Observing the First Major Thundersnow Outbreak of the 2019-2020 Winter Season

Written by Sebastian Harkema and Emily Berndt

The first major heavy-banded snowfall event of the 2019-2020 winter season occurred from Oct. 9-12 and produced over two feet of snowfall in North Dakota. Throughout the event, the NESDIS merged snowfall rate (mSFR; Meng et al. 2017) product tracked the heaviest snowfall rates, including bands with snowfall rates greater than 2 in/hr. With a temporal resolution of 10 minutes, this product can be used in real-time to forecast the location and evolution of snowbands producing heavy snowfall, and even anticipate cloud-seeding. SPoRT has collaborated closely with NESDIS to experimentally transition and assess the passive microwave and merged snowfall rate products with NWS forecast offices (Ralph et al. 2018).  Therefore, this product is available in AWIPS and forecasters can select different snow-to-liquid ratio values to best fit the situation.

Figure 1: NESDIS mSFR product and GOES-EAST ABI (Ch. 13) on October 9, 2019.

Figure 1 demonstrates the mSFR product overlapping GOES-East ABI (channel 13) for October 9th as the snowband traversed across Montana. While the mSFR product provides a unique way to monitor snowfall, the phenomenon known as thundersnow captivated the attention of some operational forecasters as well as the general public, in part by the availability of Geostationary Lightning Mapper (GLM) observations. Recent work from NASA SPoRT has shown that the overlap of GLM and mSFR data can be used to objectively identify and characterize electrified snowfall (i.e., thundersnow; Harkema et al. 2019a). In fact, Harkema et al. 2019a demonstrated that thundersnow flashes identified by GLM contain on average more total optical energy per flash area than other flashes in the GLM field-of-view. Harkema et al. 2019a also demonstrate that thundersnow flashes observed by GLM are spatially larger compared to non-thundersnow flashes and is likely a result of weaker mesoscale updrafts and slower charging rates compared to severe summertime convection.

Figure 2: NESDIS mSFR product, GOES-EAST ABI (Ch. 13), and GLM flash extent density observations on October 10, 2019.

Figure 2 demonstrates the objective identification of thundersnow based on the overlap of mSFR and GLM flash extent density observations on October 10th around the Colorado/Nebraska/Wyoming border region. From the loop, this region experiences an enhancement of snowfall rates approximately 30-40 minutes after the first occurrence of thundersnow. Even though it appears as though thundersnow can be used as a precursor for enhancement of snowfall rates in the near future, thundersnow has a spatial offset of 131±65 km from the heaviest snowfall rates (Harkema et al. 2019b, In Review). This spatial offset is evident when examining the thundersnow that occurred along the Minnesota/Manitoba border between 12-15 UTC on October 11th (Fig. 3).

Figure 3: NESDIS mSFR product, GOES-EAST ABI (Ch. 13), and GLM flash extent density observations on October 11, 2019.

The thundersnow observed by GLM occurs on the northern extent of the heaviest snowfall rates (purples/whites). The separation of thundersnow and the heaviest snowfall rates is likely caused by hydrometeor lofting of the snowfall as it descends to the surface because of the low terminal fall speed of the ice crystals.

Winter is fast approaching and the NESDIS mSFR product and GLM can be used in tangent with each other to improve situation awareness. NASA SPoRT is at the forefront of understanding the operational implications of electrified snowfall and continues to investigate the thermodynamic and microphysical properties that are associated with it. See the official JPSS Quick Guide and a past JPSS Science Seminar for more product information.

References

Harkema, S. S., C. J. Schultz, E. B. Berndt, and P. M. Bitzer, 2019a: Geostationary Lightning Mapper Flash Characteristics of Electrified Snowfall Events. Wea. Forecasting, 43(5), 1571–1585, https://doi.org/10.1175/WAF-D-19-0082.1.

Harkema, S. S., E. B. Berndt, and C. J. Schultz, 2019b: Characterization of Snowfall Rates, Totals, and Snow-to-Liquid Ratios in Electrified Snowfall Events from a Geostationary Lightning Mapper Perspective. Wea. Forecasting. In Review.

Meng, H., Dong, J., Ferraro, R., Yan, B., Zhao, L., Kongoli, C., Wang, N.‐Y., and Zavodsky, B. ( 2017), A 1DVAR‐based snowfall rate retrieval algorithm for passive microwave radiometers, J. Geophys. Res. Atmos., 122, 6520– 6540, doi:10.1002/2016JD026325.

NASA SPoRT’s SST Composite Maps Capture Upwelling in the Wakes of Hurricanes Dorian and Humberto

NASA SPoRT’s SST Composite Maps Capture Upwelling in the Wakes of Hurricanes Dorian and Humberto

Written by Patrick Duran, Frank LaFontaine, and Erika Duran

Category 5 Hurricane Dorian passed over the Bahamas between September 1 and 3 2019, producing catastrophic destruction and causing at least 60 direct fatalities in the island nation. In addition to the impacts on human life, strong, slow-moving hurricanes like Dorian can leave lasting effects on the ocean over which they travel. Through a process known as upwelling, hurricanes bring colder water from below the surface up to the top layer of the ocean.  As a result, a trail of cooler sea surface temperatures (SSTs), also referred to as a “cold wake,” is often visible behind a passing storm. Meteorologists and oceanographers can monitor changes in SST and identify a cold wake following tropical cyclones using satellite data.

NASA SPoRT produces composite maps of SST twice daily using data from the VIIRS-NPP, MODIS-Aqua, and MODIS-Terra instruments, along with OSTIA-UKMO data obtained from the GHRSST archive at NASA’s Jet Propulsion Laboratory and the NESDIS GOES-POES SST product. The input data are weighted by latency and resolution to produce the composite, which is available at 2 km resolution.

Figure 1 shows a loop of the SPoRT SST composite from August 31 – September 23, 2019 over a region that includes the Bahamas and the Southeast United States. Two rounds of SST cooling are observed as Hurricanes Dorian and Humberto move through the region.

Figure 1: Animation of NASA SPoRT SST Composite Maps from August 21 through September 23, 2019. Daily images are displayed at 1800 UTC.

On August 31, very warm SSTs of around 29-30 deg Celsius (84-86 deg Fahrenheit) overspread the waters surrounding the Bahamas (Fig. 2).

Figure 2: SST Composite Map at 1800 UTC on August 31, 2019.

After Hurricane Dorian tracked through the region and made landfall in North Carolina on September 6, the waters north of the Bahamas were considerably cooler – in the 26–29 deg Celsius (79–84 deg Fahrenheit) range (Fig. 3).

Figure 3: As in Fig. 2, but for September 6, 2019.

Over the next week, the surface waters warmed a degree or two (Fig. 4), but did not fully recover to the same temperature observed on 31 August.

As in Figs. 2-3, but for September 13, 2019.

On September 13, Tropical Storm Humberto formed 210 km (130 miles) ESE of Great Abaco Island. As the storm tracked northeast past the Bahamas, it encountered the cold wake left by Hurricane Dorian the previous week. These cooler waters, combined with the influence of some dry air and vertical wind shear, inhibited the storm’s intensification as it passed by the Bahamas. On September 15, Humberto moved over the warmer waters of the Gulf Stream off the coast of North Florida (Fig. 5) intensified to hurricane strength.

Figure 5: As in Figs 2-4, but for September 15, 2019.

Humberto continued to strengthen, and attained a maximum sustained wind speed of 125 MPH as it passed by Bermuda on September 19. Its strong winds and associated waves overturned the same region of ocean that was previously affected by Hurricane Dorian, decreasing sea surface temperatures to as low as 25 deg Celsius (77 deg Fahrenheit) in some areas (Fig. 6).

Fig. 6: As in Figs. 2-5, but for September 19, 2019.

These images highlight the effect that tropical cyclones can have on SST, and how a hurricane can make it more difficult for any subsequent storms to intensify over the same region. Satellite analyses of SSTs (such as those produced by NASA SPoRT) allow forecasters to monitor SST across the globe, helping them to produce better forecasts of tropical cyclone intensity in all ocean basins.

Snowfall Rate Provides Guidance for New Mexico Snow Event

Forecaster Jennifer Palucki from Albuquerque, New Mexico submitted a nice case study to our online evaluation form being used during the current 2016 NESDIS Snowfall Rate Evaluation.  Here are some of her discussion and impressions of using the product:

A very well defined band of snow developed along a frontal boundary extending from the southern Sangre de Cristo Mountains, toward Las Vegas, and continued southeastward toward Melrose. Initially the southeast part of the band was rain, but as temps dropped it changed to snow. At 0052z (552pm MST; see image below) the merged SFR likely did very well distinguishing where there was snow and no snow, however, in areas that there was snow, amounts were way underdone. At 545pm, approximately 4″ of snow had fallen in Sapello in the southern Sangre de Cristo Mtns. Snow likely started around 1 or 2pm, which is an average of about 1″/hr compared to the 0.3″/hr the SFR product was showing with an 18:1 ratio. Thus, the amounts via the SFR product were largely underdone. It was still snowing heavily according to the spotter at 545pm. At 645pm, approximately 1.5 inches of snow was reported in Las Vegas. The SFR product was showing around 0.1″/hr for this area.

ABQ_160203_0052Z_annotated_zoom

NESDIS SFR Product at 0052 UTC on 03 February 2016 showing light snow over Las Vegas, NM.

Another pass at 0330z (830pm MST; see image below), the SFR product missed the southeastern extent of the snowfall, and again had amounts that were likely underdone. A report of 0.5 inches of snow in the last hour was reported at 841pm in Taos. The SFR product showed around 0.02 liquid equivalent, or around 0.3″/hr snowfall rate given 18:1 ratio (which should be close to the snow ratios in that area).

ABQ_160203_0330Z_annotated

NESDIS SFR Product at 0330 UTC on 03 February 2016 showing some heavier snow over Taos, NM.

Really like using this product to gather intel on where it is snowing in areas without radar coverage. Do have some concerns about the amounts, especially in these scenarios where the heavier amounts are likely isolated. In this case, the band was very narrow, likely no more than 10 to 15 miles wide.

Life of Winter Storm Jonas as seen by the NESDIS Snowfall Rate Product

Winter Storm Jonas tracked across the eastern United States this past weekend dropping near-record amounts of snowfall in a track from West Virginia through southern New York.  Two things about this storm are particularly interesting:  1) the heavy amounts of snow that fell for long periods of time and 2) the relatively narrow swath of the heaviest snows.  Below is the 48-hour snow accumulations from the National Weather Service ending Sunday, January 24.  It is striking that New York City received on the order of 30 inches of snow, while areas less than 100 miles to the north received little if any snow.

NWS_SnowTotals

48-hour snowfall totals ending Sunday, January 24, 2016 (from NWS Central Region).  Contours are every 3″ with the darkest reds indicating 30″ of snow.

Select Eastern Region WFOs are currently evaluating the NESDIS Snowfall Rate product, which uses passive microwave observations from 5 sensors, to observe total column snowfall rates.  Below is a series of images from this past weekend showing the SFR product displayed as a 10:1 solid/liquid conversion.  The darkest greens indicate snowfall rates at the top of the sensor detection range at approximately 2″/hr.  Depending on the actual solid/liquid ratio in individual areas, rates may have been higher.

SFR_Collage_first4

SFR_Collage_second4

NESDIS SFR Product showing the evolution of Winter Storm Jonas from late on Friday through early Sunday.  The darkest greens indicate solid snowfall rates of around 2″/hr.

In the images, the NESDIS SFR product shows very good agreement with the location and track of the heaviest snows (greens) compared to the heaviest totals in the ground reports.  Additionally, the SFR product does well in picking up the abrupt northern edge of the snowfall (especially across southern New York).

UPDATE:  The Sterling, VA WFO included mention of the SFR product in a forecast discussion to confirm snowfall rates that would cause white out conditions:

Sterling_AFD

NESDIS Snowfall Product Captures Unfolding Winter Weather in the South

Beginning in the morning hours of 22 January 2016, rain began to change to snow across Mississippi, Tennessee, and Alabama.  The NESDIS Snowfall Rate, which is currently being evaluated by a handful of Weather Forecast Offices, has the ability to differentiate rain from snow.  This ability was particularly important for the large winter storm impacting much of the eastern half of the United States.  The animation below shows the 10:1 Solid SFR Product with METAR station observations indicating temperatures and precipitation.

AL_TN_SFR_Example_20160122_07-19Z_slower

The animation shows the evolution of snow across the area beginning with snow in Western Tennessee and Eastern Mississippi at around 1200 UTC (6:00a local time).  Also of note at that same time is that the SFR Product indicates relatively heavy snow (~1.5 in./hr. solid snow) directly over the Nashville area; however, the METAR site at the airport is still reporting rain.  In the following hour (1300 UTC; not shown in the loop here because there was no SFR product valid near 1300 UTC) Nashville was reporting snow.  Thus, the SFR product was seeing in-cloud snow in that area that began to reach the ground within an hour of the observation.  This is one way forecasters can use the product to view in-cloud snow to determine the potential for snow to reach the ground.

Later in the period, a similar set up appears in the Huntsville area at the Madison County Executive Airport (KMDQ).  The 1853 UTC SFR product shows light snow over Madison County, but the 1900 UTC METAR was not yet reporting any snow.  However, the 2000 UTC METAR showed snow beginning to fall across the Huntsville area.  The change over to snow falling across Western Madison county into Central Madison county was between 1830 and 1900 UTC, verified as I drove home from work.

The NESDIS SFR product will continue to be evaluated as blizzard conditions begin to set up along parts of the East Coast.

SFR Product Verifies Snow Coverage over Four Corners

The NESDIS Snowfall Rate (SFR) product assessment is in full swing at NWS Albuquerque and forecasters are already capturing some good cases over data sparse regions. The first week of January 2016 was very active across New Mexico as back to back winter storm systems crossed the area. The second system in the series crossed over the Four Corners region on 4 January 2016, producing light to moderate snowfall rates for several hours. The forecaster on shift noted the observation at Farmington, NM (KFMN) indicated light snow with a visibility of 5 statute miles. A quick glance at the SFR procedure used in Figure 1a shows the extent of any precipitation echoes well to the east of KFMN at 0000 UTC 5 January 2016. The nearest radar (KABX, not shown) is located roughly 150 miles southeast of KFMN near Albuquerque, NM. The arrival of a SFR product at 0010 UTC 5 January 2016 showed the extent of the precipitation was much greater with the merged POES image overlaid on the radar data (Figure 1b). Sampled liquid equivalent values in the light green areas to the east of KFMN were near 0.03″/hour.

Figure 1a. Liquid equivalent values of the merged SFR product valid 0000 UTC 5 January 2016. KFMN is denoted by the white circle. Note the extent of the radar coverage is well east of KFMN.

Figure 1b. Liquid equivalent values of the merged SFR product valid 0010 UTC 5 January 2016. KFMN is denoted by the white circle. Note the extent of the snowfall coverage is much greater with the addition of the POES image.

The Terminal Aerodrome Forecast (TAF) issued for KFMN shortly before the receipt of this image indicated temporary fluctuations in the visibility to 1 statute mile with light snow and an overcast ceiling near 1,200 ft between 0000 UTC and 0400 UTC (Instrument Flight Rules, IFR). It is not clear whether any operational changes occurred based on the receipt of the merged SFR product or whether the product increased confidence on the IFR forecast. However, it is entirely possible given the improvement in product latency compared to the 2015 assessment that the imagery could be used in this way.

The webcam available at San Juan College just a short distance from the KFMN observation showed significant decreases in the visibility between 330pm and shortly after sunset (Figure 2a and 2b). The two images below show the decrease in surface visibility as well as notable accumulations on grassy surfaces in front of the college. An observer 3 miles southeast of Farmington did report a total accumulation of 1″ from this event. The merged SFR product did in fact show higher rates immediately to the east of KFMN. The last image in the series shows the impact on travel conditions noted by the NM Department of Transportation web page (Figure 3). The areal coverage of the difficult travel impacts (yellow highlights) was greater than that depicted by what can be seen based on poor radar coverage.

Figure 2a. Webcam at San Juan College around 330pm. Note the light snowfall beginning to develop over the distant mesas behind the college.

Figure 2a. Webcam at San Juan College around 330pm. Note the light snowfall beginning to develop over the distant mesas behind the college.

Figure 2b. Webcam at San Juan College shortly after sunset. Note the dramatic decrease in visibility and light snow accumulations on grassy surfaces in front of the college.

Figure 2b. Webcam at San Juan College shortly after sunset. Note the dramatic decrease in visibility and light snow accumulations on grassy surfaces in front of the college.

Figure 3. Screen capture of NM DOT web page showing areal coverage of difficult travel conditions (yellow highlights) and some text summaries detailing the impacts.

Post Frontal Orographic Snowfall Impacts NM

A slow-moving upper level storm system tracked east across northern NM and southern CO on 14-15 December 2015. A weak tap of subtropical moisture ahead of this system provided light to moderate snowfall mainly along the Continental Divide of western NM and the higher terrain running north-south through central and northern NM. Snow accumulations of 3 to 8 inches were reported ahead of and immediately behind the surface front and the mid level trough passage. A classic westerly,upslope flow event developed behind the upper wave as moist, unstable flow interacted with the north-south oriented higher terrain. Winter weather advisories and winter storm warnings were in effect over much of northern NM for the expectation of storm total snowfall of 8 to 12″ with locally higher amounts. Figure 1 depicts the distribution of advisories and warnings over northern NM on the Albuquerque National Weather Service public page.

Advisory and warning map for the ABQ CWA valid 15 December 2015.

Figure 1. Advisory and warning map for the ABQ County Warning Area valid 15 December 2015.

Poor radar coverage over northern and western NM makes it a challenge for assessing winter precipitation patterns and snowfall rates. Figure 2 shows a radar mosaic valid 1800 UTC 15 December 2015 utilizing an enhanced color curve to identify areas of lighter snowfall. Automated surface observations are sparse in this area however there are at least a few observations reporting snow where nothing is present in the radar reflectivity. Webcams at ski resorts serve as an excellent near real-time proxy for visualizing active snow accumulations in these poor radar coverage regions. Additionally, once daily snow accumulation reports from ski resorts aid the verification process following the winter event.

Figure 2. Winter radar mosaic from KABX valid 1800 UTC 15 December 2015. Note the orange circle depicting a large area of poor radar coverage.

The integration of satellite data allows forecasters to supplement these data void areas. The most recent interation of the NESDIS snowfall rate products available at WFO Albuquerque illustrate the snowfall rate derived from radar (Figure 3a) and the snowfall rate available from merging the POES satellite data with the radar data (Figure 3b). Note the grey areas overlaid on the map in Figure 3a indicate areas of reliable radar coverage. The snowfall rate derived from satellite data in Figure 3b clearly shows coverage outside of the area with reliable radar coverage. A very cold and unstable airmass in association with this precipitation suggested snowfall rates in the higher terrain would average between 20-30:1. The 18:1 image in the lower right of Figure 3b indicated rates around 0.4/hr.

FIgure 3a. Radar derived snowfall rate product over northern NM valid 1750 UTC 15 December 2015.

Figure 3a. Radar derived snowfall rate product over northern NM valid 1750 UTC 15 December 2015. Note the grey areas overlaid on the map indicating where reliable radar coverage exists. Upper left (liquid equivalent), upper right (10:1), lower right (18:1), lower left (36:1).

NESDIS snowfall rate product filling in the radar gaps over northern NM valid 1750 UTC 15 December 2015. Note the circles in the upper left image are the location of the webcams in Figure 4.

Although there is sparse coverage of automated surface observations around the higher terrain, webcams from ski resorts can verify the existence of moderate to heavy snowfall. Visibilities in the webcams below suggest snowfall rates higher than those depicted in the NESDIS products – visually, rates look closer to perhaps 1″/hr in the upper right and lower right images (Figure 4). One of our goals of this assessment is to combine information from the webcams with the more quantitative snowfall rate product to better estimate snowfall in data void areas. Snowfall reports from the Chama Railyard indicated 8.5″, Taos Ski Valley 6″, Ski Santa Fe 12″, and Pajarito Mountain 10″.

 

Figure 4. Webcams from across northern NM. Top left (Chama Railyard, yellow circle), Top right (Taos Ski Valley, white circle), Bottom right (Ski Santa Fe, red circle), Bottom left (Pajarito Mt, orange circle).