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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c4481c2c-d123-43ee-a810-5078b3861d39",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "866cfcb4-e973-4a63-849c-5494ea2daa92",
+ "metadata": {},
+ "source": [
+ "# The multipurpose ARM trajectory value-added product ([ARMTRAJ](https://www.arm.gov/data/science-data-products/vaps/armtraj))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e231f47d-3ae0-4bf5-bfcd-70ed93fb2e50",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5d42a115-2490-4878-8e6e-df0e3f75d9f4",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "This notebook demonstrates the use of some of the datasets included in the multipurpose ARM trajectory value-added product (ARMTRAJ; [Silber et al., 2025](https://doi.org/10.5194/essd-17-29-2025)) to visualize and analyze the plausible source origin and properties of airmasses that overpassed the EPC ([EPCAPE](https://www.arm.gov/research/campaigns/amf2023EPCAPE)) deployment on August 20, 2023 (Hurricane Hilary — see [ARM's field note about the event](https://www.arm.gov/news/blog/post/91659)).\n",
+ "\n",
+ "1. Introduction to ARMTRAJ\n",
+ "1. How to access the data and load using Xarray\n",
+ "1. Processing, analysis, and visualization\n",
+ "\n",
+ "
\n",
+ "VAP references:\n",
+ "1. Silber, Israel, Comstock, Jennifer M., Kieburtz, Michael R., and Russell, Lynn M.: ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements, Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, 2025.\n",
+ "1. Silber, Israel, Comstock, Jennifer M, Kieburtz, Michael R, Gaustad, Krista L, and Mei, Fan.: ARM Trajectories Data Set Value-Added Product Report, DOE/SC-ARM-TR-314, https://doi.org/10.2172/2498401, 2024.\r\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d250388f-3a31-48c5-bde3-269c15008773",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b33443a5-b086-45a3-b37c-88ad90a045de",
+ "metadata": {},
+ "source": [
+ "### Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "8d1f1f5d-1233-435e-b32e-0c609d479715",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import act\n",
+ "import os\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import xarray as xr\n",
+ "import matplotlib\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.dates as mdates\n",
+ "from matplotlib.gridspec import GridSpec"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d62300ac-e6be-42bf-8460-5e0aa2ea0e9e",
+ "metadata": {},
+ "source": [
+ "## Overview of ARMTRAJ\n",
+ "\n",
+ "The multipurpose ARM trajectory VAP (ARMTRAJ) ameliorates knowledge gaps resulting from the Eulerian nature of ARM observations by providing airmass back and forward trajectory calculations. The VAP consists of six trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The six trajectory datasets support aerosol, cloud, planetary boundary layer, and related research (aerosol-cloud interactions, etc.). Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). ARMTRAJ data include ensemble run statistics that enhance trajectory consistency, while ensemble variability data serve as uncertainty metrics for airmass coordinates and state variables."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0d35dbed-4d3f-4081-a058-f5f65105536e",
+ "metadata": {},
+ "source": [
+ "## How to Access ARMTRAJ Data from ARM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1c18e4c-edd1-4f2c-bc10-a722368be985",
+ "metadata": {},
+ "source": [
+ "### Use the ARM Live API to Download the Data, using ACT\n",
+ "\n",
+ "The Atmospheric Data Community Toolkit (ACT) has a helpful module to interface with the data server:\n",
+ "* [Download Data API](https://arm-doe.github.io/ACT/API/generated/act.discovery.html#module-act.discovery)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "68738ea1-33c0-4da6-bad1-be3c26366048",
+ "metadata": {},
+ "source": [
+ "### Setup our Download Query\n",
+ "Before downloading our data, we need to make sure we have an ARM Data Account, and ARM Live token. Both of these can be found using this link:\n",
+ "- [ARM Live Signup](https://adc.arm.gov/armlive/livedata/home)\n",
+ "\n",
+ "Once you sign up, you will see your token. Copy and replace that where we have `arm_username` and `arm_password` below.\n",
+ "\n",
+ "Here, we will process ARMTRAJ's planetary boundary layer, surface, and cloud layer datasets, so we also need the names of those three datastreams, which are `epcarmtrajpblM1.c1`, `epcarmtrajsfcM1.c1`, and `epcarmtrajcldM1.c1`, respectively, representing:\n",
+ "- the site (EPC) and facility (M1)\n",
+ " - This information corresponds to the EPCAPE main site in La Jolla, CA\n",
+ "- the product class (armtraj)\n",
+ " - This is the value-added product described in this notebook\n",
+ "- the product type (pbl, sfc, and cld)\n",
+ " - Those are the three datasets derived from this product. Note that ARMTRAJ also includes three additional trajectory datasets for cloud decks determined using the [ARSCL](https://www.arm.gov/data/science-data-products/vaps/arscl) VAP (arscl), ARM's [Crewed Facilities](https://www.arm.gov/capabilities/observatories/aaf/manned) and [Uncrewed Aerial Systems](https://www.arm.gov/capabilities/observatories/aaf/uas) flights (aaf), and [tethered balloon system](https://arm.gov/capabilities/instruments/tbs) flights (tbs)\n",
+ "- the data level (c1)\n",
+ " - This is the corrected, quality-controlled data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4496d09c-e459-43a5-a13a-5cfa2a744aaf",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[DOWNLOADING] epcarmtrajcldM1.c1.20230820.000000.nc\n",
+ "\n",
+ "If you use these data to prepare a publication, please cite:\n",
+ "\n",
+ "Zhang, D., & Silber, I. Airmass trajectories to support studies using ARM data\n",
+ "(ARMTRAJCLD), 2023-08-20 to 2023-08-20, ARM Mobile Facility (EPC), La Jolla, CA;\n",
+ "AMF1 (main site for EPCAPE on Scripps Pier) (M1). Atmospheric Radiation\n",
+ "Measurement (ARM) User Facility. https://doi.org/10.5439/2309851\n",
+ "\n",
+ "[DOWNLOADING] epcarmtrajsfcM1.c1.20230820.000000.nc\n",
+ "\n",
+ "If you use these data to prepare a publication, please cite:\n",
+ "\n",
+ "Zhang, D., & Silber, I. Airmass trajectories to support studies using ARM data\n",
+ "(ARMTRAJSFC), 2023-08-20 to 2023-08-20, ARM Mobile Facility (EPC), La Jolla, CA;\n",
+ "AMF1 (main site for EPCAPE on Scripps Pier) (M1). Atmospheric Radiation\n",
+ "Measurement (ARM) User Facility. https://doi.org/10.5439/2309850\n",
+ "\n",
+ "[DOWNLOADING] epcarmtrajpblM1.c1.20230820.000000.nc\n",
+ "\n",
+ "If you use these data to prepare a publication, please cite:\n",
+ "\n",
+ "Zhang, D., & Silber, I. Airmass trajectories to support studies using ARM data\n",
+ "(ARMTRAJPBL), 2023-08-20 to 2023-08-20, ARM Mobile Facility (EPC), La Jolla, CA;\n",
+ "AMF1 (main site for EPCAPE on Scripps Pier) (M1). Atmospheric Radiation\n",
+ "Measurement (ARM) User Facility. https://doi.org/10.5439/2309848\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "arm_username = os.getenv(\"ARM_USERNAME\")\n",
+ "arm_password = os.getenv(\"ARM_PASSWORD\")\n",
+ "\n",
+ "cld_datastream = \"epcarmtrajcldM1.c1\"\n",
+ "sfc_datastream = \"epcarmtrajsfcM1.c1\"\n",
+ "pbl_datastream = \"epcarmtrajpblM1.c1\"\n",
+ "\n",
+ "start_date = \"2023-08-20T00:00:00\"\n",
+ "end_date = \"2023-08-20T23:59:00\"\n",
+ "\n",
+ "cld_files = act.discovery.download_arm_data(arm_username,\n",
+ " arm_password,\n",
+ " cld_datastream,\n",
+ " start_date,\n",
+ " end_date)\n",
+ "\n",
+ "sfc_files = act.discovery.download_arm_data(arm_username,\n",
+ " arm_password,\n",
+ " sfc_datastream,\n",
+ " start_date,\n",
+ " end_date)\n",
+ "\n",
+ "pbl_files = act.discovery.download_arm_data(arm_username,\n",
+ " arm_password,\n",
+ " pbl_datastream,\n",
+ " start_date,\n",
+ " end_date)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69d96f67-6c98-402d-80f3-cd9f1527f755",
+ "metadata": {},
+ "source": [
+ "### Read and examine data files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "92ec9374-66da-4e74-b43f-ce539d8c5177",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "traj_cld_ds = xr.open_mfdataset(cld_files)\n",
+ "traj_sfc_ds = xr.open_mfdataset(sfc_files)\n",
+ "traj_pbl_ds = xr.open_mfdataset(pbl_files)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "335b6841-57d5-49bb-928e-3dff367010ea",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2acad5a0-82ba-44c6-b2bd-d973a4b33cd6",
+ "metadata": {},
+ "source": [
+ "We can start by examining the surface (SFC) 10-day back trajectory dataset. Note the fields with the `_ens` suffix, denoting ensemble run output. **We recommend using those ensemble output fields when possible, due to their robustness, and the possibility of using them (min, max, etc.) as uncertainty metrics**, as demonstrated below"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a9e99c9b-dd6d-4c28-9241-8833631f6605",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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wvert_ens_mean
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wvert_ens_std
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wvert_ens_min
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wvert_ens_max
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height_to_pblh_ratio_ens_mean
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height_to_pblh_ratio_ens_std
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height_to_pblh_ratio_ens_min
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height_to_pblh_ratio_ens_max
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sgs_orography_angle
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
radian
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sgs_orography_angle_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
radian
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sgs_orography_anisotropy
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
1
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sgs_orography_anisotropy_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
1
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sgs_orography_std
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
m
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sgs_orography_std_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
m
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sgs_orography_slope
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km
units :
1
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sgs_orography_slope_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
1
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land_sea_mask
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land)
units :
1
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land_sea_mask_ens_mean
(time, trajectory_time)
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long_name :
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land) (along ensemble mean trajectory)
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high_vegetation_type
(time, trajectory_time)
float32
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long_name :
High vegetation type in airmass column out of 6 values: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. (0 indicates lack of high vegetation)
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high_vegetation_type_ens_mean
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long_name :
High vegetation type in airmass column out of 6 values: 3 = Evergreen needleleaf trees, 4 = Deciduous needleleaf trees, 5 = Deciduous broadleaf trees, 6 = Evergreen broadleaf trees, 18 = Mixed forest/woodland, 19 = Interrupted forest. (0 indicates lack of high vegetation) (along ensemble mean trajectory)
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low_vegetation_type
(time, trajectory_time)
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long_name :
Low vegetation type in airmass column out of 10 values: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. (0 indicates lack of low vegetation)
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low_vegetation_type_ens_mean
(time, trajectory_time)
float32
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Low vegetation type in airmass column out of 10 values: 1 = Crops, Mixed farming, 2 = Grass, 7 = Tall grass, 9 = Tundra, 10 = Irrigated crops, 11 = Semidesert, 13 = Bogs and marshes, 16 = Evergreen shrubs, 17 = Deciduous shrubs, 20 = Water and land mixtures. (0 indicates lack of low vegetation) (along ensemble mean trajectory)
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high_vegetation_cover
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float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
High vegetation cover fraction in airmass column
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high_vegetation_cover_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
High vegetation cover fraction in airmass column (along ensemble mean trajectory)
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low_vegetation_cover
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Low vegetation cover fraction in airmass column
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low_vegetation_cover_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Low vegetation cover fraction in airmass column (along ensemble mean trajectory)
units :
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soil_type
(time, trajectory_time)
float32
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Soil type in airmass column out of 7 values: 1 = Coarse, 2 = Medium, 3 = Medium fine, 4 = Fine, 5 = Very fine, 6: Organic, 7: Tropical organic. (0 indicates non-land point)
units :
1
standard_name :
soil_type
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soil_type_ens_mean
(time, trajectory_time)
float32
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Soil type in airmass column out of 7 values: 1 = Coarse, 2 = Medium, 3 = Medium fine, 4 = Fine, 5 = Very fine, 6: Organic, 7: Tropical organic. (0 indicates non-land point) (along ensemble mean trajectory)
units :
1
standard_name :
soil_type
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sea_ice_cover
(time, trajectory_time)
float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Sea-ice cover fraction (based on daily means) in airmass column
units :
1
standard_name :
sea_ice_area_fraction
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sea_ice_cover_ens_mean
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float64
dask.array<chunksize=(1, 241), meta=np.ndarray>
long_name :
Sea-ice cover fraction (based on daily means) in airmass column (along ensemble mean trajectory)
Back trajectories for ARM surface deployments supporting aerosol studies
summary :
The ARMTRAJ VAP provides trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the datasets, HYSPLIT also runs at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields are reported.
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
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sgs_orography_angle_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
radian
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sgs_orography_angle_ft
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere trajectory)
units :
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sgs_orography_angle_ft_ens_mean
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long_name :
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere ensemble mean trajectory)
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
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sgs_orography_anisotropy_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
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sgs_orography_anisotropy_ft
(time, trajectory_time)
float64
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long_name :
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere trajectory)
units :
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sgs_orography_anisotropy_ft_ens_mean
(time, trajectory_time)
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Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere ensemble mean trajectory)
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
units :
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sgs_orography_std_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
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sgs_orography_std_ft
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere trajectory)
units :
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sgs_orography_std_ft_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere ensemble mean trajectory)
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km
units :
1
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sgs_orography_slope_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
units :
1
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sgs_orography_slope_ft
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere trajectory)
units :
1
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sgs_orography_slope_ft_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km (along free-troposphere ensemble mean trajectory)
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land)
units :
1
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land_sea_mask_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land) (along ensemble mean trajectory)
units :
1
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land_sea_mask_ft
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land) (along free-troposphere trajectory)
units :
1
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land_sea_mask_ft_ens_mean
(time, trajectory_time)
float64
dask.array<chunksize=(1, 121), meta=np.ndarray>
long_name :
Land-sea mask in airmass column (area fraction per ERA5 ~31 km grid-cell; 0 - open water, 1 - all land) (along free-troposphere ensemble mean trajectory)
Back trajectories for PBL and related aerosol and cloud studies
summary :
The ARMTRAJ VAP provides trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the datasets, HYSPLIT also runs at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields are reported.
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
Terrain orientation in the horizontal plane (from a bird's-eye view) relative to an eastwards axis in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km
Terrain distortion from a circle in the horizontal plane (from a bird's-eye view) in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
Standard deviation of orography within a grid box in airmass column; calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km
Slope of orography within a grid box in airmass column (e.g., 0 - flat, 0.5 - 45 degree slope); calculated using a minimum orographic feature horizontal scale of 5 km (along ensemble mean trajectory)
Sea-ice cover fraction (based on daily means) in airmass column (along ensemble mean trajectory)
units :
1
standard_name :
sea_ice_area_fraction
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rh_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Relative Humidity
units :
%
valid_min :
0.0
valid_max :
100.0
resolution :
1.0
standard_name :
relative_humidity
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pres_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Pressure
units :
hPa
valid_min :
0.0
valid_max :
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valid_delta :
10.0
resolution :
0.1
standard_name :
air_pressure
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temp_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Dry Bulb Temperature
units :
degC
valid_min :
-90.0
valid_max :
50.0
valid_delta :
10.0
resolution :
0.1
standard_name :
air_temperature
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alt_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Altitude above mean sea level
units :
m
standard_name :
altitude
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time_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Time offset from midnight
units :
s
standard_name :
time
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wspd_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Wind Speed
units :
m/s
valid_min :
0.0
valid_max :
100.0
resolution :
0.1
standard_name :
wind_speed
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wdir_sonde
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
Wind Direction
units :
degree
valid_min :
0.0
valid_max :
360.0
resolution :
1.0
standard_name :
wind_from_direction
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liq_cld_exist
(time, time_dim_sonde)
float64
dask.array<chunksize=(1, 10000), meta=np.ndarray>
long_name :
1 - liquid droplets in grid cell, 0 - no liquid droplets in grid cell; determined using sonde RH threshold of 96.0%; layers distant by less than 50.0 meters were concatenated; layers thinner than 25.0 meters were removed
units :
1
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liq_cld_base
(time, vert_layer)
float64
dask.array<chunksize=(1, 10), meta=np.ndarray>
long_name :
Liquid cloud base altitude; determined using sonde RH threshold of 96.0%; layers distant by less than 50.0 meters were concatenated; layers thinner than 25.0 meters were removed
units :
m
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\n",
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\n",
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\n",
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liq_cld_top
(time, vert_layer)
float64
dask.array<chunksize=(1, 10), meta=np.ndarray>
long_name :
Liquid cloud top altitude; determined using sonde RH threshold of 96.0%; layers distant by less than 50.0 meters were concatenated; layers thinner than 25.0 meters were removed
Back and forward trajectories for liquid-bearing cloud layers in full tropospheric profiles
summary :
The ARMTRAJ VAP provides trajectory datasets initialized at ARM deployment coordinates and configured using ARM datasets. The trajectory datasets support aerosol, cloud, and planetary boundary layer research. Trajectory calculations use the HYSPLIT model informed by the ERA5 reanalysis dataset at its highest spatial resolution (~31 km). For each sample in each of the datasets, HYSPLIT also runs at multiple starting locations surrounding ARM deployments, enabling an ensemble of runs from which the mean and variability (estimated uncertainty) of each sample's trajectory coordinates, thermodynamic properties, or other fields are reported.