mlsanityData to 7 Oct 2026

Nowcasting-Nets: Deep Neural Network Structures for Precipitation Nowcasting Using IMERG

arXiv 2108.06868 ↗First posted 16 Aug 2021cs.LG cs.CV cs.NEcited by 10

Not on the map. Why?

Abstract (arXiv)

Accurate and timely estimation of precipitation is critical for issuing hazard warnings (e.g., for flash floods or landslides). Current remotely sensed precipitation products have a few hours of latency, associated with the acquisition and processing of satellite data. By applying a robust nowcasting system to these products, it is (in principle) possible to reduce this latency and improve their applicability, value, and impact. However, the development of such a system is complicated by the chaotic nature of the atmosphere, and the consequent rapid changes that can occur in the structures of precipitation systems In this work, we develop two approaches (hereafter referred to as Nowcasting-Nets) that use Recurrent and Convolutional deep neural network structures to address the challenge of precipitation nowcasting. A total of five models are trained using Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data over the Eastern Contiguous United States (CONUS) and then tested against independent data for the Eastern and Western CONUS. The models were designed to provide forecasts with a lead time of up to 1.5 hours and, by using a feedback loop approach, the ability of the models to extend the forecast time to 4.5 hours was also investigated. Model performance was compared against the Random Forest (RF) and Linear Regression (LR) machine learning methods, and also against a persistence benchmark (BM) that used the most recent observation as the forecast. Independent IMERG observations were used as a reference, and experiments were conducted to examine both overall statistics and case studies involving specific precipitation events. Overall, the forecasts provided by the Nowcasting-Net models are superior, with the Convolutional Nowcasting Network with Residual Head (CNC-R) achieving 25%, 28%, and 46% improvement in the test ...

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 72 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

16 of 72 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  7. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  8. 2021Skillful Precipitation Nowcasting using Deep Generative Models of Radarcited by 308
  9. 2017Deep Learning for Precipitation Nowcasting: A Benchmark and A New Modelcited by 180
  10. 2019On Extractive and Abstractive Neural Document Summarization with Transformer Language Modelscited by 83
  11. 2018Flow-Grounded Spatial-Temporal Video Prediction from Still Imagescited by 54
  12. 2018SDCNet: Video Prediction Using Spatially-Displaced Convolutioncited by 50
4 more
  1. 2019Precipitation Nowcasting with Satellite Imagerycited by 18
  2. 2019Precipitation nowcasting using a stochastic variational frame predictor with learned prior distributioncited by 4
  3. 2019Precipitation Nowcasting with Star-Bridge Networkscited by 2
  4. 2021FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcastingcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 2.

10 papers.

  1. 2023Foundation Models for Weather and Climate Data Understanding: A Comprehensive Surveycited by 32
  2. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 15
  3. 2024Deep learning for precipitation nowcasting: A survey from the perspective of time series forecastingcited by 14
  4. 2023A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systemscited by 6
  5. 2024Large-scale flood modeling and forecasting with FloodCastcited by 5
  6. 2024Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamicscited by 3
  7. 2023Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecturecited by 1
  8. 2022Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurementscited by 0
  9. 2022Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networkscited by 0
  10. 2025Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Modelcited by 0