scholarly journals Multi‐objective Downscaling of Precipitation Time Series by Genetic Programming

2021 ◽  
Author(s):  
Tanja Zerenner ◽  
Victor Venema ◽  
Petra Friederichs ◽  
Clemens Simmer
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Precipitation Time Series ◽  
Multi Objective

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

2021 ◽  
Vol 24 ◽  
pp. 100618
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula Souza e Guimarães ◽  
Fábio Dall Cortivo ◽  
Rayana Santos Araújo Palharini ◽  
Milton Kampel
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Precipitation Time Series ◽  
New Approach

Multi-objective Synthesis of Quantum Circuits Using Genetic Programming

2018 ◽  
pp. 220-227
Author(s):  
Moein Sarvaghad-Moghaddam ◽  
Philipp Niemann ◽  
Rolf Drechsler
Keyword(s):  
Genetic Programming ◽  
Quantum Circuits ◽  
Multi Objective

scholarly journals Rehabilitation and analysis of Canadian daily precipitation time series

1999 ◽  
Vol 37 (1) ◽  
pp. 53-85 ◽  
Author(s):  
Eva Mekis ◽  
William D. Hogg
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Precipitation Time Series

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

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