Details:

Journal Title:
Journal of Applied Meteorology and Climatology
Personal Author:
Sedlar, Joseph ; Riihimaki, Laura D. ; Lantz, Kathleen ; Turner, David D.
Sedlar, Joseph ; Riihimaki, Laura D. ; Lantz, Kathleen ; Turner, David D. Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; GML (Global Monitoring Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences)
OAR (Oceanic and Atmospheric Research) ; GSL (Global Systems Laboratory) ; GML (Global Monitoring Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) Less -
Description:
Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud-radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
Keywords:
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Cloud Forcing Cloud Radiative Effects Clouds Cover Isccp Meteorology & Atmospheric Sciences Pacific Program Radiative Fluxes Radiative Forcing Satellite
Source:
Journal of Applied Meteorology and Climatology, 60(4), 477-491
DOI:
https://doi.org/10.1175/JAMC-D-20-0153.1
Document Type:
Journal Article
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:204ec81d5857321e6a826ba0a291718f2d1bacb9755c405924dd4575de0ee2fa
Download URL:
https://repository.library.noaa.gov/view/noaa/30969/noaa_30969_DS1.pdf
File Type:

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