Want to fine-tune weather forecasts with greater accuracy? This paper introduces a workstation-based Numerical Weather Prediction (NWP) scheme that enables forecasters to modify model output fields in a realistic and dynamically balanced manner. The technique revolves around distorting either geopotential height or potential vorticity (PV), allowing for targeted adjustments to features such as fronts and depressions. In the case of PV distortion, the method involves calculating PV from geopotential distribution, applying the distortion, and then inverting the modified PV to derive a consistent set of grid-point data. The consistent alteration of NWP output fields technique is grounded in fundamental meteorological principles. The scheme allows forecasters to change model fields. This approach offers a valuable tool for enhancing the accuracy and reliability of weather forecasts, enabling forecasters to leverage their expertise in refining model predictions. The ability to create dynamically balanced data sets ensures that the modified forecasts remain physically plausible.
Published in Meteorological Applications, this paper directly addresses the journal's focus on practical applications of meteorology. By presenting a technique for forecasters to improve NWP output, the research contributes to the journal's goal of bridging the gap between theoretical models and real-world forecasting. The paper also shows meteorological applications.