These monthly values are obtained by aggregating the daily values obtained from the downscaling using the Multivariate Adaptive Constructed Analogs (MACA, Abatzoglou, 2012) statistical downscaling method with the METDATA (Abatzoglou,2013) training dataset. summary : This archive contains monthly downscaled meteorological and hydrological projections for the Conterminous United States at 1/24-deg resolution. id : MACAv2-METDATA naming_authority : Metadata_Conventions : Unidata Dataset Discovery v1.0 Metadata_Link : cdm_data_type : FLOAT title : Monthly aggregation of downscaled daily meteorological data of Monthly Average of Daily Maximum Near-Surface Air Temperature from College of Global Change and Earth System Science, Beijing Normal University (BNU-ESM) using the run r1i1p1 of the historical scenario. To begin, you open up the data using xarray.open_dataset.ĭescription : Multivariate Adaptive Constructed Analogs (MACA) method, version 2.3,Dec 2013. The most current earth-analytics-python environment contains all of the packages that you need to complete this tutorial. You will use the xarray package which requires the netcdf4 package to work with netcdf data. monthly: These data are aggregated monthly (rather than daily)īelow, you will learn how to open and work with MACA 2 data using open source Python tools.
tasmax: Max temperature is the parameter contained within the data.macav2metdata: the data are the MACA version two data which are downsampled to the extent of the continental United S tates.The file name itself tells you a lot about the data. In this lesson you will work with historic projected MACA 2 data that represents maximum monthly temperature for the Continental United States (CONUS).Īgg_macav2metdata_tasmax_BNU-ESM_r1i1p1_historical_1950_2005_CONUS_monthly HOME, 'earth-analytics', 'data' )) Get Started wtih MACA Version 2 Data Using Open Source Python
# accessed lower down in this notebook (the USA state boundary data) set_style ( "white" ) # Optional - set your working directory if you wish to use the data Import xarray as xr import rioxarray as rxr import cartopy.crs as ccrs import cartopy.feature as cfeature import seaborn as sns import geopandas as gpd import earthpy as et # Plotting options Import os import numpy as np import pandas as pd import matplotlib.pyplot as plt # netCDF4 needs to be installed in your environment for this to work
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