High-resolution numerical weather prediction (NWP) with data assimilation over Placentia Bay, Newfoundland

S. Allan, D. Bryan, B. Pouliot
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引用次数: 1

Abstract

Placentia Bay, Newfoundland and Labrador, Canada contains numerous islands and shoals, and is a busy bay with large marine vessel traffic, fishers and small-craft users. Placentia Bay is second only to the Port of Vancouver in Canada in terms of the annual value of goods shipped. The SmartBay applied ocean observing system, an initiative of the Fisheries and Marine Institute of Memorial University, was created to enhance marine safety for the Placentia Bay user community, and to provide environmental information to improve the efficiency of marine operations in the region. SmartBay successes to date have relied on effective collection and distribution of meteorological and oceanographic information. A recent advance has included the assimilation of these data into ultra-high resolution wind and wave models. Key elements of the work for Placentia Bay have included: configuration of the Weather Research and Forecasting (WRF) numerical weather prediction model at 2 km resolution; adaptation of the Environment Canada Global Environmental Multiscale (GEM) Local Area Model (LAM), development of a 0.6 km grid resolution WaveWatch III (WW3) deep-water wave model; incorporation of detailed sea surface temperature (SST) analysis data into WRF's input; operational implementation of the Simulating WAves Nearshore (SWAN) shallow-water wave model at 0.5 km resolution; and implementing data assimilation within WRF. The paper focuses on the configuration, development and testing of the wind models, together with discussion of the data assimilation techniques employed and a comparison of results with met-ocean buoy measurements.
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具有同化资料的纽芬兰Placentia湾高解析度数值天气预报
加拿大纽芬兰和拉布拉多的普兰西亚湾包含许多岛屿和浅滩,是一个繁忙的海湾,有大型海上船只交通,渔民和小型船只用户。就年货物运输量而言,普拉森西亚湾仅次于加拿大的温哥华港。“智能湾”应用海洋观测系统是纪念大学渔业及海洋研究所的一项倡议,旨在加强普兰提亚湾用户社区的海上安全,并提供环境信息,以提高该地区海上作业的效率。迄今为止,智能湾的成功依赖于有效收集和分发气象和海洋信息。最近的一项进展包括将这些数据同化成超高分辨率的风和波模型。普兰西亚湾工作的主要内容包括:配置2公里分辨率的天气研究与预报(WRF)数值天气预报模式;对加拿大环境部全球环境多尺度(GEM)局部区域模式(LAM)进行改编,开发了0.6 km网格分辨率的WaveWatch III (WW3)深水波模式;将详细的海表温度(SST)分析数据纳入WRF的输入;0.5 km分辨率模拟波浪近岸(SWAN)浅水波浪模式的业务实现;并在WRF内实施数据同化。本文重点介绍了风模式的配置、开发和测试,讨论了所采用的数据同化技术,并将结果与海洋浮标测量结果进行了比较。
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