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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. Plain Language Summary: The effective assimilation of satellite radiance observations requires removal of observation biases. Past studies have shown the advantages of the online bias correction approach for the clear‐sky radiance data assimilation relative to the offline bias correction approach. The advantage is achieved by simultaneously updating the model states and the bias correction terms and through the assimilation of unbiased observations together with the radiance observations. The anchoring unbiased observation in the online bias correction allows a more effective separation of the observation biases and the model bias. In this study, we propose and implement an online nonlinear bias correction method for the rapid EnKF assimilation of the all‐sky ABI radiances. The radar reflectivity observations are explored as the anchoring observations. Our results and diagnostics from a supercell case study show the online nonlinear bias correction approach improve the analysis and forecast compared to the offline nonlinear bias correction approach. Key Points: An online nonlinear bias correction for Advanced Baseline Imager (ABI) all‐sky radiance assimilation is proposed and implemented in a rapidly updated EnKFRadar reflectivity is shown to be effective in anchoring the state variable update by distinguishing the model and ABI radiance biasesThe online bias correction improves the analysis and forecast for rapidly developing supercells relative to the offline approach [ABSTRACT FROM AUTHOR]

A spatially and temporally varying Bayesian inflation algorithm was applied to the assimilation of the real radar data of a mesoscale convective system (MCS), which occurred on May 8–9, 2007 in Oklahoma and Texas (USA), using the ensemble square root filter (EnSRF) within the advanced regional prediction system (ARPS). For comparison purposes, two radar data assimilation and forecast experiments using Bayesian inflation and regular multiplicative inflation algorithms were performed. Results indicated that the analysis reflectivity structure of the MCS using the Bayesian inflation method appeared better than when using the multiplicative inflation method. The Bayesian inflation method also reduced the root mean square innovations (RMSIs) and increased the ensemble spreads for reflectivity and radial velocity. Furthermore, the reduction of RMSIs was related to the higher Bayesian inflation parameter assigned to the background. The flow‐dependent characteristic of the Bayesian inflation parameter means it varies with space and model state variables. This successful analysis helped improve subsequent forecast reflectivity and precipitation, especially for stratiform precipitation. It also promoted the development of the MCS with a strong surface cold pool and outflow in the convective regions. Quantitative reflectivity and precipitation forecast skills were also improved in the Bayesian inflation experiment. These encouraging results demonstrated the greater potential of using the Bayesian inflation algorithm for EnSRF radar data assimilation compared with multiplicative inflation. [ABSTRACT FROM AUTHOR]

Radar observations present a way to monitor large, mobile populations across long temporal scales, and are especially valuable when individual scatterers are challenging to count visually. The focus of this study is a large and relatively homogeneous wintertime roost of American Robins (Turdus migratorius) in central Oklahoma. Radar observations are used to estimate the roost population through winter 2010–2011, and the population time series is related to weather variables and radar beam propagation. Radar‐estimated roost population gradually increased to an estimated peak of 1.5–2 million individuals from November 2010 to January 2011, and then decreased in a more stepwise manner through the spring until roost dispersal in early March. Weather conditions did not definitively explain these population decreases leading toward roost dispersal. Birds from the roost were often observed to travel >50 km away during the daytime. About 25–30% of the variability in the radar‐derived roost population estimate could be explained by atmospheric variables. This work provides an example of how radar methods may be used to estimate populations and monitor their temporal trends, which may be valuable to conservation efforts by facilitating estimates of population change through time. [ABSTRACT FROM AUTHOR]