Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.

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Related Links

Related Dataset #1 : Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3

Related Dataset #2 : PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018

Related Dataset #3 : USA National Phenology Network Extended Spring Index Gridded Data Products

Related Dataset #4 : Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"

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Author Wheeler, Kathryn
Dietze, M. C.
LeBauer, D.
Peters, J. A.
Richardson, A. D.
Ross, A. A.
Thomas, R. Q.
Zhu, K.
Bhat, U.
Munch, S.
Buzbee, R. F.
Chen, M.
Goldstein, B.
Guo, J.
Hao, D.
Jones, C.
Kelly-Fair, M.
Liu, H.
Malmborg, C.
Neupane, N.
Pal, D.
Shirey, V.
Song, Y.
Steen, M.
Vance, E. A.
Woelmer, W. M.
Wynne, J. H.
Zachmann, L.
Publisher UCAR/NCAR - Library
Publication Date 2024-02-01T00:00:00
Digital Object Identifier (DOI) Not Assigned
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T20:04:25.350752
Metadata Record Identifier edu.ucar.opensky::articles:26897
Metadata Language eng; USA
Suggested Citation Wheeler, Kathryn, Dietze, M. C., LeBauer, D., Peters, J. A., Richardson, A. D., Ross, A. A., Thomas, R. Q., Zhu, K., Bhat, U., Munch, S., Buzbee, R. F., Chen, M., Goldstein, B., Guo, J., Hao, D., Jones, C., Kelly-Fair, M., Liu, H., Malmborg, C., Neupane, N., Pal, D., Shirey, V., Song, Y., Steen, M., Vance, E. A., Woelmer, W. M., Wynne, J. H., Zachmann, L.. (2024). Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7251npp. Accessed 03 August 2025.

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