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Ground fog detection and analysis with Machine Learning (GFog-ML)

使用机器学习进行地面雾检测和分析 (GFog-ML)

基本信息

批准号：
270101240
负责人：
Professor Dr. Jörg Bendix
金额：
--
依托单位：
Fachgebiet Klimageographie und Umweltmodellierung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/270101240?language=en
关键词：
Ground fog detection analysis Machine

项目摘要

Fog and Low Stratus (LST) are of utmost importance for the radiation balance and thus, the earth's climate system. The lack of knowledge on its spatio-temporal dynamics in combination with the indirect aerosol effect is one main reason for uncertainties in the latest IPCC projections of the Fifth Assessment Report (AR5). At the same time, fog/LST are causing problems for traffic security and air quality which requires reliable and operational nowcasting procedures. Particularly a proper distinction between LST and ground fog would mean a big step forward in operational nowcasting but would also form the basis for long-term monitoring of fog/LST dynamics with the newest generation of operational weather satellite systems. The main aim of the current project is to develop a novel daylight/night technique for the discrimination of LST and ground fog based on the European geostationary Meteosat Second Generation (MSG) system with its SEVIRI / HRV (Spinning Enhanced Visible and Infrared Imager / High Resolution Visible) instrument onboard. The novel technique will be used to generate for the first time a 10 years data set of LST/ground fog (fog hours) and its properties (effective radius, optical depth). The project is based on the readily available processing chain of LST detection, a unique data set for training/validation from the Marburg Ground Truth and Profiling Station, and will make use of the machine learning technique Random Forest (RF) to calculate 3 and 1 km resolved datasets of LST, ground fog (the latter resolution during daylight only) and fog properties. The comprehensive dataset enables for the first time the spatial-explicit mapping of ground fog hours as required by operational meteorology. The climatological analysis of the generated time series will focus on the detection of changes in regional/local ground fog / LST occurrence over the last decade due to autochtonous (land used and aerosol concentration) and changes in allochthonous (atmospheric circulation) formation factors.

雾和低层云（LST）对辐射平衡和地球气候系统至关重要。缺乏关于其时空动态的知识，加上间接的气溶胶效应，是气专委第五次评估报告最新预测不确定的一个主要原因。与此同时，雾/LST对交通安全和空气质量造成问题，这需要可靠和可操作的临近预报程序。特别是适当区分LST和地面雾将意味着在业务临近预报方面向前迈出一大步，但也将构成利用最新一代业务气象卫星系统长期监测雾/LST动态的基础。目前项目的主要目的是开发一种新的昼/夜技术，用于根据欧洲地球静止气象卫星第二代系统（MSG）及其上的SEVIRI / HRV（旋转增强可见光和红外成像仪/高分辨率可见光）仪器来区分LST和地面雾。该新技术将首次用于生成10年的LST/地面雾（雾小时）及其特性（有效半径，光学厚度）数据集。该项目基于现成的LST检测处理链，这是来自马尔堡地面实况和剖面站的用于培训/验证的独特数据集，并将利用机器学习技术随机森林（RF）计算3公里和1公里分辨率的LST数据集，地面雾（后者仅在白天分辨率）和雾属性。综合数据集首次实现了业务气象学所需的地面雾小时的空间明确映射。生成的时间序列的气候分析将侧重于检测区域/本地地面雾/ LST发生的变化，在过去的十年中，由于本地（土地使用和气溶胶浓度）和外来（大气环流）形成因素的变化。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning

DOI：
10.3390/en13153859
发表时间：
2020-07
期刊：
Energies
影响因子：
3.2
作者：
B. Rösner;Sebastian Egli;B. Thies;Tina Beyer;Doron Callies;L. Pauscher;J. Bendix
通讯作者：
B. Rösner;Sebastian Egli;B. Thies;Tina Beyer;Doron Callies;L. Pauscher;J. Bendix

A 10 year fog and low stratus climatology for Europe based on Meteosat Second Generation data