Nowcasting-Nets: Deep Neural Network Structures for Precipitation
Nowcasting Using IMERG
- URL: http://arxiv.org/abs/2108.06868v1
- Date: Mon, 16 Aug 2021 02:55:32 GMT
- Title: Nowcasting-Nets: Deep Neural Network Structures for Precipitation
Nowcasting Using IMERG
- Authors: Mohammad Reza Ehsani, Ariyan Zarei, Hoshin V. Gupta, Kobus Barnard,
Ali Behrangi
- Abstract summary: We 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)
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.
- Score: 1.9860735109145415
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
...
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