Observations & remote sensing

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

Zhenglong Li, Zheng Ma, Pei Wang, Agnes H. N. Lim, Jun Li, James A. Jung, Timothy J. Schmit, Hung‐Lung Huang

Journal of Geophysical Research Atmospheres · 18 July 2022 · NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), Institute of Atmospheric Physics, Chinese Academy of Sciences, China Meteorological Administration (CMA)

PaperPDF

In one sentence. Machine-learning quality control for ABI water vapor radiances blends seven bands to reject surface-contaminated pixels, improving Hurricane Harvey track forecasts.

Abstract

Abstract 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.

Labels

Topic

Observations & remote sensing, Data assimilation

Type

New model or method

Method

Classical ML (trees, GP, linear, kernels)

Time scale

6 h–3 days

Domain

Regional

Data

Satellite, NWP analysis or forecasts (IFS, GFS, ...)

Status

Research

Identifiers

DOI:10.1029/2021jd036061 OpenAlex:W4285802394

Added

2026-10-22

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