MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

Klocek, Sylwester; Haiyu, Dong,; Dixon, Matthew; Panashe, Kanengoni,; Kazmi, Najeeb; Luferenko, Pete; Lv, Zhongjian; Sharma, Shikhar; Weyn, Jonathan A.; Xiang, Siqi

doi:10.48550/arxiv.2111.09954

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Similar contributions can also be found in [14][15][16]. Klocek et al [17] achieve 6 h precipitation nowcasting under the encoder-forecaster LSTM framework with radar mosaic sequences as input. The recently proposed MetNet [18] has also shown dramatic superiority compared with numerical weather prediction.…”

Section: Introductionmentioning

confidence: 74%

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

Wang,

Huang,

et al. 2023

Atmosphere

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Radar reflectivity plays a crucial role in detecting heavy rainfall and is an important tool for meteorological analysis. However, the coverage of a single radar is limited, leading to the use of satellite data as a complementary source. Consequently, how to bridge the gap between radar and satellite data has become a growing research focus. In this paper, we present MAFormer, a novel model for reconstructing radar reflectivity using satellite data within the Transformer framework. MAFormer consists of two modules: the Axial Local Attention Module and the Mixup Global Attention Module, which extract both local saliency and global similarity. Quantitative and qualitative experiments demonstrate the effectiveness of our proposed method. Specifically, the MAFormer model exhibits notable advancements when compared to state-of-the-art deep learning techniques. It demonstrates an improvement ranging from 0.01 to 0.05 in terms of the Heidke skill score, indicating its superior performance. Additionally, MAFormer effectively mitigates false alarm rates by approximately 0.016 to 0.04, which further highlights its enhanced accuracy and reliability.

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Section: Introductionmentioning

confidence: 74%

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

Wang,

Huang,

et al. 2023

Atmosphere

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“…The authors contrasted the performance of the FDNet with that of the ConvLSTM and other state-of-the-art methods like TrajGRU. In a more hybrid approach, MS-nowcasting was presented by Klocek et al (2021), and it is an encoderdecoder ConvLSTM architecture that allows atmospheric models such as HRRR to be included in the process. The authors demonstrated that variants of the suggested model that were combined with atmospheric models performed significantly better than the original variant in both the United States and Europe.…”

Section: Related Work 21 Rainfall Nowcasting With Deep Learningmentioning

confidence: 99%

EfficientRainNet: Smaller Neural Networks Based on EfficientNetV2 for Rainfall Nowcasting

Sit¹,

Seo²,

Demiray³

et al. 2023

Preprint

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Rainfall nowcasting provides short-term, high-resolution information on the location, intensity, and timing of rainfall, which is crucial for weather forecasting, flood warning, and emergency response. This can help people and organizations make informed decisions to mitigate the impact of severe weather events and reduce the risk of damage and loss of life. There are many attempts at tackling the problem at hand, whether it be numerical models or statistical models that also comprise deep neural networks. Even though nowcast models are quite accurate nowadays and the problem has a saturated literature, current approaches mostly focus on improving the nowcast performance while the computational burden keeps increasing. In this study, we propose EfficientRainNet, which is a convolutional neural network architecture that is based on mobile inverted residual linear bottleneck blocks with a few alterations. We show that EfficientRainNet is able to produce comparable results to those of encoder-decoder convolutional GRUs with only a fraction of the trainable parameters over a radar rainfall dataset for the State of Iowa. Also, for the most part, EfficientRainNet performs better than baselines using persistence- and optical flow-based nowcasting, along with another computational efficiency-focused neural network architecture, Small Attention UNet.

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“…The authors compared the FDNet to ConvLSTM as well as state-of-the-art methods such as TrajGRU. Klocek et al (2021) presented MS-nowcasting, which is an encoder-decoder ConvLSTM architecture where atmospheric models such as HRRR could be incorporated into the process. The authors showed versions of the proposed model that were fused with atmospheric models performed better than the vanilla version for the US and Europe.…”

Section: D Rainfall Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

Sit¹,

Demiray²,

Demir³

2022

Preprint

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With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital Earth data have increased in recent years. Due to the increased availability of computer resources and the growing popularity of deep learning applications, this data has been a crucial source for data-driven studies that have transformed the fields of climate and earth science. One of the critical data sources is precipitation supporting climate and earth science studies on modeling, forecasting, and preparedness for extreme events (i.e., floods, droughts, pollution transport). In this study, we worked on an extensive review of manuscripts focusing on use of deep learning methods to tackle challenges either to improve the quality or extrapolate (forecast) rainfall datasets. The purpose of this study is to summarize the most recent developments in deep learning approaches for forecasting rainfall or improving rainfall datasets, as well as highlighting issues, shortcomings, and open questions with insightful recommendations for future directions.

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MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather

Cited by 8 publications

References 12 publications

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

MAFormer: A New Method for Radar Reflectivity Reconstructing Using Satellite Data

EfficientRainNet: Smaller Neural Networks Based on EfficientNetV2 for Rainfall Nowcasting

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

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