EGU2017- 18343 (R4SREs) M. Zambrano- Using R for analysing spatio-temporal datasets: Bigiarini a satellite-based precipitation case study 2-minute madness Session ”R’s deliberate role in Earth sciences” Motivation Precipitation EGU2017-18343, Wien, Austria Limitations Why SREs? Datasets Selected SREs Mauricio Zambrano-Bigiarini 1 , 2 Point-to-pixel R functions 1 Universidad de La Frontera, Temuco, Chile Downloading raster 2 Center for Climate and Resilience Research, Santiago, Chile hydroTSM hydroGOF mauricio.zambrano @ ufrontera.cl Results Ongoing work April 25th, 2017 References
EGU2017- 18343 Outline (R4SREs) M. Zambrano- 1 2-minute madness Bigiarini 2 Motivation 2-minute Precipitation: a key hydrological forcing madness Limitations of station-based precipitation Motivation Precipitation Why using SREs ? Limitations Why SREs? 3 Datasets Datasets Selected SREs Selected SREs Point-to-pixel 4 Point-to-pixel comparison R functions 5 R functions and scripts Downloading raster Automatic downloading of SRE files hydroTSM hydroGOF raster package Results hydroTSM package Ongoing work hydroGOF package References 6 Results 7 Ongoing work
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. 2-minute madness Motivation Precipitation Limitations Why SREs? Datasets Selected SREs Point-to-pixel R functions Downloading raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. • Traditional (i.e., station-based) representation of the spatio-temporal variability 2-minute madness of precipitation has several limitations. Motivation Precipitation Limitations Why SREs? Datasets Selected SREs Point-to-pixel R functions Downloading raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. • Traditional (i.e., station-based) representation of the spatio-temporal variability 2-minute madness of precipitation has several limitations. Motivation Precipitation • In the last decades, several satellite-based rainfall estimates (SREs) have provided Limitations Why SREs? an unprecedented opportunity for improving the spatio-temporal representation Datasets of precipitation. Selected SREs Point-to-pixel R functions Downloading raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. • Traditional (i.e., station-based) representation of the spatio-temporal variability 2-minute madness of precipitation has several limitations. Motivation Precipitation • In the last decades, several satellite-based rainfall estimates (SREs) have provided Limitations Why SREs? an unprecedented opportunity for improving the spatio-temporal representation Datasets of precipitation. Selected SREs Point-to-pixel • State-of-the-art SREs are provided in different file formats (e.g., .bin, .nc, .tiff), R functions with different spatial extents and different temporal frequencies (e.g., Downloading raster half-hourly, 3-hours, daily, monthly). hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. • Traditional (i.e., station-based) representation of the spatio-temporal variability 2-minute madness of precipitation has several limitations. Motivation Precipitation • In the last decades, several satellite-based rainfall estimates (SREs) have provided Limitations Why SREs? an unprecedented opportunity for improving the spatio-temporal representation Datasets of precipitation. Selected SREs Point-to-pixel • State-of-the-art SREs are provided in different file formats (e.g., .bin, .nc, .tiff), R functions with different spatial extents and different temporal frequencies (e.g., Downloading raster half-hourly, 3-hours, daily, monthly). hydroTSM hydroGOF • Hydrological models usually require long time series (e.g., 30 years) of Results precipitation to run and explore climate impacts on streamflows. Ongoing work References
EGU2017- 18343 Problem description (R4SREs) M. Zambrano- Bigiarini • Precipitation is a key driver of the water and energy cycles. • Traditional (i.e., station-based) representation of the spatio-temporal variability 2-minute madness of precipitation has several limitations. Motivation Precipitation • In the last decades, several satellite-based rainfall estimates (SREs) have provided Limitations Why SREs? an unprecedented opportunity for improving the spatio-temporal representation Datasets of precipitation. Selected SREs Point-to-pixel • State-of-the-art SREs are provided in different file formats (e.g., .bin, .nc, .tiff), R functions with different spatial extents and different temporal frequencies (e.g., Downloading raster half-hourly, 3-hours, daily, monthly). hydroTSM hydroGOF • Hydrological models usually require long time series (e.g., 30 years) of Results precipitation to run and explore climate impacts on streamflows. Ongoing work References it is computationally challenging to read and analyse ∴ hundreds/thousands of station-based time series and SRE files.
EGU2017- 18343 My R solution: (R4SREs) M. Zambrano- 1 To develop R scripts to automatically download daily SRE files for a Bigiarini user-defined time period and clip them to the desired spatial extent (if 2-minute necessary). madness Motivation Precipitation Limitations Why SREs? Datasets Selected SREs Point-to-pixel R functions Downloading raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 My R solution: (R4SREs) M. Zambrano- 1 To develop R scripts to automatically download daily SRE files for a Bigiarini user-defined time period and clip them to the desired spatial extent (if 2-minute necessary). madness Motivation 2 To use the raster package to read , plot , and carry out an EDA , in order to Precipitation Limitations detect unexpected problems (e.g., rotated spatial domains, wrong order of Why SREs? Datasets variables in NetCDF files, missing NA flags). Selected SREs Point-to-pixel R functions Downloading raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 My R solution: (R4SREs) M. Zambrano- 1 To develop R scripts to automatically download daily SRE files for a Bigiarini user-defined time period and clip them to the desired spatial extent (if 2-minute necessary). madness Motivation 2 To use the raster package to read , plot , and carry out an EDA , in order to Precipitation Limitations detect unexpected problems (e.g., rotated spatial domains, wrong order of Why SREs? Datasets variables in NetCDF files, missing NA flags). Selected SREs 3 To use raster along with the hydroTSM package to aggregate SRE files and Point-to-pixel R functions rain gauge time series into different temporal scales (daily, monthly, seasonal, Downloading annual). raster hydroTSM hydroGOF Results Ongoing work References
EGU2017- 18343 My R solution: (R4SREs) M. Zambrano- 1 To develop R scripts to automatically download daily SRE files for a Bigiarini user-defined time period and clip them to the desired spatial extent (if 2-minute necessary). madness Motivation 2 To use the raster package to read , plot , and carry out an EDA , in order to Precipitation Limitations detect unexpected problems (e.g., rotated spatial domains, wrong order of Why SREs? Datasets variables in NetCDF files, missing NA flags). Selected SREs 3 To use raster along with the hydroTSM package to aggregate SRE files and Point-to-pixel R functions rain gauge time series into different temporal scales (daily, monthly, seasonal, Downloading annual). raster hydroTSM hydroGOF 4 To use hydroTSM along with the hydroGOF package to carry out a Results point-to-pixel comparison between ts observed at 366 stations and the Ongoing work corresponding grid cell of each SRE. References
EGU2017- 18343 My R solution: (R4SREs) M. Zambrano- 1 To develop R scripts to automatically download daily SRE files for a Bigiarini user-defined time period and clip them to the desired spatial extent (if 2-minute necessary). madness Motivation 2 To use the raster package to read , plot , and carry out an EDA , in order to Precipitation Limitations detect unexpected problems (e.g., rotated spatial domains, wrong order of Why SREs? Datasets variables in NetCDF files, missing NA flags). Selected SREs 3 To use raster along with the hydroTSM package to aggregate SRE files and Point-to-pixel R functions rain gauge time series into different temporal scales (daily, monthly, seasonal, Downloading annual). raster hydroTSM hydroGOF 4 To use hydroTSM along with the hydroGOF package to carry out a Results point-to-pixel comparison between ts observed at 366 stations and the Ongoing work corresponding grid cell of each SRE. References Are you curious about specific functions and results? → go to Spot A.4 .
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