Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology

The global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology with six cloud types (Solid stratus, Closed MCC, Open MCC, Disorganized MCC, Clustered Cu and Suppressed Cu). The spatial resolution is 1o × 1o and the temporal resolution is 5 minutes for the years 2018-2022. They were established based on a deep learning model ResNet-50. Trained on daytime radiance data from MODIS (Moderate Resolution Imaging Spectroradiometer) and daytime retrieved COT (Cloud Optical Thickness), this model achieved a high prediction accuracy and can be applied to nighttime cloud classification. For a detailed introduction to the model, please refer to our article: Technical info Dataset information day_2018_all.npz: Daytime classification of 2018 global mesoscale low-cloud morphology with a 1°×1° resolution date: time of the 1°×1° box, date format is the same as MODIS granule: '2018001.0025' lon: central longitude, ranging from -180 to 180 lat: central latitude, ranging from -60 to 60 cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu cert: model certainty, the probability that this cloud morphology belongs to the category low_cf: the cloud fraction of low clouds, in the unit of % COT_CNN: average cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al CER_CNN: average cloud effective radius (CER), retrieved using TIR-CNN model from Wang et al, in the unit of μm LWP_CNN: average cloud liquid path (LWP), calculated from COT_CNN and CER_CNN, in the unit of g/㎡ Sensor_zenith: average sensor zenith angle, in the unit of degree night_2018_all.npz: Nighttime classification of 2018 global mesoscale low-cloud morphology with a 1°×1° resolution same variables as daytime You can extract these variables in each file using the following python code: import numpy as np # Load the .npz file data = np.load('./night_2022_all.npz') keys = list(data.keys()) print(keys) date = data['date'] cat = data['cat'] ...