|
27 | 27 | "tags": [] |
28 | 28 | }, |
29 | 29 | "source": [ |
30 | | - "# Exploring Precipitation information in the GIRAFE dataset" |
| 30 | + "# Exploring precipitation information in the GIRAFE dataset" |
31 | 31 | ] |
32 | 32 | }, |
33 | 33 | { |
|
45 | 45 | "source": [ |
46 | 46 | "**This notebook can be run on free online platforms, such as Binder, Kaggle and Colab, or they can be accessed from GitHub. The links to run this notebook in these environments are provided here, but please note they are not supported by ECMWF.** \n", |
47 | 47 | "\n", |
48 | | - "[](https://mybinder.org/v2/gh/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/main?labpath=precipitation-girafe.ipynb)\n", |
49 | | - "[](https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/main/precipitation-girafe.ipynb)\n", |
50 | | - "[](https://colab.research.google.com/github/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/main/precipitation-girafe.ipynb)\n", |
51 | | - "[](https://github.com/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/main/precipitation-girafe.ipynb)" |
52 | | - ] |
53 | | - }, |
54 | | - { |
55 | | - "cell_type": "markdown", |
56 | | - "id": "3f32c97a", |
57 | | - "metadata": { |
58 | | - "editable": true, |
59 | | - "slideshow": { |
60 | | - "slide_type": "" |
61 | | - }, |
62 | | - "tags": [] |
63 | | - }, |
64 | | - "source": [ |
65 | | - "This notebook-tutorial provides an introduction to the use of the Global Interpolated RAinFall Estimation ([GIRAFE](https://cds.climate.copernicus.eu/datasets/satellite-precipitation-microwave-infrared?tab=overview)) data record for climate studies." |
| 48 | + "[](https://mybinder.org/v2/gh/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/develop?labpath=precipitation-girafe.ipynb)\n", |
| 49 | + "[](https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/develop/precipitation-girafe.ipynb)\n", |
| 50 | + "[](https://colab.research.google.com/github/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/develop/precipitation-girafe.ipynb)\n", |
| 51 | + "[](https://github.com/ecmwf-training/c3s-training-submodule-sat-obs-atmos-physics/blob/develop/precipitation-girafe.ipynb)" |
66 | 52 | ] |
67 | 53 | }, |
68 | 54 | { |
|
76 | 62 | "tags": [] |
77 | 63 | }, |
78 | 64 | "source": [ |
79 | | - "The Precipitation (PRE) Essential Climate Variable (ECV) and the GIRAFE product are described in introduction. Then, a first use case provides an analysis of the time averaged global and seasonal climatological distributions of the Precipitation field as well as the monthly mean climatology. The second use case presents the time series and trend analysis of Precipitation. Step-by-step instructions are provided on data preparation; the use cases are extensively documented and each line of code is explained.\n", |
| 65 | + "## Learning objectives 🎯\n", |
| 66 | + "\n", |
| 67 | + "This notebook-tutorial provides an introduction to the use of the Global Interpolated RAinFall Estimation ([GIRAFE](https://cds.climate.copernicus.eu/datasets/satellite-precipitation-microwave-infrared?tab=overview)) data record for climate studies.\n", |
| 68 | + "\n", |
| 69 | + "The Precipitation (PRE) Essential Climate Variable (ECV) and the GIRAFE product are described below. Then, a first use case provides an analysis of the time averaged global and seasonal climatological distributions of the Precipitation field as well as the monthly mean climatology. The second use case presents the time series and trend analysis of Precipitation. Step-by-step instructions are provided on data preparation; the use cases are extensively documented and each line of code is explained.\n", |
80 | 70 | "\n", |
81 | 71 | "The three figures below show some results from the use cases and illustrate the successful run of the code.\n", |
82 | 72 | "\n", |
83 | | - "" |
84 | | - ] |
85 | | - }, |
86 | | - { |
87 | | - "cell_type": "markdown", |
88 | | - "id": "8a4822f4", |
89 | | - "metadata": {}, |
90 | | - "source": [ |
91 | | - "## Introduction\n" |
92 | | - ] |
93 | | - }, |
94 | | - { |
95 | | - "cell_type": "markdown", |
96 | | - "id": "9228ff61", |
97 | | - "metadata": { |
98 | | - "editable": true, |
99 | | - "slideshow": { |
100 | | - "slide_type": "" |
101 | | - }, |
102 | | - "tags": [ |
103 | | - "request" |
104 | | - ] |
105 | | - }, |
106 | | - "source": [ |
| 73 | + "\n", |
| 74 | + "\n", |
107 | 75 | "Anthropogenic activities and natural variations from years to decades shape the Earth's climate. Water and energy cycles are central to the physics of climate change. Within the hydrological cycle, precipitation has been recognised as an Essential Climate Variable (ECV) as it is the main component of water transport from the atmosphere to the Earth’s surface. Precipitation varies strongly, depending on geographical location, season, synopsis, and other meteorological factors. The supply with freshwater through precipitation is vital for many subsystems of the climate and the environment, but there are also hazards related to extensive precipitation (floods) or to the lack of precipitation (droughts).\n", |
108 | 76 | "\n", |
109 | 77 | "In the Copernicus Climate Data Store (CDS), the EUMETSAT Satellite Application Facility on Climate Monitoring has brokered the Global Interpolated RAinFall Estimation (GIRAFE) product. It merges microwave (MW) sounder- and imager-based estimations of instantaneous surface precipitation (over land and ocean) with infrared (IR) observations from geostationary platforms along the equator. GIRAFE is a global 1° x 1° latitude-longitude data record that is produced at a daily temporal resolution, as well as on a monthly mean basis. It covers the time period January 2002 to December 2022.\n", |
|
135 | 103 | ] |
136 | 104 | }, |
137 | 105 | "source": [ |
| 106 | + "### Set up CDSAPI and your credentials\n", |
| 107 | + "\n", |
138 | 108 | "The code below will ensure that the `earthkit` package is installed. If you have not setup your `~/.cdsapirc` file with your credentials, you will be prompted to provide them when you request data from the CDS. You can find the credentials required on the [how to api](https://cds.climate.copernicus.eu/how-to-api) page (you will need to log in to see your credentials)." |
139 | 109 | ] |
140 | 110 | }, |
141 | 111 | { |
142 | 112 | "cell_type": "code", |
143 | | - "execution_count": 26, |
| 113 | + "execution_count": null, |
144 | 114 | "id": "67530afe", |
145 | 115 | "metadata": { |
146 | 116 | "editable": true, |
|
153 | 123 | }, |
154 | 124 | "outputs": [], |
155 | 125 | "source": [ |
156 | | - "!pip install -q earthkit" |
| 126 | + "!pip install -q earthkit\n", |
| 127 | + "\n", |
| 128 | + "!pip install -q cdsapi\n", |
| 129 | + "# If you have already setup your .cdsapirc file you can leave this as None\n", |
| 130 | + "cdsapi_key = None\n", |
| 131 | + "cdsapi_url = None" |
157 | 132 | ] |
158 | 133 | }, |
159 | 134 | { |
|
167 | 142 | "tags": [] |
168 | 143 | }, |
169 | 144 | "source": [ |
170 | | - "### Import libraries\n", |
| 145 | + "### (Install and) Import libraries\n", |
171 | 146 | "\n", |
172 | 147 | "The data have been stored in files written in NetCDF format. We will use [`earthkit-data`](https://earthkit-data.readthedocs.io) to download the data from the CDS, and open them with [`xarray`](http://xarray.pydata.org/en/stable/). `earthkit-data` will handle the specifics of the file and download formats, so you can focus on inspecting the data contents.\n", |
173 | 148 | "\n", |
|
245 | 220 | "source": [ |
246 | 221 | "To search for data, we will visit the CDS website: https://cds.climate.copernicus.eu/.\n", |
247 | 222 | "Here we can search for GIRAFE data using the search bar. The data we need for this use case is the [Monthly and daily global interpolated rainfall estimation data from 2002 to 2022 derived from satellite measurements](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-precipitation?tab=overview) dataset.\n", |
248 | | - "This catalogue entry provides daily and monthly accumulated precipitation amounts derieved from the combination of MW imager and sounder retrievals over land and ocean surfaces with geostationary IR observations.\n", |
| 223 | + "This catalogue entry provides daily and monthly accumulated precipitation amounts derived from the combination of MW imager and sounder retrievals over land and ocean surfaces with geostationary IR observations.\n", |
249 | 224 | "\n", |
250 | 225 | "After selecting the correct catalogue entry, we will specify the time aggregation and temporal coverage we are interested in.\n", |
251 | 226 | "These can all be selected in the **\"Download data\"** tab. In this tab a form appears in which we will select the following parameters to download:\n", |
252 | 227 | "\n", |
| 228 | + ":::{dropdown} Parameter of data to download\n", |
| 229 | + "\n", |
253 | 230 | "- Time aggregation: `Monthly`\n", |
254 | 231 | "- Year: `all` (use **\"Select all\"** button)\n", |
255 | 232 | "- Month: `all` (use **\"Select all\"** button)\n", |
256 | 233 | "- Day: `01` (or simply use **\"Select all\"** button)\n", |
257 | 234 | "\n", |
258 | | - "If you have not already done so, you will need to accept the **\"terms of use\"** of the data before you can download it.\n", |
| 235 | + ":::\n", |
| 236 | + "\n", |
| 237 | + "At the end of the download form, select **\"Show API request\"**. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cell below)\n", |
| 238 | + "\n", |
| 239 | + "::: {warning}\n", |
| 240 | + "\n", |
| 241 | + "Please remember to accept the terms and conditions of the dataset, at the bottom of the CDS download form!\n", |
259 | 242 | "\n", |
260 | | - "At the end of the download form, select **\"Show API request\"**. This will reveal a block of code, which you can simply copy and paste into a cell of your Jupyter Notebook (see cell below)" |
| 243 | + ":::" |
261 | 244 | ] |
262 | 245 | }, |
263 | 246 | { |
|
320 | 303 | "id": "95814390", |
321 | 304 | "metadata": {}, |
322 | 305 | "source": [ |
323 | | - "### Inspect the data" |
| 306 | + "### Inspect data" |
324 | 307 | ] |
325 | 308 | }, |
326 | 309 | { |
|
2631 | 2614 | "source": [ |
2632 | 2615 | "#### Plot the first time step\n", |
2633 | 2616 | "\n", |
2634 | | - "For a quick demonstration, we will use earthkit plots to produce a map of the first time step in the file. We are just using the `quickplot` method, with out of the box settings." |
| 2617 | + "For a quick demonstration, we will use earthkit plots to produce a map of the first time step in the file. We are just using the `quickplot` method, without of the box settings." |
2635 | 2618 | ] |
2636 | 2619 | }, |
2637 | 2620 | { |
|
3382 | 3365 | "tags": [] |
3383 | 3366 | }, |
3384 | 3367 | "source": [ |
3385 | | - "The global climatological field of precipitations has a dominant structure along the equator, over water masses and rain forests like Amazonia. Most of the evaporation or evapotranspiration takes place in the tropics. Indeed, Solar irradiance and its relative position on the Earth's surface (solar zenith angle) controls evaporation, leading to the transition of water from the liquid phase to the gaseous phase, which is subsequently advected to higher altitudes and then condensated into precipitations. This mechanism is the main driver of the Inter-Tropical Convergence Zone (ITCZ) along the equator.\n", |
| 3368 | + "The global climatological field of precipitations has a dominant structure along the equator, over water masses and rainforests like Amazonia. Most of the evaporation or evapotranspiration takes place in the tropics. Indeed, Solar irradiance and its relative position on the Earth's surface (solar zenith angle) controls evaporation, leading to the transition of water from the liquid phase to the gaseous phase, which is subsequently advected to higher altitudes and then condensated into precipitations. This mechanism is the main driver of the Inter-Tropical Convergence Zone (ITCZ) along the equator.\n", |
3386 | 3369 | "\n", |
3387 | 3370 | "\n", |
3388 | 3371 | "There are also moderately high values of daily accumulated precipitation amounts in the region of the Gulf Stream extension and the Kuroshio, the East of Japan, in association with the storm track regions." |
|
5403 | 5386 | "id": "3f64bf83", |
5404 | 5387 | "metadata": {}, |
5405 | 5388 | "source": [ |
5406 | | - "In the next use case, we will anlyse the temporal evolution of the precipitation amount and its annual seasonal variation." |
| 5389 | + "In the next use case, we will analyse the temporal evolution of the precipitation amount and its annual seasonal variation." |
5407 | 5390 | ] |
5408 | 5391 | }, |
5409 | 5392 | { |
|
6200 | 6183 | "id": "cf4f4ef1", |
6201 | 6184 | "metadata": {}, |
6202 | 6185 | "source": [ |
6203 | | - "**Figure 4**, shows the time series of the daily accumulated precipitation amount. From the time series, we can infer the seasonal pattern along with a (decreasing) trend in the global space and time averaged global interpolated rainfall estimation." |
| 6186 | + "**Figure 4** shows the time series of the daily accumulated precipitation amount. From the time series, we can infer the seasonal pattern along with a (decreasing) trend in the global space and time averaged global interpolated rainfall estimation." |
6204 | 6187 | ] |
6205 | 6188 | }, |
6206 | 6189 | { |
|
6310 | 6293 | "id": "43f498f8", |
6311 | 6294 | "metadata": {}, |
6312 | 6295 | "source": [ |
6313 | | - "We can also use the GIRAFE dataset to visualise and analyse the temporal evolution of precipitations over some regions of interest that have a specific regime, like, for example, the Rain Forest of Amazon in Brasil, the semi-arid region of Sahel in Africa, India for Monsoon, or Indonesia that is located in the Warm Pool.\n", |
| 6296 | + "We can also use the GIRAFE dataset to visualise and analyse the temporal evolution of precipitations over some regions of interest that have a specific regime, like, for example, the Amazon rainforest in Brasil, the semi-arid region of Sahel in Africa, India for Monsoon, or Indonesia that is located in the Warm Pool.\n", |
6314 | 6297 | "\n", |
6315 | 6298 | "\n", |
6316 | 6299 | "For this purpose, we will re-use the code from the previous section, but we will need to add one additional step: select the corresponding regions by their repective coordinates from the original global dataset." |
|
6458 | 6441 | "metadata": {}, |
6459 | 6442 | "source": [ |
6460 | 6443 | "**Figure 6** shows the temporal evolution of precipitations over Brazil, Sahel, India and Indonesia for the time period 2002-2022. <br>\n", |
6461 | | - "No trend can be infered in the precipitation amount in the Rain Forest of Amazon due to the lack of months (and even years) of GIRAFE observations over this region of Brazil. <br>\n", |
| 6444 | + "No trend can be infered in the precipitation amount in the Amazon rainforest due to the lack of months (and even years) of GIRAFE observations over this region of Brazil. <br>\n", |
6462 | 6445 | "Over Sahel, no significant trend is observed during the first decade of GIRAFE TCDR. Then, after a period of drought of ~4 years between 2014 and 2018, the amount of precipitation increases abruptly up until December 2022. <br>\n", |
6463 | 6446 | "In the last decade, extreme values of precipitation that could lead to droughts or floods tend to increase over India or Indonesia (especially)." |
6464 | 6447 | ] |
|
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