Documentation Revision Date: 2018-05-10
Data Set Version: 1
There are 11,904 footprint files in NetCDF format included in this dataset. The files are provided in one TAR/GZIP file.
Henderson, J. 2018. Pre-ABoVE: Gridded Footprints from WRF-STILT Model, Barrow, Alaska, 1982-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1544
Table of Contents
- Data Set Overview
- Data Characteristics
- Application and Derivation
- Quality Assessment
- Data Acquisition, Materials, and Methods
- Data Access
Data Set Overview
This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location.
- This dataset contains only the gridded surface influence-function footprints, called foot1 and footnearfield1 on 0.5 and 0.1-deg lat/lon grids, respectively.
- The related dataset (Henderson, J. 2018 (*/1571) provides both the gridded footprints and the particle trajectory data (the time-dependent location of each of 500 particles on their backwards-in-time trajectories).
- Users may find these gridded footprint data files more convenient since they are much smaller in size and most end-users are only interested in this final component of the model output.
Project: Arctic-Boreal Vulnerability Experiment
The Arctic-Boreal Vulnerability Experiment (ABoVE) is a NASA Terrestrial Ecology Program field campaign based in Alaska and western Canada between 2016 and 2021. Research for ABoVE links field-based, process-level studies with geospatial data products derived from airborne and satellite sensors, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications.
Henderson, J. 2018. Pre-ABoVE: Gridded Particle Trajectories for WRF-STILT Model, Barrow, AK, 1982-2011. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1571 (Provides both the gridded footprints and the particle trajectory data.)
This study was funded by NASA's Arctic-Boreal Vulnerability Experiment (grant number:NNX13AK83G).
Spatial Coverage: Circumpolar influence field and Alaska regional influence field
Spatial Resolution: 0.5-degree for circumpolar; 0.1-degree for near field receptors
Temporal Coverage: 12 selected years (15 August to 15 October) across 1982-08-10 to 2011-10-15
Temporal Resolution: Hourly
Study Area (coordinates in decimal degrees)
|Site||Westernmost Longitude||Easternmost Longitude||Northernmost Latitude||Southernmost Latitude|
|NOAA Barrow Alaska Observatory (receptor location)||-156.6114||-156.6114||71.3230||71.3230|
|Circumpolar (foot1 variable)||-180.0||180.0||90.0||30.0|
|Alaska (footnearfield1 variable)||-169.51351||-133.82992||71.35532||58.35277|
Data File Information
The TAR/GZIP file (*.tar.gz) contains 11,904 NetCDF files representing surface influence-function footprints from WRF-STILT simulations for one particle receptor location. Each file aggregates particle footprints on a lat/lon/time grid starting at the STILT simulation start time.
The first surface influence field, represented by the foot1 variable in the NetCDF files, provides 10 days of surface influence representing the response of the receptor to a unit surface emission (ppm/umol m-2 s-1) of CO2 in each 0.5- x 0.5-degree grid cell within the whole area of coverage (30N to 90N, 180E to 180W) at hourly temporal resolution.
The second surface influence field, represented by the footnearfield1 variable in the NetCDF files, provides 24 hours of surface influence representing the response of the receptor to a unit surface emission (ppm/umol m-2 s-1) of CO2 in each 0.1- x 0.1-degree grid cell within a small region close to the measurement location at hourly temporal resolution.
Data file naming convention: The files are named by year, month, day, hour, minute, latitude, longitude, and height A.G.L. in meters, separated by an x.
Example file name: foot1982x08x15x02x00x71.3230Nx156.6114Wx00016.nc. This file contains the modeled footprints for August 15, 1982 at 2:00 UTC. The observation was taken at 71.3230N, 156.6114W at 16 m above ground level.
For a description of the naming elements in the example file name, refer to Table 1.
Table 1. Description of elements in the example file name
|Name element||Example value||Units|
*Data are provided for 12 selected years (15 August to 15 October) in the dataset: 1982, 1984, 1986, 1990, 1992, 1995, 1997, 1998, 2001, 2004, 2006 and 2011.
Table 2, Variables in the data files
|foot1||ppm per (umol m-2 s-1)||Gridded STILT footprint|
|foot1date||days||Date of foot1 (days since 2000-01-01 00:00:00 UTC)|
|foot1hr||hours||Hours back from STILT start time|
|foot1lat||degrees_north||Degrees latitude of center of grid cells|
|foot1lon||degrees_east||Degrees longitude of center of grid cells|
|footnearfield1||ppm per (umol m-2 s-1)||Gridded STILT footprint|
|footnearfield1date||days||Date for 'footnearfield1' (days since 2000-01-01 00:00:00 UTC)|
|footnearfield1hr||hours||Hours back from STILT start time for 'footnearfield1'|
|footnearfield1lat||degrees_north||Degrees latitude of center of grid cells|
|footnearfield1lon||degrees_east||Degrees longitude of center of grid cells|
Application and Derivation
The NOAA Barrow Alaska Observatory was treated as receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The measurements included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function.
The Stochastic Time-Inverted Lagrangian Transport model inherently provides uncertainty in atmospheric transport path by following multiple tracer particles from a single point and defining the source area by the ensemble's spread. However, the sensitivity/uncertainty associated with changes in the meteorology or configuration of STILT (e.g., depth of the surface-influencing region) is not quantified.
Data Acquisition, Materials, and Methods
The NOAA Barrow Alaska Observatory (https://www.esrl.noaa.gov/gmd/obop/brw/) was treated as the receptor in a Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled with meteorology fields from the polar variant of the Weather and Research Forecasting (WRF; Powers et al, 2017; Skamarock et al., 2008) model, in order to model the land surface influence on observed atmospheric constituents. Receptor observations are hourly from the hours 15-03 UTC and 3-hourly otherwise (6, 9 and 12 UTC). The atmospheric model was configured to generate high-quality, high-resolution meteorological fields over Arctic and boreal Alaska.
STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field (Nehrkorn et al., 2010; Henderson et al., 2015). The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume.
The WRF-STILT coupled model is described in Nehrkorn et al. (2010). Note that the two Pre-ABoVE WRF-STILT model products were created following the same methods as for the two CARVE WRF STILT model product related datasets.
These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Pre-ABoVE: Gridded Footprints from WRF-STILT Model, Barrow, Alaska, 1982-2011
Contact for Data Center Access Information:
- E-mail: email@example.com
- Telephone: +1 (865) 241-3952
Henderson, J.M., J. Eluszkiewicz, M.E. Mountain, T. Nehrkorn, R.Y.-W. Chang, A. Karion, J.B. Miller, C. Sweeney, N. Steiner, S.C. Wofsy, and C.E. Miller. 2015. Atmospheric transport simulations in support of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Atmos. Chem. Phys. 15:4093-4116. https://doi.org/10.5194/acp-15-4093-2015, 2015
Nehrkorn, T., J. Eluszkiewicz, S.C. Wofsy, J.C. Lin, C. Gerbig, M. Longo, and S. Freitas. 2010. Coupled weather research and forecasting-stochastic time-inverted lagrangian transport (WRF-STILT) model. Meteorol. Atmos. Phys. 107:51-64. doi:10.1007/s00703-010-0068-x
Powers, J. G., J. B. Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen, D. J. Gochis, R. Ahmadov, S. E. Peckham, G. A. Grell, J. Michalakes, S. Trahan, S. G. Benjamin, C. R. Alexander, G. J. Dimego, W. Wang, C. S. Schwartz, G. S. Romine, Z. Liu, C. Snyder, F. Chen, M. J. Barlage, W. Yu, and M. G. Duda. The Weather Research and Forecasting model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98(8):1717 - 1737, 2017. doi:10.1175/BAMS-D-15-00308.1.
Skamarock, W.C. and J.B. Klemp. 2008. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics, 227(7): 3465-3485.