Climate
Climate time series¶
Through this API endpoint, you can fetch several climate variables that have been extracted for all brazilian municipalities from the satellite-based reanalysis data provided by Copernicus ERA5. These series are on a daily timescale.
Parameters Table¶
Input¶
| Parameter name | Required | Type | Description |
|---|---|---|---|
| *page | yes | int | Page to be displayed |
| *per_page | yes | int | How many items will be displayed per page (up to 100) |
| start | yes | str (YYYY-mm-dd) | Start date |
| end | yes | str (YYYY-mm-dd) | End date |
| geocode | no | int | IBGE's municipality code |
| uf | no | str (UF) | Two letters brazilian's state abbreviation. E.g: SP |
Output (items)¶
| Parameter name | Type | Description |
|---|---|---|
| date | date (YYYY-mm-dd) | Day of the year |
| geocodigo | int | IBGE's municipality code |
| temp_min | float (°C) | Minimum daily temperature |
| temp_med | float (°C) | Average daily temperature |
| temp_max | float (°C) | Maximum daily temperature |
| precip_min | float (mm) | Minimum daily precipitation |
| precip_med | float (mm) | Average daily precipitation |
| precip_max | float (mm) | Maximum daily precipitation |
| precip_tot | float (mm) | Total daily precipitation |
| pressao_min | float (atm) | Minimum daily sea level pressure |
| pressao_med | float (atm) | Average daily sea level pressure |
| pressao_max | float (atm) | Maximum daily sea level pressure |
| umid_min | float (%) | Minimum daily relative humidity |
| umid_med | float (%) | Average daily relative humidity |
| umid_max | float (%) | Maximum daily relative humidity |
Details¶
page consists in the total amount of Items returned by the request divided by per_page. The pagination information is returned alongside with the returned request. E.g.:
'pagination': {
'items': 10, # Amout of Items being displayed
'total_items': 10, # Total amount of Items returned in the request
'page': 1, # *request parameter
'total_pages': 1, # Total amount of pages returned in the request
'per_page': 100 # *request parameter
},
Note: for fetching a big amount of pages, please consider using Async code
Usage examples¶
import requests
climate_api = "https://api.mosqlimate.org/api/datastore/climate/"
page = 1 # total amount of pages is returned in the request
per_page = 100
pagination = f"?page={page}&per_page={per_page}&"
filters = "start=%s&end=%s" % ("2022-12-30", "2023-12-30")
resp = requests.get(climate_api + pagination + filters)
# Or you can add a geocode to the filters
geocode = 3304557
resp = requests.get(
climate_api +
pagination +
filters +
f"&geocode={geocode}"
)
items = resp.json()["items"] # JSON data in dict format
resp.json()["pagination"] # Pagination*
library(httr)
library(jsonlite)
climate_api <- "https://api.mosqlimate.org/api/datastore/climate/"
page <- "1"
pagination <- paste0("?page=", page, "&per_page=100&")
filters <- paste0("start=2022-12-30&end=2023-12-30")
url <- paste0(climate_api, pagination, filters)
resp <- GET(url)
content <- content(resp, "text")
json_content <- fromJSON(content)
items <- json_content$items
pagination_data <- json_content$pagination
curl -X 'GET' \
'https://api.mosqlimate.org/api/datastore/climate/?start=2022-12-30&end=2023-12-30&page=1&per_page=100' \
-H 'accept: application/json'
# Or you can add a geocode to the filters
curl -X 'GET' \
'https://api.mosqlimate.org/api/datastore/climate/?start=2022-12-30&end=2023-12-30&geocode=3304557&page=1&per_page=100' \
-H 'accept: application/json'
*The response's pagination contain information about the amount of items returned
by the API call. These information can be used to navigate between the queried
data by changing the page parameter on the URL. See details
Example using the mosqlient package¶
The mosqlient is a Python package created to facilitate the use of API.
In the package, there is a function called get_climate that returns a pandas DataFrame with the data. This function accepts as filters the parameters start_date, end_date, geocode, and uf, with the same types defined in the parameters table above.
Below is a usable example of fetching data from the 3304557 (Rio de Janeiro) city.