diff --git a/Overview.ipynb b/Overview.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ecd82138ab300acd28467a15d68c86ba23bf86cc --- /dev/null +++ b/Overview.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "For the data assimilation algorithms:\n", + "* Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Weather Rev., 129, 2884–2903. [https://doi.org/10.1175/1520-0493(2001)129%3C2884:AEAKFF%3E2.0.CO;2]\n", + "* Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Weather Rev., 131, 634–642. [https://doi.org/10.1175/1520-0493(2003)131%3C0634:ALLSFF%3E2.0.CO;2]\n", + "* Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D, 230, 112–126.\n", + "[http://dx.doi.org/10.1016/j.physd.2006.11.008]\n", + "\n", + "For the single-column model of the convective boundary layer (CBL):\n", + "* Troen, I. B., and L. Mahrt, 1986: A simple model of the atmospheric boundary layer; sensitivity to surface evaporation. Bound.-Layer Meteorol., 37, 129–148. [https://doi.org/10.1007/BF00122760]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to run a simple OSSE experiment\n", + "\n", + "The default configuration files (in `json` format) are set to compute a single analysis that assimilates a single observation. For configuration details, see below. Simply run:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading .//experiment.pickle failed, running it\n", + "Spinup : # ...done\n", + "Nature run : # ...done\n", + "Cycle 0000 : ########## ...done\n" + ] + } + ], + "source": [ + "from ENDA import experiment\n", + "import pickle\n", + "import json\n", + "from PE_CBL_graphics import *\n", + "\n", + "def get_experiment(settings_file):\n", + "\n", + " with open(settings_file, 'r') as fp:\n", + " settings = json.load(fp)\n", + " exp = load_or_run(settings)\n", + "\n", + " return exp\n", + "\n", + "def load_or_run(settings):\n", + "\n", + " try:\n", + " exp = pickle.load(open(settings['path']+'/'+settings['filename'], \"rb\"))\n", + " print('loaded experiment %s'%(settings['path']+'/'+settings['filename']))\n", + " except:\n", + " print('loading %s failed, running it'%(settings['path']+'/'+settings['filename']))\n", + " exp = experiment(settings)\n", + " pickle.dump(exp, open(settings['path']+'/'+settings['filename'], 'wb')) \n", + "\n", + " return exp\n", + "\n", + "exp = get_experiment('./default_da.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x1542d54f7850>" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 600x400 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as p\n", + "fig = p.figure(151)\n", + "fig.set_size_inches(6,4)\n", + "ax1 = fig.add_subplot(1,1,1)\n", + "ax1 = plot_CBL_assimilation(exp,_,ax = ax1)\n", + "ax1.set_xlabel(r'$\\theta$ (K)')\n", + "ax1.set_ylabel(r'$z$ (m)')\n", + "ax1.set_title(r'Ensemble state at $t=3600$ s')\n", + "ax1.legend(frameon=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration of the CBL model\n", + "\n", + "See the file `default_cbl.json`\n", + "```\n", + "{\n", + " \"g\": 9.806,\n", + " \"f\": 0.0001,\n", + " \"kvonk\": 0.4,\n", + " \"z0\": 0.1,\n", + " \"ug\": 0,\n", + " \"vg\": 10,\n", + " \"theta_0\": 290,\n", + " \"gamma\": 0.003,\n", + " \"Hmax\": 0.1,\n", + " \"H0_perturbation_ampl_init\": 0.0,\n", + " \"H0_perturbation_ampl_time\": 0.0,\n", + " \"exct\": 0.3,\n", + " \"pfac\": 1.5,\n", + " \"Kmin\": 0.1,\n", + " \"Kmax\": 200,\n", + " \"is_bwind\": false,\n", + " \"is_cgrad\": true,\n", + " \"is_cycle\": false,\n", + " \"dz\": 50,\n", + " \"ztop\": 4000,\n", + " \"rnseed\": 181612,\n", + " \"perturb_ensemble_state\": true,\n", + " \"perturbations_type\": \"smooth\",\n", + " \"perturbations_theta_amplitude\": 0.1,\n", + " \"perturbations_uv_amplitude\": 0.1,\n", + " \"perturbations_smooth_number\": 11,\n", + " \"perturbations_symmetric\": true,\n", + " \"error_growth_perturbations_amplitude\": 0.1,\n", + " \"perturb_ensemble_parameters\": true,\n", + " \"parameter_number\": 1,\n", + " \"parameter_transform\": \"pit\",\n", + " \"parameter_ensemble_min\": 0.5,\n", + " \"parameter_ensemble_max\": 4.5,\n", + " \"parameter_true\": 1.5,\n", + " \"do_parameter_estimation\": true,\n", + " \"parameter_inflation_rtps_alpha\": 0.4,\n", + " \"return_covariances_increments_and_innovations\": true\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Configuration of the data assimilation cycle\n", + "\n", + "See the file `default_da.json`\n", + "```\n", + "{\n", + " \"cbl_settings_file\": \"./default_cbl.json\",\n", + " \"type\": \"OSSE\",\n", + " \"tspinup\": 10800,\n", + " \"trun\": 3600,\n", + " \"tspinup_assim\": 0,\n", + " \"assimilation_interval\": 3600,\n", + " \"randomize_obs\": true,\n", + " \"nobs\": 1,\n", + " \"obs_coordinates_min\": 433,\n", + " \"obs_coordinates_max\": 433,\n", + " \"obs_kinds\": \"theta\",\n", + " \"obs_error_sdev_generate\": 0.2,\n", + " \"obs_error_sdev_assimilate\": 0.1,\n", + " \"localization_cutoff\": 400,\n", + " \"nens\": 50,\n", + " \"FILTER\": \"EAKF\",\n", + " \"inflation_rtps_alpha\": 0.1,\n", + " \"simulate_underdispersiveness\": false,\n", + " \"rnseed\": 181612,\n", + " \"path\": \"./\",\n", + " \"filename\": \"experiment.pickle\",\n", + " \"label\": \"X\",\n", + " \"nature_run_from_file\": false,\n", + " \"do_parameter_estimation\": true\n", + " }\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mypy310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md index 00559b6f0e00d2fd3a573d474f95b519f4a62925..3ae1543422169c3a656b97bc1eecc0a20fe97879 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,24 @@ -# Tools for idealized parameter estimation experiments +# Contents -* `PE_CBL.py` -Main driver program for parameter estimation with a convective boundary layer model. Includes a few examples. +A software package to run ensemble data assimilation and parameter estimation experiments with the EAKF and LETKF. -* `models.py` -Model code. At the moment, it only includes a CBL model. +## General-purpose code +* Module `ENDA.py`: Data assimilation code. Includes implementations of the EAKF and the LETKF. Also includes classes for data assimilation cycles, diagnostics and experiments (nature run + cycle + diagnostics). -* `ENDA.py` -Data assimilation code. Includes implementations of the EAKF and the LETKF. Also includes classes for data assimilation cycles, diagnostics and experiments (nature run + cycle + diagnostics). +## Basic parameter estimation code +These were used in a manuscript on parameter estimation for a convective boundary layer parameterization scheme (single-column model, SCM): +* Script `PE_CBL.py`: Main code. +* Module `PE_CBL_models.py`: Classes and functions for the SCM intialization and integration +* Module `PE_CBL_graphics.py`: Functions to plot results. +* A set of `*tar.gz` archives, containing `.json` configuration files for the parameter estimation experiments. -* `graphics.py` -Includes all the code for generating figures. +## Additional parameter estimation code +* Module `observations.py`: Code to ingest external observations (e.g. from an LES model, or actual observations) in parameter estimation experiments. +* Module `verification.py`: Code to verify experiments against external observations. -The code depends on `numpy`, `scipy`, `matplotlib.pyplot` \ No newline at end of file +## Other +* Script `replace_text.py`: A tool to edit a set of similar config files all at once. +* Notebook `Overview.ipynb`: Simple usage examples. Much more exhaustive examples are provided in `PE_CBL.py`. + +## Dependencies +The code depends on `numpy`, `scipy`, `matplotlib`, `json`, `pandas`, `pickle`, `os`, `glob` \ No newline at end of file