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Ge Chen
and
Haitao Li

Abstract

A natural mode refers, in this study, to a periodic oscillation of sea surface temperature (SST) that is geophysically significant on a global, regional, or local scale. Using a newly developed harmonic extraction scheme by Chen, which has the advantage of being space–time decoupled and fully data adaptive, a variety of natural modes have been recovered from global monthly SST data for the period of 1985–2003. Among them, the eight most significant modes are identified as primary modes, whose spatial patterns are presented, along with their phase distributions. At seasonal time scales, a 4-month primary mode is uncovered in addition to the well-documented annual and semiannual cycles. At interannual time scales, the dominant El Niño–Southern Oscillation (ENSO) variability is found to be composed of at least five primary modes, with well-defined central periods around 18, 25, 32, 43, and 63 months. At time scales beyond ENSO, a decadal SST signal with an average period of 10.3 yr is observed. A unique contribution of this study is the derivation and presentation of fine patterns of natural SST modes and signals in joint dimensions of time, space, period, and phase, leading to several findings and conclusions that are of potential importance: 1) The degree of separability and regularity of the sub-ENSO modes is surprising, and thus reveals new details on the nature of this event. 2) The midlatitude counterparts of the equatorial interannual and decadal SST modes/signals are found in the two hemispheres with a frequency shift toward longer periods. The “shadows” of the Pacific Ocean’s ENSO modes are also observed with some detail in the Atlantic and the Indian Oceans. All of these provide direct evidence that teleconnections exist between the equatorial and extratropical oceans, as well as among the tropical Pacific, tropical Atlantic, and tropical Indian Oceans, possibly as a result of the “atmospheric bridge.” 3) A sharply opposite anisotropy is observed in the spatiotemporal pattern between the interannual modes and decadal signals, implying that they are potentially of a categorical difference in origin. 4) Locality or regionality is a fundamental feature for most of the SST modes. Treating the interannual or decadal variability as a single ENSO or Pacific decadal oscillation mode appears to be an oversimplification, and may lead to inappropriate interpretations. The results herein represent an improved knowledge of the natural variability in sea surface temperature, which will hopefully help to enhance the understanding of natural fluctuations of the global/regional climate system in the context of ocean–atmosphere interaction.

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Lu Yi
,
Zhangyang Gao
,
Zhehui Shen
,
Haitao Lin
,
Zicheng Liu
,
Siqi Ma
,
Cunguang Wang
,
Stan Z. Li
, and
Ling Li

Abstract

Precipitation is a vital process in the water cycle. Accurate estimation of the precipitation rate underpins the success of hydrological simulations, flood predictions, and water resource management. Satellite infrared (IR) data, with high temporal resolution and wide coverages, have been commonly used in precipitation inversion. However, existing IR-based precipitation retrieval algorithms suffer from various problems such as overestimation in dry regions, poor performance in extreme rainfall events, and reliance on an empirical cloud-top brightness–rain rate relationship. To resolve these problems, we construct a deep learning model using a spherical convolutional neural network to properly represent Earth’s spherical surface. With data input directly from IR bands 3, 4, and 6 of the operational Geostationary Operational Environmental Satellite (GOES), our new model of Precipitation Estimation based on IR data with Spherical Convolutional Neural Network (PEISCNN) was first trained and tested with a 3-month-long dataset, and then validated in a 2-yr period. Compared to the commonly used IR-based precipitation product PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System), PEISCNN showed significant improvement in the metrics of POD, CSI, RMSE, and CC, especially in the dry region and for extreme rainfall events. Decomposed with the four-component error decomposition (4CED) method, the overestimation of PEISCNN was averaged 47.66% lower than the CCS at the hourly scale. The PEISCNN model may provide a promising way to produce an improved IR-based precipitation product to benefit a wide range of hydrological applications.

Significance Statement

An IR-based precipitation algorithm is irreplaceable in satellite precipitation inversion, since an IR sensor can provide observations of high frequency, fine temporal resolution, and wide coverage. Considering the spherical nature of Earth’s surface which has been overlooked in previous IR-based precipitation retrieval algorithms, we proposed a new deep learning model PEISCNN, which can address the problems that exist in IR-based precipitation estimations such as overestimation in dry regions, deficiency in extreme rainfall events, and reliance on the empirical cloud-top brightness–rain rate relationship. PEISCNN provides a new insight to improve the accuracy of the satellite IR-based or multisensor-based precipitation estimation, and it has great potential to benefit a range of related hydrological research, applications in water resource management, and flood predictions.

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