Building a continuous GOES-17 brightness temperature time series over a single point

June 28th, 2022 |

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.┬áThe 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.