Ship‐icing prediction methods applied in operational weather forecasting

结冰 环境科学 气象学 范畴变量 结冰条件 气候学 计算机科学 地质学 机器学习 地理
作者
Eirik Mikal Samuelsen
出处
期刊:Quarterly Journal of the Royal Meteorological Society [Wiley]
卷期号:144 (710): 13-33 被引量:9
标识
DOI:10.1002/qj.3174
摘要

Sea‐spray wetting of ships operating in cold environments imposes a great safety risk, due to icing. For this reason, marine‐icing warnings have been a part of operational weather forecasting for the last five decades, yet verification of such warnings has only been done sparingly. This article evaluates different ship‐icing methods applied in operational weather forecasting. The methods are tested against a unique dataset from a single ship type from Arctic–Norwegian waters and two screened datasets from several ship types from Alaska and the east coast of Canada. Missing and uncertain parameters in the latter datasets are supplemented by reanalysis data from different sources. Continuous icing‐rate verification and sensitivity tests are presented for the physical icing models alongside categorical icing‐rate verification, which is applied in order also to evaluate icing nomograms, which are still used by several forecasting agencies. Furthermore, a newly proposed definition of the boundaries between icing‐rate severity categories is applied in the categorical verification procedure. The overall best verification scores for continuous and categorical icing rates are obtained by the Marine Icing model for the Norwegian COast Guard (MINCOG) and a physically based Overland model, updated from its initial version with more realistic heat transfer. Finally, sensitivity tests highlight that very low air and sea‐surface temperatures rarely occur over sea areas together with high waves, due to fetch limitations, even for strong winds. For this reason, models and nomograms that do not treat wind speed and wave height separately will provide inaccurate predictions of the icing rate in such areas. Consequently, it is preferable that methods applied in operational weather forecasting are replaced with methods capable of taking this effect into account.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Anker发布了新的文献求助10
1秒前
2秒前
2秒前
失约于月光应助贪玩满天采纳,获得10
4秒前
舒适皮皮虾完成签到,获得积分10
4秒前
aajhajkahna应助sedrakyan采纳,获得10
4秒前
所所应助智慧美少女采纳,获得10
5秒前
yskm发布了新的文献求助10
5秒前
5秒前
ZSW完成签到,获得积分10
5秒前
5秒前
6秒前
2023204306324完成签到,获得积分10
7秒前
调皮友安发布了新的文献求助10
8秒前
9秒前
yue完成签到 ,获得积分10
9秒前
9秒前
1234完成签到,获得积分10
10秒前
桐桐应助小星星采纳,获得10
11秒前
顾矜应助七慕凉采纳,获得10
11秒前
快乐千凡完成签到,获得积分10
11秒前
12秒前
13秒前
优美亦云发布了新的文献求助10
14秒前
干雅柏完成签到,获得积分10
14秒前
MXH发布了新的文献求助10
14秒前
15秒前
15秒前
张欢馨应助阿妍采纳,获得10
17秒前
流淌的愚者完成签到,获得积分10
17秒前
干雅柏发布了新的文献求助10
18秒前
cdercder应助111采纳,获得10
19秒前
随随风发布了新的文献求助10
19秒前
20秒前
prigogin应助孔令宇采纳,获得10
20秒前
小白完成签到,获得积分10
20秒前
XMY发布了新的文献求助10
21秒前
顾矜应助枫叶采纳,获得30
21秒前
大模型应助小二采纳,获得10
21秒前
23秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
The Oxford Handbook of Digital Classical Studies 550
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7617949
求助须知:如何正确求助?哪些是违规求助? 9193175
关于积分的说明 19703263
捐赠科研通 7190429
什么是DOI,文献DOI怎么找? 3272065
关于科研通互助平台的介绍 2434843
邀请新用户注册赠送积分活动 2267229