There are a growing number of advanced imagers for geostationary meteorological satellites, which can provide water vapor radiance observations with high temporal and spatial resolutions. To assess the impact of those imagers, radiance assimilation experiments were conducted with the Advanced Baseline imager (ABI) on board the Geostationary Operational Environmental Satellite‐16. The radiances from the three water vapor absorption bands of Geostationary Operational Environmental Satellite‐16 ABI were assimilated through the National Oceanic and Atmospheric Administration Gridpoint Statistical Interpolation data assimilation system in a regional numerical weather prediction (NWP) model. The forecast impacts for Hurricane Irma (2017) and Hurricane Harvey (2017) have been studied and analyzed in this work. Due to complicated surface situations (emissivity, terrain height, etc.) over land, the infrared (IR) radiance assimilation is still limited; thus, handling surface effects in radiance assimilation needs to be considered. By analyzing the Jacobian function of skin temperature in the ABI radiance assimilation process, it is shown that assimilating water vapor IR radiances over high elevation surfaces or in dry regions is problematic even where the bands are mostly sensitive to the upper level of the atmosphere such as Band 8 (6.19 μm). Additional quality control steps using skin temperature Jacobians to eliminate the contamination from the surface impact are developed and added for ABI radiance assimilation. The results show that ABI radiance assimilation with quality controls is able to improve tropical cyclone forecasts. The methodology used in this study can be applied to the assimilation of IR radiances from other geostationary satellites or polar‐orbiting satellites.
A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 µm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 µm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 µm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 µm and 10.8 µm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 µm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds.
Hurricanes are among the most destructive natural hazards, with strong asymmetries in deep convection in the eyewall and the spiral rainbands (
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.