Validation of the multi-satellite merged sea surface salinity in the South China Sea

Sea surface salinity(SSS)is an essential variable of ocean dynamics and climate research.The Soil Moisture and Ocean Salinity(SMOS),Aquarius,and Soil Moisture Active Passive(SMAP)satellite missions all provide SSS measurements.The European Space Agency(ESA)Climate Change Initiative Sea Surface Salinity(CCI-SSS)project merged these three satellite SSS data to produce CCI L4SSS products.We validated the accuracy of the four satellite products(CCI,SMOS,Aquarius,and SMAP)using in-situ gridded data and Argo floats in the South China Sea(SCS).Compared with in-situ gridded data,it shows that the CCI achieved the best performance(RMSD:0.365)on monthly time scales.The RMSD of SMOS,Aquarius,and SMAP(SMOS:0.389;Aquarius:0.409;SMAP:0.391)are close,and the SMOS takes a slight advantage in contrast with Aquarius and SMAP.Large discrepancies can be found near the coastline and in the shelf seas.Meanwhile,CCI with lower RMSD(0.295)perform better than single satellite data(SMOS:0.517;SMAP:0.297)on weekly time scales compared with Argo floats.Overall,the merged CCI have the smallest RMSD among the four satellite products in the SCS on both weekly time scales and monthly time scales,which illustrates the improved accuracy of merged CCI compared with the individual satellite data.

Multi-Satellite and Sensor Derived Trends and Variation of Snow Water Equivalent on the High-Latitudes of the Northern Hemisphere

Utilizing more than 30 years of satellite-microwave sensor derived snow water equivalent data on the high-latitudes of the northern hemisphere we investigate regional trends and variations relative to elevation. On the low-elevation tundra regions encircling the Arctic we find high statistically significant trends of snow water equivalent. Across the high Arctic Siberia and Far East Russia through North America and northern Greenland we find increasing trends of snow water equivalent with local region variations in strength. Yet across the high Arctic of western Russia through Norway we find decreasing trends of snow water equivalent of varying strength. Power density spectra identify significant power at quasi-biennial and associated lunar nodal cycles. These cycles of the upper atmosphere circulation, ENSO and ocean circulation perturbations from tides forms the causative linkage between increasing snow water equivalent on low-elevation tundra landscapes and decreasing coastal sea ice cover as part of the Arctic system energy and mass cycles.

Simulation of Multi-satellite GNSS Reflected Signals and Design of Simulator

Using GNSS-R technology for remote sensing of surface parameters has become a new trend in the field of remote sensing.With the rapid development of GNSS-R technology,GNSS-R simulation has become one of the new hot spots.Now the researches of the GNSS-R simulation are all the simulations that consider a single star or a single frequency point,and in actual applications,the signal captured by the receiver is often the reflected signals of multiple stars.In view of this situation,from the perspective of multi-satellite simulation,this paper gives the model of GNSS-R multi-satellite ocean simulation on the basis of analyzing the remote sensing principle,reflection signal model and simulation principle of GNSS-R technology.Based on the GNSS-R multi-satellite ocean simulation model and the fast parallel computing capability of GPU,the GNSS-R multi-satellite ocean simulator was designed.Finally,the direct and reflected signals generated by the GNSS-R multi-satellite simulator were tested and verified.The results show that the positioning result of the direct signal meets the positioning accuracy requirements;The delay-related power results obtained from the simulated two-satellite reflected signals processing are in good agreement with the theoretical model,and the correlation coefficients are all above 0.99;The generated signals are used for GNSS-R height measurement technology,the height measurement error is about 1.4~1.8 m,which is in line with the accuracy of the C/A code ranging receiver;And the parallel operation of the GPU for multi-satellite simulation calculation is 800—900 times higher than the traditional CPU calculation.It proves that the proposed model and the designed simulator are feasible and accurate.

Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Feasible Multiple Satellite Mission Scenarios Flying in a Constellation for Refinement of the Gravity Field Recovery

Improving the gravity field recovery in terms of error levels and more isotropic noise distribution by adding cross-track and radial information to the satellite observables has been investigated through a number of studies by a variety of satellite constellations, i.e. satellite pairs that orbit the Earth in alternative configurations than the current GRACE (Gravity Recovery And Climate Experiment) gravity mission. This contribution gives for the first time a comparative study considering the recovery of the global gravity field from three constellations flying in satellite pairs in different directions (i.e. along-track, cross-track and radial). The three constellations include: 1) Foursatellite Bender configuration (flying in two pairs) of type along-track observations, 2) Three-satellite GRAPEN (combined GRACE with Pendulum formations) configuration of type cross-alongtrack observations, 3) Four-satellite Cartwheel configuration (flying in two pairs) of type radialalong-track observations. Additionally, a GRACE mission scenario is added as a reference “comparative” mission. The orbits of all satellites are considered to fly with drag-free system, however, realistic white noise has been added to the simulated observations to mimic the error associated with the drag-free measurement. The results are analyzed in the spectral wavelength spectrum of the gravity field up to a spherical harmonics degree of n = 100 and are plotted spatially on earth maps. The results show that the Three-satellite GRAPEN constellation provides, besides its low economically launches, an improved gravity field solution with respect to the Four-satellite Bender and the Four-satellite Cartwheel constellations.

Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Continuous high spatial-resolution 10-day precipitation data are essential for crop growth services and phenological research.In this study,we first use the bidimensional empirical mode decomposition(BEMD)algorithm to decompose the digital elevation model(DEM)data and obtain high-frequency(OR3),intermediate-frequency(OR5),and low-frequency(OR8)margin terrains.Then,we propose a refined precipitation spatialization model,which uses ground-based meteorological observation data,integrated multi-satellite retrievals for global precipitation measurement(GPM IMERG)satellite precipitation products,DEM data,terrain decomposition data,prevailing precipitation direction(PPD)data,and other multisource data,to construct China's high-resolution 10-day precipitation data from2001 to 2018.The decomposition results show mountainous terrain from fine to coarse scales;and the influences of altitude,slope,and aspect on precipitation are better represented in the model after topography is decomposed.Moreover,terrain decomposition data can be added to the model simulation to improve the quality of the simulation product;the simulation quality of the model in summer is better than that in spring and autumn,and is relatively poor in winter;and OR5 and OR8 can be improved in the simulation,with better OR5 and OR8 dynamically selected.In addition,preprocessing the data before precipitation spatialization is particularly important.For example,adding 0.01to the 0 value of precipitation,multiplying the small value of precipitation less than 1 by 10,and performing the normal distributions transform(e.g.,Yeo–Johnson)on the data can improve the simulation quality.

Satellite group autonomous operation mechanism and planning algorithm for marine target surveillance

In terms of fast response problem of unanticipated marine target, it is necessary to design the satellite-ground-combined operation mechanism and planning algorithm for autonomous task planning. Firstly, based on the autonomous operation and task planning of remote sensing satellite group, it is divided into two parts: ground planning and satellite autonomous planning. Secondly,the satellite-ground-combined operation mechanism and operation flow for task planning are proposed after fully considering the resource characteristics and task demand characteristics of the ground and satellite. The satellite autonomous task planning algorithm based on extended contract net is designed. Through the simulation operation of the self-developed distributed simulation demonstration software, it shows that the operation mechanism can coordinate and cooperate effectively between the satellite autonomous task planning and ground planning. It can give full play to the advantages of the ground computing resources, reflect the control intention, make full use of the real-time feature and flexibility of the satellite calculation, and respond fast to the unanticipated task. Besides, it has solved problems of the untimely response of ground control on unanticipated observation task, the limitation of satellite computing resources and satellite-ground planning and coordination, which can effectively improve the responsiveness of remote sensing satellite to the observation task of maritime unanticipated target.

MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP) impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address such a challenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM) for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integrated Multi-satellite Retrievals for global precipitation measurement(IMERG) data,offering high spatiotemporal resolution precipitation forecasts for upcoming periods ranging from 30 to 300 min.The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time.This issue is a common challenge in precipitation forecasting models.Furthermore,we have used multisource spatiotemporal datasets with integrated geographic elements for training and prediction to improve model accuracy.The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecasting in the TP region.The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success index,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33%,19%,and 8% compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet) models.