GOES-17 visible and infrared imagery shows Tropical Storm Estelle over the eastern Pacific to the west-southwest of Baja California. Although there are regions of strong convection, satellite presentation of the storm suggests a modest tropical storm. Center-fixing a storm such as this (with just one image vs. an animation!) is complicated by both parallax (GOES-17 is overhead at 0^oN, 137.2^oW) and the lack of an easily-discerned eye feature in this system.

Instruments that view surface winds, via Scatterometry or via Synthetic Aperture Radar (SAR), can offer better center fixes. Consider the toggle below of Radarsat-2 SAR winds over Estelle. SAR Winds (available here; SAR winds over Tropical Systems can be found here.) show a lopsided storm, with most of the strong winds on the poleward side of the center. The SAR winds that use 0.5-degree GFS wind data as a first guess show a characteristic hourglass feature near the center that advertises an ambiguity between the first guess winds and the observations. That feature is missing in the SAR wind field that is a product of the 0.25-degree GFS winds, suggesting that the 0.25-degree wind field is more accurate. Both fields show a rather baggy center at or just south of 20^oN.

Scatterometry from MetopB and HY2B between 1730 UTC 19 July and 0230 UTC 20 July, respectively show a storm moving west-northwest, passing north of 20^oN just after 0230 UTC.

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GOES-18 images shown in this blog post are preliminary and non-operational

GOES-18 Mid-level Water Vapor (6.9 µm) images during the 18 July – 19 July 2022 period (above) showed a series of impulses rotating within the broader circulation of an anomalously-deep low pressure system that meandered over the Bering Strait region. Anomalously-cold air associated with this deep low helped to produce brief periods of unusual July snow at some locations across the Seward Peninsula and northwestern Alaska.

In GOES-18 Air Mass RGB images created using Geo2Grid (below), brighter shades of red highlighted the core of this broad low pressure system, where high-altitude ozone levels were elevated (due to an unusually low tropopause).

In fact, at 12 UTC on 19 July the low 500 hPa geopotential height value of 5269.3 meters from the Nome, Alaska rawinsonde report (above) established a new July record for that site. The 12 UTC sounding also suggested that the tropopause was located at an unusually low pressure level of 483 hPa — such a low tropopause height was supported by NOAA-20 Gridded NUCAPS data from the SPoRT site (below).

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GOES-16 (GOES-East) “Red” Visible (0.64 µm), Shortwave Infrared (3.9 µm) and “Clean” Infrared Window (10.35 µm) images (above) showed that a large wildfire burning in far western Manitoba produced a series of 3 brief pyrocumulonimbus (pyroCb) clouds late in the day on 19 July 2022. The coldest pyroCb cloud-top infrared brightness temperature was -52.1ºC at 0230 UTC.

GOES-18 images shown in this blog post are preliminary and non-operational

In the corresponding imagery from GOES-18, the coldest pyroCb cloud-top infrared brightness temperature was -53.3ºC at 0230 UTC.

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In a previous post, we explored a data interpolation technique that involved running sections of missing GOES-17 Band 13 data through a shape-preserving piecewise cubic spline method in order to fill the data gaps for brightness temperature (BT). (That method was nicknamed ‘pchip’ interpolation.) In this post, we introduce a more advanced type of interpolation for data filling known as interpolation by Principal Component Analysis, or PCA interpolation. One benefit of PCA interpolation is that data does not need to be smoothed to create a believable interpolation. It is, however, more computationally intense and the interpolation requires more time.

To test PCA interpolation, artificial gaps are created in portions of the complete GOES-17 time series data and compared for accuracy. The longest real gap in the GOES-17 time series is approximately 31 hours. Twenty artificial gaps of 31 hours are created in the time series and run through PCA interpolation. Examples are shown below.

Clearly from the examples above, the PCA interpolation does not replicate the original data. Comparing the trends by eye, the PCA interpolation does not seem to mimic the original data well. However, the difference between the true BT and the interpolated BT is computed and has a mean of 1.2032 Kelvin, which is fairly low. 51.4% of all tested retrievals yielded a difference of less than 10 Kelvin. That is, for more than half of the tested retrievals, the filled interpolation is within 10 Kelvin of the original value.

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