Quality control and homogenization of the Belgian historical temperature data

2018 ◽  
Vol 39 (1) ◽  
pp. 157-171 ◽  
Author(s):  
Charles Delvaux ◽  
Romain Ingels ◽  
Vladimír Vrábeĺ ◽  
Michel Journée ◽  
Cédric Bertrand
Keyword(s):  
Quality Control ◽  
Temperature Data ◽  
Historical Temperature

Research on Wireless Temperature Measurement System for Electrical Equipment

2014 ◽  
Vol 721 ◽  
pp. 523-526
Author(s):  
Xiang Li Wang ◽  
Yu Gui Nian ◽  
Dong Dong Cai
Keyword(s):  
Energy Efficiency ◽  
Temperature Measurement ◽  
Production Efficiency ◽  
Electrical Equipment ◽  
Temperature Data ◽  
Living Standards ◽  
Measuring Point ◽  
Measurement Software ◽  
Historical Temperature ◽  
Temperature Measurement System

As one of the important parameters for the production process of industry, agriculture and military, temperature can affect the production efficiency, energy efficiency and people’s living standards. The temperature measurement has been widely noted and studied. Firstly, the software can receive the temperature of measuring point, and store the value in the database. Secondly, when the temperature exceeds the specified range, the software will give an alarm. The software can view the historical temperature data and draw the curve of temperature. Finally, the software can query and modify the information of the measuring point, such as number, name, normal temperature range of equipment, etc. Based on the existing research results, this paper describes the design and implement of wireless temperature measurement software for electrical equipment.

scholarly journals FAO-56 Penman-Monteith DailyETofrom Linear Regression Calibrated Hargreaves Equation with Wind Terms in Tropics with Limited Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Eric Kra
Keyword(s):  
Linear Regression ◽  
Quadratic Function ◽  
Constant Term ◽  
Calibration Method ◽  
Temperature Data ◽  
Limited Data ◽  
Wide Range ◽  
Hargreaves Equation ◽  
Historical Temperature ◽  
Monteith Equation

Hargreaves equation (HG), which lacks a wind speed (u2) term, was modified, through a linear regression calibration method, into LHGu which hasu2terms. LHGu is effectively a simplified method for approximating FAO-56 Penman-Monteith equation (FPM) daily reference evapotranspiration (ETo) in tropics with only temperature data. In LHGu, the “0.0023” constant term in HG was calibrated as a shifted power function ofu2, and the calibration constant was parametrized as a quadratic function ofu2. LHGu was developed using simulated constantu2data and historical temperature data for four sites in West Africa: Abidjan, Accra, Daloa, and Lome. LHGu matched FPMETobetter than HG over a wide range ofu2: for Accra, foru2range 0.5–6.0 m/s, the modified coefficient of efficiency,E1, varied narrowly (0.83–0.98) for LHGu but widely (0.14–0.95) for HG optimized foru2=2.0 m/s; the corresponding MBE ranges were −0.05–0.01 mm/d for LHGu and 0.02–0.63 mm/d for HG which cannot respond to varying dailyu2. LHGu is useful for quickly computing practically accurate estimates of FPMETofor varying dailyu2where only temperature data are available.

Classification of Surface Temperature for the Flexible Pavement Design

2017 ◽  
Author(s):  
Rita Kleizienė ◽  
Audrius Vaitkus ◽  
Jurgita Židanavičiūtė ◽  
Evaldas Marcinkevičius
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Pavement Design ◽  
Traffic Loading ◽  
The Road ◽  
Weather Information ◽  
Cause Of Failure ◽  
Pavement Structures ◽  
Historical Temperature ◽  
The Impact

Surface temperature significantly affects the asphalt layers modulus and entire pavement structure response to vehicles traffic loading. Because of the rheological properties of bitumen binders, the asphalt performs similarly to temperature-susceptible visco-elastic materials. The historical temperature data of local regions is necessary to design sustainable pavement structures. Likewise, the layers’ material mechanical properties determined at specific temperatures is essential for proper design too. This paper presents an analysis of pavement surface temperature classification results. Data analysis covers temperature data from the Road Weather Information Stations from the past ten years. An analysis of various temperature profile forecast methods is presented, followed by a review of recent research on the impact of temperature and cause of failure. Particular emphasis is laid on sorting the qualitative temperature data. The complete linkage clustering method had been used for establishing the most similar pairs for classification. Accordingly, the territory of Lithuania was divided into three main regions with different pavement temperature distributions for each temperature interval. Temperature classification along these lines enables pavement responses to be estimated over the pavement design life.

scholarly journals Quality control of 10-min air temperature data at RMI

2013 ◽  
Vol 10 (1) ◽  
pp. 1-5 ◽  
Author(s):  
C. Bertrand ◽  
L. Gonzalez Sotelino ◽  
M. Journée
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Data ◽  
Temporal Consistency ◽  
Decision Algorithm ◽  
A Value ◽  
Spatial Consistency ◽  
Meteorological Measurement ◽  
Human Decision ◽  
Human Operators

Abstract. In the '90s, the Royal Meteorological Institute (RMI) of Belgium started to replace its conventional ''manual'' meteorological network by automated weather stations (AWSs). The meteorological measurement network is now fully automated. RMI counts 18 AWSs that made automated observations centrally available in our headquarters in Uccle, Brussels to internal as well as external users. Due to the large increase in the data amount associated with the automation, quality assurance (QA) procedures are being automated. However, human operators continue to play an essential role in the data validation processes. This contribution describes our newly developed semi-automatic quality control (QC) of 10-min air temperature data. After an existence test, the data are checked for limits consistency, temporal consistency and spatial consistency. At the end of these automated checks, a decision algorithm attributes a flag to each particular data. Each day the QC staff analyzes the preceding day observations in the light of the quality flags assigned by automated QA procedures during the night. It is the human decision whether or not a value is accepted.

scholarly journals Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

2021 ◽  
Vol 893 (1) ◽  
pp. 012063
Author(s):  
M Halida ◽  
SA Pramono
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Increase ◽  
Data Distribution ◽  
Threshold Value ◽  
Temperature Data ◽  
Temperature Decrease ◽  
Climatological Station ◽  
Check Method ◽  
The Difference

Abstract All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

scholarly journals Stability and Complexity Analysis of Temperature Index Model Considering Stochastic Perturbation

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Jing Wang
Keyword(s):  
Control Method ◽  
Complexity Analysis ◽  
Stochastic Perturbation ◽  
Periodic Variation ◽  
Stability Control ◽  
Temperature Data ◽  
Anhui Province ◽  
Temperature Index ◽  
Index Model ◽  
Historical Temperature

A temperature index model with delay and stochastic perturbation is constructed in this paper. It explores the influence of parameters and stochastic factors on the stability and complexity of the model. Based on historical temperature data of four cities of Anhui Province in China, the temperature periodic variation trends of approximately sinusoidal curves of four cities are given, respectively. In addition, we analyze the existence conditions of the local stability of the temperature index model without stochastic term and estimate its parameters by using the same historical data of the four cities, respectively. The numerical simulation results of the four cities are basically consistent with the descriptions of their historical temperature data, which proves that the temperature index model constructed has good fitting degree. It also shows that unreasonable delay parameter can make the model lose stability and improve the complexity. Stochastic factors do not usually change the trend in temperature, but they can cause high frequency fluctuations in the process of temperature evolution. Stability control is successfully realized for unstable systems by the variable feedback control method. The trend of temperature changes in Anhui Province is deduced by analyzing four typical cities.

Sign in / Sign up

Export Citation Format

Share Document