Multiple perspectives on Hurricane Dorian

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Data from a combination of EUMETSAT-operated satellites contributed to monitoring of the record-breaking Hurricane Dorian during September 2019

Date & Time
2 September 2019 02:53 UTC–3 September 15:29 UTC
Satellites
Metop-A, Sentinel-3B, GOES-17
Instruments
ASCAT, OLCI, SLSTR, SRAL, ABI
Channels/Products
ASCAT winds, Enhanced infrared, Brightness Temperature, Chlorophyll concentration, Significant Wave Height (SWH) and Sea Surface Height Anomaly (SSHA)

By Hayley Evers-King (EUMETSAT), Ben Loveday (PML) and Ivan Smiljanic (SCISYS)

The first major hurricane of the 2019 season arrived in the form of Hurricane Dorian in early September 2019 (also the fourth named tropical storm and second hurricane of the season).

Dorian developed from a tropical wave in the central Atlantic on 24 August. The system gradually intensified while moving toward the Lesser Antilles, before becoming a hurricane on 28 August. Following rapid intensification Dorian became a Category 4 hurricane on 31 August, before reaching Category 5 intensity the following day.

At 16:40 UTC the intense hurricane made landfall at Elbow Cay, Bahamas with one-minute sustained winds of 295 km/h and a minimum central pressure of 910 millibars. Several hours later it made another landfall on Grand Bahama with the same intensity, where it stalled for a day. It was the strongest known tropical system to impact the Bahamas. The impacts were devastating, with large swathes of the island completely destroyed. The official death toll by 6 September was 30, but it was expected to rise significantly.

A combination of cold water upwelling and an eyewall replacement cycle weakened Dorian to a Category 2 hurricane by the next day. On the morning of 3 September, Dorian began to move slowly towards the north-northwest, and subsequently completed its eyewall replacement cycle, before moving over warmer waters, regaining Category 3 intensity by midnight on 5 September. The following day, Dorian weakened to Category 1 intensity as it picked up speed and turned northeast.

Hurricanes can be readily identified in meteorological satellite data by their cloud characteristics and winds. The active radar instrument, ASCAT, on board the Metop-A polar-orbiting satellite (Figure 1) reveals the low level wind pattern, with wind barbs wrapping around the low pressure centre. Colour-coded areas of blue-to-yellow-to-red (warmer to colder), sensed by the ABI geostationary imager, show the coldest parts of the clouds associated with the hurricane. The clouds were mostly arranged in a rain band that also wrapped around the centre of the hurricane. The colder the cloud the higher the chance that it is producing a large amount of rainfall, however, this relationship is not linear and depends on many other factors.

Figure 1
 
Figure 1: Metop-A ASCAT winds laid over the GOES-17 ABI enhanced 10.35 µm channel (ch.13), 2 September 06:00 UTC
 

Other satellite-based instruments can be used to investigate different characteristics of hurricanes, and their impacts. Instruments with visible channels, such as the Sea and Land Surface Temperature Radiometer (SLSTR), and the Ocean and Land Colour imager (OLCI), both on board the Sentinel-3 satellite, can be used to capture images of the hurricane (see Figure 2 for an example night time pass).

The RGB image from SLSTR during the night on the 2 September (Figure 2) shows hurricane Dorian over the Bahamas. The overlaid data from the STM(SRAL) shows higher than normal sea levels (red), and high waves (indicated by larger bubbles).

Figure 2
 
Figure 2: Sentinel-3B SLSTR level 1 RGB, with overlaid SRAL derived SSHA and SWH, 2 September 02:53 UTC
 

SLSTR can also be used to measure the sea surface temperature in the potential path of the hurricane, which is vital for models that seek to predict how the storm will evolve.

The Sentinel-3 surface topography mission (STM), including the Synthetic Aperture Radar altimeter (SRAL) provides data on the sea surface height (SSH) and roughness which can be used to derive products including SSH anomalies, significant wave heights (SWH) and wind speeds.

Overlaying this along-track data on top of the swath RGB images provided by SLSTR (Figure 2) and OLCI (Figure 3), helps identify the storm surge preceding the storm, and high waves associated with the strongest winds (closer to the centre). This data is incorporated into many numerical weather prediction models.

Figure 3
 
Figure 3: Sentinel-3A OLCI level 1 RGB, with overlaid SRAL derived SSHA and SWH, 2 September 15:29 UTC
 

The image from OLCI during the night on 2 September 2019, shows hurricane Dorian moving towards the coast of the United States of America. Again the overlaid data from the SRAL shows sea level anomalies (increasingly red), and waves (indicated by larger bubbles).

For complex phenomena such as hurricanes, and their impacts, the use of multiple data sources can drastically improve prediction and monitoring of their development, and to help mitigate impacts.

No one sensor can do everything, but together they can provide a much fuller picture. The SLSTR and OLCI instruments can provide visible imagery of the clouds associated with hurricanes, but cannot provide their key derived products (sea surface temperature and ocean colour respectively), through clouds. The surface topography mission aboard Sentinel-3 is based on SRAL, an active instrument sending microwave pulses through the clouds to measure the surface topography.

Figure 4 shows the effects of this on the chlorophyll-a products derived from an OLCI image. The hurricane's classic shape is clearly visible in the cloud mask (white) while the altimeter is able to provide data as it passes over. Measurements of visible light, collected by the OLCI sensor on Sentinel-3 can be used to derive chlorophyll-a concentration in the ocean (indicative of photosynthesis by plant-like microorganisms). The hurricane is flagged in this product as cloud (white).

Figure 4
 
Figure 4: Sentinel-3B OLCI level 2 chlorophyll neural network product, overlaid with SRAL derived SSHA and SWH, 3 September 15:16 UTC
 
 
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