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wradlib notebooks 2.8.0
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Python Intro
A quick start to Python
Numpy: manipulating numerical data
Visualising data with Matplotlib
Dealing with time series
wradlib Basics
Get wradlib version
Typical Workflow for Radar-Based Rainfall Estimation
From Reflectivity to Rainfall
wradlib in an hour
Data Input/Output
Legacy readers
DWD DX
Reading NetCDF
HDF5
OPERA ODIM
GAMIC HDF
Leonardo Rainbow
Vaisala IRIS/Sigmet
Xarray readers
RADOLAN
ODIM
GAMIC
CfRadial1
CfRadial2
Iris/Sigmet
Rainbow5
Furuno SCN/SCNX
GIS Vector Data
GIS Raster Export
Visualization
Plot PPI
Plot RHI
Plot Curvelinear Grids
Plot geodata
Plot geodata with cartopy
Plot radar scan strategy
Attenuation Correction
Beam Blockage
Echo Classification
Clutter detection using Gabella approach
Clutter detection by using space-born cloud images
Heuristic clutter detection
Fuzzy echo classification
2D Membershipfunction HMC
Hydrometeor partitioning ratio - GPM
Hydrometeor partitioning ratio retrievals for Ground Radar
Georeferencing
Computing Cartesian Coordinates from Polar Data
Georeferencing a Radar Dataset
Overlay Ancillary Data
Managing Georeferenced Data
Quickstart
Cartesian Grid
Polar Grid
Interpolation
Gauge Adjustment
Verification
Zonal Statistics
Quickstart
Cartesian Grid
Polar Grid
Recipes
Recipe1: Clutter and attenuation correction plus composition for two DWD radars
Recipe2: Reading and visualizing an ODIM_H5 polar volume
Recipe3: Match spaceborn SR (GPM/TRRM) with ground radars GR
Load ODIM_H5 Volume data from German Weather Service
Recipe5: Zonalstats on Cartesian Grid
Recipe6: Zonalstats on Polar Grid
RADOLAN Guide
.md
.pdf
Contents
Contents