Investigation of Low-Cloud Characteristics Using Mesoscale Numerical Model Data for Improvement of Fog-Detection Performance by Satellite Remote Sensing

Abstract

The comprehensive relationship between meteorological conditions and whether low water cloud touches the surface, particularly at sea, is examined with the goal of improving low-cloud detection by satellite. Gridpoint-value data provided by an operational mesoscale model with integration of Multifunction Transport Satellite-2 data can provide sufficient data for statistical analyses to find general parameters that can discern whether low clouds touch the surface, compensating for uncertainty due to the scarcity of observation sites at sea and the infrequent incidence of fog. The analyses reveal that surface-touching low clouds tend to have lower cloud-top heights than those not touching the surface, although the frequency distribution of cloud-top height differs by season. The bottom of the G . G m layer (where G and G m are the vertical gradient and the moist-adiabatic lapse rate of the potential temperature, respectively) with surface-touching low-cloud layers tends to be very low or almost attached to the surface. In contrast, the tops of low-cloud layers not touching the surface tend to occur near the bottom of the G . G m layer. Mechanisms to correlate these meteorological conditions with whether low clouds touch the surface are inferred from investigations into the vertical structure of equivalent potential temperature. These results indicate that the temperature difference between cloud-top height and the surface can be an appropriate parameter to infer whether low clouds touch the surface. It is also suggested that only a little addition of meteorological ancillary data, such as the forecast sea surface temperature, to satellite data allows successful performance of the discrimination.

Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

“…Above all, this LSF PI can be readily presented on a 2D map. This 2D map is especially useful for the west coast of the country and the Yellow Sea, where the ground observations are rare, despite frequent fog occurrences [16,28].…”

“…However, this method was unable to essentially improve the fog detection at the time zone, because of the SNR limitation [8]. Indeed, most of the single-satellite LSF sensing methods was inaccurate at sunrise in previous studies [26][27][28].…”

A persistent wintertime fog episode at Lisbon airport (Portugal): performance of ECMWF and AROME models

“…In order to reduce the impacts of fog on air traffic at some airports, less computationally demanding statistical (Miao et al, 2012;Dutta and Chaudhuri, 2015) and 1-D (Bergot et al, 2005;Terradellas and Cano, 2007) models have been developed and used operationally to improve short-term forecasts of fog and low clouds. Recent improvements on fog/low cloud detection methodologies based on satellite remote sensing (Ishida et al, 2014;Wu and Li, 2014) can also significantly contribute to more accurate fog nowcasting.…”