The Arctic is recognized as one of the key areas of the globe, both in terms of its sensitivity to climate change, and by the increasing economic activity that is expected with the opening up of Arctic areas in a warming climate. In addition, Arctic weather can have important influences in winter cold outbreaks of air which can affect Northern Hemisphere countries as far south as the subtropics with serious economic implications. Therefore, a revised assessment of Arctic satellite and surface observation capabilities and requirements is warranted, especially given the Arctic surface and upper air network is sparse. Observing System Simulation Experiments (OSSEs) are a powerful tool to assess added value of planned or hypothetical observing systems for weather analysis and prediction. This white paper reviews the current state of OSSE science with respect to the Arctic, and provides lines of investigation for the future, with a focus on weather and air quality observations in the Arctic.Recommendations are based on perceived observation gaps in the Arctic, and the experience gained by the World Weather Research Programme-The Observing Systems Research and Predictability Experiments/Polar Prediction Project (WWRP-THORPEX/PPP) and the broader OSSE scientific community.1. Definition and motivation of OSSEs Observing System Simulation Experiments (OSSEs) are designed to use data assimilation ideas to investigate the potential impact of future observing systems (observation types and deployments)based on existing observation systems using a model simulation. They may also be used to evaluate current observational and data assimilationsystems. The information obtained from OSSEs is generally difficult, or in some contexts impossible, to obtain inany other way. Although OSSEs require significant resources in computing power and human resources, the cost is a small fraction of actual observing systems. OSSEs also allow preparation of data assimilation system for afuture observing system.Although originally of interest to the meteorological agencies, more recently OSSEs have started to be of interest to the space agencies, forming a key element in the design of future satellite missions. In an OSSE, simulated rather than real observations are theinput to a data assimilation system (DAS); an extended review of the observation types comprising the Global Observing System (GOS) and state-of-the-art data assimilation systems is provided in Lahoz et al.(2010). OSSEs are closely related to Observing System Experiments (OSEs), which test the value of current observation types using data denial experiments. An OSSE is formally similar to an OSE with one important difference: OSSEs assess newdata, i.e., data obtained by hypothetical observing systems that do not yet exist. The OSSE methodology consists of:•Generation and verification of reference atmospheric states. This is usually done with a forecast from a good-quality, realistic atmospheric model in a free-runningmode without data assimilation. This forecast is the Nature Run (NR), providing the “truth,” from which observations are simulated and against which subsequent OSSE assimilation experiments are verified;•The NR is then used to generate simulated observations, including realistic errors, for existing observing systems and for the hypothetical future observing system;•A control experiment in which current observational data are included;

Manual (incl. handbook, guide, cookbook etc)

Non Refereed

Published

Current

14.A

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