Month: September 2019

Figure 1: Map of age of recent lightning strikes in Texas on 19 September 2019 at 2110 UTC (map created through analysis of lightning detection sensors) (https://www.weathertap.com)

Cloud Height and Reflectivity Act as Maps of Lightning for Imelda

Imelda made landfall in Houston, TX as a tropical depression on Wednesday, 18 September 2019. It brought heavy precipitation and severe weather, including multiple tornado reports. Maps of lightning strikes associated with the storm within the hours before 2110 UTC 19 September 2019 display the effectiveness of using satellite imagery and radar imagery to identify areas of severe weather (Figure 1). The locations of lightning strikes within the 12-30 minutes prior to 2110 UTC (marked in white) display concentrations of lightning to the southwest, south, southeast, and northwest of Houston, as well as further north over Austin, TX. Satellite imagery of Texas within the IR Cloud Top channel display high clouds in the same areas, including significant cloud heights off the coast south of Galveston, TX, which may be too far from land to detect lightning strikes as there is not much recent lightning displayed in that location (Figure 2). Lightning is caused when deep convective clouds have large quantities of ice which aids in the polarization of the cloud structure. These ice particles result from water droplets high in the atmosphere freezing, but first they must reach altitudes cold enough. This is why deep clouds with significant heights are associated with lightning causing dynamics.

Figure 2: GOES-16 Infrared Cloud Top Imagery on 19 September 2019 at 2116 UTC (https://www.star.nesdis.noaa.gov/GOES/)

The base radar reflectivity is a less effective method of predicting the locations of large quantities of lightning strikes in the case of Imelda on 19 September 2019, with more areas of significant rainfall identified than had lightning strikes, such as to the north of Houston (Figure 3). The largest cell associated with the storm with respect to horizontal scale, just south of Houston and over Galveston, is easily apparent on the radar reflectivity image as a large area of reflectivity values over 40 Dbz.  It is less easy to identify cells further west that match up with the individual cells identifiable by the red colors indicating tall clouds on the satellite imagery. Radar also has the disadvantage of limited range and the fact that radar signals can be blocked from reaching regions on the opposite side of heavy precipitation from the radar. This indicates that in the case of Imelda, radar may have been unable to detect precipitation in regions with frequent lightning strikes; satellite imagery of cloud height acted as a fairly accurate map of regions with a high volume of lightning strikes.

Figure 3: Radar reflectivity map from KHGX (Houston, Tx) on 19 September 2019 at 2111 UTC (https://radar.weather.gov/ridge/radar.php?product=NCR&rid=HGX&loop=yes)

Late Tuesday night, on September 10^th, 2019, three tornadoes touched down and lifted over the course of five and a half minutes. All three were listed as an EF-2 on the Enhanced Fujita scale, and each tornado lasted roughly a minute. However, a tornado warning was not issued until two of the three tornadoes had already touched down and lifted, causing significant damage to the city of Sioux Falls. This delay in warning was due to the fact that tornadoes produced quasi-linear convective systems, or QLCS, are considerably harder to forecast than those formed in supercell thunderstorms.

Figure 1: Doppler radar (left) and storm relative velocity (right) of a supercell producing a tornado, Raleigh NC, April 16^th 2011. Source: USTornadoes.com

Unlike supercells (Figure 1), where the location and formation of these tornadoes can be more easily seen on radars with enough time to issue a warning, a QLCS can produce tornadoes with little to no warning. Some of these tornadoes can form in the time it takes the radar to complete a cycle the area, and disappear just as quickly. In Sioux Falls, they experienced just that. An initial scan with a storm relative velocity radar shows an area of developing circulation at 11:25pm (Figure 2, left), but was significant enough to send out a warning. One minute and 26 seconds later, the following scan at 11:26pm (Figure 2, right) indicated a tornado had already formed, and by the time the tornado warning was sent out another minute and 35 seconds later, a second tornado had touched down. Luckily, while the city suffered considerable property damage, no deaths or serious injuries have been reported due to this outbreak of tornadoes in Sioux Falls.

Figure 2: Storm Relative Velocity at 11:25pm (left) and 11:26pm (right), Sioux Falls, September 10^th 2019. Green indicated movement toward the radar site, while red indicates movement away from the radar site. Source: NOAA/GR2 Analyst/Matthew Cappucci

Sources:
https://www.washingtonpost.com/weather/2019/09/12/trio-tornadoes-tear-up-sioux-falls-sd/
https://www.ustornadoes.com/2013/02/14/understanding-basic-tornadic-radar-signatures/
https://www.washingtonpost.com/news/capital-weather-gang/wp/2017/04/20/americans-are-getting-less-advance-notice-for-tornadoes-as-researchers-struggle-to-understand-why/
https://www.usatoday.com/story/news/nation/2019/09/11/sioux-falls-south-dakota-tornado/2283760001/

Hurricane Dorian made history earlier this week as one of the most intense Atlantic basin hurricanes on record. In fact, it was the second strongest storm by wind speeds since 1950 in the Atlantic Basin. Dorian also made history as the strongest hurricane to make landfall in the Bahamas. The hurricane caused mass destruction to the Bahamas, striking the islands as a strong category 5 hurricane, and with winds peaking at 185 mph. The hurricane moved slowly over the region, contributing to major flooding on the islands. From 12am 2 September 2019 to 6am local time 3 September 2019, Dorian moved just 33 miles in 30 hours. A satellite image from ICEYE taken 2 September shows the extent of the flooding that occurred on Grand Bahama.

Grand Bahama satellite image depicting flooding. The yellow-green outline is the islands original coastline before Dorian. Taken 2 September 2019.

Satellites play a key role in tracking hurricane development, as it’s hard to get radar imaging of hurricanes when they are over the open ocean. We can track Dorian as it makes its way along the eastern seaboard of the United States using GOES satellites. The GOES-16 visible imagery of Dorian shows just how massive the system has become. Satellite imagery is also useful in understanding eyewall replacement throughout a hurricanes lifespan. This is often tracked using a combination of visible satellite imagery and infrared imagery. The GOES-16 infrared imagery is also helpful to understand the convection within the storm. Red and black hues on the color bar indicate lower temperatures and therefore higher cloud tops. This can help us determine if the hurricane is dissipating or strengthening. If there are lower temperatures developing within the storm, we can assume the hurricane is growing as cloud tops are developing at higher levels in the atmosphere.

GOES-16 visible satellite imagery of United States east coast, featuring Hurricane Dorian. Taken at 1621 Z 4 September 2019

GOES-16 IR Longwave IR imagery. Taken at 16:26Z 4 September 2019.

While Hurricane Dorian continues to make headlines after making landfall over Cape Hatteras, NC at 8:35 am EDT, NASA released an animation taken of the hurricane from one of their experimental satellites. This satellite, known as the Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D), is part of NASA’s class of nanosatellites known as CubeSats. CubeSats are small in size, where the largest qualifying dimensions (known as 6U) are 60 cm x 60 cm x 60 cm with a weight of approximately 8 kgs, and can shrink to as small of dimensions (known as 1U) as 10 cm x 10 cm x 10 cm and weigh about 1.3 kg. As a result of their small size, CubeSats are being tested as a low cost alternative to current operational weather satellites. TEMPEST-D is one of the larger CubeSats, labelled as a 6U, but as can be seen in the image on the right, it is still the approximate size of a cereal box.

The completed TEMPEST-D CubeSat satellite with the solar panels deployed. Image Credit: Blue Canyon Technologies

In the animation of Hurricane Dorian, the CubeSat TEMPEST-D captured areas of light to heavy rainfall within the hurricane at four different atmospheric layers. Designed to measure convective precipitation and structure of a storm in three dimensions, TEMPEST-D used a miniaturized microwave radiometer to scan the atmosphere at various wavelengths, resulting in the layers of precipitation intensity within Hurricane Dorian shown below.

CubeSats are still in the experimental stage, but should they be successful with tracking storms like Hurricane Dorian, CubeSats would help expand our network of satellites to include a stronger network of high quality data at the same relative cost as a single satellite. TEMPEST-D in particular could provide scientists with a better understanding of the processes that govern the formation and dissipation of clouds, which is currently a large source of uncertainty that leads to considerable changes in future climate models. Overall, CubeSats like TEMPEST-D have the potential to help scientists better understand our global climate, and in turn, help forecasters provide more accurate forecasts for storms and hurricanes just like Dorian.

Hurricane Dorian as seen by TEMPEST-D on 9/03/2019 at 2am EDT. High intensity rain is shown in yellow, red, and pink while low intensity rain is shown in green. Image Credit: NASA/JPL-Caltech/NRL-MRY. The full animation can be viewed here: https://photojournal.jpl.nasa.gov/archive/PIA23431.gif

Sources:

https://www.npr.org/2019/09/06/758240435/hurricane-dorian-finally-makes-landfall-in-n-c

https://www.nasa.gov/mission_pages/cubesats/overview

https://www.nasa.gov/feature/jpl/an-inside-look-at-hurricane-dorian-from-a-mini-satellite

https://www.cnbc.com/2019/09/06/animation-nasa-made-of-hurricane-dorian-with-an-experimental-satellite.html

https://www.jpl.nasa.gov/cubesat/missions/tempest-d.php

Figure 1: MODIS aboard the Aqua satellite captures smoke plumes in imagery from August 13, 2019. Source: NASA Earth Observatory (https://earthobservatory.nasa.gov/images/145464/fires-in-brazil)

Satellite and radar technologies provide a unique and valuable perspective for detecting and monitoring fire events. A satellite-based instrument known as MODIS has captured the abnormal fire activity in the Amazon this month. According to the NASA Earth Observatory, this region is on track to mark 2019 as a record high year for fire activity in the Amazon. By leveraging satellite data in combination with meteorological data, it is possible to better understand, monitor, and evaluate this hot topic.

Located in the tropics and near the equator, the Amazon experiences year-round rainfall events due to the low pressure environment and convergence. A recent GFS model run for South America on 27 August, 2019 shows how the Amazon experiences high 850 hPa Air Temperature readings during this time of year. Cold air from the arctic doesn’t penetrate this central, equatorial region, which allows the area to remain warm and rainy. However, July and August are considered to be the “dry season,” which unfortunately coincides with habitual land clearing practices via burning that have likely initiated the intense fire activity.

Figure 2: GFS 850 hPa air temperature data for South America on 27 August, 2019. Source: Tropical Tidbits (tropicaltidbits.com)

The Moderate Resolution Imaging Spectroradiometer (MODIS), an instrument aboard two polar-orbiting satellites called Terra and Aqua, captured the positive fire detections displayed in orange in the image below. Each orange dot represents a square kilometer area with at least one detected thermal anomaly. This data is overlaid on top of the nighttime VIIRS imagery, acquired via a satellite called the Suomi National Polar-orbiting Partnership (NPP). The nighttime VIIRS imagery, which highlights cities and populated regions, can be used in combination with the MODIS data to better identify Amazonian communities at risk.

Figure 3:VIIRS Imagery overlaid with MODIS fire detections in South America. Source: NASA Earth Observatory (https://earthobservatory.nasa.gov/images/145464/fires-in-brazil)