Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales

Abstract

This study explores the structures of the correlations between infrared (IR) brightness temperatures (BTs) from the three water vapor channels of the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite and the atmospheric state. Ensemble-based data assimilation techniques such as the ensemble Kalman filter (EnKF) rely on correlations to propagate innovations of BTs to increments of model state variables. Because the three water vapor channels are sensitive to moisture in different layers of the troposphere, the heights of the strongest correlations between these channels and moisture in clear-sky regions are closely related to the peaks of their respective weighting functions. In cloudy regions, the strongest correlations appear at the cloud tops of deep clouds, and ice hydrometeors generally have stronger correlations with BT than liquid hydrometeors. The magnitudes of the correlations decrease from the peak value in a column with both vertical and horizontal distance. Just how the correlations decrease depend on both the cloud scenes and the cloud structures, as well as the model variables. Horizontal correlations between BTs and moisture, as well as hydrometeors, in fully cloudy regions decrease to almost 0 at about 30 km. The horizontal correlations with atmospheric state variables in clear-sky regions are broader, maintaining non-zero values out to ∼100 km. The results in this study provide information on the proper choice of cut-off radii in horizontal and vertical localization schemes for the assimilation of BTs. They also provide insights on the most efficient and effective use of the different water vapor channels.

Ensemble‐Based Assimilation of Satellite All‐Sky Microwave Radiances Improves Intensity and Rainfall Predictions for Hurricane Harvey (2017)

“…For these conditions, techniques like the ensemble Kalman filter (EnKF) rely on ensemble covariances to update the model state underneath the cloud tops. Unfortunately, these covariances are sometimes erroneous because of limited ensemble sizes (Y. Zhang, Clothiaux, & Stensrud, 2021; Y. Zhang, Stensrud, & Clothiaux, 2021).…”

Potential Impacts of Lightning Flash Observations on Numerical Weather Prediction With Explicit Lightning Processes

“…As shown by Y. Zhang et al (2021), the correlation characteristics of the IR radiances in the cloudy regions were independent of the choice of water vapor bands (Figure S1). These results indicate that the GLM observations provide valuable information below the cloud top, especially for hydrometeors and moisture, unlike the IR radiance observations.…”

“…(2018), IR radiances were calculated using a radiative transfer model, RTTOV 12.3 (Saunders et al., 2018). Previous studies showed that moisture‐sensitive bands such as the 6.2 and 6.9 μm are closely correlated with the atmospheric state (e.g., F. Zhang et al., 2016; Y. Zhang et al., 2021). We focused on a moisture‐sensitive band of 6.9 μm, which has been successfully assimilated by several previous studies (Honda, Kotsuki, et al., 2018; Honda, Miyoshi, et al., 2018; Honda et al., 2019).…”

“…Following and Honda, Kotsuki, et al (2018), IR radiances were calculated using a radiative transfer model, RTTOV 12.3 (Saunders et al, 2018). Previous studies showed that moisture-sensitive bands such as the 6.2 and 6.9 μm are closely correlated with the atmospheric state (e.g., F. Zhang et al, 2016;Y. Zhang et al, 2021).…”

Potential impact of all‐sky assimilation of visible and infrared satellite observations compared with radar reflectivity for convective‐scale numerical weather prediction

“…Furthermore, the vertical resolution is a weak spot of visible and infrared satellite observations. Different channels provide information about different atmospheric levels as shown by their correlation structures (Zhang et al ., 2022). For example, while the 6.2‐$$$ \upmu $$$m channel is mostly sensitive to upper tropospheric water vapor, the 7.3‐$$$ \upmu $$$m channel sees further down into the lower troposphere.…”