API testing - Rwema25/AE-project GitHub Wiki
Climate Datasets and API Availability
| Dataset | API Available? | API Access Registration Required? | Notes / Source |
|---|---|---|---|
| CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) | No official public API | N/A | Data usually accessed via FTP or direct downloads, not REST APIs. |
| CHIRTS (CHIRPS + Temperature) | No official public API | N/A | Similar access methods as CHIRPS; no dedicated API documented. |
| AgERA5 | Yes (via Copernicus Climate Data Store API) | Yes (CDS registration and API key required) | Access through CDS API with Python cdsapi client; requires license acceptance and .cdsapirc setup. See CDS API documentation and ag5Tools. |
| ERA5 | Yes | Yes (registration and API key required) | Available via Copernicus Climate Data Store API; requires personal access token setup. |
| TerraClimate | No official public API | N/A | Data usually accessed via direct download; no standard public API. |
| IMERG (Integrated Multi-satellitE Retrievals for GPM) | Yes | Yes (registration and API key required) | NASA provides APIs via GES DISC; EarthData login and token required. |
| TAMSAT (Tropical Applications of Meteorology using SATellite data) | No official public API | N/A | Data typically accessed via downloads; no public API documented. |
| NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) | No official public API | N/A | Data accessible via EarthData portal; APIs limited or indirect; registration usually required. |
| NASA POWER (Prediction Of Worldwide Energy Resources) | Yes | No API key required for basic use; optional registration for higher limits | NASA POWER API available; no EarthData login or token needed for standard access |
| CMIP6 (Coupled Model Intercomparison Project Phase 6) | Yes (via some services) | Depends on service (e.g., Open-Meteo no key, others yes) | Accessible via APIs like Open-Meteo Climate API (no key) and others that may require registration. |
Open-Meteo Climate API Testing with CMIP6 Climate Model Data
In this section, we present the results of our testing of the Open-Meteo Climate API, which provides high-resolution climate model data from CMIP6 simulations. The API allows querying daily climate variables such as maximum temperature and precipitation for specified locations and time periods. Our testing focused on retrieving data for Nairobi, Kenya, over the year 2024, handling API constraints, and evaluating performance and data quality.
The following table summarizes the key aspects of the API testing process, including request parameters, data retrieval, error handling, and observed limitations.
| Test Aspect | Description / Result |
|---|---|
| API Endpoint | - https://climate-api.open-meteo.com/v1/climate |
| Authentication | - No authentication or API key required for non-commercial use |
| Request Parameters | - Latitude: -1.2921 (Nairobi) - Longitude: 36.8219 - Date range: chunked intervals (initially 10-day limit observed, monthly chunks implemented for convenience) - Model: MRI_AGCM3_2_S - Variables: daily max temperature, precipitation - Units: Celsius, mm - Time format: ISO8601 |
| Data Retrieval | - Successfully retrieved daily climate model data in chunks - Combined monthly chunks to form a full year dataset |
| Data Format | - JSON response parsed into R list - Converted into clean data frame with columns: date, temperature_max_c, precipitation_mm |
| Response Time | - Average ~0.19 seconds per monthly chunk - First request slightly longer (~1 second) |
| Error Handling | - Used tryCatch() to handle request failures gracefully - Warnings printed for failed requests without stopping the loop |
| Data Completeness | - Full year (2024) daily data obtained by aggregating chunked requests - Duplicate rows removed |
| Data Accuracy | - Data matches expected climate model outputs for Nairobi region and specified variables |
| Limitations | - API enforces maximum date range per request of approximately 10 days (empirically observed) - Chunking required for longer periods (months, years) |
| Usability | - Easy integration with R packages (httr, jsonlite, dplyr) - No API key needed - Flexible parameterization |
| Scalability | - Chunked requests enable multi-year dataset retrieval - Total time scales with number of chunks requested |
| Documentation | - API usage and testing process documented with clear tables and explanations - Official API docs: Open-Meteo Climate API - Markdown format used for easy integration and version tracking |
To illustrate the practical implementation of this testing approach, the following R code demonstrates how to retrieve and aggregate monthly climate data chunks from the Open-Meteo Climate API for Nairobi in 2024, including error handling and timing of requests.
library(httr)
library(jsonlite)
library(dplyr)
url <- "https://climate-api.open-meteo.com/v1/climate"
# Function to get data for a given date range
get_climate_data <- function(start_date, end_date) {
params <- list(
latitude = -1.2921,
longitude = 36.8219,
start_date = start_date,
end_date = end_date,
models = "MRI_AGCM3_2_S",
daily = "temperature_2m_max,precipitation_sum",
temperature_unit = "celsius",
precipitation_unit = "mm",
timeformat = "iso8601"
)
response <- GET(url, query = params)
if (status_code(response) == 200) {
data <- content(response, as = "parsed", type = "application/json")
dates <- unlist(data$daily$time)
temp_max <- unlist(data$daily$temperature_2m_max)
precip <- unlist(data$daily$precipitation_sum)
df <- data.frame(
date = as.Date(dates),
temperature_max_c = temp_max,
precipitation_mm = precip
)
return(df)
} else {
warning(paste("Request failed with status:", status_code(response), "for", start_date, "to", end_date))
return(NULL)
}
}
# Create a sequence of monthly intervals for 2024
start_dates <- seq(as.Date("2024-01-01"), as.Date("2024-12-01"), by = "month")
end_dates <- seq(as.Date("2024-01-31"), as.Date("2024-12-31"), by = "month")
# Make sure both vectors are the same length
if(length(end_dates) < length(start_dates)) {
end_dates <- c(end_dates, as.Date("2024-12-31"))
}
# Initialize empty data frame
full_year_data <- data.frame()
# Loop over each month and append data with timing
for (i in seq_along(start_dates)) {
cat("Fetching data from", as.character(start_dates[i]), "to", as.character(end_dates[i]), "\n")
time_taken <- system.time({
monthly_data <- tryCatch(
get_climate_data(as.character(start_dates[i]), as.character(end_dates[i])),
error = function(e) {
cat("Error for", as.character(start_dates[i]), "to", as.character(end_dates[i]), "\n")
return(NULL)
}
)
})
cat("Time taken:", time_taken["elapsed"], "seconds\n\n")
if(!is.null(monthly_data)) {
full_year_data <- bind_rows(full_year_data, monthly_data)
}
}
# Remove duplicate rows if any
full_year_data <- distinct(full_year_data)
print(full_year_data)
Below is a sample of the output generated by the R code, showing daily maximum temperature and precipitation data retrieved from the Open-Meteo Climate API for Nairobi in 2024.
> print(full_year_data)
date temperature_max_c precipitation_mm
1 2024-01-01 29.3 0.00
2 2024-01-02 28.5 1.46
3 2024-01-03 26.8 2.19
4 2024-01-04 26.7 0.00
5 2024-01-05 27.1 0.00
6 2024-01-06 29.8 0.88
7 2024-01-07 28.1 6.00
8 2024-01-08 24.8 9.36
9 2024-01-09 19.7 0.44
10 2024-01-10 22.0 0.88
11 2024-01-11 23.1 0.44
12 2024-01-12 23.9 1.32
13 2024-01-13 25.1 1.76
14 2024-01-14 26.0 0.15
15 2024-01-15 27.3 0.00
16 2024-01-16 28.1 0.00
17 2024-01-17 27.9 0.00
18 2024-01-18 29.0 0.00
19 2024-01-19 29.6 0.00
20 2024-01-20 29.7 0.00
21 2024-01-21 29.5 0.00
22 2024-01-22 28.4 0.00
23 2024-01-23 29.2 0.00
24 2024-01-24 30.5 0.00
25 2024-01-25 29.2 0.00
26 2024-01-26 28.4 0.00
27 2024-01-27 27.4 0.00
28 2024-01-28 28.1 0.00
29 2024-01-29 28.5 0.00
30 2024-01-30 29.1 0.00
31 2024-01-31 28.7 0.00
32 2024-02-01 26.9 0.00
33 2024-02-02 25.1 0.15
34 2024-02-03 27.6 0.00
35 2024-02-04 25.3 0.74
36 2024-02-05 26.4 0.30
37 2024-02-06 27.1 0.00
38 2024-02-07 29.4 0.00
39 2024-02-08 27.7 0.15
40 2024-02-09 27.1 0.15
41 2024-02-10 27.9 0.00
42 2024-02-11 28.3 0.00
43 2024-02-12 29.6 0.00
44 2024-02-13 30.3 0.89
45 2024-02-14 31.1 0.15
46 2024-02-15 29.8 0.00
47 2024-02-16 29.5 0.00
48 2024-02-17 30.6 0.00
49 2024-02-18 30.0 0.45
50 2024-02-19 29.6 0.00
51 2024-02-20 29.0 0.00
52 2024-02-21 30.4 0.00
53 2024-02-22 31.5 0.00
54 2024-02-23 31.5 0.30
55 2024-02-24 29.9 12.19
56 2024-02-25 22.5 21.86
57 2024-02-26 25.6 10.56
58 2024-02-27 26.1 4.61
59 2024-02-28 25.1 4.17
60 2024-02-29 24.6 3.87
61 2024-03-01 27.0 2.53
62 2024-03-02 26.2 2.53
63 2024-03-03 26.1 0.15
64 2024-03-04 26.3 0.30
Overview of AgERA5 API Access and Usage
The following table summarizes the key aspects of accessing and using the AgERA5 dataset through the Copernicus Climate Data Store (CDS) API. It highlights important details about authentication, request parameters, data retrieval, and practical considerations to help users effectively download and utilize AgERA5 climate data programmatically.
| Test Aspect | Description / Result |
|---|---|
| API Endpoint | - Accessed via CDS API client to dataset "sis-agrometeorological-indicators" |
| Authentication | - Requires registration on Copernicus Climate Data Store (CDS) and API key configured in .cdsapirc file |
| Request Parameters | - Variable: "2m_temperature" - Statistic: "24_hour_minimum" - Year: "1980" - Months: All 12 months - Days: All days 1-31 - Version: "1_0" - Area: Bounding box around Nairobi (N: -1.2, W: 36.74, S: -1.37, E: 36.9) |
| Data Retrieval | - Requested full-year daily minimum 2m temperature data for Nairobi region - Uses client.retrieve() method with .download() to save data locally |
| Data Format | - Data downloaded as NetCDF file (default format for AgERA5) |
| Response Time | - Dependent on data volume; full year and area request may take several minutes to complete |
| Error Handling | - Errors raised if .cdsapirc file missing or API key invalid - Network or quota errors possible during download |
| Data Completeness | - Full year coverage requested; actual completeness depends on dataset availability and license acceptance |
| Data Accuracy | - Data sourced from Copernicus AgERA5 reanalysis product, widely validated for agricultural climate applications |
| Limitations | - Must manually accept dataset license on CDS website before API download allowed - Large requests may be throttled or require chunking |
| Usability | - Python cdsapi client provides straightforward API access - Requires basic Python programming knowledge and environment setup |
| Scalability | - Supports requests for multiple years, variables, and spatial areas with appropriate chunking |
| Documentation | - Official CDS API documentation: https://cds.climate.copernicus.eu/how-to-api - Dataset page includes "Show API request code" feature for custom queries |
The following Python script demonstrates how to use the cdsapi client to request and download daily minimum 2-meter temperature data for the year 1980 over the Nairobi region from the AgERA5 dataset.
import cdsapi
dataset = "sis-agrometeorological-indicators"
request = {
"variable": "2m_temperature",
"statistic": ["24_hour_minimum"],
"year": ["1980"],
"month": [
"01", "02", "03",
"04", "05", "06",
"07", "08", "09",
"10", "11", "12"
],
"day": [
"01", "02", "03",
"04", "05", "06",
"07", "08", "09",
"10", "11", "12",
"13", "14", "15",
"16", "17", "18",
"19", "20", "21",
"22", "23", "24",
"25", "26", "27",
"28", "29", "30",
"31"
],
"version": "1_0",
"area": [-1.2, 36.74, -1.37, 36.9]
}
client = cdsapi.Client()
client.retrieve(dataset, request).download()
NASA POWER API testing process
The following table summarizes the key aspects and results of testing the NASA POWER API for retrieving daily temperature data at a specific location, highlighting the API’s usability, data quality, and practical considerations for integration in climate data analysis workflows.
| Test Aspect | Description / Result |
|---|---|
| API Endpoint | - https://power.larc.nasa.gov/api/temporal/daily/point?start=20150101&end=20150630&latitude=-1.2921&longitude=36.8219&community=ag¶meters=T2M&format=json&user=Michel&header=true&time-standard=lst |
| Authentication | - No API key required; simple user identifier optional (user=Michel) |
| Request Parameters | - Latitude: -1.2921 (Nairobi) - Longitude: 36.8219 - Date range: 2015-01-01 to 2015-06-30 - Community: ag (Agroclimatology) - Parameters: T2M (2m air temperature) - Format: JSON - Time standard: Local Solar Time (LST) |
| Data Retrieval | - Successful GET request with HTTP 200 status code - Data returned as nested JSON with daily temperature values keyed by date |
| Data Format | - JSON response parsed using jsonlite::fromJSON() - Extracted temperature data converted to data frame with columns: date, temperature_c |
| Response Time | - Typically under a few seconds for daily point data requests |
| Error Handling | - Checked HTTP status code before parsing - Used conditional stop on failure |
| Data Completeness | - Returned data covers entire requested date range - Dates correctly parsed and ordered |
| Data Accuracy | - Temperature values consistent with expected climatology for Nairobi region |
| Limitations | - Data availability depends on spatial resolution (~0.5° x 0.625°) - Exact coordinates may yield empty data if outside grid - No bulk or multi-point queries in single request (point data only) |
| Usability | - Easy integration with R packages (httr, jsonlite, dplyr) - No complex authentication or API keys needed - Clear parameterization and documentation |
| Scalability | - Suitable for single-point daily data retrieval - Larger spatial or temporal coverage requires multiple requests |
| Documentation | - API usage documented with example R code and troubleshooting notes - Official docs: NASA POWER API, and API get start |
To illustrate the practical implementation of this testing approach, the following R code demonstrates how to retrieve and aggregate monthly climate data chunks from the Open-Meteo Climate API for Nairobi in 2024, including error handling and timing of requests.
library(httr)
library(jsonlite)
library(dplyr)
# Define the API URL (copied directly from your query)
api_url <- "https://power.larc.nasa.gov/api/temporal/daily/point?start=20150101&end=20150630&latitude=-1.2921&longitude=36.8219&community=ag¶meters=T2M&format=json&user=Michel&header=true&time-standard=lst"
# Send GET request to NASA POWER API
response <- GET(api_url)
# Check if request was successful
if (status_code(response) == 200) {
json_data <- content(response, as = "text", encoding = "UTF-8")
parsed_data <- fromJSON(json_data, flatten = TRUE)
temp_data <- parsed_data$properties$parameter$T2M$data
temperature_df <- data.frame(
date = as.character(names(temp_data)),
temperature_c = as.numeric(unlist(temp_data))
) %>%
mutate(
date = as.Date(date, format = "%Y%m%d")
) %>%
arrange(date)
head(temperature_df)
} else {
stop(paste("API request failed with status:", status_code(response)))
}
The following is a sample JSON response from the NASA POWER API, showing daily 2-meter air temperature (T2M) values for Nairobi, Kenya, over the first month of 2015, including geographic coordinates and elevation metadata.
{
"type": "Feature",
"geometry": {
"type": "Point",
"coordinates": [36.822, -1.292, 1642.2]
},
"properties": {
"parameter": {
"T2M": {
"20150101": 19.56,
"20150102": 19.63,
"20150103": 20.4,
"20150104": 21.33,
"20150105": 20.41,
"20150106": 20.89,
"20150107": 21.16,
"20150108": 21.25,
"20150109": 21.64,
"20150110": 22.14,
"20150111": 21.48,
"20150112": 21.56,
"20150113": 21.01,
"20150114": 20.76,
"20150115": 21.24,
"20150116": 22.95,
"20150117": 23,
"20150118": 23.38,
"20150119": 23.88,
"20150120": 21.16,
"20150121": 20.88,
"20150122": 21.35,
"20150123": 21.58,
"20150124": 21.15,
"20150125": 20.51,
"20150126": 21.26,
"20150127": 22.12,
"20150128": 22.69,
"20150129": 22.12,
"20150130": 22.25,
"20150131": 22.48,
Crop Calendar Datasets: API Availability and Access
The following table summarizes key crop calendar datasets, highlighting their API availability, registration requirements, and access methods.
| Dataset | API Available? | API Access Registration Required? | Notes / Source |
|---|---|---|---|
| AgMIP-GGCMI Crop Calendar | No official public API | N/A | Provided as R-package and static gridded datasets; crop calendars at 0.5° resolution for 18 crops with rainfed/irrigated separation; used for crop model calibration; data downloadable from GitHub and Zenodo repositories. |
| MIRCA-OS (Monthly Irrigated and Rainfed Cropped Area, Open Source) | No official public API | N/A | Dataset provides monthly cropped area for irrigated and rainfed systems globally; data accessed via direct download (e.g., from FAO or associated portals); no REST API documented. |
| WorldCereal Project Crop Calendars | No official public API | N/A | Crop calendar data developed for global cereal crops; typically accessed via project portals or data repositories; no dedicated API available publicly. |
This table presents the testing results and key characteristics of the WorldCereal Project Crop Calendars dataset, highlighting its access method, data format, and usability in comparison to API-based climate data services.
| Test Aspect | Description / Result |
|---|---|
| API Endpoint | - No official API endpoint available for WorldCereal Crop Calendars. - Dataset downloadable as a ZIP file from: https://tandf.figshare.com/articles/dataset/Global_crop_calendars_of_maize_and_wheat_in_the_framework_of_the_WorldCereal_project/20005293 |
| Authentication | - No authentication or API key required; data accessed via direct download |
| Request Parameters | - Not applicable; data provided as static gridded maps and shapefiles |
| Data Retrieval | - Download ZIP file containing crop calendar maps for maize and wheat - Maps include Start of Season (SOS), End of Season (EOS), location info, and agro-ecological zones (AEZ) metadata |
| Data Format | - ZIP archive containing GeoTIFF maps and shapefiles - Maps display SOS, EOS in day of year (DOY) format - AEZ polygons with crop calendar attributes |
| Response Time | - Not applicable; download speed depends on user internet connection and file size |
| Error Handling | - No API errors; download failures depend on server or network issues |
| Data Completeness | - Covers global maize and wheat crop calendars at 0.5° resolution - Includes major cereal seasons and up to two maize seasons - Stratified into 106 agro-ecological zones |
| Data Accuracy | - Crop calendars derived from multiple reputable sources (GEOGLAM, USDA-FAS, FAO, JRC-ASAP) and machine learning models trained on ERA5 climate data - Widely used for global crop mapping and agro-ecological stratification |
| Limitations | - Static dataset; no temporal updates via API - Focused on maize and wheat only - Requires GIS software or programming tools to read and process maps |
| Usability | - Suitable for GIS analysis and crop modeling - Requires manual download and local processing - No programmatic access for automated workflows |
| Scalability | - Scalable for global coverage but limited by manual download and local processing resources |
| Documentation | - Detailed documentation and methodology available in project publications and ESA WorldCereal website - Dataset description: WorldCereal Crop Calendars on Figshare |
Climate Dataset Availability and API Access via Google Earth Engine
Google Earth Engine (GEE) is a powerful cloud-based platform that hosts a vast collection of geospatial datasets, including numerous key climate data products relevant to agroecological research. This section summarizes the availability of important climate datasets within GEE, highlighting which datasets can be accessed directly through the platform, their corresponding Earth Engine asset IDs, and the programmatic interfaces (APIs) supported for data retrieval and analysis. For datasets not currently hosted on GEE, alternative access methods and APIs are noted to guide integration into agroecological workflows.
| Dataset Name | Available in GEE? | Earth Engine Dataset ID / Notes | Provider / Source | Data Type | Access Method (API) | Notes / Comments |
|---|---|---|---|---|---|---|
| CHIRPS Precipitation | Yes | UCSB-CHG/CHIRPS/DAILY |
UCSB/CHG | ImageCollection | Python, JavaScript, R | Daily precipitation, 1981-present, ~5.5 km resolution |
| ERA5 Climate Reanalysis | Yes | ECMWF/ERA5_LAND_HOURLY |
ECMWF | ImageCollection | Python, JavaScript, R | Land reanalysis data with temperature, precipitation, etc. |
| MODIS NDVI | Yes | MODIS/006/MOD13Q1 |
NASA | ImageCollection | Python, JavaScript, R | Vegetation index, 250m resolution |
| SRTM Digital Elevation | Yes | USGS/SRTMGL1_003 |
USGS | Image | Python, JavaScript, R | 30m resolution digital elevation model |
| AgERA5 Agricultural Data | No | N/A | Various | TBD | External APIs (Copernicus CDS) | Accessed via Copernicus Climate Data Store API; not currently in GEE |
| CMIP6 Climate Projections | Yes | NASA/NEX-GDDP-CMIP6 |
NASA | ImageCollection | Python, JavaScript | Downscaled CMIP6 climate model projections |
| NASA POWER | No | N/A | NASA | Various | NASA POWER API (external) | Not in GEE; accessible via NASA POWER API without EarthData login |
| NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) | Yes | NASA/NEX-GDDP |
NASA | ImageCollection | Python, JavaScript | Downscaled CMIP5 climate projections |
| TAMSAT | No | N/A | TAMSAT | Various | Downloads only | Not available in GEE; data accessed via downloads |
| IMERG (Integrated Multi-satellitE Retrievals for GPM) | Yes | NASA/GPM_L3/IMERG_V06 |
NASA | ImageCollection | Python, JavaScript | Satellite precipitation estimates |
| TerraClimate | Yes | IDAHO_EPSCOR/TERRACLIMATE |
University of Idaho | ImageCollection | Python, JavaScript | Monthly climate and water balance data |
| CHIRTS (CHIRPS + Temperature) | No | N/A | UCSB/CHG | TBD | External APIs (ClimateSERV) or R package chirps |
Not available in GEE; accessed via ClimateSERV API or R package chirps |
Notably, while many key datasets such as CHIRPS, ERA5, and TerraClimate are fully integrated into GEE and accessible via its Python and JavaScript APIs, some important datasets like AgERA5 and NASA POWER require external APIs or direct downloads. To demonstrate practical usage and confirm data accessibility, we conducted a test extracting daily precipitation data from the CHIRPS dataset using the Earth Engine Python API. The following code snippet illustrates this process and highlights the ease of programmatic access to high-quality climate data through GEE.
CHIRPS
!pip install earthengine-api
import ee
import datetime
ee.Initialize()
# Load CHIRPS daily dataset
chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY')
# Define Nairobi point geometry (longitude, latitude)
nairobi = ee.Geometry.Point(36.817223, -1.286389)
# Define date range
start_date = datetime.date(2020, 1, 1)
end_date = datetime.date(2020, 1, 31)
delta = datetime.timedelta(days=1)
current_date = start_date
print("Date, Precipitation (mm)")
while current_date <= end_date:
next_date = current_date + delta
# Filter image for one day
image = chirps.filterDate(str(current_date), str(next_date)).first()
if image is None:
print(f"{current_date}, No image found")
else:
# Extract precipitation at Nairobi point
precip = image.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=nairobi,
scale=5566, # CHIRPS native resolution ~5.55 km
maxPixels=1e9,
bestEffort=True
).get('precipitation').getInfo()
print(f"{current_date}, {precip}")
current_date = next_date
The above code queries the CHIRPS daily precipitation dataset for Nairobi over the month of January 2020. It filters the dataset by date and extracts the mean precipitation value at the specified geographic point for each day. This approach demonstrates how Google Earth Engine enables efficient, programmatic access to high-resolution climate data, facilitating detailed temporal analysis at specific locations relevant to agroecological studies.
Date, Precipitation (mm)
2020-01-01, 0
2020-01-02, 0
2020-01-03, 0
2020-01-04, 0
2020-01-05, 0
2020-01-06, 0
2020-01-07, 19.17416000366211
2020-01-08, 0
2020-01-09, 0
2020-01-10, 0
2020-01-11, 0
2020-01-12, 0
2020-01-13, 28.82002067565918
2020-01-14, 0
2020-01-15, 0
2020-01-16, 0
2020-01-17, 0
2020-01-18, 0
2020-01-19, 0
2020-01-20, 0
2020-01-21, 0
2020-01-22, 0
2020-01-23, 0
2020-01-24, 0
2020-01-25, 34.24251937866211
2020-01-26, 26.745319366455078
2020-01-27, 0
2020-01-28, 0
2020-01-29, 0
2020-01-30, 0
2020-01-31, 26.745319366455078
ERA5
import ee
import datetime
# Initialize the Earth Engine API
ee.Initialize()
# Load ERA5 daily dataset and select the total precipitation band
era5 = ee.ImageCollection('ECMWF/ERA5/DAILY').select('total_precipitation')
# Define Nairobi point geometry (longitude, latitude)
nairobi = ee.Geometry.Point(36.817223, -1.286389)
# Define date range
start_date = datetime.date(2020, 1, 1)
end_date = datetime.date(2020, 1, 31)
delta = datetime.timedelta(days=1)
current_date = start_date
print("Date, Total Precipitation (mm)")
while current_date <= end_date:
next_date = current_date + delta
# Filter image for one day
image = era5.filterDate(str(current_date), str(next_date)).first()
if image is None:
print(f"{current_date}, No image found")
else:
# Extract total precipitation at Nairobi point
precip_m = image.reduceRegion(
reducer=ee.Reducer.mean(),
geometry=nairobi,
scale=10000, # ERA5 native resolution ~9 km; 10,000m used here
maxPixels=1e9,
bestEffort=True
).get('total_precipitation').getInfo()
# Convert from meters to millimeters
precip_mm = precip_m * 1000 if precip_m is not None else None
print(f"{current_date}, {precip_mm}")
current_date = next_date
Date, Total Precipitation (mm)
2020-01-01, 0.000609084963798523
2020-01-02, 0.3779754042625427
2020-01-03, 0.6145723164081573
2020-01-04, 0.2590324729681015
2020-01-05, 0.30939653515815735
2020-01-06, 3.970738500356674
2020-01-07, 1.8196124583482742
2020-01-08, 3.096519038081169
2020-01-09, 1.582600176334381
2020-01-10, 3.0573997646570206
2020-01-11, 2.657245844602585
2020-01-12, 6.174053996801376
2020-01-13, 7.1694739162921906
2020-01-14, 4.740696400403976
2020-01-15, 0.8538719266653061
2020-01-16, 1.3177990913391113
2020-01-17, 7.076513022184372
2020-01-18, 4.429824650287628
2020-01-19, 3.7414096295833588
2020-01-20, 0.0985506922006607
2020-01-21, 0.4185140132904053
2020-01-22, 1.1481251567602158
2020-01-23, 2.039514482021332
2020-01-24, 0.9816642850637436
2020-01-25, 0.28690509498119354
2020-01-26, 1.4135781675577164
2020-01-27, 3.6820396780967712
2020-01-28, 9.058376774191856
2020-01-29, 8.030232042074203
2020-01-30, 12.607492506504059
2020-01-31, 9.994294494390488