climateservaccess 1.0.4

A custom library for accessing the ClimateSERV API

climateservAccess

About

A python package to access data through ClimateSERV API. Built as a more complete, customized version of the existing climateserv package by SERVIR, which did not support all datatypes.

Documentation

datatypeDict

Stores a dictionary with all datatype numbers and names (see Reference)

getDataFrame

Accesses requested data through ClimateSERV API and returns it in a pandas dataframe (see Example Code). Returns None if no data found.

Parameters

• data_type (int): Datatype number
• start_date (str): Start date in MM/DD/YYYY format
• end_date (str): End date in MM/DD/YYYY format
• operation_type (string): 'Average', 'Min', or 'Max'
• geometry_coords (list): List of coordinates for polygon

getCSV

Accesses requested data through ClimateSERV API and saves it to a csv file.

Parameters

• data_type (int): Datatype number
• start_date (str): Start date in MM/DD/YYYY format
• end_date (str): End date in MM/DD/YYYY format
• operation_type (string): 'Average', 'Min', or 'Max'
• geometry_coords (list): List of coordinates for polygon
• filename (str): Name of the CSV file to be saved

getBox

Returns a list with coordinates for a square centered at (lon, lat), with width res

Parameters

• lat (float): Latitude.
• lon (float): Longitude.
• res (float): Resolution.

Example Code

This code snippet retrieves Stonehenge precipitation data from ClimateSERV (NASA_IMERG_Late), stores it in a pandas dataframe, and plots the data for the month of January 2023.

import pandas as pd
import matplotlib.pyplot as plt
import climateservaccess as ca

# Define parameters
data_type = 26 # see ca.datatypeDict for data types
start_date = '01/01/2023' 
end_date = '01/30/2023'
operation_type = 'average' # valid options are: 'average', 'max', 'min'
lat = 51.17912455395276 # latitude of Stonehenge
lon = -1.8262705029300066 # longitude of Stonehenge
res = 0.01 # resolution in degrees
polygon = ca.getBox(lat, lon, res) # defines box of width res around lat, lon

# Get dataframe with data from ClimateSERV
df = ca.getDataFrame(data_type, start_date, end_date, operation_type, polygon)

# Select data from df and store inside data_df
data_df = pd.DataFrame(df['data'].to_list())
# Convert the date column to datetime format
data_df['date'] = pd.to_datetime(data_df['date'])

# Plot the data
plt.figure(figsize=(10,5))
plt.plot(data_df['date'], data_df['raw_value'])
plt.xlabel('Date')
plt.ylabel('Precipitation (mm)')
plt.title('Average Daily Precipitation of Stonehenge')
plt.show()

Reference

List of datatypes also available under ClimateSERV Developers API.

Historical Datasets

Datatype Number	Datatype	Data Availability	Date Range
0	CHIRPS_Rainfall	Every 1 day	1981 - Near Present
1	eMODIS_NDVI_W_Africa	Every 10 days	2002 - September 2022
2	eMODIS_NDVI_E_Africa	Every 10 days	2002 - September 2022
5	eMODIS_NDVI_S_Africa	Every 10 days	2002 - September 2022
26	NASA_IMERG_Late	Every 1 day	2000 - Near Present
28	eMODIS_NDVI_Central_Asia	Every 10 days	2002 - September 2022
29	ESI_4WEEK	Every 7 days	2000 - Present
31	CHIRPS_GEFS_Forecast_Mean_Anom	Every 1 day	1985 - Near Present
32	CHIRPS_GEFS_Forecast_Mean_Precip	Every 1 day	1985 - Near Present
33	ESI_12WEEK	Every 7 days	2000 - Present
37	USDA_SMAP_Soil_Moisture_Profile	Every 3 days	March 2015 - August 2022
38	USDA_SMAP_Surface_Soil_Moisture	Every 3 days	March 2015 - August 2022
39	USDA_SMAP_Surface_Soil_Moisture_Anom	Every 3 days	March 2015 - August 2022
40	USDA_SMAP_Sub_Surface_Soil_Moisture	Every 3 days	March 2015 - August 2022
41	USDA_SMAP_Sub_Surface_Soil_Moisture_Anom	Every 3 days	March 2015 - August 2022
90	UCSB_CHIRP_Rainfall	Every 1 day	1981 - Near Present
91	NASA_IMERG_Early	Every 1 day	2000 - Near Present
541	NSIDC_SMAP_Sentinel_1Km	Every 1 day	2015 - Near Present
542	NSIDC_SMAP_Sentinel_1Km_15_day	Every 15 days	2015 - Near Present
661	LIS_ET	Every 1 day	2000 - Near Present
662	LIS_Baseflow	Every 1 day	2000 - Near Present
663	LIS_Runoff	Every 1 day	2000 - Near Present
664	LIS_Soil_Moisture_0_10cm	Every 1 day	2000 - Near Present
665	LIS_Soil_Moisture_10_40cm	Every 1 day	2000 - Near Present
666	LIS_Soil_Moisture_40_100cm	Every 1 day	2000 - Near Present
667	LIS_Soil_Moisture_100_200cm	Every 1 day	2000 - Near Present

Forecast Datasets

These NMME (North American Multi-Model Ensemble) datasets all provide daily forecasts up to 6 months out.

Datatype Number	Datatype
6	CCSM_Ensemble_1_Temperature
7	CCSM_Ensemble_1_Precipitation
8	CCSM_Ensemble_2_Temperature
9	CCSM_Ensemble_2_Precipitation
10	CCSM_Ensemble_3_Temperature
11	CCSM_Ensemble_3_Precipitation
12	CCSM_Ensemble_4_Temperature
13	CCSM_Ensemble_4_Precipitation
14	CCSM_Ensemble_5_Temperature
15	CCSM_Ensemble_5_Precipitation
16	CCSM_Ensemble_6_Temperature
17	CCSM_Ensemble_6_Precipitation
18	CCSM_Ensemble_7_Temperature
19	CCSM_Ensemble_7_Precipitation
20	CCSM_Ensemble_8_Temperature
21	CCSM_Ensemble_8_Precipitation
22	CCSM_Ensemble_9_Temperature
23	CCSM_Ensemble_9_Precipitation
24	CCSM_Ensemble_10_Temperature
25	CCSM_Ensemble_10_Precipitation
42	CFSv2_Ensemble_1_Temperature
43	CFSv2_Ensemble_1_Precipitation
44	CFSv2_Ensemble_2_Temperature
45	CFSv2_Ensemble_2_Precipitation
46	CFSv2_Ensemble_3_Temperature
47	CFSv2_Ensemble_3_Precipitation
48	CFSv2_Ensemble_4_Temperature
49	CFSv2_Ensemble_4_Precipitation
50	CFSv2_Ensemble_5_Temperature
51	CFSv2_Ensemble_5_Precipitation
52	CFSv2_Ensemble_6_Temperature
53	CFSv2_Ensemble_6_Precipitation
54	CFSv2_Ensemble_7_Temperature
55	CFSv2_Ensemble_7_Precipitation
56	CFSv2_Ensemble_8_Temperature
57	CFSv2_Ensemble_8_Precipitation
58	CFSv2_Ensemble_9_Temperature
59	CFSv2_Ensemble_9_Precipitation
60	CFSv2_Ensemble_10_Temperature
61	CFSv2_Ensemble_10_Precipitation
62	CFSv2_Ensemble_11_Temperature
63	CFSv2_Ensemble_11_Precipitation
64	CFSv2_Ensemble_12_Temperature
65	CFSv2_Ensemble_12_Precipitation
66	CFSv2_Ensemble_13_Temperature
67	CFSv2_Ensemble_13_Precipitation
68	CFSv2_Ensemble_14_Temperature
69	CFSv2_Ensemble_14_Precipitation
70	CFSv2_Ensemble_15_Temperature
71	CFSv2_Ensemble_15_Precipitation
72	CFSv2_Ensemble_16_Temperature
73	CFSv2_Ensemble_16_Precipitation
74	CFSv2_Ensemble_17_Temperature
75	CFSv2_Ensemble_17_Precipitation
76	CFSv2_Ensemble_18_Temperature
77	CFSv2_Ensemble_18_Precipitation
78	CFSv2_Ensemble_19_Temperature
79	CFSv2_Ensemble_19_Precipitation
80	CFSv2_Ensemble_20_Temperature
81	CFSv2_Ensemble_20_Precipitation
82	CFSv2_Ensemble_21_Temperature
83	CFSv2_Ensemble_21_Precipitation
84	CFSv2_Ensemble_22_Temperature
85	CFSv2_Ensemble_22_Precipitation
86	CFSv2_Ensemble_23_Temperature
87	CFSv2_Ensemble_23_Precipitation
88	CFSv2_Ensemble_24_Temperature
89	CFSv2_Ensemble_24_Precipitation

License

Distributed under the MIT License. See LICENSE.txt for more information.