I haven't been posting for a while as I now realise.
So here a new post about a fairly simple and/but old model to calculate the net primary productivty (NPP), the so called Miami Model (Lieth, 1973):
NPP(T) = 3000(1+\exp(1.315-0.119 T)) \\
NPP(P) = 3000 (1-\exp(-0.000664 P)) \\
NPP = \min(NPP(T),NPP(P))
After reading through the more recent literature, it turns out that the Miami-Model is still regarded as useful proxy for annual NPP.
Global NPP observations are available, for example the Terra/MODIS Net Primary Production Yearly L4 Global 1km.
But these are currently not available as global data set.
Instead you can download all tiles (i.e., chunks of 1km by 1km tiles).
I have no idea why, but apparently, nobody is interested in compiling it from the available tile data.
(Maybe a topic for another blog post?).
Anyways, here is what the annual NPP looks like for a present-day climatology, i.e. the Ten Minute Climatology.
Here is a simple Python implementation:
import numpy as np
npp_temp = a/(1.+np.exp(b-c*temp))
npp_prec = a*(1.-np.exp(-d*prec))
Hello everyone. This is my first posting on my blog where I want to provide some useful information about my job search.
I started of as a physicist (TU-Berlin) about six years ago. Afterwards I went to Hamburg to do my graduate studies at the Hamburg University and the Max Planck Institute for Meteorology. After finishing my Ph.D. I went back to Berlin to work at the Potsdam Institute for Climate Impact Research as a postdoctoral researcher. My project about the melting of the Greenland Icesheet on different time scales ended two weeks ago. Now I'm taking my time to recollect what I've done so far and what I want to do next. This blog should help me and others to keep track about the expected and unexpected things to come, difficulties and the joy and pain of finding a new job outside of academia.
As of this week, I started writing a static blog engine in Python (pystable), which can be found on Github and cloned via
git clone https://github.com/mkrapp/pystable.git
Actually, this very web page is based on pystable.
And I like coding. I've been working with complex computer models ever since my undergrad and I enjoy data exploration and data analysis to gain insights into the underlying principles.
Feel free to contact me.