Journal Article

Sampling Bias From Satellite Retrieval Failures of Cloud Properties and Its Implications for Aerosol‐Cloud Interactions

Goren, Tom, Choudhury, Goutam

2025

Overview

Satellite radiometers like MODIS use a bi‐spectral retrieval algorithm to simultaneously retrieve cloud optical thickness and cloud effective radius re $\\left({r}_{\\mathrm{e}}\\right)$. However, retrievals fail for liquid clouds when the re ${r}_{\\mathrm{e}}$ observation exceeds the maximum threshold of 30 μ ${\\upmu }$m in MODIS's solution space, leading to a sampling bias. Here, we quantify this bias by reconstructing pixels with failed retrievals using two methods: a conservative approach assigning a fixed minimum re ${r}_{\\mathrm{e}}$ threshold to failed pixels, and a representative approach modeling failed re ${r}_{\\mathrm{e}}$ using CloudSat radar measurements. We show that MODIS overestimates cloud droplet number concentration by 8%–9% and underestimates liquid water path by 8%–11% globally. We demonstrate that this bias can introduce erroneous correlations between cloud properties that may be misinterpreted as causal processes. Accordingly, we show that accounting for this bias increases the cloud water adjustments by 24%–36%, highlighting the crucial need to expand the solution space in MODIS and similar sensors.

• Publisher: John Wiley & Sons, Inc
• Subject: Bias / Cloud droplet concentration / Cloud droplets / Cloud properties / Cloud water / Microwave imagery / MODIS / Optical thickness / Pixels / Radar measurement / Radiometers / Retrieval / Sampling / Satellites / Solution space / Water