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malmans2malmans2
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More templates (#8)
* add seasonal forecast * cleanup * renaming
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‎notebooks/01-Application_Template_ERA5.ipynb‎ renamed to ‎notebooks/01-Application_Template_Overview.ipynb‎

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"id": "ae8bf7b2",
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"metadata": {},
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"source": [
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"## APPLICATION TEMPLATE ERA5 - GLOBAL DAILY MEAN TEMPERATURE\n",
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"## APPLICATION TEMPLATE OVERVIEW - GLOBAL DAILY MEAN TEMPERATURE\n",
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"\n",
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"package c3s_eqc_automatic_quality_control (https://github.com/bopen/c3s-eqc-automatic-quality-control)"
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"Templates are built upon `c3s_eqc_automatic_quality_control` (https://github.com/bopen/c3s-eqc-automatic-quality-control)"
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]
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},
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{
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "29a64fdc",
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"metadata": {},
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"source": [
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"For request definition we use **`c3s_eqc_toolbox_template.update_request_date`**.\n",
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"For request definition we use **`download.update_request_date`**.\n",
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"\n",
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"It allows to generate the requests for a given period `[start, stop]` if stop is `None` then compute the stop month as follows:\n",
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"if `current day > switch_month_day`: then `stop_month = current_month - 1`\n",
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "df75d54b",
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"metadata": {},
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"source": [
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"The processing will be perform as follows:\n",
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"\n",
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"**Definition of the transformation `f`** to be applied to each single chunk\n",
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"**Definition of the transformation function `transform_func`** to be applied to each single chunk\n",
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"\n",
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"**Chunk definition**: define how to chunk the request along time dimension (year, month, day):\n",
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"e.g. `chunks={\"year\": 1,\"month\": 1}`\n",
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"e.g. `chunks={\"year\": 1,\"month\": 1}`. Alternatively, use `split_all=True` to chunk as much as possible.\n",
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"\n",
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"**Download and processing**: for each chunk in time\n",
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"\n",
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"```\n",
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"- Download chunk\n",
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"- Cache the downloaded chunk (optional)\n",
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"- Apply the operation `f` to the chunk\n",
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"- Cache transformed chunk, `f(chunks)` (optional)\n",
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"- Apply the operation `transform_func` to the chunk\n",
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"- Cache transformed chunk, `transform_func(chunks)` (optional)\n",
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"```\n",
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"\n",
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"Merge all the transformed chunk"
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"fig.show()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "84465449",
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"metadata": {},
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"source": [
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"#### Save figure"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎notebooks/Deliverables/C-In_situ_ungridded.ipynb‎ renamed to ‎notebooks/in_situ/01-Application_Template_Global_Timeseries_Ungridded.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Ungridded In Situ: Global timeseries"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libraries\n",
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"\n",
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"Switch warnings off for better readability."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define request"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"stop = \"2020-01\""
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate requests"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"requests = download.update_request_date(request, start=start, stop=stop)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define transform function:\n",
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"\n",
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"1. Convert timestamp to datetime\n",
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"1. Rename variables using station names\n",
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"1. Compute chunk mean and std"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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" return xr.merge(dataarrays)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute monthly mean and std timeseries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Plot and save figure"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"fig.show()\n",
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"fig.write_image(filename + \".png\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎notebooks/Deliverables/D-In_situ_gridded.ipynb‎ renamed to ‎notebooks/in_situ/02-Application_Template_Global_Timeseries_Gridded.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Gridded In Situ: Global timeseries"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libraries\n",
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"\n",
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"Switch warnings off for better readability."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define request"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"}"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute spatially-weighted global mean timeseries"
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]
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},
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{
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")"
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]
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute spatially-weighted global std timeseries"
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]
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},
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")"
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]
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Plot and save figure"
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]
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},
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"execution_count": null,

‎notebooks/Deliverables/A-Reanalysis_single_level.ipynb‎ renamed to ‎notebooks/renalysis/01-Application_Template_Global_Timeseries_Single_Level.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"# Reanalysis single level: Global timeseries"
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"# Single Level Reanalysis: Global timeseries"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import libraries\n",
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"\n",
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"Switch warnings off for better readability."
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define request"
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]
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},
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"cell_type": "code",
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"stop = None"
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]
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generate requests"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"requests = download.update_request_date(request, start=start, stop=stop)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute spatially-weighted global mean timeseries"
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]
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},
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"execution_count": null,
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Compute spatially-weighted global std timeseries"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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")"
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Plot and save figure"
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]
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},
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"fig.show()\n",
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"fig.write_image(filename + \".png\")"
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"cell_type": "code",
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"metadata": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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"version": "3.10.8 | packaged by conda-forge | (main, Nov 22 2022, 08:27:35) [Clang 14.0.6 ]"
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},
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"vscode": {
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"interpreter": {

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