Anomalously-deep upper low brings light snow to northwestern Alaska

July 19th, 2022 |

GOES-18 Mid-level (6.9 µm) Water Vapor images, with plots of hourly surface weather type [click to play animated GIF | MP4]

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).

GOES-18 Air Mass RGB images [click to play animated GIF | MP4]


Plots of rawinsonde data at Nome, Alaska at 00 UTC and 12 UTC on 19 July 2022 [click to enlarge]

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).

NOAA-20 Gridded NUCAPS Tropopause Height at 1236 UTC on 19 July [click to enlarge]

Pyrocumulonimbus clouds in Manitoba

July 19th, 2022 |

GOES-16 “Red” Visible (0.64 µm, top), Shortwave Infrared (3.9 µm, center) and “Clean” Infrared Window (10.35 µm, bottom) images [click to play animated GIF | MP4]

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.

GOES-18 “Red” Visible (0.64 µm, top), Shortwave Infrared (3.9 µm, center) and “Clean” Infrared Window (10.35 µm, bottom) images [click to play animated GIF | MP4]

Testing an interpolation technique for building a continuous GOES-17 data series

July 19th, 2022 |

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.

Example 1 of PCA-filled data (red) compared to the original true data (blue) for 31 hours of data on May 22, 2020. The red line is an interpolation or ‘educated guess’ of the blue line values, using Principal Component Analysis.
Example 2 of PCA-filled data (red) compared to the original true data (blue) for 31 hours of data on July 8, 2020. The red line is an interpolation or ‘educated guess’ of the blue line values, using Principal Component Analysis.

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.

A distribution of the differences between interpolated BT and true BT for the retrievals tested.