On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction
- Autor(en)
- Guannan Hu, Sarah L. Dance, Alison Fowler, David Simonin, Joanne Waller, Thomas Auligne, Sean Healy, Daisuke Hotta, Ulrich Löhnert, Takemasa Miyoshi, Nikki C. Prive, Olaf Stiller, Xuguang Wang, Martin Weissmann
- Abstrakt
In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, the assessment can be challenging in convection-permitting NWP. First, the strong nonlinearity in the forecast model limits the methods available for the assessment. Second, convection-permitting NWP typically uses a limited area model and provides short forecasts, giving problems with verification and our ability to gather sufficient statistics. Third, convection-permitting NWP often makes use of novel observations, which can be difficult to simulate in an observing system simulation experiment (OSSE). We compare methods that can be used to assess the value of observations in convection-permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection-permitting NWP. We also identify several future research directions: comparison of forecast validation using analyses and observations, the effect of ensemble size on assessing the value of observations, flow-dependent covariance localization, and generation and validation of the nature run in an OSSE.
- Organisation(en)
- Institut für Meteorologie und Geophysik
- Externe Organisation(en)
- University of Reading, Met Office, Université de Toulouse, Meteorological Research Institute - Japan Meteorological Agency, Morgan State University, University of Oklahoma, European Centre for Medium-Range Weather Forecasts (ECMWF), Universität zu Köln, RIKEN, Deutscher Wetterdienst
- Journal
- arxiv accessibility Forum
- DOI
- https://doi.org/10.48550/arXiv.2309.16433
- Publikationsdatum
- 2023
- Peer-reviewed
- Ja
- ÖFOS 2012
- 105206 Meteorologie
- Link zum Portal
- https://ucrisportal.univie.ac.at/de/publications/on-methods-for-assessment-of-the-influence-and-impact-of-observations-in-convectionpermitting-numerical-weather-prediction(2a793605-a592-44d5-a9f6-b85a967c4d13).html