An Objective Quality Control of Surface Contamination Observations for ABI Water Vapor Radiance Assimilation

A quality control (QC) process which handles surface impacts is an important step toward successful assimilation of the Advanced Baseline Imager (ABI) water vapor (WV) band radiances. If the QC is too relaxed, many surface contaminated radiances get assimilated. If the QC is too stringent, useful radiances are rejected. Either way can result in reduced or even compromised observation impacts. A new machine learning‐based QC scheme for the three ABI WV bands is developed and optimized to help understand the importance and effectiveness of the scheme. Unlike previous schemes which are dependent on the background, this scheme extracts and blends the surface information from 7 ABI bands (bands 8–10, 13–16) to determine if a WV radiance is affected by the surface. Simulation studies show that the new QC scheme is effective in retaining radiances that are either unaffected by the surface or have very small surface contamination. It is highly effective in rejecting radiances with large surface contamination. Numerical experiments from a single case study of Hurricane Harvey (2017) were carried out to optimize the QC and to understand the potential impacts on forecasts. The use of the new QC scheme shows that radiances from each WV band have substantially added value. Combining them has a positive impact on hurricane track forecasts compared with existing QC schemes. Hence, it is critical that an optimized QC scheme is used for infrared WV radiance assimilation. It provides a balance between positive impacts from useful radiances and negative impacts from surface contaminated radiances. Plain Language Summary Surface affected (contaminated) infrared water vapor radiances are usually not assimilated into numerical weather prediction models, or are assimilated with reduced observation weights due to negative impacts on the analysis and forecast. The existing quality control schemes to find such radiances are not objective because they rely on background field. The quality control affects both the quantity (the number of observations) and the quality (the cleanness of the observations) of the radiances for assimilation. An objective quality control provides optimized balance between the quantity and quality to maximize the observation impacts. A machine learning‐based quality control scheme is developed and used to understand the importance and effectiveness of optimized quality control. It is objective because it relies only on observations. Simulation studies show