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Abstract Convective initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, an object-based probabilistic deep learning model is developed to predict CI based on multichannel infrared GOES-16 satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning model significantly outperforms the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits dependence on the characteristics of clouds and moisture at multiple altitudes. Model explanation further reveals that the contribution of features to model predictions is significantly dependent on the baseline, a reference point against which the prediction is compared. Under a moist baseline, moisture gradients in the lower and middle troposphere contribute most to correct CI forecasts. In contrast, under clear-sky baselines, correct CI forecasts are dominated by cloud-top features, including cloud-top glaciation, height, and cloud coverage. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights. 

Abstract Arctic single‐layer mixed‐phase clouds were studied using a one‐dimensional model that incorporated the adaptive habit growth model for ice microphysics. The base case was from the Indirect and Semidirect Aerosol Campaign, and it was perturbed over a range of cloud‐average temperatures, maximum (per model run) ice nuclei (IN) concentrations, and large‐scale subsidence velocities. For each parameter combination, the model was iterated out to 48 hr, and the time, called the glaciation time, to complete disappearance of liquid recorded if this occurred within the 48 hr. Dependence of glaciation times on cloud‐average temperatures from −30°C to −5°C, maximum IN concentrations from 0.10 to 30 L⁻¹, and strong–no subsidence, with both isometric and habit‐dependent ice crystal growth, were investigated. For isometric crystal growth, the relationship between the critical maximum IN concentration (IN_crit), the maximum (per model run) IN concentration above which a mixed‐phase cloud glaciated within a fixed model runtime, and cloud‐average temperature was monotonic. IN_critdecreased with decreasing cloud‐average temperature. Strengthening of subsidence led to a further decrease in IN_critfor every cloud‐average temperature. For habit‐dependent ice crystal growth, the relationship between IN_critand cloud‐average temperature was nonmonotonic. Ice crystals develop dendritic and columnar habits near −15°C and −7°C, respectively, and at these two temperatures, ice crystals grew and depleted supercooled liquid water faster than the case when ice crystals grew isometrically. This led to deep local minima in IN_critaround these two temperatures in the model runs. Habit‐dependent ice crystal growth, coupled with changes in cloud‐average temperature, IN_crit, and subsidence strength, led to significant changes in Arctic single‐layer mixed‐phase cloud lifetimes. 

Abstract Ensemble‐based data assimilation of radar observations across inner‐core regions of tropical cyclones (TCs) in tandem with satellite all‐sky infrared (IR) radiances across the TC domain improves TC track and intensity forecasts. This study further investigates potential enhancements in TC track, intensity, and rainfall forecasts via assimilation of all‐sky microwave (MW) radiances using Hurricane Harvey (2017) as an example. Assimilating Global Precipitation Measurement constellation all‐sky MW radiances in addition to GOES‐16 all‐sky IR radiances reduces the forecast errors in the TC track, rapid intensification (RI), and peak intensity compared to assimilating all‐sky IR radiances alone, including a 24‐hr increase in forecast lead‐time for RI. Assimilating all‐sky MW radiances also improves Harvey's hydrometeor fields, which leads to improved forecasts of rainfall after Harvey's landfall. This study indicates that avenues exist for producing more accurate forecasts for TCs using available yet underutilized data, leading to better warnings of and preparedness for TC‐associated hazards in the future. 

Abstract Intrusions of warm, moist air into the Arctic during winter have emerged as important contributors to Arctic surface warming. Previous studies indicate that temperature, moisture, and hydrometeor enhancements during intrusions all make contributions to surface warming via emission of radiation down to the surface. Here, datasets from instrumentation at the Atmospheric Radiation Measurement User Facility in Utqiaġvik (formerly Barrow) for the six months from November through April for the six winter seasons of 2013/14–2018/19 were used to quantify the atmospheric state. These datasets subsequently served as inputs to compute surface downwelling longwave irradiances via radiative transfer computations at 1-min intervals with different combinations of constituents over the six winter seasons. The computed six winter average irradiance with all constituents included was 205.0 W m−2, close to the average measured irradiance of 206.7 W m−2, a difference of −0.8%. During this period, water vapor was the most important contributor to the irradiance. The computed average irradiance with dry gas was 71.9 W m−2. Separately adding water vapor, liquid, or ice to the dry atmosphere led to average increases of 2.4, 1.8, and 1.6 times the dry atmosphere irradiance, respectively. During the analysis period, 15 episodes of warm, moist air intrusions were identified. During the intrusions, individual contributions from elevated temperature, water vapor, liquid water, and ice water were found to be comparable to each other. These findings indicate that all properties of the atmospheric state must be known in order to quantify the radiation coming down to the Arctic surface during winter.