Open Access
28 May 2022 Altitude-dependent probability of vertical, cloud-free line-of-sight from European Centre for Medium Range Weather Forecasting Re-Analysis-interim cloud cover
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Abstract

A method to calculate the altitude-dependent, vertical, cloud-free line-of-sight (CFLOS) using the fractional cloud cover from the European Centre for Medium Range Weather Forecasting Re-Analysis Interim (ERA-I) dataset has been developed. This method enables users of airborne and satellite collections of optical ground data to understand the statistical coverage limitations of these collection systems by informing them of global probabilities of CFLOS versus altitude as well as time of year. This method is accurate for regions between ±60  deg of latitude; it should not be applied to polar regions due to limitations in the underlying ERA-I data. Our CFLOS calculations have been compared to the results of CloudSAT/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation analysis and with Moderate Resolution Imaging Spectroradiometer (MODIS) total cloud cover data. It is shown that the ERA-I reports on average less cloud cover by about 7.5% (absolute) for regions within   ±  60 deg of the equator relative to MODIS cloud cover retrievals. Our CFLOS calculation leverages the resolution and diversity of ERA-I that enables spatial coverage as well as frequency of occurrence CFLOS calculations for nearly all non-polar regions on earth.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Adam H. Willitsford, Gregory Hicks, and Walt Bowen "Altitude-dependent probability of vertical, cloud-free line-of-sight from European Centre for Medium Range Weather Forecasting Re-Analysis-interim cloud cover," Journal of Applied Remote Sensing 16(2), 028502 (28 May 2022). https://doi.org/10.1117/1.JRS.16.028502
Received: 2 November 2021; Accepted: 13 May 2022; Published: 28 May 2022
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Cited by 1 scholarly publication.
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KEYWORDS
Clouds

MODIS

Satellites

Weather forecasting

Data modeling

Spatial resolution

Infrared imaging

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