Direct Assimilation of All-Sky GOES-R ABI Radiances in GSI EnKF for the Analysis and Forecasting of a Mesoscale Convective System

Abstract

In this study, all-sky GOES-R ABI infrared radiances at their native resolution are assimilated using an enhanced GSI Ensemble Kalman Filter (EnKF) data assimilation (DA) system, and the impacts of the data on the analysis and forecast of a mesoscale convective system (MCS) are explored. Results show that all-sky ABI BT data can correctly build up observed storms within the model and effectively remove spurious storms in model background through frequent DA cycles. Both bias and root-mean-squared innovation of the background and analysis are significantly reduced during the DA cycles, and free forecasts are improved when verified subjectively and objectively against observed ABI BTs and independent radar reflectivity observations. A horizontal localization radius of 30 km is found to produce the best results while 5-minute DA cycles improve the storm analyses over 15-minute cycles, but the differences in forecasts are small. Further analyses show that the clearing of spurious clouds by ABI radiance is correctly accompanied by reduction in moisture through background error cross-covariance, but over-drying often occurs which can cause spurious storm decay in the forecast. The problem is reduced when the ensemble mean of observation prior instead of observation prior of the ensemble mean state is used in the ensemble mean state update equation of EnKF. The significant difference between the two ways that the ensemble mean of observation prior is calculated when the observational operator is very nonlinear has not been recognized in earlier cloudy radiance DA studies.

Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

“…In addition, in this study, the same cloud predictor ACA is used for both channels 9 and 10, so the development of a channel‐dependent all‐sky BC scheme can be considered in the future by using the corresponding CA as the cloud predictor for each channel, respectively. Moreover, the ensemble DA method (Johnson et al., 2022; Y. Wang et al., 2017; L. Zhu et al., 2023) or the hybrid ensemble‐variational DA method (Y. Wang et al., 2022) is expected to be employed in future work to provide the flow‐dependent background error covariances to improve all‐sky assimilation. The presence of cloud may lead to interchannel correlations (Bormann et al., 2011; Eyre et al., 2022; Geer, 2019) which are neglected in our all‐sky assimilation scheme.…”

“…Moreover, the ensemble DA method (Johnson et al, 2022;Y. Wang et al, 2017;L. Zhu et al, 2023) or the hybrid ensemble-variational DA method (Y.…”

“…Many efforts were made on observation error models (Harnisch et al., 2016; Okamoto, 2017) and assimilation approaches (Kumar & Shukla, 2019; Y. Wang et al., 2022) to improve the all‐sky IR data assimilation results. Positive impacts were proven on predictions of severe weather systems such as tropical cyclone (TC; Honda et al., 2018; Zhang et al., 2016), mesoscale convective systems (L. Zhu et al., 2023), and severe storm (D. Xu et al., 2021) when assimilating all‐sky IR radiances from geostationary satellites IR imagers. And Kumar et al.…”

“…Wang et al, 2022) to improve the all-sky IR data assimilation results. Positive impacts were proven on predictions of severe weather systems such as tropical cyclone (TC; Honda et al, 2018;Zhang et al, 2016), mesoscale convective systems (L. Zhu et al, 2023), and severe storm (D. Xu et al, 2021) when assimilating all-sky IR radiances from geostationary satellites IR imagers. And Kumar et al (2022) found all-sky assimilation experiments with IR imagers data from Indian geostationary satellites provided better forecast results than clear-sky assimilation experiments for short-range forecasts.…”

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

“…Otkin (2012aOtkin ( , 2012b pioneered direct assimilation using four channels between 6.2 and 8.5 μm, albeit at a resolution of 15 km that resolves deep convection only partly. In convection-permitting models, infrared observations had a positive analysis and forecast impact for the prediction of typhoons, mesoscale convective systems, and severe weather events under weak and strong large-scale forcing (Jones et al, 2015(Jones et al, , 2016(Jones et al, , 2020Honda et al, 2018;Sawada et al, 2019;Zhu et al, 2022;Eure et al, 2023). Comparing the assimilation of infrared and radar observations, Zhang et al (2019) found that assimilating infrared observations before storm initiation can advance the warning time for mesocyclones by several tens of minutes compared with assimilating radar observations only.…”