A Note on the Relation between the Cold-V Brightness Temperature Feature and the Above-Anvil Cirrus

Abstract Severe convective storms, including supercell thunderstorms, are known to produce distinctive features in satellite imagery, one of which is the cold (enhanced) “V” brightness temperature anomaly pattern. This feature is frequently used by weather forecasters to aid severe weather warnings and is often attributed to the above-anvil cirrus (AAC). However, multiple explanations of the cold-V feature have been proposed, and its relation to AAC continues to be debated. This note aims to clarify their relation, by using the satellite images synthesized from the high-resolution simulation of overshooting convective storms by the Global Environmental Multiscale model combined with the Moderate Spectral Resolution Transmittance radiative transfer model. It is found that most of the AAC are optically too thin to create the cold-V temperature contrast in the brightness temperature field. As the cloud body that contributes the most to satellite-measured radiance locates at the effective emission level, the cloud temperature at this level is found to best explain the brightness temperature features, with a spatial correlation generally exceeding 0.80. Therefore, the temperature inhomogeneity inside the anvil cloud, as opposed to the AAC, is found to be the cause of the cold-V feature. This finding cautions against the notion of a causality relation between the AAC and the cold-V feature and suggests they should be considered as separate evidence in interpreting the satellite images for severe weather forecasts. Significance Statement The cold-V feature in infrared satellite imagery is an important indicator used for severe weather warnings. It was believed that this pattern is caused by specific high-altitude clouds known as the above-anvil cirrus. However, our study suggests otherwise. We found that the variations in temperature within the main body of the anvil cloud, not the above-anvil cirrus, are actually responsible for this pattern. This is important because it changes how meteorologists interpret satellite images, potentially leading to more accurate weather forecasts. Our findings encourage further research into understanding cloud temperatures and their impact on weather prediction, which is vital for public safety and preparedness for extreme weather.