Using GLM for Airport Weather Warnings

As part of our impact weather responsibilities, NWS Huntsville issues Airport Weather Warnings (AWWs) for Huntsville International Airport (KHSV) and Northwest Alabama Regional Airport (Muscle Shoals; KMSL).  AWWs are issued for the threat to personnel working outside at the terminal and neighboring operations, not for the threat to aviation as a whole.  One of our criteria for AWW issuance is the threat of cloud-to-ground lightning within 5 miles.

On May 30, a relatively small line of thunderstorms developed over northeast Mississippi and tracked to the east, producing a few pockets of straight-line wind damage along the way.  The line approached the 10-nm range of KHSV at 2320 UTC (seen below).  At this point, GLM Flash Extent Density (FED) data wasn’t the most helpful data set for deciding to issue an AWW; the National Lightning Detection Network (NLDN) was already detecting a great deal of cloud-to-ground lightning with the storms.


Multi-Radar/Multi-Sensor (MRMS) Reflectivity At Lowest Altitude (RALA) valid 2320 UTC 30 May; 5-minute NLDN flashes valid 2321 UTC; and GLM Flash Extent Density valid 2321 UTC.  The 5 and 10-nautical mile range rings around KHSV are illustrated in coral near the center of the image.

The GLM FED data were far more useful later on in the event, after the line passed well east of KHSV and the Huntsville metro area.  With nightfall, the convective updrafts weakened and lightning along the line generally decreased–but flashes within the trailing stratiform region of the quasi-linear convective system (QLCS) did not.  One of the more spectacular examples occurred around 0059 UTC (seen below).  The FED product really illustrated the spatial threat simply and effectively, especially when combined with the NLDN data.  (This example uses a slightly different color curve from the GLM baseline to enhance lower flash rates.)


MRMS RALA valid 0058 UTC 31 May; 5-minute NLDN valid 0059 UTC; and GLM FED valid 0059 UTC.  5 and 10-nm range rings around KHSV are also noted.

GLM helped forecasters acquire and retain situational awareness of these trailing stratiform “long flashes”, which helped with AWW extension/reissuance.  As a result, the airport weather warning for Huntsville was re-issued until the trailing stratiform region cleared the airport and the threat subsided.

Geostationary Lightning Mapper detects lightning in the Volcanic Plume from the Fuego Volcano in Guatemala (3 June 2018)

During the eruption of the Fuego Volcano on the afternoon of Sunday June 3, 2018, the Geostationary Lightning Mapper observed a rare, but important, phenomenon: volcanic lightning.  A total of five lightning flashes were observed between 1814 and 1834 UTC with the rising plume.  The first three flashes between 1814 and 1818 UTC were 8 to 10 km north-northeast of the volcano. Then as the plume continued to advect to the northeast of the volcano, the position of the lightning followed. A flash at 1822 UTC was 13 km from the cone of the volcano, and the flash at 1833 UTC was farthest away from the cone of the volcano at 15 km.

Animation of GLM group densities from 1810 to 1835 UTC on 3 June 2018. The location of the volcano is circled.

One of the unique features of GLM is the ability to measure the size of the flash directly as each flash is observed.  In thunderstorms, flash size is a good indicator of vertical motion, which is often hard to directly measure within thunderstorms, or in this case, a volcanic plume.  Between 1814 and 1822 UTC, flashes were occurring approximately every 2 to 4 minutes, and their size was on the order of 500-1000 km2.  Then between 1822 and 1834 UTC there was a lull in GLM-detected lightning activity, followed by the largest flash detected by GLM during the event at 1500 km2 as the plume continued to expand over central Guatemala.

There was a 3 hour lull in GLM-detected activity, until 2141 UTC, when a second set of 21 flashes was detected by GLM between 2141 and 2203 UTC. These flashes were located between 1 and 8 km south-southeast from the highest point of the volcano and correspond in time and reported location of the deadly lahar and pyroclastic flow that came down the south side of the volcano. GLM flash sizes ranged from 64 km2 up to 1500 km2 and there was not as clear of an increase in size as observed with the volcanic plume.

Animation of GLM group densities from 2139 to 2205 UTC on 3 June 2018. The location of the volcano is circled.

Operational Utility of GLM Flash Extent Density on June 1

For the seventh consecutive year, NWS Huntsville provided on-site weather support for a large outdoor country music concert in Cullman, Alabama this past weekend. This concert is usually held in June, one of the most problematic times of year for forecasting due to the seemingly-random nature of summertime convection. Public safety officials have had to stop the concert once for the threat of cloud-to-ground lightning and gusty winds, and have come close on several other occasions.

This year’s event was no exception. On Friday night, June 1, a small multi-cell cluster of storms containing cloud-to-ground lightning developed approximately 30 miles to the west and moved steadily east, putting forecasters and public safety officials alike on alert.

In past years, NWS Huntsville forecasters have used the North Alabama Lightning Mapping Array for situational awareness. Unfortunately, the NALMA is no longer available, but NWS Huntsville is a Preliminary Test & Evaluation site for the GOES-16 Geostationary Lightning Mapper (GLM). So forecasters at both the NWS office and the concert used GLM to evaluate the threat to almost 30,000 people.

Four Panel image of GOES-16 Clean IR (top-left), MRMS radar reflectivity (top-right), Earth Networks 8-km total flash density (bottom-right), and GLM FED and NLDN plot (bottom-left), vallid 0113 UTC 1 June.  The concert is denoted by the crosshairs marked “Home”.

Fortunately, the initial cluster of storms to the west essentially “split”, with one updraft gaining dominance to the south, and the rest weakening.  There were some “long flashes” extending far to the north from the southern storm, and far to the south from the northern storm, as seen in the image above.

The storm to the south produced a great deal of lightning, but thanks to the GLM Flash Extent Density product, forecasters were able to determine that the concert would not be affected.

As the storm to the south was weakening, the northern storm regained strength and intensified.  GLM FED data shows several flashes moving into the 10 nautical mile range ring, and one moving within the 5-nm ring.  Public safety officials and forecasters were certainly concerned.  However, forecasters were able to combine GLM FED information with GOES-16 IR and Multi-Radar/Multi-Sensor radar data to determine that the storm was moving away, the updraft was intensifying (which typically leads to smaller flashes), and the lightning threat would gradually diminish.

Four Panel image of GOES-16 Clean IR (top-left), MRMS radar reflectivity (top-right), Earth Networks 8-km total flash density (bottom-right), and GLM FED and NLDN plot (bottom-left), vallid 0151 UTC 1 June.  The concert is denoted by the crosshairs marked “Home”.

The northern storm would eventually produce quite a light show for skywatchers in the Huntsville metro area–but it also did much more.  Later on, GLM FED data indicated a lightning increase at 0207 UTC, followed by a more pronounced increase at 0216 UTC.

Animation 0156-0259 UTC of GOES-16 Clean IR 1-minute imagery (top-left), MRMS radar reflectivity (top-right), Earth Networks 8-km Total Flash Density (bottom-right), GLM FED and NLDN plot (bottom-left)

Indeed, this storm eventually downed numerous trees along the Madison-Marshall county line around 0240-0250 UTC.

With our legacy of using LMA data for almost 15 years, NWS Huntsville forecasters have embraced GLM FED data eagerly.  We hope to share more operational examples in the future.

Wet streaks in soil moisture observed in west Texas…

This morning I observed some rather odd looking streaks in the 10.3 µm imagery in western portions of Texas.  Sampling the 10.3 µm data revealed alternating areas of relative warm/cool skin surface temperatures in the cloud free conditions in the area on the upstream side of departing deep convection.  The temperature difference in the skin temperatures were around 5 C at this time.  The 10.3 µm image below was taken from appox. 1357 UTC this morning (3 May 2018).

Image 1.  10.3 um imagery from GOES-16, 1357 UTC 3 May 2018

Arguably, the streaks of lower temperature values showed up better in the 3.9 µm imagery.  Notice the streaks or alternating bands of yellow/orange colors in portions of west Texas.

Image 2.  3.9 um imagery from GOES-16, 1357 UTC 3 May 2018

Realizing these temperature differences were likely due to the recent convective rainfall, I looked up the SPoRT LIS 0-10 cm volumetric soil moisture data, which corresponded nearly perfectly with the streaks of relative lower temperature values (Image 3).  So indeed, this was due to the recent heavy, convective rainfall across the area.

Image 3.  SPoRT LIS 0-10 cm Volumetric Soil Moisture, 15 UTC 3 May 2018


Extreme Wildfire Setup over Southern High Plains for 17 April

The fire weather outlook for today (17 April 2018) looks very dire over the Southern High Plains of western Texas, New Mexico, and portions of western Oklahoma, southwestern Kansas, and southeastern Colorado. The combination of very little precipitation in recent months along with expected strong winds and extremely low relative humidities will set the stage for potentially dangerous wildfires over this region. The NCEP Storm Prediction Center has the highest threat category in today’s fire weather outlook across the region, with a large swath of extremely critical fire weather conditions expected (Fig. 1).

The persistent lack of precipitation over the Southern High Plains and Desert Southwest regions and its impact on deep-layer soil moisture is captured by the SPoRT-LIS 6-month change in total column relative soil moisture, as posted on the SPoRT-LIS graphics web page (Fig. 2;  A sharp transition lies across Kansas, Oklahoma and Texas, where a strong drying signal is seen across western portions of these states into New Mexico, Arizona, and Mexico, whereas dramatic moistening is prevalent in the last 6 months over the Mississippi, Ohio, and Tennessee River Valleys.  Substantial drying is also noted over the southern Florida Peninsula, with wetting seen over the West Coast and Pacific Northwest (Fig. 2).

Since the unusual and persistent dry pattern over the Southern Plains and Desert Southwest has occurred during the winter months when vegetation is typically dormant (which taps into the deeper soil moisture layers), the anomalously dry conditions are best captured by soil moisture percentiles in the near-surface layer of the SPoRT-LIS.  The total column SPoRT-LIS soil moisture percentiles does not depict an overly dramatic anomaly over the Desert Southwest (Fig. 3; unusual dryness is most prevalent in the deep layers from Oklahoma/Kansas up to Wisconsin/Illinois); however, the shallow soil moisture percentiles capture the anomalous drying over these regions near the surface, as seen in the animation of daily 0-10 cm percentiles for April  in Fig. 4, especially over West Texas, New Mexico and Arizona.  Medium-range forecasts suggest there could be precipitation over the Southern High Plains this weekend, but numerous wetting events will be needed to relieve the ongoing drought conditions.

Figure 1. NCEP Storm Prediction Center’s Day-1 fire weather outlook map for 17 April 2018.

Figure 2. Six-month change in SPoRT-LIS total column relative soil moisture for the period ending 16 April 2018.

Figure 3. SPoRT-LIS total column relative soil moisture percentiles, valid for 16 April 2018.

Figure 4. Daily animation of top-layer (0-10 cm) SPoRT-LIS soil moisture percentiles for the period 1 April to 16 April 2018.




Dust RGB via GOES-16 from 1732-2232 UTC on 12 April 2018 over the U.S. Southwest

SPoRT used the MODIS and VIIRS imagers (on NASA’s Aqua & Terra satellites, and NOAA’s S-NPP satellite, respectively) within the NOAA’s Satellite Proving Ground to assess the value of a “Dust RGB Imagery” product for potential use with GOES-R (now GOES-16).  The Dust RGB proved useful on 13 April 2018 (animation above) where many dust plumes developed in the U.S. Southwest and Mexico.  Forecasters were able to monitor dust plume initiation and issue advisories and warnings.  In addition, several plumes continued to have impacts after sunset, and the Dust RGB, which uses only IR window channels (see Dust RGB Quick Guide), was able to continue monitoring the event at night while the visible imagery was no longer valuable.  Some advisories were extended beyond their original expiration time.  NASA SPoRT is using the NASA CALIPSO satellite and associated CALIOP lidar on board to validate and categorize dust signatures seen in the RGB and examine quantitative aspects like plume height and thickness.  The image below shows an event from 3 April 2018 where forecasters from the NWS Albuquerque WFO and CWSU evaluated the Dust RGB impact to operations as part of SPoRT’s assessment activities, and the CALIOP lidar backscatter captured the dust plume over west Texas.  From CALIOP the dust plume appears to be about 2 km thick in most locations, but the most concentrated region reached a height of about 3 km above ground.


Dust RGB via GOES-16 (upper) over the CONUS and lidar backscatter via CALIOP (on NASA’s CALIPSO satellite) for 2007 UTC on 3 April 2018.  Annotations in yellow point out the dust plume and clouds along the path of CALIOP shown by white arrow/text.

Assimilation of NASA Soil Moisture Active Passive (SMAP) Retrievals to Improve Modeled Soil Moisture Estimates and Short-term Forecasts

For several years, SPoRT has been running a real-time simulation of the NASA Land Information System (hereafter, “SPoRT-LIS“), over a Continental U.S. domain at a ~3-km spatial resolution.  The SPoRT-LIS product is a Noah land surface model climatological and real-time simulation over 4 model soil layers (0-10, 10-40, 40-100, and 100-200 cm). For real-time output, the Noah simulation is updated four times per day as an extension of the long-term climatology simulation.  It ingests NOAA/NESDIS daily global VIIRS Green Vegetation Fraction data, and the real-time SPoRT-LIS component also incorporates quantitative precipitation estimates (QPE) from the Multi-Radar Multi-Sensor (MRMS) gauge-corrected radar product.  The long-term, climatological SPoRT-LIS is based exclusively on atmospheric analysis input from the NOAA/NASA North American Land Data Assimilation System – version 2.

Over the last 1-2 years, SPoRT has been conducting applied research to improve its SPoRT-LIS analyses by assimilating Level 2, enhanced-resolution soil moisture retrievals from the NASA Soil Moisture Active Passive (SMAP) mission.  Comparisons between the “SMAP-LIS” (assimilating the SMAP soil moisture retrievals) and the current SPoRT-LIS (without data assimilation) are available in a near real-time research page.  Data assimilation experiments are being conducted over both Continental U.S. and East Africa domains, and the utility of SMAP data assimilation over Alaska during the warm season is being explored as well.  Besides producing more accurate soil moisture estimates, the project also seeks to improve short-term numerical weather prediction (NWP) models by comparing model runs initialized with SPoRT-LIS enhanced with SMAP data assimilation against model runs initialized with the current SPoRT-LIS soil moisture fields.  This blog post provides a status of this ongoing research, highlighting recently-published work and preliminary NWP model results.

Improvements to Modeled Soil Moisture Estimates

[The data assimilation discussion below represents an excerpt from Blankenship et al. 2018]

To reduce model forecast error in a land surface or atmospheric model, it is essential to periodically update model states with independent observations (Fig. 1).  Data assimilation methods are used to combine an existing model state (background) with a set of observations in order to produce a new model analysis.  The SMAP-LIS uses an Ensemble Kalman Filter to combine SMAP observations with the modeling capabilities of the previous SPoRT-LIS, with the goal of improving states of soil moisture , soil temperature, and fluxes of moisture and energy at the land surface.


Figure 1.  Conceptual diagram of data assimilation (DA).  The black curve represents the true state of some model variable over time at a single point.  The red curve represents a forecast unconstrained by observations and whose error grows with time.  With data assimilation, the observations (blue diamonds) are used to adjust the model value at each assimilation cycle (gray arrows), producing a new forecast (purple curve) that is closer to reality.

An important step in data assimilation is to perform a bias correction to adjust the observations to match a known model distribution.  This is desirable because the solution for the new model analysis makes the assumption that the model and observation are unbiased relative to each other.  The choice of the temporal and spatial scales for this correction are somewhat subjective.  If the initial model climatology has biases built in, e.g., a systematic wet bias or a regionally-varying bias, a strict correction to the model climatology will maintain the model’s previous bias.  Since we seek to take advantage of the global consistency of SMAP observations, we have implemented a non-local bias correction by aggregating points to generate a location-independent correction curve for each soil type (since many modeling errors are related to soil type).  Figure 2 shows the resulting correction curves for 8 broad soil-type categories.

An advantage of applying the weaker non-local constraint when performing the bias correction is that it allows SMAP to influence the climatology of the soil moisture.  We have identified some geographic model biases in our existing SPoRT LIS run, forced by NLDAS-2 analyses.  One example involves the blending of disparate US and Canadian precipitation observations in the Great Lakes region.  This blending produced a persistent dry anomaly in the southern Ontario region (between Lakes Superior, Erie, and Ontario) in the SPoRT-LIS.  This is seen in Fig. 3a, which shows the monthly average 0-100 cm soil moisture for June 2016.  The SMAP retrievals (A single overpass is shown in Fig. 3c.), while relatively dry in this region, are more consistent with nearby areas in Michigan and the northern parts of Indiana and Ohio.  (Note that the color scale is different since this figure represents the top 5 cm only.)  As the result of repeated data assimilation, the SMAP-LIS soil moistures (Fig. 3b) in Southern Ontario over the 0-100 cm layer are more consistent with those in neighboring regions to the south and west.  This example illustrates how the non-local bias correction can help correct spatially varying errors in the model soil moisture.

A quantitative validation (Table 1) was performed against a soil moisture gauge at Elora, Ontario (depicted by the star in Fig. 3c) for two summers.  Results show that the SMAP-LIS soil moistures were more accurate in terms of bias and RMSE for 2015 and 2016.  Results for unbiased RMSE, correlations, and anomaly correlations were mixed from year to year but all three metrics performed better in the second year of the experiment.


Figure 2. Bias correction curves for 8 soil type groupings used to convert SMAP retrievals to model-equivalent values.


Figure 3. Long-term impact of SMAP data assimilation on root zone soil moisture. (a) Average of 1200 UTC 0-100 cm relative soil moisture (%) for June 2016 from SPoRT-LIS (no data assimilation), (b) Same quantity but for SMAP-LIS, (c) SMAP retrieved soil moisture on 4 June 2016. (m3 m-3 x100).  The star shows the location of a soil moisture gauge used for validation. (Click on image for full size view)

TABLE 1. Validation statistics (bias, RMSE, unbiased RMSE, correlation, anomaly correlation) from Elora, Ontario, Canada soil moisture gauge for summer 2015 (30 May-4 Sep) and summer 2016 (2 May-31 Aug).  For each pair of measurements, the better value is in bold type.



Bias -0.096 -0.077 -0.083 -0.043
RMSE 0.102 0.088 0.115 0.086
ubRMSE 0.036 0.042 0.079 0.075
RCORR 0.76 0.69 0.38 0.48
ACORR 0.77 0.67 0.55 0.57

Impacts on Short-term NWP Model Forecasts

The second component of this research involves compared NWP model simulations initialized with SPoRT-LIS and SMAP-Enhanced DA fields, using the NASA Unified-Weather Research and Forecasting (NU-WRF) modeling framework for the experiments.  NU-WRF simulations are currently focused on the CONUS during the warm season (May to August) to document impacts of SMAP data assimilation on short-term regional NWP.  A case study of improved timing of a mesoscale convective system (MCS) is highlighted here.  Ongoing work involves examining other high-impact convective cases during the 2015 and 2016 warm seasons, and conducting comprehensive model verification statistics.

The case highlighted here is from a severe MCS over the Midwest from 13-14 July 2016.  The NCEP/Storm Prediction Center reports from this day are available at An MCS developed over Missouri and Illinois during the afternoon of 13 July, and quickly moved eastward into Indiana, Michigan, and Ohio and southern Ontario province into the evening.  The initial surface soil moisture differences between the SMAP-LIS and SPoRT-LIS (Fig. 4) show that a distinct drying occurred in the data assimilation output over the Midwest, compared to the SPoRT-LIS output.  Meanwhile, a moistening occurred from SMAP DA over portions of Southern Ontario, as illustrated above.  (Note that the moist “stripe” surrounding the Great Lakes is consistent with the appearance of a moist bias near coastlines found within the SMAP Enhanced Resolution Level 2 product).  A similar signal is seen in the deeper soil layers as well.


Figure 4.  Difference in initial 0-10 cm volumetric soil moisture between SMAP-LIS and SPoRT-LIS for the model runs initialized at 0000 UTC 13 July 2016.

The soil drying signal over the Midwest led to a corresponding increase in 2-m temperatures, decrease in 2-m dew points, and overall decrease in surface convective available potential energy (CAPE), as seen in the NU-WRF 18-h forecast (Fig. 5).  Meanwhile, over southern Ontario, the more moist soils in the SMAP-LIS initialized run led to an opposite response.  These changes to the simulated boundary layer environment led to an overall faster propagation of the MCS across Illinois and Indiana in the SMAP-LIS initialized NU-WRF runs, as highlighted in Fig. 6.  This faster solution was in better agreement with the observed radar reflectivity at 0000 UTC 14 July, as the NU-WRF run initialized with SPoRT-LIS data had too slow of a solution at this time.  While the two solutions converged to a slow-biased placement and timing after dark, secondary development over Southern Ontario was more aggressive in the SMAP-LIS initialized run, again in better agreement with reality (Fig. 7).  More comprehensive analysis and model verification will help us better understand the cause and effect relationship between the soil moisture initialization and the resulting NU-WRF simulation differences for this case, as well as composite results during the 2015 and 2016 warm seasons.


Figure 5.  Differences in NU-WRF simulated 2-m temperature (left panel; SMAP-Enhanced minus SPoRT-LIS), surface CAPE (middle), and 2-m dew point (right) for the 18-h forecast valid 1800 UTC 13 July 2016.  (Click on image for full size view)


Figure 6.  Comparison of NU-WRF simulated composite radar reflectivity for the (a) SPoRT-LIS initialized run, (c) SMAP-LIS initialized run, and (b) validating radar reflectivity observation, for the 24-h forecast valid 0000 UTC 14 July 2016. (click on image for full size view)


Figure 7.  Same as in Fig. 6, except for the 28-h forecast valid 0400 UTC 14 July 2016. (click on image for full size view)

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