The Advanced Baseline Imager (ABI) instrument on the geostationary National Oceanographic and Atmospheric Administration (NOAA) GOES-16 satellite provides continuous coverage over the United States with unprecedentedly high spatial (about 2 km) and temporal (about every 5 min) coverage. The ABI infrared (IR) observations are expected to be particularly well suited to address the challenges of predicting the early stages of convection initiation (CI) and the rapid evolution of convective-scale systems (Schrӧttle et al., 2021). Such systems typically evolve on time scales of minutes and space scales of kilometers. These space and time scales are consistent with the ABI resolution and are much finer than existing in situ observation networks.

The present study introduces the online non‐linear bias correction for the assimilation of all‐sky GOES‐16 Advanced Baseline Imager (ABI) channel 9 (6.9 μm) radiances in a rapidly cycled EnKF for convective scale data assimilation (DA). This study is the first to explore the use of the radar reflectivity as the anchoring observation for ABI all sky radiance assimilation. The online and offline nonlinear bias correction methods are compared and evaluated for a case of rapidly developing supercells over Oklahoma and Texas. The analysis and background of the online bias correction perform better than the offline approach during the suppression of spurious clouds and the establishment of non‐precipitating and precipitating regions when the supercell storms are observed to develop. The online approach not only improves the analysis and background over the radar anchored region but also the unanchored non‐precipitating regions compared to the offline approach. Both quantitative and subjective verification of the deterministic forecasts showed consistent superior performance from the online bias correction over the offline approach. Diagnostics reveal that the online bias correction retains useful information in the innovation, which in turn improves subsequent analysis, background and background ensemble spread for both the thermodynamic and dynamic fields. The effect is accumulated during the DA cycling that is responsible for the superior analysis and forecast of the supercells.

Accurate forecasts of the intensity of tropical cyclones (TCs) are important in both early warning systems and impact assessments because the damage caused by these storms is significantly associated with their intensity. Previous studies have suggested that changes in the intensity of TCs are affected by the dynamic and thermodynamic structures of the inner core, multiscale interactions between the storm and the environmental flow, and the asymmetry of the storm (

Recently, multiple ensemble-based data assimilation (DA) methods, such as the ensemble Kalman filter (e.g.,

This study evaluates simulated radiance forecasts from a series of controlled experiments consisting of FV3‐LAM forecasts with different configurations of model physics and vertical resolution. The forecasts were produced during the 2020 Hazardous Weather Testbed Spring Forecasting Experiments on the same forecast cases. The evaluation includes grid‐point, neighborhood‐based and object‐based verification. The experiments include forecasts that were identical except for the physics (EMC‐LAM vs. EMC‐LAMx), vertical resolution (EMC‐LAMx vs. NSSL‐LAM), or combined initial conditions, physics and vertical resolution (GSL‐LAM). It is found that the EMC‐LAM generally provided better simulated radiance forecasts than the other three configurations at most forecast lead times, due to its unique physics configuration. All configurations generally over‐forecasted high level clouds. EMC‐LAM reduced the over‐forecasting of high clouds, but also under‐forecasted the coverage of mid‐level clouds. In contrast, at early lead times the EMC‐LAM had relatively poor performance relative to the other forecasts. Furthermore, EMC‐LAM was an outlier in terms of the vertical structure of clouds. It is also found that the NSSL‐LAM consistently improved upon the EMC‐LAMx, which had fewer vertical levels than NSSL‐LAM. Compared to EMC‐LAMx, NSSL‐LAM had less cloud over‐forecasting bias, especially with small cloud objects, and less overall error. The differences between EMC‐LAMx and GSL‐LAM were generally much smaller than the differences between EMC‐LAMx and EMC‐LAM/NSSL‐LAM. Finally, it is found that a non‐linear bias correction conditioned on symmetric brightness temperature reduced the overall root‐mean‐square error by about a factor of 2 while improving the unrealistic vertical structure of clouds in the EMC‐LAM.