GOES-16 ABI and collocated SNPP-VIIRS imagery for evaluating the emulation of daytime cloud products at night

This dataset contains the testing set for Emulating Daytime ABI Cloud Optical Properties at Night with Machine Learning, currently in preparation for peer review. These files contain the information needed for reproducing the analysis and all figures in the manuscript. These files primarily contain Advanced Baseline Imager (ABI) brightness temperatures for 9 channels, cloud properties from the NOAA operational products, cloud optical depth, cloud effective radius, and derived cloud water path, including accompanying estimates from the machine learning emulator. Daytime evaluation files are for testing the effectiveness of a machine learning emulator during the daytime. Twilight files are for illustrating the impact of the day/night terminator on cloud water path distributions. Nighttime files are for evaluating the machine learning emulator in the target domain by comparing to a physical retrieval using lunar reflectance on the Visible Imaging Infrared Radiometer Suite (VIIRS) Day/Night..., All ABI brightness temperatures are obtained from the NOAA Comprehensive Large Array Data Stewardship System (CLASS; www.aev.class.noaa.gov). For the daytime dataset, cloud properties are obtained using the Clouds for AVHRR Extended (CLAVR-x; https://cimss.ssec.wisc.edu/clavrx/documentation/) software. For the twilight dataset, NOAA operational cloud properties, daytime and nighttime cloud properties, are obtained from CLASS. For the nighttime dataset, Suomi-NPP VIIRS data is obtained from CLASS, processed through CLAVR-x, and collocated to ABI imagery using nearest-neighbor resampling.In this dataset, we define daytime observations as having a solar zenith angle less than 82 degrees. Twilight observations have a solar zenith angle greater than 82 degrees and less than 90 degrees. Nighttime observations have a solar zenith angle greater than 90 degrees. Machine learning emulators are trained to match the Daytime Cloud Optical and Microphysical Properties (DCOMP) algorithm during the day..., , # GOES-16 ABI and collocated SNPP-VIIRS imagery for evaluating the emulation of daytime cloud products at night

[https://doi.org/10.5061/dryad.gf1vhhmz6](https://doi.org/10.5061/dryad.gf1vhhmz6)

## Description of the data and file structure

There are four sets of .tar data archives within this repository: Daytime, Twilight, Nighttime, and ground-based evaluation data. A fifth .tar file contains files needed for running the four machine learning emulators.

The first three files primarily contain GOES-16 Advanced Baseline Imager (ABI) brightness temperatures for 9 channels, cloud properties from the NOAA operational products, including cloud optical depth, cloud effective radius, and derived cloud water path. They also include accompanying estimates from a machine learning emulator. Daytime evaluation files are used for testing the effectiveness of the machine learning emulator during the daytime. Twilight files are used for illustrating the impact of the day/night terminator on clou...,