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From today’s inbox: This morning was interesting because I saw very little indication in the vis channel or microphysics RGBs to indicate fog was present over far SW Lower MI this morning. Any thoughts why? When fog is difficult to view in satellite imagery, as on 2 July 2022, it’s... Read More
GOES-16 IFR Probability (top) and GOES-16 Night Microphysics RGB (bottom), both with surface observations of ceilings and visibilities, 0551 – 0956 UTC on 2 July 2022 (click to enlarge)
From today’s inbox: This morning was interesting because I saw very little indication in the vis channel or microphysics RGBs to indicate fog was present over far SW Lower MI this morning. Any thoughts why? When fog is difficult to view in satellite imagery, as on 2 July 2022, it’s usually because the fog is very thin (but even a very thin fog can cause transportation issues). It might also be that thin cirrus is obscuring the satellite view of low-level fog. The animation above shows some indication of high cirrus (as purple features). Note however, that IFR Probability fields (on top) do highlight a region over southern lower Michigan near station KIRS (Kirsch, MI, near Sturgis). This is, however, after the station there started to report IFR conditions — but before a signal appeared in the Night Microphysics RGB. In cases when satellite detection might not identify fog, numerical prediction fields (such as those used in the computation of IFR Probability) can give a forecaster an earlier alert on the presence of fog.
The animation below, from the CIMSS CSPP Geosphere site, shows the region through sunrise. The low clouds burn off quickly after sunrise, reinforcing the idea that they were very thin.
Night Microphysics RGB (nighttime) and True-Color imagery (daytime) 0601 – 1231 UTC on 2 July 2022
Thanks to TJ Turnage, WFO Grand Rapids, for the alert about this challenging case. When satellite detection isn’t working, webcams and surface observations still do. Some imagery in this post was created using the NOAA/TOWR-S Cloud instance of AWIPS.
GOES-17 (GOES-West) True Color RGB images created using Geo2Grid (above) showed a widespread pall of smoke covering much of southern Alaska — from above-normal fire activity during the preceding several days, amid drought conditions — along with the growth of new smoke plumes from many of the larger fires later in... Read More
GOES-17 True Color RGB images [click to play animated GIF | MP4]
GOES-17 (GOES-West) True Color RGB images created using Geo2Grid(above) showed a widespread pall of smoke covering much of southern Alaska — from above-normal fire activity during the preceding several days, amid drought conditions — along with the growth of new smoke plumes from many of the larger fires later in the day on 29 Jun 2022. This dense smoke was restricting the surface visibility at numerous sites, creating hazards to aviation and poor air quality for inhabitants.
GOES-17 True Color RGB images [click to play animated GIF | MP4]
On the following day, GOES-17 True Color RGB images (above) showed an increasing trend in the areal coverage of smoke — and also revealed the marked re-intensification of a 40-50 mile long line of the combined Koktuli River and Pike Creek fires in southwestern Alaska (northwest of Iiamna Lake).
A closer view of the Koktuli River and Pike Creek fire line is shown below, using 1-minute Mesoscale Sector GOES-17 “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm), Day Cloud Land Fire RGB and Fire Temperature RGB images. This was reportedly the largest wildfire complex in that area of Alaska in the past 70 years.
GOES-17 “Red” Visible (0.64 µm, top left), Shortwave Infrared (3.9 µm, top right), Day Cloud Land Fire RGB (bottom left) and Fire Temperature RGB (bottom right) images [click to play animated GIF | MP4]
The dense smoke plume was transported northwestward toward the Seward Peninsula, where it contributed to very poor air quality in Nome on the following day.
This post explores data-filling methods for missing data in a GOES-17 Level 1B radiance time series dataset.Background: SSEC’s Data Center is home to a near complete record of GOES-16 and GOES-17 Full Disk Level 1B data. With these data capabilities, it is possible to compile a time series or ‘short climatology’ of GOES data... Read More
This post explores data-filling methods for missing data in a GOES-17 Level 1B radiance time series dataset.
Background: SSEC’s Data Center is home to a near complete record of GOES-16 and GOES-17 Full Disk Level 1B data. With these data capabilities, it is possible to compile a time series or ‘short climatology’ of GOES data over a given location. In examples shown here, Pago Pago has been chosen. (Pago Pago, the capital of American Samoa, holds significance due to its data sparseness. Weather forecasting and nowcasting in remote Pacific regions such as Pago Pago are heavily reliant on satellite data because of a lack of ground-based observations.)
Long-term purpose: The purpose of building a continuous satellite time series is to perform a time series analysis, i.e. to determine any periodicity or patterns in the data.
Short-term goal: The goal is to build a continuous, gap-free, time record for GOES-17 Band 13 radiance over Pago Pago from 2019-2021. Brightness temperature is computed from radiance. Compiling the existing data is trivial, but when data is missing, it can present problems. The data must be gap-free for a time series analysis. For the GOES-17 Level 1 record from January 1, 2019 – December 31, 2021, 1.14% of radiance data is missing. Some gaps are as large as 31 hours.
Example 1 of missing GOES-17 data. There is a gap between 05-Jan-2019 03:30Z and 06-Jan-2019 09:45Z, corresponding to about 30 hours of missing brightness temperature data. [click to enlarge]. Example 2 of missing GOES-17 data. There is a gap between 22-Jul-2021 05:15Z and 23-Jul-2021 13:00Z, corresponding to about 31 hours of missing brightness temperature data [click to enlarge].
Approach: To build a continuous data record, data gaps must be identified and filled. The original raw data, which exists as frequently as every ten minutes, is first sampled at every fifteen minutes. Then, it is smoothed using a moving mean method. The moving mean computes a mean over a sliding window of length k. As a last step, the smoothed data are run through a shape-preserving piecewise cubic spline interpolation (nicknamed ‘pchip’ interpolation) to fill remaining gaps [1].
Results: In examples below, we investigate samples of the time series for k = 6 hours, 12 hours, 24 hours, and 48 hours over the two example gaps: January 2019 and July 2021. As the data is smoothed, precision is lost when comparing to the original time series. However, smoother data also results in a more “natural” looking interpolation. For example, when examining the 48-hr smoothed data [red lines in panels (d)], the data hardly looks interpolated.
Brightness temperature is smoothed and interpolated over missing sections during the January 2019 example. Panels (a, b, c, d) correspond to smoothing the data with window length k = 6 hours, 12 hours, 24 hours, and 48 hours, respectively. Such that progressing from panels (a) through (d), the data is ‘smoother,’ that is, smoothed over a longer period of time [click to enlarge].Brightness temperature is smoothed and interpolated over missing sections during the July 2021 example. Panels (a, b, c, d) correspond to smoothing the data with window length k = 6 hours, 12 hours, 24 hours, and 48 hours, respectively. Such that progressing from panels (a) through (d), the data is ‘smoother,’ that is, smoothed over a longer period of time [click to enlarge].
How smoothed must the data be for the time series to look convincingly ‘real’ after interpolation? And will the data smoothing and loss of precision affect results from a time series analysis? These questions remain unanswered.
GOES-18 images in this blog post are preliminary and non-operational GOES-18 “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.35 µm) images (above) showed the formation of a small pyrocumulonimbus (pyroCb) cloud — generated by a wildfire that was burning near the northwest coast of Great... Read More
GOES-18 “Red” Visible (0.64 µm, top), Shortwave Infrared (3.9 µm, center) and “Clean” Infrared Window (10.35 µm, bottom) images, which include hourly plots of surface reports [click to play animated GIF | MP4]
GOES-18 images in this blog post are preliminary and non-operational
GOES-18 “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.35 µm) images (above) showed the formation of a small pyrocumulonimbus (pyroCb) cloud — generated by a wildfire that was burning near the northwest coast of Great Slave Lake in the Northwest Territories of Canada — on 24 June 2022. The pyroCB cloud then drifted southeastward across the lake, toward Resolute Bay (CYFR). Incidentally, this was Canada’s first documented pyroCb of the 2022 wildfire season.
Suomi-NPP VIIRS Visible (0.64 µm), Shortwave Infrared (3.74 µm) and Infrared Window (11.45 µm) images valid at 1930 UTC, with plots of surface reports [click to enlarge]
Suomi-NPP VIIRS Visible (0.64 µm), Shortwave Infrared (3.74 µm) and Infrared Window (11.45 µm) images valid at 1930 UTC (above) showed the pyroCb shortly after it formed (when it exhibited a minimum cloud-top 11.45 µm infrared brightness temperature of -49C, lighter shades of red) — and Suomi-NPP VIIRS images valid at 1930 UTC (below) displayed the pyroCb over Great Slave Lake at 2110 UTC (when it exhibited a minimum cloud-top 11.45 µm infrared brightness temperature of -54C, darker shades of violet).
Suomi-NPP VIIRS Visible (0.64 µm), Shortwave Infrared (3.74 µm) and Infrared Window (11.45 µm) images valid at 2110 UTC, with plots of surface reports [click to enlarge
