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Cyclone Gillian in the Indian Ocean

McIDAS images of MTSAT-2 10.8 µm IR channel data (above; click image to play animation) showed the southward motion of Cyclone Gillian as it intensified over the far eastern Indian Ocean to Category 5 intensity on 23 March 2014 (Read More

MTSAT-2 10.8 µm IR channel images (click to play animation)

MTSAT-2 10.8 µm IR channel images (click to play animation)

McIDAS images of MTSAT-2 10.8 µm IR channel data (above; click image to play animation) showed the southward motion of Cyclone Gillian as it intensified over the far eastern Indian Ocean to Category 5 intensity on 23 March 2014 (Joint Typhoon Warning Center advisory). A Category 5 tropical cyclone in the Southern Hemisphere (and in the Indian Ocean basin) is a relatively rare event.

An MTSAT-2 IR image from the CIMSS Tropical Cyclones site with an overlay of Metop ASCAT surface scatterometer winds at 14:20 UTC (below) showed the tight radius of high winds around the center of circulation.

MTSAT-2 10.8 µm IR image with Metop ASCAT surface scatterometer winds

MTSAT-2 10.8 µm IR image with Metop ASCAT surface scatterometer winds

MTSAT-2 0.675 µm visible channel images (below; click image to play animation) revealed the formation of a well-defined eye on 23 March.

MTSAT-2 0.675 µm visible channel images [click to play animation]

MTSAT-2 0.675 µm visible channel images [click to play animation]

A plot of the Advanced Dvorak Technique (ADT) satellite-based intensity estimate (below) showed the period of rapid intensification on 23 March, with tropcial cyclone Gillian reaching its peak intensity late on 23 March.

Advanced Dvorak Technique (ADT) plot for Cyclone Gillian

Advanced Dvorak Technique (ADT) plot for Cyclone Gillian

A comparison of MTSAT-2 10.8 µm IR channel data and DMSP SSMIS-16 85 GHz microwave brightness temperature data (below) demonstrated the ability of microwave imagery to show important storm details (such as the closed eyewall, and curved spiral bands) that might be obscured by clouds on conventional IR images.

MTSAT-2 10.8 µm IR image and DMSP SSMIS-16 85 GHz microwave brightness temperature image

MTSAT-2 10.8 µm IR image and DMSP SSMIS-16 85 GHz microwave brightness temperature image

The MIMIC Total Precipitable Water (TPW) product (below; click image to play animation) showed the circulation of high TPW values as Cyclone Gillian began to move southward from Indonesia on 21 March. As the tropical cyclone began to encounter an environment of increasing vertical wind shear poleward of about 20º S latitude, the storm began to rapidly decrease in intensity — and on 26 March Gillian was downgraded to a tropical low.

MIMIC Total Precipitable Water product (click to play animation)

MIMIC Total Precipitable Water product (click to play animation)

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Fix for GOES-13 Sounder Pixel Drop-outs

The GOES-13 (GOES-East) Sounder instrument has been experiencing data anomalies that manifest themselves as missing pixels (link, link). These errors occurred because of slight fluctuations in the speed of the sounder filter wheel, resulting in a time offset. Processing software expects the data to be present... Read More

GOES-13 Sounder 10.7 µm imagery without and with Processing Software changes (click to enlarge)

GOES-13 Sounder 10.7 µm imagery without and with Processing Software changes (click to enlarge)

The GOES-13 (GOES-East) Sounder instrument has been experiencing data anomalies that manifest themselves as missing pixels (link, link). These errors occurred because of slight fluctuations in the speed of the sounder filter wheel, resulting in a time offset. Processing software expects the data to be present at a certain time, but because of the filter wheel speed fluctuations, data were not present when expected. All 19 spectral bands on the GOES-13 Sounder were affected.

Missing data can now be reclaimed using a modified version of the SPS (Sensor Processing System), the ground software that makes the GVAR data stream. GOES Engineers have been testing this software change. The test software modifications properly handle the slight differences in the timing of the data. As of this time, a date has not been slated for operational implementation by NOAA NESDIS.

An example with all 19 bands of the GOES-13 Sounder is shown below, with the current pixel drop-outs (top) and after the software changes were applied (bottom) as part of off-line testing (image toggle).

GOES-13 Sounder imagery (all 19 bands) [click to enlarge]

GOES-13 Sounder imagery (all 19 bands) [click to enlarge]

GOES-13 Sounder imagery (all 19 bands) produced with new processing software [click to enlarge]

GOES-13 Sounder imagery (all 19 bands) produced with new processing software [click to enlarge]

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Landsat-8 images of Washington State landslide site

Kudos to Russ Dengel of the SSEC RealEarth web map server development team for spotting this: a relatively cloud-free overpass of the Landsat-8 satellite which revealed the site of the massive landslide/mudslide near the small town... Read More

Landsat-8 0.59 µm panochromatic visible image of the Washington State landslide site

Landsat-8 0.59 µm panochromatic visible image of the Washington State landslide site

Kudos to Russ Dengel of the SSEC RealEarth web map server development team for spotting this: a relatively cloud-free overpass of the Landsat-8 satellite which revealed the site of the massive landslide/mudslide near the small town of Oso in northwestern Washington State (north of Seattle). The animation shown above was made using RealEarth to zoom in with the Google Maps base layer, and then toggle between the base map and an overlay of 15-meter resolution Landsat-8 0.59 µm (Band 8) panochromatic visible imagery at 19:03 UTC or 12:03 PM local time on 23 March 2014. It can be seen that debris from the landslide — which occurred a day earlier — covered one mile of State Road 530, cutting off access to the town of Darlington (located east of the landslide site); it also blocked the North Fork of the Stillaguamish River, leading to fears of localized flooding both upstream and downstream of the landslide site.

The Landsat-8 visible image is shown below. The landslide was blamed on ground saturation due to heavy rainfall in the region over the past month (30-day total rainfall | depature from normal).

Landsat-8 0.59 µm panochromatic visible image

Landsat-8 0.59 µm panochromatic visible image

===== 01 April Update =====

Landsat-8 0.59 µm panochromatic visible image on 01 April

Landsat-8 0.59 µm panochromatic visible image on 01 April

14 days later, there was another overpass of the Landsat-8 satellite; it could be seen on the 0.59 µm panochromatic visible image (above) that the mudslide still covered a significant portion of State Road 530. The 1.61 µm near-IR image (below) revealed that the mudslide had altered the course of the North Fork of the Stillaguamish River, and highlighted areas where some localized flooding was occurring due to a widening of the river (water is a strong absorber at the 1.61 µm wavelength, so it appears dark on the near-IR image).

Landsat-8 1.61 µm near-IR image on 01 April

Landsat-8 1.61 µm near-IR image on 01 April

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Suomi NPP VIIRS Sea Surface Temperature product flowing into AWIPS

Suomi NPP VIIRS data are being used to compute sea-surface temperature (SST), and those fields are now being input into AWIPS for evaluation. The Suomi NPP images as displayed in AWIPS are labeled as MODIS SSTs (this was done to speed the injection process into AWIPS; however, note that the... Read More

Suomi NPP Sea Surface Temperature product, 0727 UTC 21 March 2014

Suomi NPP Sea Surface Temperature product, 0727 UTC 21 March 2014

Suomi NPP VIIRS data are being used to compute sea-surface temperature (SST), and those fields are now being input into AWIPS for evaluation. The Suomi NPP images as displayed in AWIPS are labeled as MODIS SSTs (this was done to speed the injection process into AWIPS; however, note that the labels on VIIRS images for this blog post were modified to display the correct satellite source). Even though the labels are the same (both MODIS and VIIRS products are labeled “MODIS Sea Sfc Temperature” in AWIPS), the data sources are different, and the user can learn to identify the data being used.

A user can match the time of the image to overpass times for the Aqua, Terra or Suomi NPP satellites. Overlaying a different Suomi NPP image, for example 11.45 µm IR brightness temperature, below, that covers the same geographic region will also tell the user which data source — from VIIRS or MODIS — is being used to construct the SST product.

VIIRS-based SSTs and 11.45 µm IR brightness temperatures (click to enlarge)

VIIRS-based SSTs and 11.45 µm IR brightness temperatures (click to enlarge)

A MODIS-based SST from approximately the same time is shown below. The VIIRS swath is much wider than the MODIS swath. This will always be true.

MODIS and VIIRS-based SSTs at ~0805 UTC 20 February 2014 (click to enlarge)

MODIS and VIIRS-based SSTs at ~0730 UTC 21 March 2014 (click to enlarge)

Values for MODIS SSTs and VIIRS SSTs are similar. In general, the cloud-clearing with Suomi NPP VIIRS is more accurate, meaning there are more clear pixels with the Suomi NPP data and therefore more SST pixels. Note in particular differences in the strong temperature gradient along the edges of the Gulf Stream where MODIS algorithms mistakenly flag pixels as cloudy.

[Added: This animation cycles through the SSTs and the Window Channel IR images from both VIIRS and MODIS]

2 days earlier, a nighttime/daytime Suomi NPP VIIRS SST comparison on 19 March — magnified to provide a closer look at the Gulf of Mexico (below) — revealed intricate structure associated with the Loop Curent (the large darker red feature, with SST values around 80º F), as well as other small-scale eddys in the surrounding Gulf waters.

Suomi NPP VIIRS SST images at 08:03 UTC and 19:27 UTC on 19 March

Suomi NPP VIIRS SST images at 08:03 UTC and 19:27 UTC on 19 March

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