denis-bz

Cosmopy: clip and regrid COSMO_REA6 wind data to python numpy

Keywords: Cosmo_rea6 DWD wind-data python numpy

Purpose: DWD, the German Meteorological Service, has wind data for Europe on a 6 x 6 km grid, from 1995 to August 2019, hourly, at heights 10 100 .. 200m. These are in 2 gigabyte files like
https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/converted/hourly/2D/WS_100m.2D.201908.nc4
-- windspeed data at 100m for 2019 August.

Old mst.py with Kruskal's algorithm

mst.py is simple old (2011) code for MSTs using Kruskal's algorithm -- see the animation there.

Kleinberg-Tardos:

single-link k-clustering == MST k-1 most expensive edges

but min( max spacing ), Theorem 1, may be wrong or at least odd for k-clustering ?

Windspeeds in Bayern, COSMO_REA6 vs. ERA5

Here are plots of average windspeeds in Bayern in January 2018 at 100m,
COSMO_REA6 on its ~ 6 × 6 km grid
ERA5 on its 0.25° × 0.25° grid.
We see big differences over the Alps in the south and over hilly areas in north Bayern.

Wind speeds at 100m in Bayern, ERA5 vs. globalwindatlas

Keywords: wind power, Germany, python, ERA5, globalwindatlas

Purpose: look at windspeed estimates at 100m in Bayern from two different sources:
ERA5: https://confluence.ecmwf.int/display/CKB/ERA5
GWA: https://globalwindatlas.info/area/Germany/Bayern

How efficient are wind, sun, biomass in the German provinces in 2019 - 2020 ?

Keywords: Renewable energy, wind energy, electricity, Germany

Purpose: look at the efficiency of electricity produced in the German provinces from sun, wind and biomass, with files from official data:
LAK, real: https://www.lak-energiebilanzen.de 2019, Länderarbeitskreis Energiebilanzen Bruttostromerzeugung nach Erneuerbaren Energieträgern
BMWK, max 31.12.2020,

Windspeeds and capacity factors in the Netherlands from wikipedia, KNMI and globalwindatlas

Keywords: wind, wind farm, wind speed, capacity factor, onshore, Netherlands, globalwindatlas, GWA, KNMI

What I want to do is show my neighbors around Munich that windfarms make more sense where there's more wind. GWA has a nice GUI, which everyone can understand. But are their wind speed and capacity factors accurate enough to convince anyone ?

nl.wikipedia Windturbines_in_Nederland,

Introduction to Netherlands wind data with xarray

Keywords: Netherlands, wind, wind speed, KNMI, python, xarray, data-carpentry

Purpose: introduce Netherlands wind data from KNMI, the Royal Netherlands$ Meteorological Institute, with the steps:

download 2 \.nc files by hand, one day and one gridpoint
look at them in xarray

Windspeeds and windfarms in Bayern

Keywords: wind power, Germany, Bayern, globalwindatlas, GWA

Purpose: I'm trying to show my neighbors around Munich that windfarms make more sense where there's more wind. To this end, here's a map of GWA windspeeds at 150m, together with the sites of 202 windfarms in Bayern:

Poisson2 - 4 I: nice test matrices for linear solvers and eigensolvers

Purpose: a simple generator of test matrices for linear solvers and eigensolvers

Keywords: sparse-matrix, linear-solver, eigensolver, arpack, nullspace, python

poisson2( n ) below generates sparse $n^2 \ x \ n^2$ matrices $A$ with $A$ symmetric (aka Hermitian) and positive-definite; solving $A \ x = b$ is then relatively easy. $A_4 = A - 4 I$ is more interesting: there are $n$ vectors $z_i$

	#!/usr/bin/env python3
	""" Usage:
	python3 ssquery.py '(posdef == 1) & (1000 <= rows <= 20000)' posdef.csv
	reads http://sparse.tamu.edu/files/ssstats.csv
	queries it
	writes "posdef.csv" like

	# SuiteSparse posdef & (1000 <= rows <= 20000)
	id,group,name,rows,cols,nnz,real,bool,is2d3d,posdef,symm,nsymm,kind
	29156,Boeing,msc01050,1050,1050,26198,1,0,1,1,1.0,1.0,structural