edu.ucar.opensky::articles:26897 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T18:55:33.769042 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7251npp https://doi.org/10.3334/ORNLDAAC/1328 https Related Dataset #1 Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 https://doi.org/10.3334/ORNLDAAC/1689 https Related Dataset #2 PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018 https://doi.org/10.5066/F7XD0ZRK https Related Dataset #3 USA National Phenology Network Extended Spring Index Gridded Data Products https://doi.org/10.5281/zenodo.8200101 https Related Dataset #4 Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge" Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge 2024-02-01T00:00:00Z publication https://n2t.org/ark:/85065/d7251npp https://doi.org/10.1016/j.agrformet.2023.109810 Wheeler, Kathryn author Dietze, M. C. author LeBauer, D. author Peters, J. A. author Richardson, A. D. author Ross, A. A. author Thomas, R. Q. author Zhu, K. author Bhat, U. author Munch, S. author Buzbee, R. F. author Chen, M. author Goldstein, B. author Guo, J. author Hao, D. author Jones, C. author Kelly-Fair, M. author Liu, H. author Malmborg, C. author Neupane, N. author Pal, D. author Shirey, V. author Song, Y. author Steen, M. author Vance, E. A. author Woelmer, W. M. author Wynne, J. H. author Zachmann, L. author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation