Simulation Modeling of Aerial Work Completed by Helicopters in the Construction Industry Focused on Weather Conditions

The aim of this research was to develop a simulation model of the works carried out by helicopters, which are used in the construction of buildings under harsh natural conditions. This work identified that even technologies that we do not normally encounter, such as aerial work using helicopters, can have a major impact on ensuring the requirement of sustainability within the overall environmental and economic context. In the environment of protected landscape areas and national parks, in particular, where all sites are sensitive to human intervention, the use of helicopters in construction functions is an irreplaceable aid. Preparations for aerial work are very demanding and require the use of more sophisticated tools to achieve optimal results consistent within the paradigm of long-term sustainability. Simulation modeling is one such option, thanks to the considerable advancements made in information technology. A simulation model of aerial work was compiled within the presented work, and its functionality was verified using specific examples that confirmed in full the suitability of using simulations in the preparation of aerial work within construction. A detailed analysis of helicopter operations showed that an algorithm that accounts for future weather conditions at the construction site, and specifically focused on the conditions at the given altitude above the ground, should be a dominant feature of simulation models. It is exceptionally important that such data be known within the preparations for aerial work as accurately as possible, and, as such, this article describes the process of obtaining meteorological information for simulation models in detail using a numerical weather forecast and the reliability of data obtained in this manner. Based on the results obtained during this research, the proposed simulation model can be recommended as a suitable tool in the preparation of buildings. Its use is especially important if construction takes place under difficult natural conditions, where work cannot be carried out without the use of helicopters. We perceive the simulation model as a potential tool for digitizing construction preparations in the age of Industry 4.0.

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.

Application of Kalman Filter and Breeding Ensemble Technique to Forecast the Tropical Cyclone Activity

Tropical cyclone (TC) is one of the major meteorology disasters, as they lead to deaths, destroy the infrastructure and the environment. Therefore, how to improve the predictability of TC’s activities, such as formation, track, and intensity, is very important and is considered an important task for current operational predicting TC centers in many countries. However, predicting TC’s activities has remained a big challenge for meteorologists due to our incomplete understanding of the multiscale interaction of TCs with the ambient environment and the limitation of numerical weather forecast tools. Hence, this chapter will exhibit some techniques to improve the ability to predict the formation and track of TCs using an ensemble prediction system. Particularly, the Local Ensemble Transform Kalman Filter (LETKF) scheme and its implementation in the WRF Model, as well as the Vortex tracking method that has been applied for the forecast of TCs formation, will be presented in subSection 1. Application of Breeding Ensemble to Tropical Cyclone Track Forecasts using the Regional Atmospheric Modeling System (RAMS) model will be introduced in subSection 2.

Studies of FY-3 Observations over the Past 10 Years: A Review

With the rapid advances and abundant observations from Chinese Fengyun-3 (FY-3) meteorological satellites, it is of great interest to summarize a decade of quality assessments of FY-3 observations. The topics covered are noise characterization, bias estimation, striping noise detection and mitigation of striping noise, radio frequency interference detection, geolocation accuracy estimation and improvement, data assimilation cloud detection and quality control for observations from the MicroWave Temperature Sounder (MWTS), the MicroWave Humidity Sounder (MWHS), the MicroWave Radiation Imager (MWRI) and the Hyperspectral Infrared Atmospheric Sounder (HIRAS) instruments on board FY-3A/B/C/D. Whether and how much FY-3 data assimilation could improve the numerical weather forecast skill strongly depends on how well the FY-3 data characteristics and errors listed above are known. This review article shall contribute to promoting internal and national usages of FY-3 observations for weather and climate studies.

Technical note: Lowermost-stratosphere moist bias in ECMWF IFS model diagnosed from airborne GLORIA observations during winter–spring 2016

Numerical weather forecast systems like the ECMWF IFS (European Centre for Medium-Range Weather Forecasts – Integrated Forecasting System) are known to be affected by a moist bias in the extratropical lowermost stratosphere (LMS) which results in a systematic cold bias there. We use high-spatial-resolution water vapor measurements by the airborne infrared limb-imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) during the PGS (POLSTRACC/GW-LCYCLE-II/SALSA) campaign to study the LMS moist bias in ECMWF analyses and 12 h forecasts from January to March 2016. Thereby, we exploit the two-dimensional observational capabilities of GLORIA, when compared to in situ observations, and the higher vertical and horizontal resolution, when compared to satellite observations. Using GLORIA observations taken during five flights in the polar sub-vortex region around Scandinavia and Greenland, we diagnose a systematic moist bias in the LMS exceeding +50 % (January) to +30 % (March) at potential vorticity levels from 10 PVU (∼ highest level accessed with suitable coverage) to 7 PVU. In the diagnosed time period, the moist bias decreases at the highest and driest air masses observed but clearly persists at lower levels until mid-March. Sensitivity experiments with more frequent temporal output, and lower or higher horizontal and vertical resolution, show the short-term forecasts to be practically insensitive to these parameters on timescales of < 12 h. Our results confirm that the diagnosed moist bias is already present in the initial conditions (i.e., the analysis) and thus support the hypothesis that the cold bias develops as a result of forecast initialization. The moist bias in the analysis might be explained by a model bias together with the lack of water vapor observations suitable for assimilation above the tropopause.