Lessons and recommendations for modellers

Model builders

Design your models for flexibility and ease of use.
Develop your models incrementally.
Analysize and document the model sensitivity to each new component or modified parameterization.
Write out the key algorithms.
Document your source code. And make good use of blank lines to compartimentalize the logic/algorithms
Write a publication quality "Methods" or "Model description" while you're developing the code. It's easier than coming back to it later, and it helps to catch algorithmic problems
Use short descriptive variable names
All parameters should have their units explicitly indicated in the code
Use F90 (or C++), implicit none, explicit intents, and modules (not common blocks)
Order arguments consistently, eg: inputs, outputs, control flags
Use existing software libraries whenever possible
Design your own standard data formats for IO (or consider using standards such as netcdf)
Design and implement strong model tests such as flux conservation, sensitivity to time-step and grid resolution...
Test array index ranges with compiler flags
Check all compiler warning messages
Plot key fields

Model based analyses

Consider ensemble-based analyses. There are too many papers published that are based on model analyses with no rational error bars and many hidden parameters.
Before carrying out sensitivity analyses, put careful thought into the choice of parameter ranges
Write out all variable parameters to an ID file for each model run
Build scripts for automated output analysis
Save lots of output especially at the beginning. You can always dump data later. And some of the most interesting results (and subtlest bugs) show up in the least suspected fields.
Plot results. Make movies. Get a visual sense of the model dynamics.

Open-source (or at least free) software recommendations

I'm picky about reasonable learning curves, logical syntax, and simple scripts. These packages are my favourites:

1D (timeseries,..) plots: gnuplot

scriptable, logical syntax

2D area map graphics and netcdf access/regridding...: Ferret

Ferret has a short learning curve and is script based (with optional GUI interface for those so inclined). It is also backed by a very helpful user/developer community

matrix/numerical analysis: Octave

Octave is an open-source partial clone of Matlab

data management/scripting: Python

Python is much more numerically oriented than Perl and as far as I'm concerned, has a more logical syntax

vector graphics: xfig

xfig is easy to use and can import many types of graphic files

GIS: grass5

Powerful, scriptable, relatively easy learning curve, diverse set of toolboxes

Statistical Computing: R

Similar to the S language and environment but open-sourced and with loads of tool-boxes.

general scripting: awk

awk, csh, sed, join, cut are all easy to use and powerful tools for managing large datasets, automating dataprocessing,...

Artificial Neural Networks

Netlab is a freely available Neural Toolbox for Matlab (that unfortunately doesn't work on Octave). It's biggest plus is the associated text: Netlab: Algorithms for Pattern Recognition. All the algorithms are spelled out in detail in the textbook and for me this was a great learning aid. Phil Goodman's Nevprop is the easiest package that I've found to use. It is a good starting point for someone who wants to get quick results even though it is limited to standard feedforward multilayer perceptron networks. For Bayesian neural networks and Markov chain sampling I reccomend Radford Neal's software for flexible Bayesian modelling which is being used in our calibration project.

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