The Orinoco low-level jet (OLLJ) is characterized using finer horizontal, vertical, and temporal resolution than possible in previous studies via dynamical downscaling. The investigation relies on a 5-month-long simulation (November 2013-March 2014) performed with the WRF model, with initial and boundary conditions provided by the GFS analysis. Dynamical downscaling is demonstrated to be an effective method not only to better resolve the horizontal and vertical characteristics of the Orinoco low-level jet but also to determine the mechanisms leading to its formation. The OLLJ is a single stream tube over Colombia and Venezuela with wind speeds greater than 8 m s-1 , and four distinctive cores of higher wind speeds varying in height under the influence of sloping terrain. It is an austral summer phenomenon that exhibits its seasonal maximum wind speed and largest spatial extent (2100 km × 450 km) in January. The maxima diurnal mean wind speeds (13–17 m s-1) at each core location occur at different times during the night (2300–0900 LST). The momentum balance analysis in a natural coordinate system reveals that the OLLJ results from four phenomena acting together to accelerate the wind: a sea-breeze penetration, katabatic flow, three expansion fans, and diurnal variation of turbulent diffusivity. The latter, in contrast to the heavily studied nocturnal low-level jet in the U.S. Great Plains region, plays a secondary role in OLLJ acceleration. These results imply that LLJs near the equator may originate from processes other than the inertial oscillation and topographic thermal forcing.

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 cutoff 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.