scholarly journals Uso de parâmetros estatísticos para a classificação de regiões homogêneas de temperatura do ar em Santa Catarina

2020 ◽  
Vol 42 ◽  
pp. e10
Author(s):  
Carlos Eduardo Salles de Araujo
Keyword(s):  
Time Series ◽  
Pearson Correlation ◽  
Weather Forecast ◽  
Data Representation ◽  
Temperature Time Series ◽  
Santa Catarina ◽  
Homogeneous Groups ◽  
Hourly Temperature ◽  
Weather Stations ◽  
Temperature Time

Time series of hourly temperature from 146 weather stations located in Santa Catarina State – South Brazil were used to show that a compact data representation, using probability density functions (PDF) parameters, could be useful to classify homogeneous air temperature areas. The normal distribution fitted well the 146 weather stations temperature time series, presenting a median value of 0.9721 for the Pearson correlation coefficient.  The means and standard deviations obtained by adjusting the Gaussian functions for the 146 stations were used as input parameters for two different classifiers: hierarchical and k-means. Both classifiers separated Santa Catarina's weather stations into four distinct groups. These groups had direct relationship with altitude ranges and with the influence of sea. The classification of weather stations in different homogeneous groups was useful to identify climatic behaviors of hourly temperatures. In addition to the characterization of the climate itself, this classification can be useful as a support for the validation of numerical weather forecast models, and for the identification of abnormal temperature time series in a regional spatial context.

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>

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 Warming: Is There Evidence in Africa?

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Hector Carcel ◽  
Luis A. Gil-Alana
Keyword(s):  
South Africa ◽  
Time Series ◽  
Climate Warming ◽  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Fractional Integration ◽  
Temperature Time Series ◽  
Cote D’Ivoire ◽  
Weather Stations ◽  
Temperature Time

We have examined the temperature time series across several locations in Africa. In particular, we focus on three countries, South Africa, Kenya, and Côte d’Ivoire, examining the monthly averaged temperatures from three weather stations at different locations in each country. We examine the presence of deterministic trends in the series in order to check if the hypothesis of warming trends for these countries holds; however, instead of using conventional approaches based on stationaryI(0)errors, we allow for fractional integration, which seems to be a more plausible approach in this context. Our results indicate that temperatures have only significantly increased during the last 30 years for the case of Kenya.

scholarly journals The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Exchange ◽  
Temperature Time Series ◽  
Temperature Time ◽  
Streambed Heterogeneity

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

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