Data-driven weather prediction (DDWP) models are increasingly becoming popular for weather forecasting. While DDWPs primarily forecast prognostic parameters, many diagnostic meteorological parameters (such as precipitation) are dependent on the most recent weather state and are modeled by learning a data-driven functional mapping of the current meteorological state (c.f. FourCastNet). However, the cost of training bespoke models for diagnostic variables can scale significantly and further limit the use during operationalizing these forecasts. This presents an opportunity to learn dense representations of essential meteorological parameters in a latent space, and using learned representations to model diagnostic parameters, or any other dependent variables. Using learned representations of weather allows for efficient prediction of dependent variables, while dramatically lowering the training cost for such models as well. In this paper, we present one such weather embedding model, WeatherX, trained on decades of reanalysis data that is used to train multiple diagnostic variables. The results indicate that models trained using learned representations of weather offer performance comparable to bespoke models, while leading to significant reduction in resource utilization during training and inference. Further lower memory footprint during operationalization leads to additional gain of running larger ensembles during inference thereby further improving uncertainty quantification of the said forecasts.