Provenance Collection Platform for the Weather Research and Forecasting Model

Abstract

Loss of life and property, disruptions to transportation and trading operations, etc. caused by meteorological events increasingly highlight the importance of fast and accurate weather forecasting. For this reason, there are various Numerical Weather Prediction (NWP) models worldwide that are run on either a local or a global scale. NWP models typically take hours to finish a complete run, however, depending on the input parameters and the size of the forecast domain. Provenance information is of central importance for detecting unexpected events that may develop during the course of model execution, and also for taking necessary action as early as possible. In addition, the need to share scientific data and results between researchers or scientists also highlights the importance of data quality and reliability. This can only be achieved through provenance information collected during the entire lifecycle of the data of interest. The Weather Research and Forecasting (WRF) Model is a Numerical Weather Prediction model developed as open source. In this study, we develop a framework for tracking the WRF model and for generating, storing and analyzing provenance data. The proposed system enables easy management and understanding of numerical weather forecast workflows by providing provenance graphs. By analyzing these graphs, potential faulty situations that may occur during the execution of WRF can be traced to their root causes. Our proposed system has been evaluated and has been shown to perform well even in a high-frequency provenance information flow.