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.

Artificial intelligence reconstructs missing climate information

2021 ◽  
Author(s):  
Christopher Kadow ◽  
David Hall ◽  
Uwe Ulbrich
Keyword(s):  
Artificial Intelligence ◽  
Twentieth Century ◽  
Time Series ◽  
Missing Values ◽  
Pearson Correlation ◽  
Image Inpainting ◽  
Temperature Time Series ◽  
Climate Information ◽  
Mean Temperature ◽  
Temperature Time

<p>Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.</p><p>From:</p><p>Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. <em>Nature Geoscience</em> <strong>13, </strong>408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5</p><p>The presentation will tell from the journey of changing an image AI to a climate research application.</p>

scholarly journals A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

2010 ◽  
Vol 23 (19) ◽  
pp. 5325-5331 ◽  
Author(s):  
Andrea Toreti ◽  
Franz G. Kuglitsch ◽  
Elena Xoplaki ◽  
Jürg Luterbacher ◽  
Heinz Wanner
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Eastern Mediterranean ◽  
Higher Order ◽  
Temperature Time Series ◽  
Daily Temperature ◽  
Change Analysis ◽  
Higher Order Moments ◽  
The Impact ◽  
Temperature Time

Abstract Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

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 Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

Artificial intelligence reconstructs missing climate information - newest developments

2021 ◽  
Author(s):  
Christopher Kadow ◽  
David M. Hall ◽  
Uwe Ulbrich ◽  
Johannes Meuer ◽  
Thomas Ludwig
Keyword(s):  
Artificial Intelligence ◽  
Twentieth Century ◽  
Time Series ◽  
Missing Values ◽  
Pearson Correlation ◽  
Image Inpainting ◽  
Temperature Time Series ◽  
Climate Information ◽  
Mean Temperature ◽  
Temperature Time

<p>Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.</p> <p>As published in:</p> <p>Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. <em>Nat. Geosci.</em> <strong>13, </strong>408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5</p> <p>Newest developments around the technology will be presented.</p> <p> </p>

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.

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