PythonicDISORT 0.2.0

Discrete Ordinates Solver for the (1D) Radiative Transfer Equation in a single or multi-layer atmosphere.

Introduction

The PythonicDISORT package is a Discrete Ordinates Solver for the (1D) Radiative Transfer Equation in a single or multi-layer atmosphere. It is coded entirely in Python 3 and in as "Pythonic" a manner as possible: we vectorize as well as use list comprehension and map as much as possible.

PythonicDISORT is based on Stamnes' FORTRAN DISORT (see References, in particular [2, 3, 8]) and has its main features: delta-M scaling, Nakajima-Tanaka (NT) corrections, only flux option, isotropic internal sources (thermal source), Bi-Directional Reflectance Function (BDRF) and more.

This repository also includes our F2PY-wrapped Stamnes DISORT (version 4.0.99) in the disort4.0.99_f2py directory. The original was downloaded from http://www.rtatmocn.com/disort/. The wrapper was inspired by https://github.com/kconnour/pyRT_DISORT.

Documentation

https://pythonic-disort.readthedocs.io/en/latest/

Also see the accompanying Jupyter Notebook: Pythonic-DISORT.ipynb in the docs directory at https://github.com/LDEO-CREW/Pythonic-DISORT. The Jupyter Notebook provides comprehensive documentation, suggested inputs, explanations, mathematical derivations and verification tests. We highly recommend reading the non-optional parts of sections 1 and 2 before use.

PyTest

Separate from the verification tests in the Jupyter Notebook, we used PyTest to recreate most of the test problems from Stamnes et. al.'s disotest.f90. With PyTest installed, execute the console command pytest in the pydisotest directory to run these tests.

Installation

• From PyPI: pip install PythonicDISORT
• From Conda-forge: (TODO: we need to first publish on Conda-forge)
• By cloning repository: pip install . in the Pythonic-DISORT directory; pip install -r all_optional_dependencies.txt to install all optional dependencies

Requirements to run PythonicDISORT

• Python 3.8+
• numpy >= 1.17.3
• scipy >= 1.8.0
• (OPTIONAL) joblib >= 1.0.0 (Required for parallelization)
• (OPTIONAL) pytest >= 6.2.5 (Required for non-Notebook tests)

Additional requirements to run the Jupyter Notebook

• autograd >= 1.5
• jupyter > 1.0.0
• notebook > 6.5.2

In addition, our F2PY-wrapped Stamnes' DISORT (in the disort4.0.99_f2py directory) must be set up to run the last section (section 6).

Compatibility

The PythonicDISORT package should be system agnostic given its minimal dependencies and pure Python code, but we cannot say the same for our Jupyter Notebook and F2PY-wrapped Stamnes' DISORT. Everything was built and tested on Windows 11 and not yet tested on other systems.

References

1. S. Chandrasekhar. 1960. Radiative Transfer.

2. Knut Stamnes and S-Chee Tsay and Warren Wiscombe and Kolf Jayaweera. 1988. Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. http://opg.optica.org/ao/abstract.cfm?URI=ao-27-12-2502.

3. Stamnes, S.. 1999. LLLab disort website. http://www.rtatmocn.com/disort/.

4. Knut Stamnes and Paul Conklin. 1984. A new multi-layer discrete ordinate approach to radiative transfer in vertically inhomogeneous atmospheres. https://www.sciencedirect.com/science/article/pii/0022407384900311.

5. W. J. Wiscombe. 1977. The Delta–M Method: Rapid Yet Accurate Radiative Flux Calculations for Strongly Asymmetric Phase Functions. https://journals.ametsoc.org/view/journals/atsc/34/9/1520-0469_1977_034_1408_tdmrya_2_0_co_2.xml.

6. J. H. Joseph and W. J. Wiscombe and J. A. Weinman. 1976. The Delta-Eddington Approximation for Radiative Flux Transfer. https://journals.ametsoc.org/view/journals/atsc/33/12/1520-0469_1976_033_2452_tdeafr_2_0_co_2.xml.

7. Sykes, J. B.. 1951. Approximate Integration of the Equation of Transfer. https://doi.org/10.1093/mnras/111.4.377.

8. Stamnes, Knut and Tsay, Si-Chee and Wiscombe, Warren and Laszlo, Istvan and Einaudi, Franco. 2000. General Purpose Fortran Program for Discrete-Ordinate-Method Radiative Transfer in Scattering and Emitting Layered Media: An Update of DISORT.

9. Z. Lin and S. Stamnes and Z. Jin and I. Laszlo and S.-C. Tsay and W.J. Wiscombe and K. Stamnes. 2015. Improved discrete ordinate solutions in the presence of an anisotropically reflecting lower boundary: Upgrades of the DISORT computational tool. https://www.sciencedirect.com/science/article/pii/S0022407315000679.

10. Trefethen, L. N.. 1996. Finite difference and spectral methods for ordinary and partial differential equations. https://people.maths.ox.ac.uk/trefethen/pdetext.html.

11. T. Nakajima and M. Tanaka. 1988. Algorithms for radiative intensity calculations in moderately thick atmospheres using a truncation approximation. https://www.sciencedirect.com/science/article/pii/0022407388900313.

12. K. Connour and A. Stcherbinine. 2022. pyRT_DISORT. https://github.com/kconnour/pyRT_DISORT.