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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The animation above shows ACSPO estimates of Lake Surface Temperature (LST) over Lake Michigan from 3 different VIIRS overpasses: NPP at 0703 and 0844, and NOAA-20 at 0753 UTC (despite the AWIPS label!) early on 18 July 2022; VIIRS data were downloaded at the CIMSS Direct Broadcast antennae, and ACSPO SST fields were created using CSPP software; AWIPS-ready tiles are available via an LDM feed from CIMSS. Orange to red enhancement values show lake surface temperature at/above 20^oC/68^oF (the color bar limits are 41^oF/77^oF). Two NOAA buoys are also present within Lake Michigan, and both show lake temperatures of 69^oF; satellite values show far greater variability within the lake than can be deduced from just two buoy-based observations.

GOES-R data are used to create lake-surface (and sea-surface) temperatures. The toggle below over Green Bay and central Lake Michigan shows the superior spatial resolution of the VIIRS instrument. Over the open lake, GOES-R SSTs run about 1/2^oF warmer than SSTs from VIIRS.

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Similar imagery (also using Direct Broadcast data and created using CSPP software) was available slightly later off the coast of Oregon, as shown below. Three consecutive passes of VIIRS, at about 45-minute intervals, allow for changes in the surface temperature fields to be monitored with high spatial and temporal resolution.

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ACSPO SST (and individual channel, and true/false color) imagery is routinely available at the CIMSS direct broadcast site at this link for NOAA-20 and this link for NPP. You can also find True Color imagery over the Great Lakes, such as the image below from NOAA-20, posted for your viewing pleasure.

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

Overlapping 1-minute Mesoscale Sectors provided 30-second images of GOES-18 “Red” Visible (0.64 µm) data, which showed the circulation of a Mesoscale Convective Vortex (MCV) that moved northward across southern Nevada early in the day on 17 July 2022. Ongoing thunderstorms along the periphery of this MCV slowly dissipated as they moved across California and Nevada — and according the morning NWS Las Vegas forecast discussion, some of these storms produced rare July rainfall in parts of Death Valley, California.

According to NAM40 model winds, this MCV was moving through an environment of generally low wind shear (below), where there was also sufficient moisture and instability.

The GOES-18 Total Precipitable Water (source) derived product (below) also showed abundant moisture across much of the Desert Southwest as the MCV approached — which helped to sustain the MCV as it moved across southern Nevada.

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