AGU期刊一周Research Spotlight
AGU发布最新Editor's Highlight和国外工作学习机会(Oct 25~Oct 31, 2019)
时间:2019年11月01日 11:15来源: 点击数:

Machine Learning Improves Satellite Rainfall Estimates

A new deep learning approach bridges ground rain gauge and radar data with spaceborne radar observations of Tropical Rainfall Measuring Mission to improve precipitation estimation.

SOURCE: Geophysical Research Letters

An illustration of rainfall estimates from ground-based radar and spaceborne Tropical Rainfall Measuring Mission (TRMM) radar on 20 May 2009. (a) Ground radar rainfall estimates obtained with a machine learning approach trained with ground rain gauge data; (b) space radar estimates obtained with a machine learning model trained using ground radar estimates; and (c) standard TRMM product. An assessment of machine learning approach performance using hourly validation data for 2009 shows (d) ground radar rainfall estimates versus rain gauge data and (e) TRMM rainfall estimates versus ground radar product. Credit: Chen et al. [2019], Figure 2

Spaceborne precipitation observing systems can provide global coverage but estimates typically suffer from uncertainties and biases. Conversely, ground based systems such as rain gauges and precipitation radar have higher accuracy but only limited spatial coverage. Chen et al. [2019] have developed a novel deep learning algorithm designed to construct a hybrid rainfall estimation system, where the ground radar is used to bridge the scale gaps between (accurate) rain gauge measurements and (less accurate) satellite observations.

Such a non-parametric deep learning technique shows the potential for regional and global rainfall mapping and can also be expanded as a data fusion platform through incorporation of additional precipitation estimates such as outputs of numerical weather prediction models.

Citation: Chen, H., Chandrasekar, V., Tan, H., & Cifelli, R. [2019]. Rainfall estimation from ground radar and TRMM Precipitation Radar using hybrid deep neural networks. Geophysical Research Letters, 46. https://doi.org/10.1029/2019GL084771

—Valeriy Ivanov, Editor, Geophysical Research Letters


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