# Climate Model Driven Seasonal Forecasting Approach with Deep Learning

arXiv: https://arxiv.org/abs/2302.10480 · HTML: https://mlsanity.com/paper/2302.10480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [A Bayesian Deep Learning Approach to Near-Term Climate Prediction](https://mlsanity.com/paper/2202.11244/) (2022)
- [Improving seasonal forecast using probabilistic deep learning](https://mlsanity.com/paper/2010.14610/) (2020)

## Built on by (most cited first, 4 of 4)

- [ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning](https://mlsanity.com/paper/2311.03721/) (2023)
- [Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting](https://mlsanity.com/paper/2403.16612/) (2024)
- [DUNE: A Machine Learning Deep UNet++ based Ensemble Approach to Monthly, Seasonal and Annual Climate Forecasting](https://mlsanity.com/paper/2408.06262/) (2024)
- [SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting](https://mlsanity.com/paper/2608.26594/) (2026)

## Abstract (arXiv)

Understanding seasonal climatic conditions is critical for better management of resources such as water, energy and agriculture. Recently, there has been a great interest in utilizing the power of artificial intelligence methods in climate studies. This paper presents a cutting-edge deep learning model (UNet++) trained by state-of-the-art global CMIP6 models to forecast global temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was also used for finetuning as well performance analysis in the validation dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation + ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in six different models. For each model 14 different sequential and non-sequential temporal settings were used. The Mean Absolute Error (MAE) analysis revealed that UNet++ with CMIP6 with elevation and ERA5 finetuning model with "Year 3 Month 2" temporal case provided the best outcome with an MAE of 0.7. Regression analysis over the validation dataset between the ERA5 data values and the corresponding AI model predictions revealed slope and R2 values close to 1 suggesting a very good agreement. The AI model predicts significantly better than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the summer months more accurately than the winter months.