purple color (i.e. increased red contribution), and the moist side appears more blue (i.e. less red). This dryline can be noted more easily than in visible imagery (Fig. 5) due to the sensitivity of the 12.3 micron channel used in the 12.3 – 10.35 micron difference within the Dust RGB red component.  The 12.3 micron channel goes from warmer to cooler brightness temperatures with changes in density from very dry to very moist air. The blue contribution is consistent on each side of the line because the surface temperature, and hence the 10.35 micron channel, does not change much from either side of the dryline. There is limited ability to identify drylines using high resolution visible imagery, as seen in the Midland WFO Graphicast (Fig. 6) where cumulus clouds are documented forming along the dryline. Unfortunately, visible imagery is only useable during daylight hours and a user is dependent on cloud features along the dryline in order to analyze its position. However, aside from the obvious value of the color difference in cloud free areas to depict the dryline, the Dust RGB, is viable both during daytime and nighttime hours.

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