Anaconda Downloads
PIP Downloads:
(C) Eric J. Drewitz 2025-2026
An open-source package that helps meteorologists and weather enthusiats download, pre-process and post-process various types of weather data.
This package only retrieves open-source publicly available weather data.
This package provides the following extra functionality compared to existing packages for downloading weather data:
How To Install
Copy and paste either command into your terminal or anaconda prompt:
Install via Anaconda
conda install wxdata
Install via pip
pip install wxdata
How To Update To The Latest Version
Copy and paste either command into your terminal or anaconda prompt:
Update via Anaconda
This is for users who initially installed WxData through Anaconda
conda update wxdata
Update via pip
This is for users who initially installed WxData through pip
pip install --upgrade wxdata
Important Compatibility Information
When a new version of Python comes out, it might not be compatible with the C++ eccodes library immediately (especially on pip/pypi versions).
This issue arises when the user is post-processing GRIB data.
There are two options to resolve this issue:
i) Install wxdata via Anaconda/Miniconda3 --> conda install wxdata
ii) Set up a new environment with an earlier version of Python (must be Python >= 3.10) and then pip install wxdata
Friendly for users working on VPN/PROXY connections
Depending on which client, the proxy-address:port must be entered as either a dictionary or a string.
The clients that use a string for proxies are:
-
All ECMWF clients
-
METAR Observations Client
-
pixel_query()tool if the user needs to download the airport station codes list.All other clients use proxies as a dictionary
Example: We want to download the latest Observed Sounding Data for San Diego, CA (NKX) proxies=None ---> proxies={ 'http':'http://your-proxy-address:port', 'https':'http://your-proxy-address:port' } sounding_data = get_observed_sounding_data('nkx', proxies=proxies) Example: We want to download the ECMWF IFS Data: proxies=None ---> proxies="http://your-proxy-address:port" ---> ds = ecmwf_ifs(proxies=proxies)
For more information on configuring proxies: https://requests.readthedocs.io/en/latest/user/advanced/#proxies
-
Converts GRIB variable keys into variable keys that are in plain language.
- (e.g. 'r2' ---> '2m_relative_humidity')
-
Has a scanner that checks if the data files on your PC are up to date with those on the data server.
- This is a safeguard to protect newer developers from getting temporary IP address bans from the various data servers.
- Improves performance by preventing the potential of repetative downloading the same dataset.
-
Preserves system memory via the following methods:
- Clears out old data files before each new data download.
- Optional setting
clear_recycle_binin all functions.- When
clear_recycle_bin=Truethe computer's recycle/trash bin is cleared with each run of the script using any WxData function. - If a user wishes to not clear out their recycle bin
set clear_recycle_bin=False. - Default:
clear_recycle_bin=False.
- When
Regular Users
- Downloading METAR Data
- Downloading Observed Sounding Data
- Downloading the first 72 hours of the ECMWF IFS and ECMWF AIFS
- Downloading the GEFS members p01 and p02 for only Temperature
- Downloading the Real-Time Mesoscale Analysis (RTMA)
- Downloading the RAWS SIG Group Fuels Data and the NFDRS Forecast for the RAWS SIG Groups for the South Ops Geographic Area Coordination Center
- Downloading the SPC Convective Outlook for CONUS
- Downloading NWS Maximum Temperature Forecast for Hawaii
- Downloading the GFS0P25 then performing pixel and line queries on the data
- Downloading various datasets from the Applied Climate Information System (ACIS)
- Downloading AIGFS Data
- Downloading AIGEFS Data
- Downloading and plotting the Climate Prediction Center 6-10 Day Precipitation Outlook
- Downloading OUN Sounding Data and Using The WxData Linear Anti Aliasing Tool To Interpolate 100 Points Between Each Observed Data Point And Visualize Both Data Sets
- Downloading Subsets Of ECMWF IFS Ensemble and AIFS Ensemble Data
- Downloading the ECMWF IFS 500 mb Geopotential Height Initial Analysis And Plot A North Pole Stereographic Resolving The Meridian With The WxData Cyclic Point Tool
Advanced Users
- Using the
clientmodule to download the latest HadCRUT5 Analysis netCDF file and open this dataset in xarray - Downloading the GFS0P25 for temperature fields and using run_external_scripts() to post-process this GFS0P25 dataset in an external Python script
Global Forecast System (GFS)
AI Global Forecast System (AIGFS)
Hybrid Global Ensemble Forecast System (HGEFS)
Global Ensemble Forecast System (GEFS)
AI Global Ensemble Forecast System (AIGEFS)
- AIGEFS Members (Pressure Parameters)
- AIGEFS Members (Surface Parameters)
- AIGEFS Ensemble Mean & Ensemble Spread
ECMWF Open Data
Real-Time Mesoscale Analysis (RTMA)
NOAA Storm Prediction Center Outlooks/Climate Prediction Center Outlooks/National Weather Service Forecasts
METAR Observations
FEMS RAWS Network
- Get Single Station RAWS Data
- Get Each SIG of RAWS Data for a Geographic Area Coordination Center
- Get NFDRS Forecast Data for Each SIG for a Geographic Area Coordination Center
Observed Atmospheric Soundings
GFS Post-Processing
AIGFS Post-Processing
GEFS Post-Processing
AIGEFS Post-Processing
HGEFS Post-Processing
ECMWF Post-Processing
Real-Time Mesoscale Analysis Post-Processing
xmACIS2 Climate Data
Custom Gridded Data
Custom CSV Data
Cyclic Points For Hemispheric Plots
Shifting Longitude From 0 to 360 --> -180 to 180
Pixel Query
Line Query
Linear Anti-Aliasing Between Two Points
Running External Python Scripts In Your Workflow
"""
This file hosts all of the functions in the WxData Python library that directly interact with the user.
(C) Eric J. Drewitz 2025-2026
"""
"""
This section of functions are for users who want full wxdata functionality.
These functions do the following:
1) Scan for the latest available data.
- If the data on your local machine is not up to date, new data will download automatically.
- If the data on your local machine is up to date, new data download is bypassed.
- This is a safeguard that prevents excessive requests on the data servers.
2) Builds the wxdata directory to store the weather data files.
- Scans for the directory branch and builds the branch if it does not exist.
3) Downloads the data.
- Users can define their VPN/PROXY IP Address as a (dict) in their script and pass their
VPN/PROXY IP address into the function to avoid SSL Certificate errors when requesting data.
- Algorithm allows for up to 5 retries with a 30 second break between each retry to resolve connection
interruptions while not overburdening the data servers.
4) Pre-processes the data via filename formatting and correctly filing in the wxdata directory.
5) Post-processing by doing the following tasks:
- Remapping GRIB2 variable keys into plain language variable keys.
- Fixing dataset build errors and grouping all variables together.
- Transforms longitude from 0 to 360 degrees into -180 to 180 degrees.
- Subsetting the data to the latitude/longitude boundaries specified by the user.
- Converting temperature from kelvin to units the user wants (default is Celsius).
- Returning a post-processed xarray.array to the user.
6) Preserves system memory by doing the following:
- Deleting old data files before each new download.
- When clear_recycle_bin=True, the user's recycle bin is also cleared.
"""
# Global Forecast System (GFS)
# - GFS 0.25x0.25 Degree Primary Parameters
# - GFS 0.25x0.25 Degree Secondary Parameters
# - GFS 0.5x0.5 Degree
from wxdata.gfs.gfs import(
gfs_0p25,
gfs_0p25_secondary_parameters,
gfs_0p50
)
# AI Global Forecast System (AIGFS)
from wxdata.aigfs.aigfs import aigfs
# Hybrid Global Ensemble Forecast System (HGEFS)
from wxdata.hgefs.hgefs import hgefs_mean_spread
# Global Ensemble Forecast System (GEFS)
# - GEFS 0.5x0.5 Degree Primary Parameters
# - GEFS 0.5x0.5 Degree Secondary Parameters
# - GEFS 0.25x0.25 Degree
from wxdata.gefs.gefs import(
gefs_0p50,
gefs_0p50_secondary_parameters,
gefs_0p25
)
# AI Global Ensemble Forecast System (AIGEFS)
# - AIGEFS Pressure Members (Pressure Level Variables)
# - AIGEFS Surface Members (Surface Level Variables)
# - AIGEFS Single (AIGEFS Ensemble Mean & AIGEFS Ensemble Spread)
from wxdata.aigefs.aigefs import(
aigefs_pressure_members,
aigefs_surface_members,
aigefs_single
)
# European Centre for Medium-Range Weather Forecasts (ECMWF)
# - ECMWF IFS
# - ECMWF IFS Ensemble
# - ECMWF AIFS
# - ECMWF AIFS Ensemble
# - ECMWF IFS Wave
# - ECMWF IFS Wave Ensemble
from wxdata.ecmwf.ecmwf import(
ecmwf_ifs,
ecmwf_ifs_ens,
ecmwf_aifs,
ecmwf_aifs_ens,
ecmwf_ifs_wave,
ecmwf_ifs_wave_ens
)
# FEMS RAWS Network
# - Single RAWS Station Data
# - A SIG Group of RAWS Data by GACC
# - NFDRS Forecast Data For a RAWS Station
from wxdata.fems.fems import(
get_single_station_data,
get_raws_sig_data,
get_nfdrs_forecast_data
)
# Real-Time Mesoscale Analysis (RTMA)
# - RTMA Latest
# - RTMA Comparison Between Two Times
from wxdata.rtma.rtma import(
rtma,
rtma_comparison
)
# NOAA
# - Storm Prediction Center Outlooks
# - Climate Prediction Center Outlooks
# - National Weather Service Forecasts
from wxdata.noaa.nws import(
get_ndfd_grids,
get_cpc_outlook
)
# Observed Upper-Air Soundings
# (University of Wyoming Database)
from wxdata.soundings.wyoming_soundings import get_observed_sounding_data
# METAR Observational Data (From NOAA)
from wxdata.metars.metar_obs import download_metar_data
"""
This section hosts all the functions and modules that involve post-processing the data.
These are the functions and modules that:
1) Re-map the GRIB2 Variable Keys into Plain Language Keys
2) Build the xarray.array of the various datasets.
"""
# Global Forecast System (GFS)
import wxdata.post_processors.gfs_post_processing as gfs_post_processing
# AI Global Forecast System (AIGFS)
import wxdata.post_processors.aigfs_post_processing as aigfs_post_processing
# Hybrid Global Ensemble Forecast System (HGEFS)
import wxdata.post_processors.hgefs_post_processing as hgefs_post_processing
# Global Ensemble Forecast System (GEFS)
import wxdata.post_processors.gefs_post_processing as gefs_post_processing
# AI Global Ensemble Forecast System (AIGEFS)
import wxdata.post_processors.aigefs_post_processing as aigefs_post_processing
# European Centre for Medium-Range Weather Forecasts (ECMWF)
import wxdata.post_processors.ecmwf_post_processing as ecmwf_post_processing
# Real-Time Mesoscale Analysis (RTMA)
from wxdata.post_processors.rtma_post_processing import process_rtma_data
"""
This section hosts the utility functions accessable to the user.
These functions provide helpful utilities when analyzing weather data.
Utility functions are geared towards the following types of users:
1) Users who want to use their own scripts to download the data however, they
would like to use the wxdata post-processing capabilities.
2) Users who want to make hemispheric graphics or any graphics where cyclic points
resolve missing data along the prime meridian or international dateline.
"""
# WxData function using cartopy to make cyclic points
# This is for users who wish to make graphics that cross the -180/180 degree longitude line
# This is commonly used for Hemispheric graphics
# Function that converts the longitude dimension in an xarray.array
# From 0 to 360 to -180 to 180
from wxdata.utils.coords import(
cyclic_point,
shift_longitude
)
# Functions to pixel query and query pixels along a line between points A and B
# Function to interpolate to n amount of points in between x and y values respectively
from wxdata.utils.tools import(
pixel_query,
line_query,
linear_anti_aliasing
)
"""
This section hosts the various data clients that retrieve various types of data.
These clients can be easily configured to work on VPN/PROXY connections.
"""
# These are the wxdata HTTPS Clients with full VPN/PROXY Support
# Client List:
# - get_gridded_data()
# - get_csv_data()
# - get_xmacis_data()
import wxdata.client.client as client
# This function executes a list of Python scripts in the order the user lists them
from wxdata.utils.scripts import run_external_scripts
MetPy: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.
xarray: Hoyer, S., Hamman, J. (In revision). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software.
cartopy: Phil Elson, Elliott Sales de Andrade, Greg Lucas, Ryan May, Richard Hattersley, Ed Campbell, Andrew Dawson, Bill Little, Stephane Raynaud, scmc72, Alan D. Snow, Ruth Comer, Kevin Donkers, Byron Blay, Peter Killick, Nat Wilson, Patrick Peglar, lgolston, lbdreyer, … Chris Havlin. (2023). SciTools/cartopy: v0.22.0 (v0.22.0). Zenodo. https://doi.org/10.5281/zenodo.8216315
NumPy: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).
Pandas: Pandas: McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).
dask: Dask Development Team (2016). Dask: Library for dynamic task scheduling. URL http://dask.pydata.org
cfgrib: Author: ECMWF, Year: (2025), Title: cfgrib: A Python interface to map GRIB files to xarray, Source: https://github.com/ecmwf/cfgrib
requests: K. Reitz, "Requests: HTTP for Humans". Available: https://requests.readthedocs.io/.
shapeography: Eric J. Drewitz. (2026). edrewitz/shapeography: shapeography 1.0 Released (shapeography1.0). Zenodo. https://doi.org/10.5281/zenodo.18676845
geopandas: Kelsey Jordahl, Joris Van den Bossche, Martin Fleischmann, Jacob Wasserman, James McBride, Jeffrey Gerard, … François Leblanc. (2020, July 15). geopandas/geopandas: v0.8.1 (Version v0.8.1). Zenodo. http://doi.org/10.5281/zenodo.3946761
tqdm: da Costa-Luis, (2019). tqdm: A Fast, Extensible Progress Meter for Python and CLI. Journal of Open Source Software, 4(37), 1277, https://doi.org/10.21105/joss.01277
ecmwf-opendata: European Centre for Medium-Range Weather Forecasts (2026). ecmwf-opendata[Computer software]. GitHub. https://github.com/ecmwf/ecmwf-opendata
- National Oceanic and Atmospheric Administration/National Center for Environmental Prediction
- European Centre for Medium-Range Weather Forecasts
- University of Wyoming
- National Oceanic and Atmospheric Administration/National Weather Service
- National Oceanic and Atmospheric Administration/Aviation Weather Center
- National Oceanic and Atmospheric Administration/Climate Prediction Center
- Applied Climate Information System (ACIS)