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Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks
Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. Our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product
Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
Spatio-temporal sequence forecasting is one of the fundamental tasks in
spatio-temporal data mining. It facilitates many real world applications such
as precipitation nowcasting, citywide crowd flow prediction and air pollution
forecasting. Recently, a few Seq2Seq based approaches have been proposed, but
one of the drawbacks of Seq2Seq models is that, small errors can accumulate
quickly along the generated sequence at the inference stage due to the
different distributions of training and inference phase. That is because
Seq2Seq models minimise single step errors only during training, however the
entire sequence has to be generated during the inference phase which generates
a discrepancy between training and inference. In this work, we propose a novel
curriculum learning based strategy named Temporal Progressive Growing Sampling
to effectively bridge the gap between training and inference for
spatio-temporal sequence forecasting, by transforming the training process from
a fully-supervised manner which utilises all available previous ground-truth
values to a less-supervised manner which replaces some of the ground-truth
context with generated predictions. To do that we sample the target sequence
from midway outputs from intermediate models trained with bigger timescales
through a carefully designed decaying strategy. Experimental results
demonstrate that our proposed method better models long term dependencies and
outperforms baseline approaches on two competitive datasets.Comment: ECAI 2020 Accepted, preprin
PAUNet: Precipitation Attention-based U-Net for rain prediction from satellite radiance data
This paper introduces Precipitation Attention-based U-Net (PAUNet), a deep
learning architecture for predicting precipitation from satellite radiance
data, addressing the challenges of the Weather4cast 2023 competition. PAUNet is
a variant of U-Net and Res-Net, designed to effectively capture the large-scale
contextual information of multi-band satellite images in visible, water vapor,
and infrared bands through encoder convolutional layers with center cropping
and attention mechanisms. We built upon the Focal Precipitation Loss including
an exponential component (e-FPL), which further enhanced the importance across
different precipitation categories, particularly medium and heavy rain. Trained
on a substantial dataset from various European regions, PAUNet demonstrates
notable accuracy with a higher Critical Success Index (CSI) score than the
baseline model in predicting rainfall over multiple time slots. PAUNet's
architecture and training methodology showcase improvements in precipitation
forecasting, crucial for sectors like emergency services and retail and supply
chain management
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