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README

What is this repository for?

This repository contains all the necessary code and data files to run Bio-SNICAR.

IMPORTANT NOTE

Development of BioSNICAR has continued in the form of BioSNICAR_GO (https://github.com/jmcook1186/BioSNICAR_GO_PY). BioSNICAR_GO includes options to run in Mie scattering or Geometric Optics modes, allowing better modellng of dry or wet snow or ice. There are also significant updates to the bio-optical model and empirical pigment absorption coefficients for glacier algae in that repository. Running BioSNICAR_GO in Mie mode is equivalent to running the original BiOSNICAR model provided here. Since I have been refining, debugging and extending BioSNICAR under the BioSNICAR_GO framework, I strongly suggest using that model in preference to the original code provided here.

How do I get set up?

The essential scripts are all written for Matlab. This includes the driver software, the radiative transfer code, which are all that are needed to run model experiments using our existing impurity lookup library. However, if new impurities are to be created additional scripts are used. A few of these are currently coded in Python, but will be translated for Matlab. All the necessary data files and scripts are included in this repository. Basic usage guidelines are provided below, but for detailed instructions we direct the user to the instruction manual available in the repo.

The software versions used to write the codes were Matlab R2016b and Python 3.4 using the Anaconda 3.4.3 distribution (Windows 64bit).

Contribution guidelines

We welcome collaborations and contributions from other developers. We highlight the incorporation of grain shape effects and translation of BioSNICAR into Python as particularly useful development goals.

Who do I talk to?

Please direct queries to Joseph Cook (University of Sheffield) at the following email address: [email protected]

Usage Guidelines

To run this model, open 'BioSNICAR_driver_MASTER.m'. This is the driver software where the model parameters are defined and formatted so that they can be read by the radiative transfer code (snicar_8d.m). When the driver is run, the user-defined input values are sent to the radiative transfer model. The spectral albedo is plotted in a new window and a numeric value for the broadband albedo is printed in the command window.

By default, the incoming solar irradiance is for a mid-latitude site with a 60 degree zenith angle. The snow grain size in each vertical layer is defined in an array in line 84. The impurity concentrations in each layer are defined in lines 112 - 125. The model is run simply by pressing F5 or clicking the green 'run' arrow in the toolbar.

Disclaimer

This software is in active development. We provide free and open access to this software on the understanding that the authors assume no responsibility for downstream usage and, while we welcome collaboration, we are not obliged to provide training or support for users. We hope that this code and data will be useful and encourage collaboration, but we provide it without warranty or guarantee, nor the implied warranty of merchantability or fitness for any particular purpose.

Joseph Cook's Projects

albedo_neuralnet icon albedo_neuralnet

Uses full RTMs to generate training data, then trains neural nets as lightweight alternatives to full models.

azure-sci-framework icon azure-sci-framework

a python implementation for https://github.com/Green-Software-Foundation/ief/tree/dev

biodisortpy icon biodisortpy

Python implementation of DISORT including biological impurities from BiOSNICAR_GO

biosnicar icon biosnicar

1-D radiative transfer model for predicting albedo of snow contaminated with various algal cells

biosnicar_go icon biosnicar_go

An implementation of the BioSNICAR model using geometric optics to determine ice optical properties

book icon book

The Rust Programming Language

brightlink icon brightlink

PoC project that incentivizes communities to "brighten "or "green" their environments as a local response to climate change.

caspa icon caspa

Cellular Automaton for SnowPack Albedo: A coupled cellular automaton, radiative transfer model and grain growth model for simulating biomass growth, dust scavenging, snowpack albedo, microstructure and radiative forcing over space and time

cohort-one icon cohort-one

Repository for the second cohort of the apprenticeship program

community-dashboard icon community-dashboard

a nextjs dashboard app tracking participation on Github and Discord using Orbit API

cryoconite_ca icon cryoconite_ca

A simple cellular automaton showing that complex cryoconite hole shapes can be explained using simple sediment transfer rules

cryoconitertm icon cryoconitertm

Radiative transfer model for calculating energy fluxes in an Arctic cryoconite hole

data_archive_tc2019 icon data_archive_tc2019

Data in support of Cook et al (2019) Algae accelerate melting of the Greenland Ice Sheet

ethernaut icon ethernaut

ethernaut solutions and notes (https://ethernaut.openzeppelin.com/)

flowmodel icon flowmodel

Model subsurface flow through a glacier weathering crust

icesurfclassifiers icon icesurfclassifiers

Code for applying ML algorithms trained on ground spectra to autoclassify ice surface type in UAV or Sentinel-2 derived multispectral imagery

if icon if

impact engine framework

imp-shell icon imp-shell

impact engine framework plugin: calls executable conforming to imp spec and returns ompl data to stdout/disk

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