On the modern deep learning approaches for precipitation downscaling

Kumar, Bipin; Kaustubh, Atey,; Singh, Bhupendra Bahadur; Chattopadhyay, Rajib; Acharya, Nachiket; Singh, Manmeet; Nanjundiah, Ravi S.; Rao, Suryachandra A.

doi:10.1007/s12145-023-00970-4

Cited by 23 publications

(3 citation statements)

References 33 publications

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“…DL-based downscaling methods demonstrate a compelling capability to capture nonlinear relationships. Additionally, the effectiveness of DL methods in predicting SM has been extensively validated in numerous studies [76,78]. In this study, the accuracy and efficiency of the SMNet model are highly dependent on those optical RS data utilized and on data preprocessing [79,80].…”

Section: Uncertainty Of the Downscaling Resultsmentioning

confidence: 92%

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Zhang,

Lu,

et al. 2024

Remote Sensing

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Soil moisture (SM) is a critical variable affecting ecosystem carbon and water cycles and their feedback to climate change. In this study, we proposed a convolutional neural network (CNN) model embedded with a residual block and attention module, named SMNet, to spatially downscale the European Space Agency (ESA) Climate Change Initiative (CCI) SM product. In the SMNet model, a lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism was integrated to comprehensively extract the spatial and channel information from the high-resolution input remote sensing products, the reanalysis meteorological dataset, and the topographic data. The model was employed to downscale the ESA CCI SM from its original spatial resolution of 25 km to 1 km in California, USA, in the annual growing season (1 May to 30 September) from 2003 to 2021. The original ESA CCI SM data and in situ SM measurements (0–5 cm depth) from the International Soil Moisture Network were used to validate the model’s performance. The results show that compared with the original ESA CCI SM data, the downscaled SM data have comparable accuracy with a mean correlation (R) and root mean square error (RMSE) of 0.82 and 0.052 m3/m3, respectively. Moreover, the model generates reasonable spatiotemporal SM patterns with higher accuracy in the western region and relatively lower accuracy in the eastern Nevada mountainous area. In situ site validation results in the SCAN, the SNOTEL network, and the USCRN reveal that the R and RMSE are 0.62, 0.63, and 0.77, and 0.077 m3/m3, 0.093 m3/m3, and 0.078 m3/m3, respectively. The results are slightly lower than the validation results from the original ESA CCI SM data. Overall, the validation results suggest that the SMNet downscaling model proposed in this study has satisfactory performance in handling the task of soil moisture downscaling. The downscaled SM model not only preserves a high level of spatial consistency with the original ESA CCI SM model but also offers more intricate spatial variations in SM depending on the spatial resolution of model input data.

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Section: Uncertainty Of the Downscaling Resultsmentioning

confidence: 92%

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Zhang,

Lu,

et al. 2024

Remote Sensing

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“…Furthermore, with the advent of the "big data" era, deep learning has shown great potential in the downscaling of precipitation in recent studies [73]. The application of many deep learning algorithms, such as convolutional neural networks (CNNs) [74], long short-term memory (LSTM) networks [75] and generative adversarial network (GAN) [76], has displayed high accuracy and efficiency of precipitation downscaling in comparison to traditional regression-based models. Evaluating the performance of different deep learning models is beyond the scope of this study, but it could be explored in our future work.…”

Section: Future Perspectivesmentioning

confidence: 99%

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

et al. 2023

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Accurate high-spatial-resolution precipitation is significantly important in hydrological and meteorological modelling, especially in rain-gauge-sparse areas. Some methods and strategies have been applied for satellite-based precipitation downscaling, residual correction and precipitation calibration. However, which downscaling scheme can provide reliable high-resolution precipitation efficiently remains unanswered. To address this issue, this study aimed to present a framework combining the machine learning downscaling algorithm and post-process procedures. Firstly, four ML-based models, namely support vector regression, random forest, spatial random forest (SRF) and eXtreme gradient boosting (XGBoost), were tested for downscaling and compared with conventional downscaling methods. Then, the effectiveness of the residual correction process using ordinary Kriging and the calibration process using the geographical difference analysis (GDA) method was investigated. The results showed that the ML-based methods had better performance than the conventional regression and interpolation approaches. The SRF and XGBoost outperformed others in generating accurate precipitation estimation with a high resolution. The GDA calibration process significantly improved the downscaled results. However, the residual correction process decreased the downscaling performance of the ML-based models. Combining the SRF or XGBoost downscaling algorithm with the GDA calibration method could be a promising downscaling scheme for precipitation data. The scheme could be used to generate high-resolution precipitation, especially in areas urgently requiring data, which would benefit regional water resource management and hydrological disaster prevention.

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“…Ledig et al (2017) present SRGAN to get realistic images with 4x higher resolution. Kumar et al (2023) showed that SRGAN outperforms DeepSD and ConvLSTM in downscaling 0.25-degree IMD rainfall to 0.0625-degree rainfall (4 times higher) over the India region. In the probabilistic forecasting sense, GANs are used as stochastically downscaling techniques to generate an ensemble of high-resolution precipitations from coarsen-resolution meteorological variables, which present the small-scale samples from the same large-scale distribution.…”

Section: Introductionmentioning

confidence: 99%

Downscaling Taiwan Precipitation with a Residual Deep Learning Approach

Hsu,

Chiang,

Lin

et al. 2024

Preprint

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In response to the growing demand for high-resolution rainfall data to support disaster prevention in Taiwan, this study presents an innovative approach for downscaling precipitation data. We employed a hierarchical architecture of Multi-Scale Residual Networks (MSRN) to downscale rainfall from a coarse 0.25-degree resolution to a finely 0.0125-degree resolution, representing a substantial challenge due to a resolution increase of over 20 times. Our results demonstrate that the hierarchical MSRN outperforms both the one-step MSRN and linear interpolation methods when reconstructing high-resolution daily rainfall. It surpasses the linear interpolation method by 15.1% and 9.1% in terms of mean absolute error and root mean square error, respectively. Furthermore, the hierarchical MSRN excels in accurately reproducing high-resolution rainfall for various rainfall thresholds, displaying minimal biases. The threat score (TS) highlights the hierarchical MSRN's capability to replicate extreme rainfall events, achieving TS scores exceeding 0.54 and 0.46 at rainfall thresholds of 350 and 500 mm per day, outperforming alternative methods. This method is also applying on an operational global model, the ECMWF’s daily rainfall forecasts over Taiwan. The evaluation results indicate that our approach is effective at improving rainfall forecasts for thresholds greater than 100 mm per day, with more significant improvement for the1-3-day lead forecast. This approach also offers a realistic visual representation of fine-grained rainfall distribution, showing promise for making significant contributions to disaster preparedness and weather forecasting in Taiwan.

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On the modern deep learning approaches for precipitation downscaling

Cited by 23 publications

References 33 publications

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism

Towards an Accurate and Reliable Downscaling Scheme for High-Spatial-Resolution Precipitation Data

Downscaling Taiwan Precipitation with a Residual Deep Learning Approach

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