With climate change, there will be higher requirements for monitoring storm surges (SSs) in nearshore areas. However, this capability is limited by the sparseness of tide gauge (TG) stations. ...Establishing and maintaining a permanent, high‐spatial coverage, in situ TG network is complex and expensive. Here, we propose a joint modeling method developed from the all‐site modeling data‐driven framework by importing temporary TGs into coastal regions with insufficient permanent TG stations. The assessments show that this method can significantly optimize the capability of extreme SS monitoring during typhoons and hurricanes. Moreover, the evaluation based on Coupled Model Intercomparison Project Phase 6 data indicates that it will monitor extreme SSs more effectively during 2025–2050 compared with only using existing permanent in situ TGs (reducing root mean square error and absolute mean bias by ∼50%). The joint modeling method provides an applicable and sustainable solution for optimizing the SS monitoring capability in coastal areas.
Plain Language Summary
Each year, storm surges (SSs) generated by typhoons and hurricanes cause loss of life and property in coastal areas. With global warming, the destruction from extreme events will increase in the future, thus posing challenges to nearshore SS monitoring. High spatiotemporal resolution and high‐precision monitoring data are crucial for early warning and forecasting, which can better prepare coastal communities for incoming storms. However, as the only means to observe continuous and high‐frequency sea levels, tide gauges often provide limited information due to insufficient spatial resolution. In addition, establishing and maintaining a permanent tide gauge (TG) monitoring network with high spatial coverage is expensive and unrealistic. A joint modeling method based on artificial intelligence technology through importing temporary tide gauges established during extreme events to the existing TG networks can significantly optimize the future SS monitoring capability, providing a valuable and applicable reference for regions affected by powerful typhoons and hurricanes and thereby improving the nearshore SS monitoring system.
Key Points
A storm surge (SS) joint modeling method developed from the all‐site modeling data‐driven framework
Temporary tide gauge (TG) stations can reduce the cost of establishing and maintaining permanent TGs
Improve extreme SS monitoring precision by ∼50% from 2025 to 2050 compared with only using permanent TGs
The presence of a static tilt between the inner core and mantle is an ongoing discussion encompassing the geodynamic state of the inner core. Here, we confirm an approximate 8.5 yr signal in polar ...motion is the inner core wobble (ICW), and find that the ICW is also contained in the length-of-day variations of the Earth's rotation. Based on the determined amplitudes of the ICW and its good phase consistency in both polar motion and the length-of-day variations, we infer that there must be a static tilt angle θ between the inner core and the mantle of about 0.17 ± 0.03°, most likely towards ~90°W relative to the mantle, which is two orders of magnitude lower than the 10° assumed in certain geodynamic research. This tilt is consistent with the assumption that the average density in the northwestern hemisphere of the inner core should be greater than that in the other regions. Further, the observed ICW period (8.5 ± 0.2 yr) suggests a 0.52 ± 0.05 g/cm
density jump at the inner core boundary.
GNSS time-series prediction plays an important role in the monitoring of crustal plate movement, and dam or bridge deformation, and the maintenance of global or regional coordinate frames. Deep ...learning is a state-of-the-art approach for extracting high-level abstract features from big data without any prior knowledge. Moreover, long short-term memory (LSTM) networks are a form of recurrent neural networks that have significant potential for processing time series. In this study, a novel prediction framework was proposed by combining a multi-scale sliding window (MSSW) with LSTM. Specifically, MSSW was applied for data preprocessing to effectively extract the feature relationship at different scales and simultaneously mine the deep characteristics of the dataset. Then, multiple LSTM neural networks were used to predict and obtain the final result by weighting. To verify the performance of MSSW-LSTM, 1000 daily solutions of the XJSS station in the Up component were selected for prediction experiments. Compared with the traditional LSTM method, our results of three groups of controlled experiments showed that the RMSE value was reduced by 2.1%, 23.7%, and 20.1%, and MAE was decreased by 1.6%, 21.1%, and 22.2%, respectively. Our results showed that the MSSW-LSTM algorithm can achieve higher prediction accuracy and smaller error, and can be applied to GNSS time-series prediction.
The Global Positioning System (GPS) is an important tool to observe and model geodynamic processes such as plate tectonics and post-glacial rebound. In the last three decades, GPS has seen tremendous ...advances in the precision of the measurements, which allow researchers to study geophysical signals through a careful analysis of daily time series of GPS receiver coordinates. However, the GPS observations contain errors and the time series can be described as the sum of a real signal and noise. The signal itself can again be divided into station displacements due to geophysical causes and to disturbing factors. Examples of the latter are errors in the realization and stability of the reference frame and corrections due to ionospheric and tropospheric delays and GPS satellite orbit errors. There is an increasing demand on detecting millimeter to sub-millimeter level ground displacement signals in order to further understand regional scale geodetic phenomena hence requiring further improvements in the sensitivity of the GPS solutions. This paper provides a review spanning over 25 years of advances in processing strategies, error mitigation methods and noise modeling for the processing and analysis of GPS daily position time series. The processing of the observations is described step-by-step and mainly with three different strategies in order to explain the weaknesses and strengths of the existing methodologies. In particular, we focus on the choice of the stochastic model in the GPS time series, which directly affects the estimation of the functional model including, for example, tectonic rates, seasonal signals and co-seismic offsets. Moreover, the geodetic community continues to develop computational methods to fully automatize all phases from analysis of GPS time series. This idea is greatly motivated by the large number of GPS receivers installed around the world for diverse applications ranging from surveying small deformations of civil engineering structures (e.g., subsidence of the highway bridge) to the detection of particular geophysical signals.
On 22nd May 2021 (local time), an earthquake of
M
s
7.4 struck Maduo county in Qinghai Province, China. This was the largest earthquake in China since the 2008 Wenchuan earthquake. In this study, ...ascending/descending Sentinel-1 and advanced land observation satellite-2 (ALOS-2) synthetic aperture radar (SAR) images were used to derive the three-dimensional (3-D) coseismic displacements of this earthquake. We used the differential interferometric SAR (InSAR, DInSAR), pixel offset-tracking (POT), multiple aperture InSAR (MAI), and burst overlap interferometry (BOI) methods to derive the displacement observations along the line-of-sight (LOS) and azimuth directions. To accurately mitigate the effect of ionospheric delay on the ALOS-2 DInSAR observations, a polynomial fitting method was proposed to optimize range-spectrum-split-derived ionospheric phases. In addition, the 3-D displacement field was obtained by a strain model and variance component estimation (SM-VCE) method based on the high-quality SAR displacement observations. Results indicated that a left-lateral fault slip with the largest horizontal displacement of up to 2.4 m dominated this earthquake, and the small-magnitude vertical displacement with an alternating uplift/subsidence pattern along the fault trace was more concentrated in the near-fault regions. Comparison with the global navigation satellite system data indicated that the SM-VCE method can significantly improve the accuracy of the displacements compared to the classical weighted least squares method, and the incorporation of the BOI displacements can substantially benefit the accuracy of north-south displacement. In addition to the displacements, three coseismic strain invariants calculated based on the strain model parameters were also investigated. It was found that the eastern and western parts of the faults suffered more significant strains compared with the epicenter region.
The BeiDou navigation satellite system (BDS) currently has 41 satellites in orbits and will reach its full constellation following the launch of the last BDS satellite in June 2020 to provide ...navigation, positioning, and timing (PNT) services for global users. In this contribution, we investigate the characteristics of inter-system bias (ISB) between BDS-2 and BDS-3 and verify whether an additional ISB parameter should be introduced for the BDS-2 and BDS-3 precise point positioning (PPP). The results reveal that because of different clock references applied for BDS-2 and BDS-3 in the International GNSS Service (IGS) precise satellites clock products and the inconsistent code hardware delays of BDS-2 and BDS-3 for some receiver types, an ISB parameter needs to be introduced for BDS-2 and BDS-3 PPP. Further, the results show that the ISB can be regarded as a constant within a day, the value of which is closely related to the receiver type. The ISB values of the stations with the same receiver type are similar to each other, but a great difference may be presented for different receiver types, up to several meters. In addition, the impact of ISB on PPP has also been studied, which demonstrates that the performance of kinematic PPP could be improved when ISB is introduced.
Abstract
Tropospheric hydrostatic delay is one of the major source of errors in Global Navigation Satellite System (GNSS) navigation and positioning, and an important parameter in GNSS meteorology. ...This work first proposes a new method of computing zenith hydrostatic delay (ZHD) based on precipitable water vapor (PWV), using radiosonde data. Next, using these calculations as a reference, the performance of three empirical ZHD models and three ZHD integral models in China is assessed using benchmark values obtained from 8 years (2010-2017) of radiosonde data recorded at 75 stations across China. Finally, we provide a new revised ZHD model that can be applied to China and validate its performance using radiosonde data collected in China in 2018. The statistical results indicate that the ZHD can be estimated by this new model with an accuracy of several millimeters. Due to its performance and simplicity, this new model is shown to be the optimal ZHD model for use in China.
Analyzing the features of extreme events and estimating their probabilities robustly require high spatial coverage, high temporal resolution, and sufficiently long storm surge (SS) records. However, ...in situ observations cannot always meet these demands due to spatiotemporal sparseness. Here, we proposed a novel regional all‐site modeling framework based on a machine learning method, the extreme gradient boosting tree. This framework can reconstruct long SS records simply and quickly and can estimate storm surges simultaneously at both gauged and ungauged locations. Compared to in situ observations, the distribution patterns of SS variations during extreme events can be recognized easily from the reconstructed hourly SS data set. Since its available record is longer than 60 years (1959–2020), the estimation uncertainties of extreme event probabilities are significantly decreased. Noticeably high extreme SS return levels were found along the coast of the northern Gulf of Mexico, which should be given great attention.
Plain Language Summary
High spatial‐coverage/temporal‐resolution and long storm surge records are crucial for coastal protection because they are the basis for analyzing the spatiotemporal characteristics of extreme sea‐level events and estimating their occurrence probabilities robustly. Due to spatiotemporal sparseness, tide gauge observations cannot always meet these demands. A novel regional all‐site modeling framework based on artificial intelligence technology can address this challenge. The novel framework allows the data‐driven model to reconstruct high‐precision and long‐term hourly storm surges at both gauged and ungauged locations, which can provide valuable information for disaster prevention in coastal areas.
Key Points
A novel regional all‐site storm surge (SS) modeling framework based on machine learning
Over 60‐year reconstructed hourly SS records with high spatial coverage
More clear spatiotemporal characteristics of extreme sea‐level events and lower estimation uncertainty of their probabilities
Analysis of Global Positioning System (GPS) position time series and its common mode components (CMC) is very important for the investigation of GPS technique error, the evaluation of environmental ...loading effects, and the estimation of a realistic and unbiased GPS velocity field for geodynamic applications. In this paper, we homogeneously processed the daily observations of 231 Crustal Movement Observation Network of China (CMONOC) Continuous GPS stations to obtain their position time series. Then, we filtered out the CMC and evaluated its effects on the periodic signals and noise for the CMONOC time series. Results show that, with CMC filtering, peaks in the stacked power spectra can be reduced at draconitic harmonics up to the 14th, supporting the point that the draconitic signal is spatially correlated. With the colored noise suppressed by CMC filtering, the velocity uncertainty estimates for both of the two subnetworks, CMONOC-I (≈16.5 years) and CMONOC-II (≈4.6 years), are reduced significantly. However, the CMONOC-II stations obtain greater reduction ratios in velocity uncertainty estimates with average values of 33%, 38%, and 54% for the north, east, and up components. These results indicate that CMC filtering can suppress the colored noise amplitudes and improve the precision of velocity estimates. Therefore, a unified, realistic, and three-dimensional CMONOC GPS velocity field estimated with the consideration of colored noise is given. Furthermore, contributions of environmental loading to the vertical CMC are also investigated and discussed. We find that the vertical CMC are reduced at 224 of the 231 CMONOC stations and 170 of them are with a root mean square (RMS) reduction ratio of CMC larger than 10%, confirming that environmental loading is one of the sources of CMC for the CMONOC height time series.
The Global Positioning System (GPS) records monsoonal precipitable water vapor (PWV) and vertical crustal displacement (VCD) due to hydrological loading, and can thus be applied jointly to diagnose ...meteorological and hydrological droughts. We have analyzed the PWV and VCD observations during 2007.0-2015.0 at 26 continuous GPS stations located in Yunnan province, China. We also obtained equivalent water height (EWH) derived from the Gravity Recovery And Climate Experiment (GRACE) and precipitation at these stations with the same period. Then, we quantified the annual variations of PWV, precipitation, EWH and VCD and provided empirical relationships between them. We found that GPS-derived PWV and VCD (positive means downward movement) are in phase with precipitation and GRACE-derived EWH, respectively. The annual signals of VCD and PWV show linearly correlated amplitudes and a two-month phase lag. Furthermore, the results indicate that PWV and VCD anomalies can also be used to explore drought, such as the heavy drought during winter/spring 2010. Our analysis results verify the capability of GPS to monitor monsoon variations and drought in Yunnan and show that a more comprehensive understanding of the characteristics of regional monsoon and drought can be achieved by integrating GPS-derived PWV and VCD with precipitation and GRACE-derived EWH.
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