The assessment of rainfall extremes at sub-hourly scales is generally hindered by a lack data small timescale resolutions. This study proposes methodology for assessing mean annual maximum the scale blending historical time series maxima recorded mechanical stations (operating hourly scales) up to end past century with newer higher resolutions automatic installed over twenty years. A linear cor...

Sub-daily rainfall observations are vital to help us understand, model and adapt changing climate extremes. However, gauge records often have quality issues, for example due equipment malfunctions recording errors. This paper presents a new, open-source control algorithm (GSDR-QC) identify these issues in hourly data, along with an application of the Global Sub-Daily Rainfall (GSDR) observation...

Power consumption forecasting is a crucial need for power management to achieve sustainable energy. The demand increasing over time, while the of possesses challenges with nonlinearity patterns and various noise in datasets. To this end, paper proposes RobustSTL temporal convolutional network (TCN) model forecast hourly consumption. Through RobustSTL, instead standard STL, decomposition method ...