💧🌏 Greg CocksBURGER - A Bottom-Up Regionalization Approach for Global Sub-Daily Intensity-Duration-Frequency Data
--
https://doi.org/10.1029/2024WR039773 <-- shared paper
--
https://zenodo.org/records/15473689 <-- shared dataset
--
"KEY POINTS
• BURGER is the first global IDF data set derived from observations of sub-daily rainfall
• Sub-daily rainfall intensities are computed with the Simplified Metastatistical Extreme Value (SMEV) approach for a range of return periods
• BURGER intensities compare well with expected intensities, indicated by an interquartile range of the percentage bias between −5% and 5%..”
#GIS #spatial #data #mapping #global #exteremeweather #precipitation #rainfall #gauge #intensity #model #modeling #spatialanalysis #spatiotemporal #BURGER #geostatistics #spatialstatistics # statistics #GSDR #IDF #remotesensing #earthobservation #machinelearning #AI #weather #climate #prediction #usecase #flood #risk #hazard #SMEV #accuracy #metrology
--
https://doi.org/10.1029/2024WR039773 <-- shared paper
--
https://zenodo.org/records/15473689 <-- shared dataset
--
"KEY POINTS
• BURGER is the first global IDF data set derived from observations of sub-daily rainfall
• Sub-daily rainfall intensities are computed with the Simplified Metastatistical Extreme Value (SMEV) approach for a range of return periods
• BURGER intensities compare well with expected intensities, indicated by an interquartile range of the percentage bias between −5% and 5%..”
#GIS #spatial #data #mapping #global #exteremeweather #precipitation #rainfall #gauge #intensity #model #modeling #spatialanalysis #spatiotemporal #BURGER #geostatistics #spatialstatistics # statistics #GSDR #IDF #remotesensing #earthobservation #machinelearning #AI #weather #climate #prediction #usecase #flood #risk #hazard #SMEV #accuracy #metrology
Ruben Zondervan
Sustainable Development MA