scholarly journals Climate reference stations in Germany: Status, parallel measurements and homogeneity of temperature time series

2016 ◽  
Vol 13 ◽  
pp. 163-171 ◽  
Author(s):  
Frank Kaspar ◽  
Lisa Hannak ◽  
Klaus-Jürgen Schreiber
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Type A ◽  
Temperature Time Series ◽  
Current Status ◽  
Maximum Temperature ◽  
Specific Task ◽  
New Instrumentation ◽  
Temperature Time ◽  
Parallel Measurements

Abstract. Germany's national meteorological service (Deutscher Wetterdienst, DWD) operates a network of so-called "climate reference stations". These stations fulfill several tasks: At these locations observations have already been performed since several decades. Observations will continuously be performed at the traditional observing times, so that the existing time series are consistently prolonged. Currently, one specific task is the performance of parallel measurements in order to allow the comparison of manual and automatic observations. These parallel measurements will be continued at a subset of these stations until at least 2018. Later, all stations will be operated as automatic stations but will also be used for the comparison of subsequent sensor technologies. New instrumentation will be operated in parallel to the previously used sensor types over sufficiently long periods to allow an assessment of the effect of such changes. Here, we present the current status and an analysis of parallel measurements of temperature at 2 m height. The analysis shows that the automation of stations did not cause an artificial increase in the series of daily mean temperature. Depending on the screen type, a bias with a seasonal cycle occurs for maximum temperature, with larger differences in summer. The effect can be avoided by optimizing the position of the sensor within the screen.

Relationship between heating/cooling period and changing temperature conditions in the urban areas of Hungary

2020 ◽  
Author(s):  
Csenge Dian ◽  
Attila Talamon ◽  
Rita Pongrácz ◽  
Judit Bartholy
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Energy Consumption ◽  
Extreme Values ◽  
Building Design ◽  
Temperature Time Series ◽  
Maximum Temperature ◽  
Design Parameters ◽  
Daily Average ◽  
Temperature Time

<p>Climate change, extreme weather conditions, and local scale urban heat island (UHI) effect altogether have substantial impacts on people’s health and comfort. The urban population spends most of its time in buildings, therefore, it is important to examine the relationship between weather/climate conditions and indoor environment. The role of buildings is complex in this context. On the one hand UHI effect is mostly created by buildings and artificial surfaces. On the other hand they account for about 40% of energy consumption on European average. Since environmental protection requires increased energy efficiency, the ultimate goal from this perspective is to achieve nearly zero-energy buildings. When estimating energy consumption, daily average temperatures are taken into account. The design parameters (e.g. for heating systems) are determined using temperature-based criteria. However, due to climate change, these critical values are likely to change as well. Therefore, it is important to examine the temperature time series affecting the energy consumption of buildings. For the analysis focusing on the Carpathian region within central/eastern Europe, we used the daily average, minimum and maximum temperature time series of five Hungarian cities (i.e. Budapest, Debrecen, Szeged, Pécs and Szombathely). The main aim of this study is to investigate the effect of changing daily average temperatures and the rising extreme values on building design parameters, especially heating and cooling periods (including the length and average temperatures of such periods).</p>

scholarly journals Newest developments of ACMANT

2011 ◽  
Vol 6 (1) ◽  
pp. 7-11 ◽  
Author(s):  
P. Domonkos ◽  
R. Poza ◽  
D. Efthymiadis
Keyword(s):  
Time Series ◽  
Seasonal Changes ◽  
Seasonal Cycle ◽  
Test Procedure ◽  
Temperature Time Series ◽  
Temperature Series ◽  
Substantial Portion ◽  
Indirect Connection ◽  
Efficiency Test ◽  
Temperature Time

Abstract. The seasonal cycle of radiation intensity often causes a marked seasonal cycle in the inhomogeneities (IHs) of observed temperature time series, since a substantial portion of them have direct or indirect connection to radiation changes in the micro-environment of the thermometer. Therefore the magnitudes of temperature IHs tend to be larger in summer than in winter. A new homogenisation method, the Adapted Caussinus – Mestre Algorithm for Networks of Temperature series (ACMANT) has recently been developed which treats appropriately the seasonal changes of IH-sizes in temperature time series. The performance of ACMANT was proved to be among the best methods (together with PRODIGE and MASH) in the efficiency test procedure of COST ES0601 project. A further improved version of the ACMANT is described in this paper. In the new version the ANOVA procedure is applied for correcting inhomogeneities, and with this change the iterations applied in the earlier version have become unnecessary. Some other modifications have also been made, from which the most important one is the new way for estimating the timings of IHs. With these modifications the efficiency of the ACMANT has become even higher, therefore its use is strongly recommended when networks of monthly temperature series from mid- or high geographical latitudes are subjected to homogenisation. The paper presents the main properties and the operation of the new ACMANT.

scholarly journals Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series?

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1072
Author(s):  
Trang Thi Kieu Tran ◽  
Taesam Lee ◽  
Jong-Suk Kim
Keyword(s):  
Time Series ◽  
Weather Forecasting ◽  
Human Life ◽  
Linear Process ◽  
Temperature Time Series ◽  
Maximum Temperature ◽  
Ann Model ◽  
Natural Ecosystems ◽  
Performance Effects ◽  
Temperature Time

Weather forecasting, especially that of extreme climatic events, has gained considerable attention among researchers due to their impacts on natural ecosystems and human life. The applicability of artificial neural networks (ANNs) in non-linear process forecasting has significantly contributed to hydro-climatology. The efficiency of neural network functions depends on the network structure and parameters. This study proposed a new approach to forecasting a one-day-ahead maximum temperature time series for South Korea to discuss the relationship between network specifications and performance by employing various scenarios for the number of parameters and hidden layers in the ANN model. Specifically, a different number of trainable parameters (i.e., the total number of weights and bias) and distinctive numbers of hidden layers were compared for system-performance effects. If the parameter sizes were too large, the root mean square error (RMSE) would be generally increased, and the model’s ability was impaired. Besides, too many hidden layers would reduce the system prediction if the number of parameters was high. The number of parameters and hidden layers affected the performance of ANN models for time series forecasting competitively. The result showed that the five-hidden layer model with 49 parameters produced the smallest RMSE at most South Korean stations.

scholarly journals Analysis of Long Term Temperature Trend for Madhya Pradesh, India (1901-2005)

2017 ◽  
Vol 12 (1) ◽  
pp. 68-79
Author(s):  
Rituraj Shukla ◽  
Deepak Khare ◽  
Priti Tiwari ◽  
Prabhash Mishra ◽  
Sakshi Gupta
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Madhya Pradesh ◽  
Maximum Temperature ◽  
Significance Level ◽  
Monotonic Trend ◽  
Mean Temperature ◽  
The Mean ◽  
The Impact ◽  
Temperature Time

The paper examines the impact of climatic change on the mean temperature time series for Pre-monsoon (Mar-May), Monsoon (Jun-Sept), Post-monsoon (Oct-Nov), winter (Dec-Feb) and Annual (Jan-Dec) at 45 stations in the state of Madhya Pradesh, India. Impact detection is accomplished by using the Mann-Kendall method to find out the monotonic trend and Sen’s slope is method is to identify the grandeur of trend for the period 1901 to 2005 (105 years). Prior to the trend analysis prominence of eloquent lag-1 serial correlation are eradicated from data by the pre-whitening method. In addition, shift year change has also been examined in the study using Pettitt’s test. From 45 stations, most of the station show symbolic hike trend at 5% significance level in the mean temperature time series for Madhya Pradesh region. During peak summer months the maximum temperature touches 40°C in the entire Madhya Pradesh. The magnitudes of annual increase in temperature in the majority of the stations are about 0.01°C.The analysis in the present study indicated that the change point year of the significant upward shift changes was 1963 for annual mean temperature time series, which can be very useful for water resources planners in the study area. The finding of the study provides more insights and inputs for the better understanding of regional temperature and shift behavior in the study area.

scholarly journals Updated Moving Forecasting Model of Air Maximum Temperature

2021 ◽  
Vol 877 (1) ◽  
pp. 012032
Author(s):  
Khalid Hashim ◽  
Hussein Al-Bugharbee ◽  
Salah L. Zubaidi ◽  
Nabeel Saleem Saad Al-Bdairi ◽  
Sabeeh L. Farhan ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
High Performance ◽  
Training Sample ◽  
Temperature Time Series ◽  
Ar Model ◽  
Maximum Temperature ◽  
Forecasting Model ◽  
Temperature Time ◽  
Maximum Air Temperature

Abstract In the current study, a moving forecasting model is used for the purpose of forecasting maximum air temperature. A number of recordings are used for building the AR model and next, to forecasting some temperature values ahead. Then the AR model coefficients are updating due to shifting the training sample by adding new temperature values in order to involve the change in temperature time series behaviour. The current work shows a high performance all over the temperature time series, which considered in the analysis.

scholarly journals Modelling and Forecasting of Monthly Rainfall and Temperature Time Series Using SARIMA for Trend Detection- A Case Study of Umiam, Meghalaya (India)

2020 ◽  
pp. 155-172
Author(s):  
P. P. Dabral ◽  
Issac Tabing
Keyword(s):  
Time Series ◽  
Minimum Temperature ◽  
Moving Average ◽  
Temperature Time Series ◽  
Monthly Rainfall ◽  
Trend Detection ◽  
Maximum Temperature ◽  
Lm Test ◽  
Temperature Time ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average Models (SARIMA) were developed for monthly rainfall, mean monthly maximum and minimum temperature time series for Umiam (Barapani), Meghalaya (India). The best model was selected based on the minimum values of AIC and BIC criteria as well as based on observing the ACF and PACF plot of residuals. SARIMA (5,1,2) x (1,1,1)12, SARIMA (2,1,2) x (2,1,1)12, SARIMA (6,1,4) x (2,1,3)12 models were found to be the best fit model for the monthly rainfall, mean monthly maximum  and minimum temperatures time series respectively. The adequacy of the SARIMA models was also verified using the Ljung-Box (Q) statistic test. McLeod-Li test and Engle’s ARCH LM test were carried out for residuals. The results indicated that there was no Arch effect in the established SARIMA models and models can be used for forecasting the future values for the year 2013 to 2028. The determination of trend in monthly rainfall, mean maximum and minimum temperatures in the forecasted series were done using different trend analysis techniques. For monthly rainfall and mean monthly minimum temperature time series, all the selected methods supported no significant trend. However, in the case of mean monthly maximum temperature time series, three selected methods supported falling trend.

Sign in / Sign up

Export Citation Format

Share Document