Advanced infrared sounder subpixel cloud detection with imagers and its impact on radiance assimilation in NWP

Abstract

Accurate cloud detection is very important for infrared (IR) radiance assimilation; improved cloud detection could reduce cloud contamination and hence improve the assimilation. Although operational numerical weather prediction (NWP) centers are using IR sounder radiance data for cloud detection, collocated high spatial resolution imager data could help sounder subpixel cloud detection and characterization. IR sounder radiances with improved cloud detection using Atmospheric Infrared Sounder (

Satellite-Based Atmospheric Infrared Sounder Development and Applications

“…Assimilation of CrIS data has become operational at some NWP centers (Collard et al 2012). Meanwhile, more observations from hyperspectral instruments have been used in some regional forecast models with positive impact on precipitation forecasts; examples include convective-scale models with a horizontal resolution of 1.5-2.5 km (Guidard et al 2011) and prediction models for hurricane tracking (Li and Liu 2009;Liu and Li 2010;Xu et al 2013;Wang et al 2015;Zheng et al 2015). As the NWP model vertical resolution continues to improve, the information from hyperspectral instruments is expected to become even more significant.…”

“…Errors arising when clouds are mistaken for clear sky or from observation data without an accurate description impede improvement in the assimilation analysis and produce insignificant improvement or even failure in the forecast (Wang et al 2014). In most of the current operational systems, the observation operators of the radiative transfer in the assimilation have difficulty in simulating cloud-affected radiation accurately.…”

“…In the latter, the cloud-cleared (or equivalent clear sky) IR sounder radiances for cloudy regions were estimated with additional information [either microwave sounders such as AMSU for AIRS/ IASI and ATMS for CrIS or imagers such as MODIS for AIRS, the Advanced Very High Resolution Radiometer (AVHRR) for IASI, and the Visible Infrared Imaging Radiometer Suite (VIIRS) for CrIS]. Assimilation of cloud-cleared radiances has been found to improve the accuracy of tropical storm-track forecasts (Wang et al 2015).…”

“…Similarly, the atmospheric stability index derived from AIRS data can also distinguish stable and unstable regions in the atmosphere, which is especially important for decreasing false alarms of severe convective storms. Furthermore, the track and intensity forecasts for typhoons/hurricanes can be improved when AIRS soundings at single field-of-view resolution are assimilated in a regional NWP (Li and Liu 2009;Liu and Li 2010;Wang et al 2015).…”

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

“…For instance, high resolution numerical weather prediction model results have been used to calculate temperature (T), relative humidity (RH), vertical velocity (VV), and supercooled liquid water (SLW) as input parameters in potential icing calculation algorithms [10,11]. However, numerical models often provide inaccurate results [12], which increase the uncertainty of potential icing clouds identified by the icing algorithms [13].…”