An approach for meteorological data integration and stream processing

Meteorological hazard has been frequently occurred in Indonesia due to torrential rains. It is important to examine the characteristics of the atmosphere during rainy seasons for hazard mitigation. National Laboratory of Weather Modification Technology has conducted a short Intensive Observation Program (IOP) from January 18th to February 16th, 2016 to collect meteorological data in the vicinity of Jakarta Region. During that period several instruments have been used, such as Radar, Microwave Profiling Radiometer, Automatic Weather Station, and Radiosonde. This paper examines the comparison of atmospheric parameters obtained from Radiosonde and Profiling Radiometer during extreme weather days. The results showed that there were significant differences of instability indices of Radiosonde and Profiling Radiometer data: 15 points for KI, 6 points for TT and 100 points for SWEAT. The atmospheric stability indices of the Profiling Radiometer tended to be lower than Radiosonde. A radar image showing a rainstorm as well as rain rate information validates atmospheric index stability data. Radar and atmospheric instability indices data integration can be used as one of the parameters to forecast extreme weather events and as an early warning system of hazard mitigation.

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Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

Energies ◽

10.3390/en14020468 ◽

2021 ◽

Vol 14 (2) ◽

pp. 468

Author(s):

Antonio Manuel Gómez-Orellana ◽

Juan Carlos Fernández ◽

Manuel Dorado-Moreno ◽

Pedro Antonio Gutiérrez ◽

César Hervás-Martínez

Keyword(s):

Machine Learning ◽

Data Integration ◽

Soft Computing ◽

Energy Flux ◽

Meteorological Data ◽

Software Tool ◽

Quality Data ◽

Coastal Protection ◽

Ocean Engineering

Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.

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Constructing the CityGML ADE for the Multi-Source Data Integration of Urban Flooding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060359 ◽

2020 ◽

Vol 9 (6) ◽

pp. 359

Author(s):

Jie Shen ◽

Jingyi Zhou ◽

Jiemin Zhou ◽

Lukas Herman ◽

Tomas Reznik

Keyword(s):

Data Integration ◽

Meteorological Data ◽

Unified Modeling Language ◽

Prototype System ◽

Markup Language ◽

Urban Flooding ◽

Application Domain ◽

Data Types ◽

Domain Extension ◽

Source Data

Urban flooding, as one of the most serious natural disasters, has caused considerable personal injury and property damage throughout the world. To better cope with the problem of waterlogging, the experts have developed many waterlogging models that can accurately simulate the process of pipe network drainage and water accumulation. The study of urban waterlogging involves many data types. These data come from the departments of hydrology, meteorology, planning, surveying, and mapping, etc. The incoordination of space–time scale and format standard has brought huge obstacles to the study of urban waterlogging. This is not conducive to interpretation, transmission, and visualization in today’s network environment. In this paper, the entities and attributes related to waterlogging are defined. Based on the five modules of urban drainage network, sub basin, dynamic water body, time series, and meteorological data, the corresponding UML (Unified Modeling Language) model is designed and constructed. On this basis, the urban waterlogging application domain extension model city waterlogging application domain extension (CTWLADE) is established. According to the characteristics of different types of data, two different methods based on FME object and citygml4j are proposed to realize the corresponding data integration, and KML (Keyhole Markup Language) /glTF data organization form and the corresponding sharing method are proposed to solve the problem that the CTWLADE model data cannot be visualized directly on the web and cannot interact in three-dimensional format. To evaluate the CTWLADE, a prototype system was implemented, which can convert waterlogging-related multi-source data in extensible markup language (XML) files conform. The current CTWLADE can map the data required and provided by the hydraulic software tool storm water management model (SWMM) and is ready to be integrated into a Web 3D Service to provide the data for 3D dynamic visualization in interactive scenes.

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