Generation of a critical weather year for hygrothermal simulations using partial weather data sets

2014 ◽  
Vol 76 ◽  
pp. 54-61 ◽  
Author(s):  
Jan Kočí ◽  
Jiří Maděra ◽  
Robert Černý
Keyword(s):  
Weather Data ◽  
Data Sets

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

scholarly journals Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 621
Author(s):  
Elaheh Talebi ◽  
W. Pratt Rogers ◽  
Tyler Morgan ◽  
Frank A. Drews
Keyword(s):  
Health And Safety ◽  
Time Of Day ◽  
Weather Data ◽  
Data Sets ◽  
Leading Indicators ◽  
Safety Control ◽  
Model Complex ◽  
Fatigue Monitoring ◽  
The Impact ◽  
Operational Data

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

scholarly journals Development of a Global Fire Weather Database

2015 ◽  
Vol 15 (6) ◽  
pp. 1407-1423 ◽  
Author(s):  
R. D. Field ◽  
A. C. Spessa ◽  
N. A. Aziz ◽  
A. Camia ◽  
A. Cantin ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Large Scale ◽  
Prediction Models ◽  
Dry Season ◽  
Weather Data ◽  
Data Sets ◽  
Fire Weather ◽  
Fire Weather Index ◽  
Mato Grosso ◽  
Global Database

Abstract. The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

scholarly journals VARIABILITY OF BUILDING SIMULATION RESULTS DEPENDING ON SELECTED WEATHER FILES AND CONDITIONING SET POINTS – A CASE STUDY FOR A RESIDENTIAL BUILDING IN VICTORIA, AUSTRALIA

2016 ◽  
Vol 11 (4) ◽  
pp. 91-108
Author(s):  
Astrid Roetzel
Keyword(s):  
Residential Building ◽  
Building Simulation ◽  
Weather Data ◽  
Data Sets ◽  
Heating And Cooling ◽  
Data Files ◽  
Final Energy Consumption ◽  
Simulation Results ◽  
Simulation Input ◽  
Heating Loads

Building simulation is a powerful way to evaluate the performance of a building. The quality of simulation results however strongly depends on the accuracy of simulation input data. Especially for weather data files and occupant behaviour it is difficult to obtain accurate data. This paper evaluates the variability of building simulation results with regards to different weather data sets as well as different heating and cooling set points for a residential building in Victoria, Australia. Thermal comfort according to ASHRAE Standard 55, final energy consumption and peak cooling and heating loads are assessed. Simulations have been performed with Energy-Plus, and weather data for a multi-year approach have been generated with the software Meteonorm. The results show that different weather files for the same location as well as different conditioning set points can influence the results by approximately a factor of 2.

scholarly journals Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

2020 ◽  
Vol 12 (17) ◽  
pp. 6788 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Vicente Gutiérrez González ◽  
Antonis Peppas ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Meteorological Data ◽  
Building Energy ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Energy Models ◽  
The Impact ◽  
Energy Strategies

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

scholarly journals Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

2020 ◽  
Vol 51 (4) ◽  
pp. 648-665
Author(s):  
Min Wu ◽  
Qi Feng ◽  
Xiaohu Wen ◽  
Ravinesh C. Deo ◽  
Zhenliang Yin ◽  
...  
Keyword(s):  
Random Forest ◽  
Uncertainty Analysis ◽  
Predictive Model ◽  
Air Temperature ◽  
Model Development ◽  
Influential Factor ◽  
Weather Data ◽  
Data Sets ◽  
Oasis Area ◽  
Lower Uncertainty

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.

scholarly journals Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5

2015 ◽  
Vol 8 (2) ◽  
pp. 151-170 ◽  
Author(s):  
J. R. Buzan ◽  
K. Oleson ◽  
M. Huber
Keyword(s):  
Heat Stress ◽  
Land Surface ◽  
Climate Models ◽  
Extreme Temperature ◽  
Industrial Applications ◽  
Weather Data ◽  
Data Sets ◽  
Thermodynamic Quantities ◽  
Community Land Model ◽  
Common Framework

Abstract. We implement and analyze 13 different metrics (4 moist thermodynamic quantities and 9 heat stress metrics) in the Community Land Model (CLM4.5), the land surface component of the Community Earth System Model (CESM). We call these routines the HumanIndexMod. We limit the algorithms of the HumanIndexMod to meteorological inputs of temperature, moisture, and pressure for their calculation. All metrics assume no direct sunlight exposure. The goal of this project is to implement a common framework for calculating operationally used heat stress metrics, in climate models, offline output, and locally sourced weather data sets, with the intent that the HumanIndexMod may be used with the broadest of applications. The thermodynamic quantities use the latest, most accurate and efficient algorithms available, which in turn are used as inputs to the heat stress metrics. There are three advantages of adding these metrics to CLM4.5: (1) improved moist thermodynamic quantities; (2) quantifying heat stress in every available environment within CLM4.5; and (3) these metrics may be used with human, animal, and industrial applications. We demonstrate the capabilities of the HumanIndexMod in a default configuration simulation using CLM4.5. We output 4× daily temporal resolution globally. We show that the advantage of implementing these routines into CLM4.5 is capturing the nonlinearity of the covariation of temperature and moisture conditions. For example, we show that there are systematic biases of up to 1.5 °C between monthly and ±0.5 °C between 4× daily offline calculations and the online instantaneous calculation, respectively. Additionally, we show that the differences between an inaccurate wet bulb calculation and the improved wet bulb calculation are ±1.5 °C. These differences are important due to human responses to heat stress being nonlinear. Furthermore, we show heat stress has unique regional characteristics. Some metrics have a strong dependency on regionally extreme moisture, while others have a strong dependency on regionally extreme temperature.

scholarly journals Seamless climate change projections and seasonal predictions for bushfires in Australia

2020 ◽  
Vol 70 (1) ◽  
pp. 120
Author(s):  
Andrew J. Dowdy
Keyword(s):  
Climate Change ◽  
Long Range ◽  
Time Scales ◽  
Weather Conditions ◽  
Weather Data ◽  
Future Climate Change ◽  
Data Sets ◽  
Fire Weather ◽  
Climate Change Projections ◽  
Different Time Scales

Spatio-temporal variations in fire weather conditions are presented based on various data sets, with consistent approaches applied to help enable seamless services over different time scales. Recent research on this is shown here, covering climate change projections for future years throughout this century, predictions at multi-week to seasonal lead times and historical climate records based on observations. Climate projections are presented based on extreme metrics with results shown for individual seasons. A seasonal prediction system for fire weather conditions is demonstrated here as a new capability development for Australia. To produce a more seamless set of predictions, the data sets are calibrated based on quantile-quantile matching for consistency with observations-based data sets, including to help provide details around extreme values for the model predictions (demonstrating the quantile matching for extremes method). Factors influencing the predictability of conditions are discussed, including pre-existing fuel moisture, large-scale modes of variability, sudden stratospheric warmings and climate trends. The extreme 2019–2020 summer fire season is discussed, with examples provided on how this suite of calibrated fire weather data sets was used, including long-range predictions several months ahead provided to fire agencies. These fire weather data sets are now available in a consistent form covering historical records back to 1950, long-range predictions out to several months ahead and future climate change projections throughout this century. A seamless service across different time scales is intended to enhance long-range planning capabilities and climate adaptation efforts, leading to enhanced resilience and disaster risk reduction in relation to natural hazards.

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