GEOS5FP 2.4.0

generates rasters of near-real-time GEOS-5 FP near-surface meteorology

GEOS5FP Python Package

The GEOS5FP Python package generates rasters of near-real-time GEOS-5 FP near-surface meteorology.

Gregory H. Halverson (they/them)
[e-mail]
NASA Jet Propulsion Laboratory 329G

Installation

This package is available on PyPi as a pip package called GEOS5FP.

pip install GEOS5FP

Usage

Import this package as GEOS5FP.

from GEOS5FP import GEOS5FPConnection
from datetime import datetime

Creating a Connection

# Create connection to GEOS-5 FP data
conn = GEOS5FPConnection()

Generating Raster Data

Generate georeferenced raster data for a specific time and optional target geometry:

from rasters import RasterGeometry

# Define target geometry (optional - if not provided, uses native GEOS-5 FP grid)
target_geometry = RasterGeometry.open("target_area.tif")

# Get air temperature raster for a specific time
time_utc = datetime(2024, 11, 15, 12, 0)
temperature_raster = conn.Ta_K(time_UTC=time_utc, geometry=target_geometry)

# Get soil moisture raster
soil_moisture_raster = conn.SM(time_UTC=time_utc, geometry=target_geometry)

# Get leaf area index raster
lai_raster = conn.LAI(time_UTC=time_utc, geometry=target_geometry)

# Save raster to file
temperature_raster.to_geotiff("temperature.tif")

Available raster methods include:

• Ta_K() - Air temperature (Kelvin)
• Ts_K() - Surface temperature (Kelvin)
• SM() / SFMC() - Soil moisture
• LAI() - Leaf area index
• NDVI() - Normalized difference vegetation index
• RH() - Relative humidity
• Ca() / CO2SC() - Atmospheric CO2 concentration (ppmv)
• And many more (see variables.csv for complete list)

Generating Table Data

Query point locations or time series to generate tabular data as pandas DataFrames:

Single Point Query

from shapely.geometry import Point

# Define point location (longitude, latitude)
point = Point(-118.25, 34.05)  # Los Angeles

# Get data for single point at specific time
time_utc = datetime(2024, 11, 15, 12, 0)
result = conn.Ta_K(time_UTC=time_utc, geometry=point)
print(result)  # Returns DataFrame with temperature value

Multiple Points Query

from shapely.geometry import MultiPoint

# Define multiple points
points = MultiPoint([
    (-118.25, 34.05),   # Los Angeles
    (-122.42, 37.77),   # San Francisco
    (-73.94, 40.73)     # New York
])

# Query multiple points at once
results = conn.Ta_K(time_UTC=time_utc, geometry=points)
print(results)  # Returns DataFrame with one row per point

Time Series Query

from datetime import timedelta

# Define time range
end_time = datetime(2024, 11, 15, 0, 0)
start_time = end_time - timedelta(days=7)  # 7 days of data

# Get time series for a point location
lat, lon = 34.05, -118.25
df = conn.query(
    target_variables="Ta_K",
    time_range=(start_time, end_time),
    lat=lat,
    lon=lon
)
print(df)  # Returns DataFrame with time series

Multi-Variable Query

# Query multiple variables at once
variables = ["Ta_K", "SM", "LAI"]
df_multi = conn.query(
    target_variables=variables,
    time_range=(start_time, end_time),
    lat=lat,
    lon=lon
)
print(df_multi)  # Returns DataFrame with columns for each variable

Vectorized Spatio-Temporal Query

import pandas as pd
import geopandas as gpd

# Load spatio-temporal data from CSV
data = pd.read_csv("locations.csv")  # Should have columns: time_UTC, lat, lon
data['time_UTC'] = pd.to_datetime(data['time_UTC'])

# Create geometries
gdf = gpd.GeoDataFrame(
    data,
    geometry=gpd.points_from_xy(data['lon'], data['lat'])
)

# Query all points and times at once (vectorized operation)
results = conn.query(
    target_variables=["Ta_K", "SM", "LAI"],
    time_UTC=gdf['time_UTC'],
    geometry=gdf['geometry']
)
print(results)  # Returns DataFrame with results for all locations and times

Using Raw GEOS-5 FP Variables

You can also query variables directly by their GEOS-5 FP product and variable names:

# Query specific humidity from tavg1_2d_slv_Nx product
df = conn.query(
    target_variables="QV2M",  # Raw GEOS-5 FP variable name
    time_range=(start_time, end_time),
    dataset="tavg1_2d_slv_Nx",
    lat=lat,
    lon=lon
)

See GEOS5FP/variables.csv for the complete list of available variables and their mappings.

Computed Variables

The package automatically computes derived meteorological variables from base GEOS-5 FP data. You can query these just like any other variable:

# Query computed variables
results = conn.query(
    target_variables=["wind_speed_mps", "Ta_C", "RH"],
    time_UTC=time_utc,
    lat=lat,
    lon=lon
)

Available computed variables:

• wind_speed_mps - Wind speed in m/s (from U2M and V2M components)
• Ta_C - Air temperature in Celsius (from Ta_K)
• RH - Relative humidity (from Q, PS, Ta)
• VPD_kPa - Vapor pressure deficit in kPa (from SVP and Ea)
• Ea_Pa - Actual vapor pressure in Pascals
• SVP_Pa - Saturated vapor pressure in Pascals
• Td_K - Dew point temperature in Kelvin

The package automatically retrieves only the necessary base variables and returns just the computed results.

Data Source & Citation

This package accesses GEOS-5 FP (Forward Processing) data produced by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center.

Data Access

GEOS-5 FP data is accessed through:

• OPeNDAP Server: https://opendap.nccs.nasa.gov/dods/GEOS-5/fp/
• HTTP Server: https://portal.nccs.nasa.gov/datashare/gmao/geos-fp/das

Data is provided by NASA's Center for Climate Simulation (NCCS).

Citation

When using GEOS-5 FP data in publications, please cite:

Data Product:

Global Modeling and Assimilation Office (GMAO) (2015), GEOS-5 FP: GEOS Forward 
Processing for Instrument Support, Greenbelt, MD, USA, Goddard Earth Sciences 
Data and Information Services Center (GES DISC). 
Accessed: [Date]

Acknowledgment:

GEOS-5 FP data used in this study were provided by the Global Modeling and 
Assimilation Office (GMAO) at NASA Goddard Space Flight Center through the 
NASA Center for Climate Simulation (NCCS).

For more information about GEOS-5 FP, visit: https://gmao.gsfc.nasa.gov/GEOS/