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.

With the improvement in numerical weather prediction models and high-performance computing technology, ensemble modeling and probabilistic forecasts have taken on some of the most challenging tasks, such as weather model uncertainty estimation and the global climate projection. High-resolution model simulations that were deemed impossible to complete within a reasonable amount of time in the old days are now running as an ensemble to better characterize the model uncertainty. However, with advances in computation which make large parallel computing widely accessible, important questions are being increasingly addressed on how to interpret each forecast ensemble member, instead of relying on a summarization of all ensemble members. The analysis of individual ensemble members allows for an in-depth analysis of specific possible future outcomes. Thus, it is desirable to have the ability to generate a large forecast ensemble in order to help researchers understand the forecast uncertainty. But it is also crucial to determine which ensemble members are the better ones and to identify metrics to assess the uncertainty captured by each ensemble member. This work proposes the Empirical Inverse Transform (EITrans) function to address these questions. EITrans is a technique for ensemble transformation and member selection based on knowledge from historical forecasts and the corresponding observations. This technique is applied to a particular ensemble forecast to select ensemble members that would offer a sharper and more reliable distribution without compromising the accuracy of the prediction.