Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests
- URL: http://arxiv.org/abs/2401.03960v1
- Date: Mon, 8 Jan 2024 15:29:23 GMT
- Title: Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests
- Authors: Christian Reimers, David Hafezi Rachti, Guahua Liu, Alexander J.
Winkler
- Abstract summary: We train a deep neural network to predict a phenological index from meteorological time series.
We find that this approach outperforms traditional process-based models.
- Score: 47.285748922842444
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Understanding the future climate is crucial for informed policy decisions on
climate change prevention and mitigation. Earth system models play an important
role in predicting future climate, requiring accurate representation of complex
sub-processes that span multiple time scales and spatial scales. One such
process that links seasonal and interannual climate variability to cyclical
biological events is tree phenology in deciduous broadleaf forests.
Phenological dates, such as the start and end of the growing season, are
critical for understanding the exchange of carbon and water between the
biosphere and the atmosphere. Mechanistic prediction of these dates is
challenging. Hybrid modelling, which integrates data-driven approaches into
complex models, offers a solution. In this work, as a first step towards this
goal, train a deep neural network to predict a phenological index from
meteorological time series. We find that this approach outperforms traditional
process-based models. This highlights the potential of data-driven methods to
improve climate predictions. We also analyze which variables and aspects of the
time series influence the predicted onset of the season, in order to gain a
better understanding of the advantages and limitations of our model.
Related papers
- VegeDiff: Latent Diffusion Model for Geospatial Vegetation Forecasting [58.12667617617306]
We propose VegeDiff for the geospatial vegetation forecasting task.
VegeDiff is the first to employ a diffusion model to probabilistically capture the uncertainties in vegetation change processes.
By capturing the uncertainties in vegetation changes and modeling the complex influence of relevant variables, VegeDiff outperforms existing deterministic methods.
arXiv Detail & Related papers (2024-07-17T14:15:52Z) - Probabilistic Emulation of a Global Climate Model with Spherical DYffusion [15.460280166612119]
We present the first conditional generative model able to produce global climate ensemble simulations that are accurate and physically consistent.
Our approach beats relevant baselines and nearly reaches a gold standard for successful climate model emulation.
arXiv Detail & Related papers (2024-06-21T00:16:55Z) - Advancing Data-driven Weather Forecasting: Time-Sliding Data
Augmentation of ERA5 [3.3748750222488657]
We introduce a novel strategy that deviates from the common dependence on high-resolution data.
This paper improves on conventional approaches by adding more variables and a novel approach to data augmentation and processing.
Our findings reveal that despite the lower resolution, the proposed approach demonstrates considerable accuracy in predicting atmospheric conditions.
arXiv Detail & Related papers (2024-02-13T03:01:22Z) - Characterizing climate pathways using feature importance on echo state
networks [0.0]
echo state network (ESN) is a computationally efficient neural network variation designed for temporal data.
ESNs are non-interpretable black-box models, which poses a hurdle for understanding variable relationships.
We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data.
arXiv Detail & Related papers (2023-10-12T16:55:04Z) - Encoding Seasonal Climate Predictions for Demand Forecasting with
Modular Neural Network [0.8378605337114742]
We propose a novel framework that encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions.
Our experiments indicate learning such representations to model seasonal climate forecast results in an error reduction of approximately 13% to 17% across multiple real-world data sets.
arXiv Detail & Related papers (2023-09-05T13:58:59Z) - ClimaX: A foundation model for weather and climate [51.208269971019504]
ClimaX is a deep learning model for weather and climate science.
It can be pre-trained with a self-supervised learning objective on climate datasets.
It can be fine-tuned to address a breadth of climate and weather tasks.
arXiv Detail & Related papers (2023-01-24T23:19:01Z) - Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z) - Forecasting large-scale circulation regimes using deformable
convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs)
We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future.
Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
arXiv Detail & Related papers (2022-02-10T11:37:00Z) - Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
We find a third intermediate stable state in one of the two climate models we consider.
The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
arXiv Detail & Related papers (2020-10-20T15:31:38Z) - Crop Yield Prediction Integrating Genotype and Weather Variables Using
Deep Learning [8.786816847837976]
We use historical performance records from Uniform Soybean Tests (UST) in North America spanning 13 years of data to build a Long Short Term Memory - Recurrent Neural Network based model to dissect and predict genotype response in multiple environments.
We deploy this deep learning framework as a 'hypotheses generation tool' to unravel GxExM relationships.
We envision broad applicability of this approach (via conducting sensitivity analysis and "what-if" scenarios) for soybean and other crop species under different climatic conditions.
arXiv Detail & Related papers (2020-06-24T16:20:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.