scholarly journals IDENTIFICATION OF RADIATIVE AND ADVECTIVE POPULATIONS IN CANADIAN TEMPERATURE TIME SERIES USING THE LINEAR PATTERN DISCRIMINATION ALGORITHM

2021 ◽  
Author(s):  
Ana Žaknić‐Ćatović ◽  
William A. Gough
Keyword(s):  
Time Series ◽  
Pattern Discrimination ◽  
Temperature Time Series ◽  
Linear Pattern ◽  
Temperature Time

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

Modeling of Predictable Variations in Multi-Time Instant Ambient Temperature Time Series

2021 ◽  
Author(s):  
Udith Shyamsukha ◽  
Nimish Jain ◽  
Tirthankar Chakraborty ◽  
B Rajanarayan Prusty ◽  
Kishore Bingi
Keyword(s):  
Time Series ◽  
Ambient Temperature ◽  
Time Instant ◽  
Temperature Time Series ◽  
Temperature Time

scholarly journals Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Information ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 60 ◽  
Author(s):  
Li Ma ◽  
Xiaodu Gu ◽  
Baowei Wang
Keyword(s):  
Time Series ◽  
Sensor Network ◽  
Sliding Window ◽  
Temperature Time Series ◽  
Temperature Time

scholarly journals The Simulation of Daily Temperature Time Series from GCM Output. Part I: Comparison of Model Data with Observations

1997 ◽  
Vol 10 (10) ◽  
pp. 2497-2513 ◽  
Author(s):  
J. P. Palutikof ◽  
J. A. Winkler ◽  
C. M. Goodess ◽  
J. A. Andresen
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Daily Temperature ◽  
Model Data ◽  
Temperature Time

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.

Climate change, trends in extremes, and model assessment for a long temperature time series from Sweden

2011 ◽  
Vol 22 (3) ◽  
pp. 456-463 ◽  
Author(s):  
Peter Guttorp ◽  
Jia Xu
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Temperature Time Series ◽  
Model Assessment ◽  
Temperature Time

Long-term variability of the temperature time series recorded in Lisbon

2009 ◽  
Vol 36 (3) ◽  
pp. 323-337 ◽  
Author(s):  
João Santos ◽  
Solange Leite
Keyword(s):  
Time Series ◽  
Temperature Time Series ◽  
Temperature Time

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>

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