Sample ClimateR-Catalog Datasets

Contents

Sample ClimateR-Catalog Datasets#

gdptools integrates with Mike Johnson’s ClimateR-catalogs:

Table 1 Sample selection of climater-catalog datasets#
Dataset (Best available reference)	Description	Search ID
BCCA	Bias Corrected Constructed Analogs V2 Daily Climate Projections (BACA) contains projections of daily BCCA CMIP3 and CMIP5 projections of precipitation, daily maximum, and daily minimum temperature over the contiguous United States	bcca
BCSD	Bias Corrected Spatially Downscaled (BCSD) Monthly CMIP5 Climate Projections	bcsd
LOCA	Statistically downscaled CMIP5 climate and hydrology projections for North America, usingLocalized Constructed Analogs (LOCA) method.	loca, loca_hydrology
Daymet	Daymet provides long-term, continuous, gridded estimates of daily weather and climatology variables by interpolating and extrapolating ground-based observations through statistical modeling techniques.	daymet4
gridMET	GridMET is a gridded meteorological data product that provides estimates of daily weather and climatology variables for the conterminous United States.	gridmet
MACA	Multivariate Adaptive Constructed Analogs (MACA) is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution that captures reflects observed patterns of daily near-surface meteorology and simulated changes in GCMs experiments.	maca_day, maca_month
TerraClimate	TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data.	terraclim, terraclim_normals
CHIRPS	Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.	chirps20GlobalPentadP05 chirps20GlobalPentadP05_Lon0360, chirps20GlobalAnnualP05, chirps20GlobalAnnualP05_Lon0360, chirps20GlobalDailyP05, chirps20GlobalDailyP05_Lon0360, chirps20GlobalMonthlyP05, chirps20GlobalMonthlyP05_Lon0360
PRISM	PRISM (Parameter-elevation Regressions on Independent Slopes Model) is a family of gridded climate data products that provide estimates of monthly climate variables for the conterminous United States (CONUS) and Alaska.	prism_monthly, prism_daily
Livneh	A data set of observed daily and monthly averaged precipitation, maximum and minimum temperature, gridded to a 1/16° (~6km) resolution that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53° N for the period 1950-2013.	Livneh_daily, Livneh_monthly, Livneh_fluxes
topowx	(“Topography Weather”) is an 800-meter resolution gridded dataset of daily minimum and maximum air temperature for the conterminous U.S.	topowx_daily, topowx_monthly, topowx_normals
WorldClim v2	Spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2)	wc2.1_10m, wc2.1_5m, wc2.1_2m, wc2.1_30s
3DEP	The 3D Elevation Program is managed by the U.S. Geological Survey (USGS) National Geospatial Program to respond to growing needs for high-quality topographic data	USGS_3DEP
LCMAP	Land Change Monitoring, Assessment, and Projection (LCMAP) represents a new generation of land cover mapping and change monitoring from the U.S. Geological Survey’s Earth Resources Observation and Science (EROS) Center. LCMAP answers a need for higher quality results at greater frequency with additional land cover and change variables than previous efforts.	LCMAP
ssebopeta	Actual Evapotranspiration (ETA) from The operational Simplified Surface Energy Balance (SSEBop)	ssebopeta
maurer	Downscaled climate projections as part of CMIP3 and CMIP5, and gridded observed data that can be used in downscaling	maurer
GLDAS	Global Land Data Assimilation System (GLDAS)	GLDAS
NLDAS	North American Land Data Assimilation System (NLDAS)	NLDAS

Data Access#

ClimateR-Catalogs are accessed using Pandas, and Pandas Query method. Once the catalog has been filtered for the dataset of interest, gdptools ClimRCatData can be used to parameterize the data.

ClimateR-Catalogs: mikejohnson51/climateR-catalogs

Usage with gdptools#

from gdptools import ClimRCatData

# Access ClimateR-catalog
climater_cat = "https://github.com/mikejohnson51/climateR-catalogs/releases/download/June-2024/catalog.parquet"
cat = pd.read_parquet(climater_cat)

# Use Pandas query function to filter datasets
_id = "gridmet"
_varname = "tmmx"

# an example query returns a pandas dataframe.
tc = cat.query("id == @_id & variable == @_varname")

# Create a dictionary of parameter dataframes for each variable
tvars = ["tmmx", "tmmn", "pr"]
cat_params = [cat.query("id == @_id & variable == @_var").to_dict(orient="records")[0] for _var in tvars]

cat_dict = dict(zip(tvars, cat_params))
user_data = ClimRCatData(
    source_cat_dict=cat_dict,
    target_gdf=huc12_basins,
    target_id="huc12",
    source_time_period=["1980-01-01", "1980-12-31"],
)

wght_gen = WeightGen(
    user_data=user_data, method="serial", output_file="gridmet_daily_wghts.csv", weight_gen_crs=6931
)

wghts = wght_gen.calculate_weights()

agg_gen = AggGen(
    user_data=user_data,
    stat_method="masked_mean",
    agg_engine="serial",
    agg_writer="netcdf",
    weights="gridmet_daily_wghts.csv",
    out_path=".",
    file_prefix="testing_gridmet_drb_huc12",
)
ngdf, ds_out = agg_gen.calculate_agg()