US-Slt: Silas Little- New Jersey
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| Tower_team: | |
| PI: | Andrew Ouimette Andrew.Ouimette@usda.gov - USDA Forest Service |
| PI: | Michael R. Gallagher michael.r.gallagher@usda.gov - USDA Forest Service |
| FluxContact: | Roel Alfredo Ruzol roel.ruzol@maine.edu - University of Maine |
| Technician: | Kevin Brown kevin.brown3@usda.gov - USDA Forest Service |
| Lat, Long: | 39.9138, -74.5960 |
| Elevation(m): | 30 |
| Network Affiliations: | AmeriFlux, Phenocam |
| Vegetation IGBP: | DBF (Deciduous Broadleaf Forests: Lands dominated by woody vegetation with a percent cover >60% and height exceeding 2 meters. Consists of broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.) |
| Climate Koeppen: | Dfa (Humid Continental: humid with severe winter, no dry season, hot summer) |
| Mean Annual Temp (°C): | 11.04 |
| Mean Annual Precip. (mm): | 1138 |
| Flux Species Measured: | CO2, H, H2O |
| Years Data Collected: | 2004 - Present |
| Years Data Available: | AmeriFlux BASE 2005 - 2014 Data Citation |
| Data Use Policy: | AmeriFlux CC-BY-4.0 Policy1 |
| Description: | Wildfires, prescribed fires, insect defoliation events and windstorms are the common disturbances in the NJ Pinelands. The oak-dominated forest at Silas ... Wildfires, prescribed fires, insect defoliation events and windstorms are the common disturbances in the NJ Pinelands. The oak-dominated forest at Silas Little Experimental Forest was most recently defoliated by Gypsy moth (Lymantria dispar L.) in 2006 to 2008, with complete defoliation occuring in 2007. Following this multi-year defoliation event, oak mortality was significant, and resulted in the death of approximately 20 % of the overstory oaks, and a similar reduction in stand biomass. Previous disturbances have included windstorms and earlier Gypsy moth defoliation events in the 1990's. The last major wildfire to occur at and near the Experimental Forest was in 1963. Since then, a number of prescribed fires have been conducted in the vicinity of the Silas Little flux site. See MoreShow Less |
| URL: | http://www.nrs.fs.fed.us/ef/locations/nj/silas-little/ |
| Research Topics: | 1) Impact of fire management activities on stand carbon dynamics, 2) Flux measurements and smoke emissions during prescribed burns, 3) Comparision of biometric, ... 1) Impact of fire management activities on stand carbon dynamics, 2) Flux measurements and smoke emissions during prescribed burns, 3) Comparision of biometric, remotely sensed (LiDAR), and flux-based measurements of stand carbon dynamics. See MoreShow Less |
| Acknowledgment: | — |
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The AmeriFlux Legacy Policy must be followed if this site’s data are combined with data from sites that require the AmeriFlux Legacy Policy.
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US-Slt: Silas Little- New Jersey
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Use the information below for citation of this site. See the Data Policy page for more details.
DOI(s) for citing US-Slt data
Data Use Policy: AmeriFlux CC-BY-4.0 License
This site’s data can also be used under the more restrictive AmeriFlux Legacy Policy.
The AmeriFlux Legacy Policy must be followed if US-Slt data are combined with data from sites that require the AmeriFlux Legacy Policy.
- AmeriFlux BASE: https://doi.org/10.17190/AMF/1246096
Citation: Ken Clark (2016), AmeriFlux BASE US-Slt Silas Little- New Jersey, Ver. 5-1, AmeriFlux AMP, (Dataset). https://doi.org/10.17190/AMF/1246096
To cite BADM when downloaded on their own, use the publications below for citing site characterization. When using BADM that are downloaded with AmeriFlux BASE and AmeriFlux FLUXNET products, use the DOI citation for the associated data product.
Publication(s) for citing site characterization
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US-Slt: Silas Little- New Jersey
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This page displays the list of downloads of data for the site US-Slt.
Note: Results are the number of downloads to distinct data users. The Download Count column indicates the number of times the data user downloaded the data. The Version column refers to the version of the data product for the site that was downloaded by the data user.
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US-Slt: Silas Little- New Jersey
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Search Images for this Site Images found : 2
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US-Slt 2013.US.Slt.sitevisit20130918__MG_008222
Description:
2013.US.Slt.sitevisit20130918__MG_008222
Keywords: —
Site ID: US-Slt
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US-Slt Silas Little AmeriFlux tower
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Credit: Stephen Chan
Site ID: US-Slt
Location: New Jersey, United States
Location: New Jersey, United States
Image Date: 2013/09/20
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US-Slt: Silas Little- New Jersey
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
MODIS NDVI
The time series shows the 16-day Normalized Difference Vegetation Index (NDVI) average from the MOD13Q1 data product.
Use the slider below the time series to zoom in and out.
Includes all pixels that have acceptable quality
To view / download these data and other MOD13Q1 products for this site, visit MODIS/Terra Vegetation Indices.
For other related products, visit MODIS/VIIRS Fixed Sites Subsets Tool.
Citation:
ORNL DAAC. 2018. Terrestrial Ecology Subsetting & Visualization Services (TESViS) Fixed Sites Subsets. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1567
MODIS NDVI subsetted data is not yet available for this site.
For a complete list of AmeriFlux sites, visit ORNL DAAC's MODIS/VIIRS Fixed Sites Subsets Tool.
PhenoCam Images and Derived Time Series Data
PhenoCams are high-resolution digital cameras that take repeated images of studied ecosystems and provide quantitative information about the canopy phenology. The PhenoCam Network coordinates the camera installation and data reporting/analyses across sites in the Americas, providing automated, near-surface remote sensing of canopy phenology across a range of ecosystems and climate zones. Use of PhenoCam images / data should follow the PhenoCam Data Use Policy .
PhenoCam sites for US-Slt:
Use the links below to explore camera images and interactive timeseries for these sites.
Citation:
B. Seyednasrollah, A. M. Young, K. Hufkens, T. Milliman, M. A. Friedl, S. Frolking, and A. D. Richardson. Tracking vegetation phenology across diverse biomes using version 2.0 of the phenocam dataset. Scientific Data, 6(1):222, 2019. doi:10.1038/s41597-019-0229-9Camera Imagery
Milliman, T., B. Seyednasrollah, A.M. Young, K. Hufkens, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J.M. Baker, D. Baldocchi, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, J. Verfaillie, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1689
Green Chromatic Coordinate Time Series
Seyednasrollah, B., A.M. Young, K. Hufkens, T. Milliman, M.A. Friedl, S. Frolking, A.D. Richardson, M. Abraha, D.W. Allen, M. Apple, M.A. Arain, J. Baker, J.M. Baker, D. Baldocchi, C.J. Bernacchi, J. Bhattacharjee, P. Blanken, D.D. Bosch, R. Boughton, E.H. Boughton, R.F. Brown, D.M. Browning, N. Brunsell, S.P. Burns, M. Cavagna, H. Chu, P.E. Clark, B.J. Conrad, E. Cremonese, D. Debinski, A.R. Desai, R. Diaz-Delgado, L. Duchesne, A.L. Dunn, D.M. Eissenstat, T. El-Madany, D.S.S. Ellum, S.M. Ernest, A. Esposito, L. Fenstermaker, L.B. Flanagan, B. Forsythe, J. Gallagher, D. Gianelle, T. Griffis, P. Groffman, L. Gu, J. Guillemot, M. Halpin, P.J. Hanson, D. Hemming, A.A. Hove, E.R. Humphreys, A. Jaimes-Hernandez, A.A. Jaradat, J. Johnson, E. Keel, V.R. Kelly, J.W. Kirchner, P.B. Kirchner, M. Knapp, M. Krassovski, O. Langvall, G. Lanthier, G.l. Maire, E. Magliulo, T.A. Martin, B. McNeil, G.A. Meyer, M. Migliavacca, B.P. Mohanty, C.E. Moore, R. Mudd, J.W. Munger, Z.E. Murrell, Z. Nesic, H.S. Neufeld, T.L. O'Halloran, W. Oechel, A.C. Oishi, W.W. Oswald, T.D. Perkins, M.L. Reba, B. Rundquist, B.R. Runkle, E.S. Russell, E.J. Sadler, A. Saha, N.Z. Saliendra, L. Schmalbeck, M.D. Schwartz, R.L. Scott, E.M. Smith, O. Sonnentag, P. Stoy, S. Strachan, K. Suvocarev, J.E. Thom, R.Q. Thomas, A.K. Van den berg, R. Vargas, J. Verfaillie, C.S. Vogel, J.J. Walker, N. Webb, P. Wetzel, S. Weyers, A.V. Whipple, T.G. Whitham, G. Wohlfahrt, J.D. Wood, S. Wolf, J. Yang, X. Yang, G. Yenni, Y. Zhang, Q. Zhang, and D. Zona. 2019. PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1674
GeoNEX Data Products
GeoNEX led by NASA Earth eXchange (NEX) is a collaborative effort for generating Earth monitoring products from the new generation of geostationary satellite sensors. GeoNEX has produced a suite of geostationary data products including surface reflectance, land surface temperature, surface solar radiation, and many others.
The GeoNEX Common Grid locates GeoNEX data in the geographic (latitude/longitude) projection. Pixels (grid cells) are created at regular 0.005°, 0.01°, and 0.02° resolutions.
GeoNEX pixels below cover the area 0.06° x 0.06° around and including site US-Slt, 39.9138, -74.596.
Click a square in the grid at left to display its data below.
Coordinates for selected GeoNEX Pixel
Graph controls:
- Zoom: click-drag
- Pan: shift-click-drag
- Restore zoom level: double-click
- Use the slider below the time series to zoom in and out.
All download requests will be logged.
NDVI: Normalized Difference Vegetation Index
No NDVI data have been received for site US-Slt.
NDVI (unitless)
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel:
NIRv Near-Infrared Reflectance of vegetation
No NIRv data have been received for site US-Slt.
NIRv (unitless)
Resolution: 0.01° x 0.01° & 10 minutes
Coordinates for pixel:
DSR: Surface downward shortwave radiation
No DSR data have been received for site US-Slt.
Shortwave Radiation (W/m2)
Resolution: 0.01° x 0.01° & Hourly
Coordinates for pixel:
LST: Land Surface Temperature
No LST data have been received for site US-Slt.
LST (K)
Resolution: 0.02° x 0.02° & Hourly
Coordinates for pixel:
Citation
This material can be used without obtaining permission from NASA. NASA should be acknowledged as the source of this material.
Subset Data Citation:
- Hashimoto, H., Wang, W., Park, T., Khajehei, S., Ichii, K., Michaelis, A.R., Guzman, A., Nemani, R.R., Torn, M., Yi, K., Brosnan, I.G. (in preparation). Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles.
Relevant Science Publication Citation:
GeoNEX Surface Reflectance for Vegetation Indices (NDVI & NIRv)- Wang, W., Wang, Y., Lyapustin, A., Hashimoto, H., Park, T., Michaelis, A., & Nemani, R. (2022). A novel atmospheric correction algorithm to exploit the diurnal variability in hypertemporal geostationary observations. Remote Sensing, 14(4), 964.
- Li, R., Wang, D., Wang, W., & Nemani, R. (2023). A GeoNEX-based high-spatiotemporal-resolution product of land surface downward shortwave radiation and photosynthetically active radiation. Earth System Science Data, 15(3), 1419-1436.
- Jia, A., Liang, S., & Wang, D. (2022). Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data. Remote Sensing of Environment, 278, 113105.
US-Slt: Silas Little- New Jersey
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Search Publications for this Site
Publications Found: 16
| AmeriFlux Publications | Add Publication |
| Year | Publication |
|---|---|
| 2022 | Clark, K. L., Aoki, C., Ayres, M., Kabrick, J., Gallagher, M. R. (2022) Insect Infestations And The Persistence And Functioning Of Oak-Pine Mixedwood Forests In The Mid-Atlantic Region, USA, Plos One, 17(5), e0265955. https://doi.org/10.1371/journal.pone.0265955 |
| 2021 | Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn, M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi, D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K., Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A., Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa, H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T., Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick, K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E., Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E. S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C., Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A., Ueyama, M., Vargas, R., Wood, J. D., Zona, D. (2021) Representativeness Of Eddy-Covariance Flux Footprints For Areas Surrounding Ameriflux Sites, Agricultural And Forest Meteorology, 301-302, 108350. https://doi.org/10.1016/j.agrformet.2021.108350 |
| 2021 | Heilman, W. E., Clark, K. L., Bian, X., Charney, J. J., Zhong, S., Skowronski, N. S., Gallagher, M. R., Patterson, M. (2021) Turbulent Momentum Flux Behavior Above A Fire Front In An Open-Canopied Forest, Atmosphere, 12(8), 956. https://doi.org/10.3390/atmos12080956 |
| 2020 | Clark, K. L., Heilman, W. E., Skowronski, N. S., Gallagher, M. R., Mueller, E., Hadden, R. M., Simeoni, A. (2020) Fire Behavior, Fuel Consumption, And Turbulence And Energy Exchange During Prescribed Fires In Pitch Pine Forests, Atmosphere, 11(3), 25. https://doi.org/10.3390/atmos11030242 |
| 2020 | Hannun, R. A., Wolfe, G. M., Kawa, S. R., Hanisco, T. F., Newman, P. A., Alfieri, J. G., Barrick, J., Clark, K. L., DiGangi, J. P., Diskin, G. S., King, J., Kustas, W. P., Mitra, B., Noormets, A., Nowak, J. B., Thornhill, K. L., Vargas, R. (2020) Spatial Heterogeneity In Co2, Ch4, And Energy Fluxes: Insights From Airborne Eddy Covariance Measurements Over The Mid-Atlantic Region, Environmental Research Letters, 15(3), 035008. https://doi.org/10.1088/1748-9326/ab7391 |
| 2019 | Guerrieri, R., Belmecheri, S., Ollinger, S. V., Asbjornsen, H., Jennings, K., Xiao, J., Stocker, B. D., Martin, M., Hollinger, D. Y., Bracho-Garrillo, R., Clark, K., Dore, S., Kolb, T., Munger, J. W., Novick, K., Richardson, A. D. (2019) Disentangling The Role Of Photosynthesis And Stomatal Conductance On Rising Forest Water-Use Efficiency, Proceedings Of The National Academy Of Sciences, 116(34), 16909-16914. https://doi.org/10.1073/pnas.1905912116 |
| 2018 | Clark, K., Renninger, H., Skowronski, N., Gallagher, M., Schäfer, K. (2018) Decadal-Scale Reduction In Forest Net Ecosystem Production Following Insect Defoliation Contrasts With Short-Term Impacts Of Prescribed Fires, Forests, 9(3), 145. https://doi.org/doi:10.3390/f9030145 |
| 2018 | Chu, H., Baldocchi, D. D., Poindexter, C., Abraha, M., Desai, A. R., Bohrer, G., Arain, M. A., Griffis, T., Blanken, P. D., O'Halloran, T. L., Thomas, R. Q., Zhang, Q., Burns, S. P., Frank, J. M., Christian, D., Brown, S., Black, T. A., Gough, C. M., Law, B. E., Lee, X., Chen, J., Reed, D. E., Massman, W. J., Clark, K., Hatfield, J., Prueger, J., Bracho, R., Baker, J. M., Martin, T. A. (2018) Temporal Dynamics Of Aerodynamic Canopy Height Derived From Eddy Covariance Momentum Flux Data Across North American Flux Networks, Geophysical Research Letters, 45, 9275–9287. https://doi.org/10.1029/2018GL079306 |
| 2016 | Wolf, S., Keenan, T.F., Fisher, J.B., Baldocchi, D.D., Desai, A.R., Richardson, A.D., Scott, R.L., Law, B.E., Litvak, M.E., Brunsell, N.A., Peters, W., van der Laan-Luijkx, I.T. (2016) Warm spring reduced carbon cycle impact of the 2012 US summer drought, Proceedings of the National Academy of Sciences, 113(21), 5880-5885. https://doi.org/10.1073/pnas.1519620113 |
| 2016 | Zscheischler, J., Fatichi, S., Wolf, S., Blanken, P., Bohrer, G., Clark, K., Desai, A., Hollinger, D., Keenan, T., Novick, K.A., Seneviratne, S.I. (2016) Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes, Journal of Geophysical Research - Biogeosciences, 121(8), 2186-2198. https://doi.org/10.1002/2016JG003503 |
| 2014 | Skowronski, N. S., Clark, K. L., Gallagher, M., Birdsey, R. A., Hom, J. L. (2014) Airborne Laser Scanner-Assisted Estimation Of Aboveground Biomass Change In A Temperate Oak–Pine Forest, Remote Sensing Of Environment, 151, 166-174. https://doi.org/10.1016/j.rse.2013.12.015 |
| 2014 | Clark, K. L., Skowronski, N. S., Gallagher, M. R., Renninger, H., Schäfer, K. V. R. (2014) Contrasting Effects Of Invasive Insects And Fire On Ecosystem Water Use Efficiency, Biogeosciences, 11(23), 6509-6523. https://doi.org/10.5194/bg-11-6509-2014 |
| 2014 | Renninger, H. J., Carlo, N., Clark, K. L., Schäfer, K. V. (2014) Modeling Respiration From Snags And Coarse Woody Debris Before And After An Invasive Gypsy Moth Disturbance, Journal Of Geophysical Research: Biogeosciences, 119(4), 630-644. https://doi.org/10.1002/2013jg002542 |
| 2012 | Clark, K. L., Skowronski, N., Gallagher, M., Renninger, H., Schäfer, K. (2012) Effects Of Invasive Insects And Fire On Forest Energy Exchange And Evapotranspiration In The New Jersey Pinelands, Agricultural And Forest Meteorology, 166-167, 50-61. https://doi.org/10.1016/j.agrformet.2012.07.007 |
| 2010 | Clark, K. L., Skowronski, N., Hom, J. (2010) Invasive Insects Impact Forest Carbon Dynamics, Global Change Biology, 16(1), 88-101. https://doi.org/10.1111/j.1365-2486.2009.01983.x |
| 2007 | SKOWRONSKI, N., CLARK, K., NELSON, R., HOM, J., PATTERSON, M. (2007) Remotely Sensed Measurements Of Forest Structure And Fuel Loads In The Pinelands Of New Jersey, Remote Sensing Of Environment, 108(2), 123-129. https://doi.org/10.1016/j.rse.2006.09.032 |
US-Slt: Silas Little- New Jersey
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BADM for This Site
Access the Biological, Ancillary, Disturbance and Metadata (BADM) information and data for this site.
BADM contain information for many uses, such as characterizing a site’s vegetation and soil, describing disturbance history, and defining instrumentation for flux processing. They complement the flux/met data.
- Download BADM for this site*
- View Site General Info for this site (Overview tab)*
- Use Online Editor to update Site General Info or DOI Authorship
- Update information about submitted data (Variable Information tool)
- More BADM resources
* Online updates are shown on the Overview tab real time. However, downloaded BADM files will not reflect those updates until they have been reviewed for QA/QC.
US-Slt: Silas Little- New Jersey
- Overview
- Windroses
- Data Citation
- Data Use Log
- Image Gallery
- Remote Sensing Data
- MODIS
- PhenoCam
- GeoNEX
- Publications
- BADM
Wind Roses
Click an image below to enlarge it, or use the navigation panel.
Wind Speed (m/s)
Navigation
Annual Average
About Ameriflux Wind Roses
Wind Rose Explanation
A wind rose gives a succinct view of how wind speed and direction are typically distributed at a particular location. Presented in a circular format, a wind rose shows the frequency and intensity of winds blowing from particular directions. The length of each “spoke” around the circle indicates the amount of time (frequency) that the wind blows from a particular direction. Colors along the spokes indicate categories of wind speed (intensity). Each concentric circle represents a different frequency, emanating from zero at the center to increasing frequencies at the outer circles
Utility
This information can be useful to gain insight into regions surrounding a flux tower that contribute to the measured fluxes, and how those regions change in dependence of the time of day and season. The wind roses presented here are for four periods of the year, and in 16 cardinal directions. Graphics are available for all sites in the AmeriFlux network based on reported wind measurements at each site.
Data from each site can be downloaded by clicking the ‘download’ button.
Hover the cursor over a wind rose to obtain directions, speeds and intensities.
Note that wind roses are not equivalent to flux footprints. Specifically, the term flux footprint describes an upwind area “seen” by the instruments measuring vertical turbulent fluxes, such that heat, water, gas and momentum transport generated in this area is registered by the instruments. Wind roses, on the other hand, identify only the direction and speed of wind.
Where do these data come from?
The wind roses are based on observed hourly data from the sites registered with the AmeriFlux Network.
Parameters for AmeriFlux Wind Roses
To use wind roses for a single AmeriFlux site, the following parameters may be most useful:
- Wind Speed Scale: Per Site
- Wind Direction Scale (%): Per Site
To compare wind roses from more than one single AmeriFlux site, the following parameters may be most useful:
- Wind Speed Scale: Non-Linear
- Wind Direction Scale (%): AmeriFlux
Mar - Jun; 6am - 6pm
Mar - Jun; 6pm - 6am
Jun - Sep; 6am - 6pm
Jun - Sep; 6pm - 6am
Sep - Dec; 6am - 6pm
Sep - Dec; 6pm - 6am
Dec - Mar; 6am - 6pm
Dec - Mar; 6pm - 6am