paper-with-me

Papers

Machine Learning for Precipitation Nowcasting from Radar Images

2019-12-11 · Shreya Agrawal, Luke Barrington, Carla Bromberg, John Burge, Cenk Gazen, Jason Hickey

High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather. As Deep Learning (DL) techniques have shown dramatic promise in many domains, including the geosciences, we present an application of DL to the problem of precipitation nowcasting, i.e., high-resolution (1 km x 1 km) short-term (1 hour) predictions of precipitation. We treat forecasting as an image-to-image translation problem and leverage the power of the ubiquitous UNET convolutional neural network. We find this performs favorably when compared to three commonly used models: optical flow, persistence and NOAA's numerical one-hour HRRR nowcasting prediction.

📄 PDF Abstract BibTeX arXiv:1912.12132

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine LearningImage-to-Image TranslationOptical Flow EstimationTranslation

Methods 이 논문이 사용한 방법론

Concatenated Skip Connection A Concatenated Skip Connection is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from…
Max Pooling Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually…
Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…
ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…
U-Net 설명 없음

Similar Papers 제목 키워드 기반

Effective Training Strategies for Deep-learning-based Precipitation Nowcasting and Estimation

2022-02-17 · Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Seok-Geun Oh 외

Deep learning has been successfully applied to precipitation nowcasting. In this work, we propose a pre-training scheme and a new loss function for improving deep-learning-based nowcasting. First, we adapt U-Net, a widel…

Deep Learning

FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting

2021-05-06 · Bi-Ying Yan, Chao Yang, Feng Chen, Kohei Takeda 외

With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. T…

Optical Flow Estimation

Deep-Learning-Based Precipitation Nowcasting with Ground Weather Station Data and Radar Data

2022-10-20 · Jihoon Ko, Kyuhan Lee, Hyunjin Hwang, Kijung Shin

Recently, many deep-learning techniques have been applied to various weather-related prediction tasks, including precipitation nowcasting (i.e., predicting precipitation levels and locations in the near future). Most exi…

Spatiotemporal Data Fusion for Precipitation Nowcasting

2018-12-28 · Vladimir Ivashkin, Vadim Lebedev

Precipitation nowcasting using neural networks and ground-based radars has become one of the key components of modern weather prediction services, but it is limited to the regions covered by ground-based radars. Truly gl…

Precipitation Nowcasting with Satellite Imagery

2019-05-23 · Vadim Lebedev, Vladimir Ivashkin, Irina Rudenko, Alexander Ganshin 외

Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service. Modern precipitation nowcasting algorithms rely on the extrapolation…

Optical Flow Estimation