From 28d8c4bcca696b41f0fd2cc26044c7b793d36a75 Mon Sep 17 00:00:00 2001 From: mgrover1 Date: Thu, 13 Oct 2022 14:22:19 -0500 Subject: [PATCH 1/2] add use case notebook --- .../SSP-Chicago-example.ipynb | 0 {scenarioMIPS => notebooks}/SSP.png | Bin notebooks/use-cases.ipynb | 1579 +++++++++++++++++ 3 files changed, 1579 insertions(+) rename {scenarioMIPS => notebooks}/SSP-Chicago-example.ipynb (100%) rename {scenarioMIPS => notebooks}/SSP.png (100%) create mode 100644 notebooks/use-cases.ipynb diff --git a/scenarioMIPS/SSP-Chicago-example.ipynb b/notebooks/SSP-Chicago-example.ipynb similarity index 100% rename from scenarioMIPS/SSP-Chicago-example.ipynb rename to notebooks/SSP-Chicago-example.ipynb diff --git a/scenarioMIPS/SSP.png b/notebooks/SSP.png similarity index 100% rename from scenarioMIPS/SSP.png rename to notebooks/SSP.png diff --git a/notebooks/use-cases.ipynb b/notebooks/use-cases.ipynb new file mode 100644 index 0000000..b4e3cb1 --- /dev/null +++ b/notebooks/use-cases.ipynb @@ -0,0 +1,1579 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "06ffda4d-52a2-43bd-9d21-c77efa13e3b1", + "metadata": {}, + "source": [ + "# Defining ESGF Use Cases\n", + "\n", + "## Motivation\n", + "It is important to consider the different use-cases scientists might have when using data from the Earth System Grid Federation (ESGF). There are two primary workflows:\n", + "- Global/regional statistics\n", + "- Timeseries for some given location\n", + "- Aggregated spatial averages/temporal means\n", + "\n", + "For this illustration, we will use ILAMB (land model diagnostic suite) output as an example.\n", + "\n", + "### Global or Regional Statistics\n", + "\n", + "The first case is one where the user would use either a single vertical level (ex. the surface of the land, ocean, atmosphere) or the average over several vertical levels (ex. vertically integrated BGC variables in the ocean). Scientists may be interested in bias, general trends, or general performance stastics compared to some baseline.\n", + "\n", + "These datasets are **aggregated over some temporal range**. From the data query side, the user would require:\n", + "- All time steps\n", + "- The entire spatial domain (or a region if interested in some regional mask ex. North America)\n", + "\n", + "**Sample Query Syntax**\n", + "\n", + "```python\n", + "subset = catalog.search(variable='tas', frequency='monthly', experiment_id=['ssp370', 'ssp585'])\n", + "\n", + "temporal_average = subset.mean(dim='time')\n", + "```\n", + "\n", + "Which would leave the dataset with the spatial dimensions, and possibly height/depth.\n", + "\n", + "\n", + "![Global mean plot map](images/global-mean-map.png)\n", + "\n", + "### Timeseries for some location\n", + "\n", + "Another case might be interest in a spatial subset (a single location, or set of locations) from the models. **This is a case where the user does not require the entire global dataset, a possible opportunity for server-side subsetting**. For example, someone may be interested in a timeseries of projected temperature or rainfall values over the grid cell closest to Chicago. Or, they may be interested in a set of locations (Chicago, New York, Los Angeles).\n", + "\n", + "These datasets are **subset from a single location**. From the data query side, the user would require:\n", + "- All time steps\n", + "- A single location, or set of locations\n", + "- A single level, or set of vertical levels\n", + "\n", + "It would be inefficient here for the user to need to **download the entire global dataset, for each model** if they just needed data for a single data point.\n", + "\n", + "The user may want to calculate an average from this timeseries as well.\n", + "\n", + "```python\n", + "subset = catalog.search(variable='tas', frequency='monthly', experiment_id=['ssp370', 'ssp585'])\n", + "\n", + "timeseries = subset.sel(lat=41.87, lon=-87.63)\n", + "```\n", + "\n", + "![Single Location timeseries](images/sample-location-timeseries.png)\n", + "\n", + "### Aggregated Spatial Averages/Integrals \n", + "The last case here is a combination of the first two. One may be interested in **global or regional statistics** over some time period, or over the entire time period of interest. For example, one might want to determine global or regional carbon sinks or sources. Another common one is global average temperature.\n", + "\n", + "These datasets are **aggregated over some spatial range**. From the data query side, the user would require:\n", + "- All time steps\n", + "- The entire spatial domain (or a region if interested in some regional mask ex. North America)\n", + "\n", + "**Sample Query Syntax**\n", + "```python\n", + "subset = catalog.search(variable='tas', frequency='monthly', experiment_id=['ssp370', 'ssp585'])\n", + "\n", + "global_average = subset.weighted.mean(dim=['lat', 'lon']) #.mean(dim='time')\n", + "```\n", + "\n", + "![Global integral of carbon over land](images/global-integral-timeseries.png)" + ] + }, + { + "cell_type": "markdown", + "id": "325999ae-69e1-494d-ba9e-1a2bd7d141f2", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "72979be9-51f0-4249-8b7a-b001a4f0f7ff", + "metadata": {}, + "source": [ + "## Sample Demo with an Intake-ESM Catalog\n", + "\n", + "Let's demo some of these illustrations with Intake-ESM.\n", + "\n", + "For this use-case illustration, we will use:\n", + "- CESM2 (NCAR's flagship climate model) and E3SM (DOE's flagship climate model)\n", + "- Rainfall (`pr`) and surface temperature (`tas`) data\n", + "- Monthly frequency data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fc0af5d4-5098-4f44-a9df-370106a490bb", + "metadata": {}, + "outputs": [], + "source": [ + "import intake\n", + "from distributed import Client, LocalCluster\n", + "import dask\n", + "from cmip6_preprocessing.preprocessing import combined_preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2e0433d2-8774-4b5a-9186-9f3199c52464", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Worker: 17

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Worker: 18

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Worker: 19

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anl-cmip6 catalog with 3 dataset(s) from 445 asset(s):

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unique
activity_id1
institution_id2
source_id2
experiment_id1
member_id11
table_id2
variable_id2
grid_label2
dcpp_init_year0
version7
time_range40
path445
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activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labeldcpp_init_yearversiontime_rangepath
0CMIPE3SM-ProjectE3SM-1-1historicalr1i1p1f1AmonprgrNaNv20191211185001-185912/eagle/projects/ESGF2/esg_dataroot/css03_data/...
1CMIPE3SM-ProjectE3SM-1-1historicalr1i1p1f1AmonprgrNaNv20191211186001-186912/eagle/projects/ESGF2/esg_dataroot/css03_data/...
2CMIPE3SM-ProjectE3SM-1-1historicalr1i1p1f1AmonprgrNaNv20191211187001-187912/eagle/projects/ESGF2/esg_dataroot/css03_data/...
3CMIPE3SM-ProjectE3SM-1-1historicalr1i1p1f1AmonprgrNaNv20191211188001-188912/eagle/projects/ESGF2/esg_dataroot/css03_data/...
4CMIPE3SM-ProjectE3SM-1-1historicalr1i1p1f1AmonprgrNaNv20191211189001-189912/eagle/projects/ESGF2/esg_dataroot/css03_data/...
.......................................
440CMIPNCARCESM2historicalr9i1p1f1daytasgnNaNv2019031119700101-19791231/eagle/projects/ESGF2/esg_dataroot/css03_data/...
441CMIPNCARCESM2historicalr9i1p1f1daytasgnNaNv2019031119800101-19891231/eagle/projects/ESGF2/esg_dataroot/css03_data/...
442CMIPNCARCESM2historicalr9i1p1f1daytasgnNaNv2019031119900101-19991231/eagle/projects/ESGF2/esg_dataroot/css03_data/...
443CMIPNCARCESM2historicalr9i1p1f1daytasgnNaNv2019031120000101-20091231/eagle/projects/ESGF2/esg_dataroot/css03_data/...
444CMIPNCARCESM2historicalr9i1p1f1daytasgnNaNv2019031120100101-20150101/eagle/projects/ESGF2/esg_dataroot/css03_data/...
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445 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " activity_id institution_id source_id experiment_id member_id table_id \\\n", + "0 CMIP E3SM-Project E3SM-1-1 historical r1i1p1f1 Amon \n", + "1 CMIP E3SM-Project E3SM-1-1 historical r1i1p1f1 Amon \n", + "2 CMIP E3SM-Project E3SM-1-1 historical r1i1p1f1 Amon \n", + "3 CMIP E3SM-Project E3SM-1-1 historical r1i1p1f1 Amon \n", + "4 CMIP E3SM-Project E3SM-1-1 historical r1i1p1f1 Amon \n", + ".. ... ... ... ... ... ... \n", + "440 CMIP NCAR CESM2 historical r9i1p1f1 day \n", + "441 CMIP NCAR CESM2 historical r9i1p1f1 day \n", + "442 CMIP NCAR CESM2 historical r9i1p1f1 day \n", + "443 CMIP NCAR CESM2 historical r9i1p1f1 day \n", + "444 CMIP NCAR CESM2 historical r9i1p1f1 day \n", + "\n", + " variable_id grid_label dcpp_init_year version time_range \\\n", + "0 pr gr NaN v20191211 185001-185912 \n", + "1 pr gr NaN v20191211 186001-186912 \n", + "2 pr gr NaN v20191211 187001-187912 \n", + "3 pr gr NaN v20191211 188001-188912 \n", + "4 pr gr NaN v20191211 189001-189912 \n", + ".. ... ... ... ... ... \n", + "440 tas gn NaN v20190311 19700101-19791231 \n", + "441 tas gn NaN v20190311 19800101-19891231 \n", + "442 tas gn NaN v20190311 19900101-19991231 \n", + "443 tas gn NaN v20190311 20000101-20091231 \n", + "444 tas gn NaN v20190311 20100101-20150101 \n", + "\n", + " path \n", + "0 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "1 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "2 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "3 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "4 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + ".. ... \n", + "440 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "441 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "442 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "443 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "444 /eagle/projects/ESGF2/esg_dataroot/css03_data/... \n", + "\n", + "[445 rows x 12 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog_subset.df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b16485b-6d6e-4138-b99d-2b1921ca8da7", + "metadata": {}, + "outputs": [], + "source": [ + "with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n", + " dsets = catalog_subset.to_dataset_dict(preprocess=combined_preprocessing)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "265233dc-b8da-4d49-8890-86b788ba400d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From a8695269d06cda5fa29850914384a3890bbca3a1 Mon Sep 17 00:00:00 2001 From: mgrover1 Date: Thu, 13 Oct 2022 14:29:47 -0500 Subject: [PATCH 2/2] add missing images --- .../images/global-integral-timeseries.png | Bin 0 -> 211571 bytes notebooks/images/global-mean-map.png | Bin 0 -> 49986 bytes .../images/sample-location-timeseries.png | Bin 0 -> 624581 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 notebooks/images/global-integral-timeseries.png create mode 100644 notebooks/images/global-mean-map.png create mode 100644 notebooks/images/sample-location-timeseries.png diff --git a/notebooks/images/global-integral-timeseries.png b/notebooks/images/global-integral-timeseries.png new file mode 100644 index 0000000000000000000000000000000000000000..07631ce622b40bfba60950f03b34bfff4d21caf1 GIT binary patch literal 211571 zcmeFZWmH_twg!sRSZFL*a1HJRcMBfeAy}|9fdGvMXe3B*w*Ub`Ab4Srv^ou?+z9d2N2%S$CagBT!u2}4+cVz-p37WCp z*t1Jhfqu+u5rHz7*x&4Njd2FE5CRA&+mJf)&=XG&?`CveF%vQ$V~q7?zY6}S1>eM^3Y`|DGLmap)C zwc)IvI*O@D$jL!}s+l^No43H9j>W_b^wyjOY-j&; 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