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Meral Demirtas and Alan J. Thorpe

Abstract

A new method is described to interpret satellite water vapor (WV) imagery in dynamical terms using potential vorticity (PV) concepts. The method involves the identification of mismatches between the WV imagery and a numerical weather prediction model description of the upper-level PV distribution at the analysis time. These mismatches are usually associated with horizontal positioning errors in the tropopause location in the oceanic storm-track region in midlatitudes. The PV distribution is locally modified to minimize this mismatch, and PV inversion is carried out to provide dynamically consistent additional initial data with which to reinitialize the numerical forecast.

One of the advantages of using this method is that it is possible to generate wind and temperature data suitable for inclusion as initial data for numerical weather forecasts. By using PV additional data can be inferred that cannot otherwise be simply derived from the WV data. In this way dynamical concepts add considerable value to the WV imagery, which by themselves would probably not have as significant a forecast impact.

Several examples of the use of this method are given here including cases of otherwise poorly forecast North Atlantic cyclones. In cases where the analysis errors occur at upper levels of the troposphere, the method leads to a significant improvement in the short-range forecast skill. In general, it is useful in highlighting where forecast problems are arising.

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Lígia Bernardet, Louisa Nance, Meral Demirtas, Steve Koch, Edward Szoke, Tressa Fowler, Andrew Loughe, Jennifer Luppens Mahoney, Hui-Ya Chuang, Matthew Pyle, and Robert Gall

The Weather Research and Forecasting (WRF) Developmental Testbed Center (DTC) was formed to promote exchanges between the development and operational communities in the field of Numerical Weather Prediction (NWP). The WRF DTC serves to accelerate the transfer of NWP technology from research to operations and to support a subset of the current WRF operational configurations to the general community. This article describes the mission and recent activities of the WRF DTC, including a detailed discussion about one of its recent projects, the WRF DTC Winter Forecasting Experiment (DWFE).

DWFE was planned and executed by the WRF DTC in collaboration with forecasters and model developers. The real-time phase of the experiment took place in the winter of 2004/05, with two dynamic cores of the WRF model being run once per day out to 48 h. The models were configured with 5-km grid spacing over the entire continental United States to ascertain the value of high-resolution numerical guidance for winter weather prediction. Forecasts were distributed to many National Weather Service Weather Forecast Offices to allow forecasters both to familiarize themselves with WRF capabilities prior to WRF becoming operational at the National Centers for Environmental Prediction (NCEP) in the North American Mesoscale Model (NAM) application, and to provide feedback about the model to its developers. This paper presents the experiment's configuration, the results of objective forecast verification, including uncertainty measures, a case study to illustrate the potential use of DWFE products in the forecasting process, and a discussion about the importance and challenges of real-time experiments involving forecaster participation.

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Dale Barker, Xiang-Yu Huang, Zhiquan Liu, Tom Auligné, Xin Zhang, Steven Rugg, Raji Ajjaji, Al Bourgeois, John Bray, Yongsheng Chen, Meral Demirtas, Yong-Run Guo, Tom Henderson, Wei Huang, Hui-Chuan Lin, John Michalakes, Syed Rizvi, and Xiaoyan Zhang

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. Air Force Weather Agency (AFWA) and United Arab Emirates (UAE) Air Force and Air Defense Meteorological Department.

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