MISR CMORization script

doc/sphinx/source/input.rst

@@ -424,6 +424,8 @@ A list of the datasets for which a CMORizers is available is provided in the fol
 +----------------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MLS-AURA* [#t3]_                       | hur, hurStderr (day)                                                                                 |   3  | Python          |
 +----------------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
+| MISR                                   | od550aer, abs550aer (AERmon)                                                                         |   3  | Python          |
++----------------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MOBO-DIC-MPIM                          | dissic (Omon)                                                                                        |   2  | Python          |
 +----------------------------------------+------------------------------------------------------------------------------------------------------+------+-----------------+
 | MOBO-DIC2004-2019                      | dissic (Omon)                                                                                        |   2  | Python          |

esmvaltool/cmorizers/data/cmor_config/MISR.yml

@@ -0,0 +1,19 @@
+# Global attributes of NetCDF file
+attributes:
+  project_id: OBS6
+  dataset_id: MISR
+  modeling_realm: atmos
+  type: sat
+  tier: 3
+  version: 'V0032'
+  source: 'https://asdc.larc.nasa.gov/project/MISR/MIL3MAEN_004'
+  reference: 'misr'
+  files: 'MISR_AM1_CGAS_*_F15_0032.nc'
+# Variables to CMORize
+variables:
+  od550aer:
+    raw_name: Aerosol_Optical_Depth
+    mip: AERmon
+  abs550aer:
+    raw_name: Absorbing_Optical_Depth
+    mip: AERmon

esmvaltool/cmorizers/data/datasets.yml

@@ -1082,6 +1082,16 @@ datasets:
     info: |
       Download the file MPI_MOBO-DIC_2004-2019_v2.nc
 
+  MISR:
+    tier: 3
+    source: https://asdc.larc.nasa.gov/project/MISR/MIL3MAEN_004
+    last_access: 2026-02-17
+    info: |
+      To obtain the data set you will need to go to the Data Distribution
+      section of the link, create an account/sign in on the earthdata.nasa.gov website
+      and then follow the instructions for downloading the data.
+      The data is currently freely available, but a registration is required.
+
   MODIS:
     tier: 3
     source: https://ladsweb.modaps.eosdis.nasa.gov/search/order

esmvaltool/cmorizers/data/formatters/datasets/misr.py

@@ -0,0 +1,166 @@
+"""ESMValTool CMORizer for MISR data.
+
+Tier
+    Tier 3
+
+Source
+    ftp://l5ftl01.larc.nasa.gov/MISR/MIL3MAEN.004/
+"""
+
+import copy
+import datetime as dt
+import logging
+import os
+from pathlib import Path
+
+import numpy as np
+import xarray as xr
+from dask import array as da
+from esmvalcore.cmor.table import CMOR_TABLES
+
+from esmvaltool.cmorizers.data import utilities as utils
+
+logger = logging.getLogger(__name__)
+
+band550 = {"name": "green_558nm", "lambda": 558}
+
+
+def _extract_variable(short_name, var, cfg, in_dir, out_dir):
+    attrs = copy.deepcopy(cfg["attributes"])
+    attrs["mip"] = var["mip"]
+    ver = attrs["version"]
+    files = attrs["files"]
+    raw_var = var.get("raw_name", short_name)
+
+    cmor_table = CMOR_TABLES[attrs["project_id"]]
+    cmor_info = cmor_table.get_variable(var["mip"], short_name)
+
+    """Extract variable."""
+    # load data
+    logger.debug(
+        "Loading data from file(s) '%s' in directory '%s' with version '%s'",
+        files,
+        in_dir,
+        ver,
+    )
+    for filepath in Path(os.path.join(in_dir)).glob(files):
+        xrds = xr.open_dataset(filepath, group="Aerosol_Parameter_Average")
+        xrvar = xrds.sel(Band=band550["name"], Optical_Depth_Range="all")[
+            raw_var
+        ]
+
+        # change order of latitude and longitude coordinates
+        # xrvar = xrvar.transpose()
+
+        # Add additional coordinates before converting to an iris cube, as this is easier with xarray
+
+        # Time not present in source data, needs to be added manually
+        # Determine time from filename:
+        fileparts = str(filepath).split("_")
+        year = int(fileparts[-3])
+        monthstr = fileparts[-4]
+        month = [
+            "JAN",
+            "FEB",
+            "MAR",
+            "APR",
+            "MAY",
+            "JUN",
+            "JUL",
+            "AUG",
+            "SEP",
+            "OCT",
+            "NOV",
+            "DEC",
+        ].index(monthstr) + 1
+        days_since_1850 = dt.date(year, month, 15) - dt.date(1850, 1, 1)
+        lb_since_1850 = dt.date(year, month, 1) - dt.date(1850, 1, 1)
+        if month == 12:
+            ub_since_1850 = dt.date(year + 1, 1, 1) - dt.date(1850, 1, 1)
+        else:
+            ub_since_1850 = dt.date(year, month + 1, 1) - dt.date(1850, 1, 1)
+
+        xrvar = xrvar.assign_coords(time=days_since_1850.days)
+        xrvar = xrvar.expand_dims("time", axis=2)
+        xrvar["time"].attrs["units"] = "days since 1850-01-01 00:00:00"
+
+        if short_name in ["od550aer", "abs550aer"]:
+            xrvar = xrvar.assign_coords(radiation_wavelength=band550["lambda"])
+            xrvar["radiation_wavelength"].attrs["units"] = "nm"
+
+        cube = xrvar.to_iris()
+
+        # Fix metadata
+        cube.coord("Geodetic Latitude").rename("latitude")
+        cube.coord("Geodetic Longitude").rename("longitude")
+
+        # add time bounds
+        cube.coord("time").bounds = np.array(
+            [ub_since_1850.days, lb_since_1850.days]
+        )
+
+        utils.fix_var_metadata(cube, cmor_info)
+        utils.set_global_atts(cube, attrs)
+
+        utils.fix_dim_coordnames(cube)
+
+        # When Dask tries to roll this cube, it fails because it can't chunk this properly
+        # So here we replicate the part of fix_coords that does that, except with numpy.roll
+        # instead of dask.roll.
+        # However, it seems that those last two lines had no effect on the results...
+        # cube.data has shape (360, 720, 1) i.e. (lat, lon, time)
+        # so the original code tried to roll the cube on the time axis
+        # I could hard-code the lon axis (like they did), but instead I try to autodetect it.
+        cube_coord = cube.coord("longitude")
+        logger.debug("Fixing longitude...")
+        if cube_coord.ndim == 1:
+            if cube_coord.points[0] < 0.0 and cube_coord.points[-1] < 181.0:
+                cube_coord.points = cube_coord.points + 180.0
+                cube.attributes["geospatial_lon_min"] = 0.0
+                cube.attributes["geospatial_lon_max"] = 360.0
+                nlon = len(cube_coord.points)
+                axis = cube.coords().index(cube_coord)
+                shift = nlon // 2
+                cube.data = da.roll(cube.core_data(), shift, axis=axis)
+
+        if np.diff(cube.coord("latitude").points)[0] < 0:
+            # convert [90,-90] to [-90,90]
+            cube.coord("latitude").points = cube.coord("latitude").points[::-1]
+            # flip the data
+            cube.data = cube.data[:, ::-1, :]  # latitude is axis=1
+
+        lat_bounds = []
+        for lat in cube.coord("latitude").points:
+            lat_bounds.append([lat - 0.25, lat + 0.25])
+        lat_bounds = np.array(lat_bounds)
+        cube.coord("latitude").bounds = lat_bounds.reshape(-1, 2)
+
+        lon_bounds = []
+        for lon in cube.coord("longitude").points:
+            lon_bounds.append([lon - 0.25, lon + 0.25])
+        lon_bounds = np.array(lon_bounds)
+        cube.coord("longitude").bounds = lon_bounds.reshape(-1, 2)
+
+        utils.fix_coords(cube)
+
+        # fix the wavelength coordinate information.
+        if short_name in ["od550aer", "abs550aer"]:
+            cube.coord("radiation_wavelength").var_name = "wavelength"
+            cube.coord("wavelength").standard_name = "radiation_wavelength"
+
+        utils.set_global_atts(cube, attrs)
+
+        # Save variable
+        logger.debug(f"Saving Cube: {cube}, in directory: {out_dir}")
+        utils.save_variable(
+            cube, short_name, out_dir, attrs, unlimited_dimensions=["time"]
+        )
+
+
+def cmorization(in_dir, out_dir, cfg, cfg_user, start_date, end_date):
+    """Run CMORizer for MISR."""
+    cfg.pop("cmor_table")
+
+    for short_name, var in cfg["variables"].items():
+        logger.info(f"CMORizing variable '{short_name}'")
+        _extract_variable(short_name, var, cfg, in_dir, out_dir)

esmvaltool/config-references.yml

@@ -229,6 +229,10 @@ authors:
     name: Winterstein, Franziska
     institute: DLR, Germany
     orcid: https://orcid.org/0000-0002-2406-4936
+  fruttarol_noah:
+    name: Fruttarol, Noah
+    institute: CCCma, ECCC, Canada
+    github: noahfruttarol
   fuckar_neven:
     name: Fuckar, Neven
     institute: BSC, Spain

esmvaltool/recipes/examples/recipe_check_obs.yml

@@ -800,6 +800,19 @@ diagnostics:
     scripts: null
 
 
+  MISR:
+    description: MISR check
+    variables:
+      od550aer:
+        mip: AERmon
+      abs550aer:
+        mip: AERmon
+    additional_datasets:
+      - {dataset: MISR, project: OBS, mip: AERmon, tier: 3,
+         type: sat, version: v0032, start_year: 2000, end_year: 2020}
+    scripts: null
+
+
   MOBO-DIC-MPIM:
     description: MOBO-DIC-MPIM check
     variables:

esmvaltool/references/misr.bibtex

@@ -0,0 +1,11 @@
+@misc{misr,
+	doi = {10.5067/Terra/MISR/MIL3MAEN_L3.004},
+	url = {https://doi.org/10.5067/Terra/MISR/MIL3MAEN_L3.004},
+	publisher = {NASA Langley Atmospheric Science Data Center DAAC},
+	title = {MISR Level 3 Component Global Aerosol product in netCDF format covering a month V004},
+	author = {NASA/LARC/SD/ASDC},
+	date = {2008-09-26},
+	year = 2008,
+	month = 9,
+	day = 26,
+}