Reconstructing a Rare Bolt from the Blue Event Using Multiple Lightning Datasets

Reconstructing a Rare Bolt from the Blue Event Using Multiple Lightning Datasets

Written by Chris Schultz

On August 20, 2019, much of the Midwest was impacted by several rounds of severe thunderstorms.  These electrically active thunderstorms produced wind damage across Iowa, Illinois, Indiana, Ohio, Kentucky, and Missouri. However, it wasn’t the large flash rates that got the attention of those of us in SPoRT, but a rare bolt from the blue event that occurred nearly 50 miles (76 km) outside any surface precipitation.

During the 40 minutes leading up to the lightning event, the closest thunderstorm activity was located approximately 50 miles south of Dittmer, MO, across parts of Phelps, Dent, Washington, St. Francois, and Ste. Genevieve Counties (Fig. 1A).   Between 400 pm and 440 pm CDT zero lightning flashes occurred in Franklin, Jefferson, Warren, or St. Charles Co., MO (Fig. 1B).


Figure 1 – A- Radar reflectivity at 0.4 degrees elevation at 2140 UTC from KLSX in Weldon Spring MO, and  B- NLDN lightning detections between 21:00:00 and 21:40:16 UTC (4:00:00-4:40:00 pm CDT).

Then at 4:40:15 pm CDT, a positive lightning flash was observed by Vaisala’s National Lightning Detection Network well outside of any precipitation (Fig. 2).  This flash was positive polarity, was approximately 136 kiloamps, and located in an area that had not observed any lightning in the previous 40 minutes. This +CG flash was accompanied by 5 additional incloud flash detections, and one negative cloud to ground flash detection by the NLDN.  All 7 detections occurred within 1 second of each other, indicating that they were part of the same lightning event.  However, the question remained, where did this flash originate? Radar and previous lightning data from the NLDN indicate that there are 2-3 areas of thunderstorm activity to the south of this location which could be a possible origination point. But there wasn’t a definitive prospect because the NLDN point locations are spatially separated by several miles. 


Figure 2 – Radar reflectivity at 0.4 degrees elevation at 2140 UTC from KLSX in Weldon Spring MO (A) and NLDN lightning detections at 21:40:15 UTC (4:40:15 pm CDT).

Bringing in Geostationary Lightning Mapper Flash Extent Density data product for the same point in time (Fig. 3), there is a better idea of which thunderstorm this flash originated from.  There is a distinct lightning path from the thunderstorms over Dent and Phelps Counties in up to the NLDN flash locations in Jefferson and Franklin Counties. This single flash travelled nearly 57 miles (~ 92 km) from its original start location to the ground location, and actually propagated further north into Warren and St. Charles Counties.  


Figure 3 – GOES GLM Flash Extent Density overlaid on 0.64 µm ABI data at 2141 UTC (441 pm CDT).

Taking a vertical slice of the radar data between the parent thunderstorm and the location where the flash came to ground, there is a distinct path of precipitation aloft between 20,000 and 30,000 ft (Fig. 4).  Thus the lightning traveled through an anvil region before coming to ground approximately 41 miles (76 km) outside of the main precipitation near the surface.  Large bolt from the blue events have been reported in the literature previously (e.g., Kuhlman et al. 2009, Weiss et al. 2012, Lang et al. 2016). This flash was also a unique event because any lightning safety protocols would not have been in place for the location due to the absence of lightning within 6 miles during the previous 40 minutes.


Figure 4 – A vertical cross section of reflectivity from KLSX at 2140 UTC (440 pm CDT)

When GLM data are combined with ground based lightning networks like the NLDN or Earth Networks Total Lightning Network, the GLM Flash Extent Density can be used to connect point locations and determine where additional electrification may be present aloft that is not readily apparent at the surface.

Transition of Research to Operations – Gridded NUCAPS

Transition of Research to Operations – Gridded NUCAPS

By Emily Berndt

SPoRT has been part of a collaborative effort within the Joint Polar Satellite System (JPSS) Proving Ground Sounding Initiative* to develop the capability for 2D display of satellite soundings in the NOAA NWS decision support system (AWIPS).  CrIS/ATMS (Cross-track Infrared Sounder/Advanced Technology Microwave Sounder) temperature and moisture soundings are processed through the NOAA Unique Combined Atmospheric Processing System (NUCAPS) and are good quality in clear to partly cloudy regions but soundings are poor quality where cloud cover is over 85% and when precipitating conditions exist.  Currently, NWS offices receive NOAA-20 CrIS/ATMS NUCAPS Soundings through the Satellite Broadcast Network for display as vertical soundings and Gridded NUCAPS is the capability to process and view these data horizontally and vertically (Fig. 1).  Up until now, Gridded NUCAPS has been pre-processed at SPoRT and provided experimentally to Alaska Region NWS offices and the Hazardous Weather Testbed.  The team worked with NOAA/CIRA/MDL to create an AWIPS plug-in to grid the soundings upon arrival and ingest in AWIPS.  Gridded NUCAPS has been a successful multi-organizational collaborative R2O/O2R project with a transition to operations in sight. With the official 19.2.1 AWIPS release coming soon SPoRT is finalizing development of training material and an NWS VLab page to highlight the Gridded NUCAPS capability, products, and helpful hints….more information will be forthcoming  as these items are completed!  NWS offices that are beta testers for new AWIPS releases, such as the Huntsville forecast office will be able to display Gridded NUCAPS with AWIPS 19.2.1-29 prior to the official release.

*including NOAA NWS, Science and Technology Corporation, the Cooperative Institute for Research of the Atmosphere, Geographic Information Network of Alaska, Space Science Engineering Center/Cooperative Institute for Meteorological Satellite Studies, and NOAA/NWS/MDL.

Blog-sounding-grid

Figure 1. Left: NOAA-20 CrIS/ATMS NUCAPS Sounding Availability in AWIPS and Right: Gridded NUCAPS plan view display of 700 mb Lapse Rates. Demontrates the NUCAPS Soundings are Gridded for plan view and cross section display.  Image courtesy of Kevin Fuell (UAH/NASA SPoRT).

Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA; Weaver et al. 2019) and NWS Anchorage Center Weather Service Unit aviation forecasters have benefited from this capability to issue public products regarding CAA. Additionally Gridded NUCAPS has been extensively evaluated at the Hazardous Weather Testbed for assessing the pre-convective environment (Berndt et al. 2017). As part of the JPSS Sounding Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS (e.g., fire weather, turbulence, and icing) and exploring the benefits of the microwave-only NUCAPS Soundings for applications in cloudy regions.  A few new capabilities of Gridded NUCAPS include display of fields such as precipitable water to diagnose moist/dry layers in the atmosphere, the Haines Index for fire weather potential (Fig.2), and SPoRT-developed ozone products (e.g., Total Ozone, Ozone Anomaly, and Tropopause Level) to diagnose the potential for tropopause folding and cyclogenesis.

Look for Gridded NUCAPS posters and presentations at the National Weather Association Annual Meeting and the AMS Joint Satellite Conference – both in September!

Picture2

Figure 2. Top: Example of Haines Index image and icons plotted with Gridded NUCAPS compared to Bottom: GFS Haines Index and Icons for a fire that began on 23 July 2018 near Northway, AK.

 

Using Synthetic Data to Prepare for the NASA TROPICS Mission

In 2021, we anticipate the launch of The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) satellite, which is a National Aeronautics and Space Administration (NASA) Earth Venture Mission designed for observing hurricanes (i.e. tropical cyclones, typhoons). TROPICS presents a unique opportunity in hurricane remote sensing though an unprecedented combination of horizontal and temporal resolution: this mission is expected to provide nearly all-weather observations of three-dimensional temperature and humidity, as well as cloud ice and precipitation horizontal structure, at a mean refresh rate of 30 minutes. These measurements will be available in both the inner-core and the external environment of hurricanes, allowing for the observation of dynamic processes and on mesoscale time scales which are often challenging to observe.

As an Early Adopter, NASA SPoRT is currently assessing the capabilities and applications of the upcoming TROPICS mission through the use of synthetic TROPICS data. Proxy data are designed to mimic the resolution, format, and accuracy of an anticipated project, and provide a way to evaluate the potential value of a mission prior to launch. Fig 1. shows an example of TROPICS proxy data, derived from a high-resolution numerical simulation of a hurricane lifecycle in the North Atlantic Ocean (proxy data are courtesy of Ralf Bennartz, Vanderbilt University, UW-Madison SSEC). The estimated channel frequency is 205 GHz, which sensitive to the distribution of ice suspended in the atmosphere.

Fig. 1: An example of TROPICS proxy data estimated at a channel frequency of 205 GHz. Image courtesy of Frank LaFontaine, Raytheon/NASA SPoRT.

Blue and green colors demonstrate deep convective clouds near the eye of the cyclone (located near 22 degrees N and 62 degrees W), and red and orange colors demonstrate drier air wrapping around the storm. These data can potentially help to accelerate our abilities to use real TROPICS data once calibrated and in orbit.

TROPICS will have 12 channel frequencies, most of which are used for temperature and humidity measurements. More information on the TROPICS mission and applications is available here.

Wide disparity in soil wetness this summer across Alabama and the Southeastern U.S.

The pattern of soil moisture across the state of Alabama and more broadly the Southeastern United States has evolved into one of marked disparity over relatively short distances (see Fig. 1d) and time frames. The SPoRT Center manages its own instance of the NASA Land Information System (i.e., “SPoRT-LIS”), which produces real-time soil moisture estimates in an observations-driven modeling framework.  Hourly and daily output fields are available on the SPoRT Center web page, and 3-hourly and daily data are delivered to select NOAA/NWS weather forecast offices for enhanced decision support in areas such as drought and hydrologic applications.

In general, 2019 has been quite a wet year across large portions of the U.S., with parts of Alabama being no exception (especially northwestern Alabama).  Beginning in May, a rapid deterioration in soil wetness occurred across many parts of the Southeast due to unusually hot and dry conditions during the last half of May through early June.  However, the latter part of June into July featured well above-average rainfall in some areas that reversed the rapid drying trends, especially over far northwestern Alabama, Mississippi, and western Tennessee.  The variations in SPoRT-LIS total-column soil moisture percentiles over the past 4 months are given in Fig. 1, illustrating the regional spatiotemporal trends described above from April through July.

Fig1_monthly-rsm02percent-apr-jul2019

Fig 1. Monthly-evolving, total-column SPoRT-LIS soil moisture percentiles (relative to a 1981-2013 climatology) over the Southeastern U.S., valid (a) 30 Apr, (b) 31 May, (c) 30 Jun, and (d) 31 Jul 2019. [Click on image for full resolution]

Interestingly, the soil moisture percentiles across far northern Alabama diminish quite substantially from west to east by the end of July (Fig. 1d), approaching the 98th percentile in western Lauderdale county (NW Alabama) to less than the 10th percentile across Jackson county (NE Alabama; counties of northern Alabama shown in Fig. 2).  The current conditions on 31 July 2019 relative to the 31 July historical soil moisture distributions from a 1981-2013 SPoRT-LIS daily climatology further illustrate this sharp zonal contrast in soil wetness anomalies within the county-based histograms of Fig. 3. The county-mean 0-2 meter relative soil moisture on 31 July 2019 in Lauderdale county is at the 87th percentile compared to the 33-year historical distribution of 31 July values (Fig. 3a).  Meanwhile, Limestone county to its east has a mean soil moisture at the 61st percentile (Fig. 3b), followed by the 51st percentile in Madison county (Fig. 3c) and only the 24th percentile in Jackson County, AL.  These results serve to illustrate the highly variable nature of rainfall and resulting soil wetness and agricultural impacts that can occur across the Southeastern U.S. during the summer months.

Fig2_NorthAlabama_counties

Fig 2. Counties of northern Alabama. The far northern counties of Lauderdale, Limestone, Madison, and Jackson are highlighted within the discussion text and in Figure 3.

Fig3_NorthAlabama_histograms

Fig. 3.  Historical distributions of 0-2 meter relative soil moisture on 31 July and present-day county means on 31 July 2019 for all SPoRT-LIS grid points within a specific county, valid for far northern Alabama counties ranging west to east from (a) Lauderdale, (b) Limestone, (c) Madison, and (d) Jackson.  Gray bars represent the frequency distributions of 1981-2013 soil moisture values, vertical colored lines are reference percentiles, and the black dashed lines are present-day, county-averaged soil moisture value, with values tabulated in the upper-right of each panel. [Click on image for full resolution]

Finally, despite the month-to-month swings in soil moisture anomalies across much of the Southeast in recent months, one corridor that has persistently experienced abnormally dry conditions extends from southeastern Alabama into southern and central Georgia and western South Carolina. In fact, since 31 May (Figs. 1b-d), southeastern Alabama has seen soil moisture percentiles less than 20%, analogous to moderate to severe (or worse) proxy drought categories based on community-accepted conventions of percentile anomalies.  These dry regional pockets in the SPoRT-LIS analysis strongly correspond to the most recent U.S. Drought Monitor weekly product, issued on 30 July (Fig. 4).

Fig4_USDM_20190730_Southeast

Fig 4.  U.S. Drought Monitor weekly product valid for the week of 30 July 2019.

 

Identifying Dust and Fog in Geostationary Imagery through Machine Learning

Identifying Dust and Fog in Geostationary Imagery through Machine Learning

written by Chris Jewett

NASA SPoRT has a long history of creating state-of-the-art multispectral (red, green, blue, or RGB) imagery derived from both polar-orbiting (MODIS, VIIRS, AVHRR) and geostationary imagers (AHI, ABI, and SEVIRI). RGB imagery is provided to forecasters to allow them to visualize different types of meteorological phenomena and hazards that are sometimes not readily apparent in single channel imagery.  Two of the multispectral composites analyzed by forecasters include dust and nighttime microphysics, the latter of which allows for the detection of low clouds and fog.

Both blowing dust and fog events can produce a substantial reduction in visibility, yielding a significant hazard to motorists. Forecasters currently investigate the RGB imagery for these hazards qualitatively to provide necessary information to the public.  As an extension to these products, SPoRT is utilizing machine learning methods to determine if these hazards can be quantitatively identified in remote sensing observations, giving forecasters a probability on whether each pixel contains a certain hazard. This probability can then be used along with the RGB to aid visual classification and interpretation.

The RGB composites are typically produced using 3-4 channels from the imagers. For example, the dust RGB composite combines two spectral differences, 12.3 – 10.3 µm (Red) and 11.2 – 8.4 µm (Green), along with the 10.3 µm channel (Blue). Using specific ranges of the spectral differences, as well as the surface temperature derived from 10.3 µm channel, allows for dust to appear as a pink/magenta color in the RGB imagery. To produce a more quantitative analysis, numerous machine learning methods (Random Forest, Logistic Regression, Naïve Bayes, Convolutional Neural Networks) were applied to both the data that makes up the RGB composites and other channels, such as the 7.3 µm band, which can help distinguish dry low-level environments typically associated with blowing dust events.

In the animated image below, an example of the results from Random Forest are shown for a dust event that occurred on April 10, 2019 as a strong extratropical cyclone traversed across the Central/Northern Plains. Note how the brighter pinks and magentas corresponding to more dust within the plumes are associated with the greatest probabilities of dust. It is exciting to see how machine learning can clearly pull out the dust signature from the satellite imagery. Moving forward, the SPoRT team is administering machine learning applications to the visibilities associated with the dust plumes, so not only would the forecaster have knowledge of the probability of the dust, but also information on the anticipated degraded visibility. Early indications also show hopeful results on applying similar methods to the nighttime microphysics composite for the detection of low clouds and fog.

Left: RGB image created from GOES-16 data. Right: Probability of dust, based on the Random Forest Machine Learning algorithm.

Geostationary Lightning Mapper (GLM) Data Used to Aid in Warning Decision…

The NWS office in Huntsville, AL (HUN) has had a long history with the use of total lightning data in operations, which stretches back to the office’s inception (after NWS modernization) in 2003.  Back then, and until its removal to South America for GOES validation testing, the HUN office largely used data from the North Alabama Lightning Mapping Array (NALMA). Lightning data sources from NLDN and ENTLN have also been used to varying degrees, but the advent of the GLM aboard GOES-16 brought a new era of lightning observations.  Because of the office’s participation in early operational testing of the GLM, its use and familiarity have gradually increased over the past year.  This was probably made easier due to our familiarity with total lightning data from the NALMA network.  Generally, GLM data have been used in much the same way as those from the NALMA network, especially with regards to situational awareness purposes (i.e., airport weather warnings, real-time weather watches for EM partners, initial cell electrification, etc.).  The use of the data to aid in severe weather warning decisions has been a bit slower to evolve, as might have been expected.  After all, there are differences in the way the NALMA observes lightning as compared to the GLM.  Values from the GLM have typically been “muted” compared to those from NALMA, so forecasters have had to make internal adjustments and recalibrate, if you will, what is considered significant.  However, the physical mechanisms that generate increases in total lightning, that is, increases in mixed-phase updraft volume, are essentially observed either way.  Thus, GLM data can still be useful to relate important information about storm/cell evolution, and can help to “tip the scales” in the balance of evidence about whether or not a warning may be needed.

This particular application of the GLM data occurred this morning with operational meteorologists at the HUN office.  The short image loop below shows thunderstorms moving across northwestern portions of Alabama between 1226 and 1300 UTC.  The top panel of the image contains data from the KGWX radar (0.5 degree reflectivity), while the bottom panel contains GLM 1-minute Flash Extent Density (FED) data.  Notice that lightning activity is relatively limited initially as the storm moves across western Franklin County, AL (near center of image), with 1-min FED values ranging between 2 and 6 flashes per minute.  Then, at the 12:37 UTC time mark, flashes begin an increase that manifests in a statistical lightning “jump” (GLM sigma > 2).  The warning meteorologist at the time was watching this cell for potential severe weather, and observed the sudden increase in FED values.  This, together with other radar and satellite observations (not shown here), suggested that a severe weather warning was necessary as wind signatures aloft gradually increased.  A warning was subsequently issued at 1245 UTC.  Incidentally, this thunderstorm did end up producing some wind damage, with trees reported down in south-central portions of Franklin County.  Notice also that a number of strong cell signals were detected by radar as indicated by the higher dBZ values across the domain.  Another use of the GLM is allowing meteorologists to focus on the cells with the strongest updrafts, making the overall radar interrogation and warning process more efficient.  This case can help to demonstrate that the GLM can be used as an important indicator of storm evolution and as a useful operational tool for the evaluation of severe weather potential.

-Kris W.

2PanelLoop_GWX0.5Refl_andGLM1Min_17July2019

[Top] KGWX 0.5 deg Refl with NWS Severe Thunderstorm Warning (yellow box), [Bottom] GLM 1-min Flash Extent Density, 1226-1300 UTC 17 July 2019.

 

Monitoring Pollution Hourly from Space: Preparing for the TEMPO mission

Monitoring Pollution Hourly from Space: Preparing for the TEMPO mission

Remote sensing using spaceborne instruments provides valuable information on the evolution of atmospheric trace gases and atmospheric pollutants. In early 2022, the launch of the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission will usher in unprecedented coverage of air quality over North America with hourly measurements available with a spatial resolution of 2.1 km x 4.7 km. Since current spaceborne instruments only provide one overpass over a given location each day, these observations are essential for monitoring the diurnal evolution of trace gases and atmospheric pollutants, and understanding their subsequent role on air quality and public health.

NASA SPoRT has begun working with the TEMPO Science Team to develop synthetic TEMPO data products to use in pre-launch activities to prepare the air quality and public health communities for TEMPO capabilities. The NASA SPoRT Center is generating a long-term archive of synthetic TEMPO Level 2 data products from the global high-resolution Goddard Earth Observing System-5 (GEOS-5) Nature Run with full chemistry (GEOS-5 NR-Chem), operated by the NASA Global Modeling Assimilation Office (GMAO). Using proxy data in pre-launch activities can help accelerate the use of TEMPO data once the mission is calibrated in orbit, and can even help identify future problems and determine solutions.

Below are two examples demonstrating the capabilities of the TEMPO mission. First, Fig. 1 provides a comparison of nitrogen dioxide (NO2) concentration over the state of Alabama between the TEMPO proxy data (left) and the Ozone Measuring Instrument (OMI; currently aboard NASA’s Aura satellite, right).

Fig. 1. Left: TEMPO proxy data, valid at 1417 UTC. Right: OMI data, valid at 1900 UTC. Data applies for September 7, 2013.

The TEMPO proxy data show a much higher spatial resolution than the OMI data, capturing higher magnitude in NO2 concentrations overall (brighter yellow colors in left image) and a stronger gradient in NO2, in particular across north-central Alabama. While OMI provides a coarse, broad distribution in NO2 concentration, the TEMPO proxy data provide detail down to the sub-urban scale, capturing a local maximum corresponding to the Alabama Power Gaston Plant (red dot near center of left image).

If TEMPO data are considered only at the county level, differences in NO2 concentration are still visible between counties (Fig 2, below). Although the emission from the Alabama Power Gaston Plant is no longer apparent, improved spatial information is still provided as compared to OMI.

Fig 2. Left: TEMPO proxy data averaged to the county level, valid at 1417 UTC. Right: OMI data, valid at 1900 UTC. Data applies for September 7, 2013.

More information about the TEMPO mission can be found here.