Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

Climatological Features of the Global Tropical Subsidence Region Based on Satellite Observations

Consisting of subtropical highs and tropical buffer zone, the global tropical subsidence region is the subsidence branches of Hadley cell. Walker circulation and monsoon circulation which are important components of the global general circulation. This region is closely connected with Asian monsoon. Based on long-term satellite observations of OLR (Outgoing Longwave Radiation) and HIRS-Tbl2 (the bright temperature from High-resolution Infra-red Radiation Sounder Channel 12 (6.7 Μm)), the climatological features over the global tropical subsidence region are studied in this paper and the main findings are as follows: Key words Satellite observation - OLR - HIRS-Tb12 - Tropical subsidence region - Monsoon This project was supported by the State Key Project of the SCSME and the National NSF No. 49875016.

Improving the Analyses and Forecasts of a Tropical Squall Line Using Upper Tropospheric Infrared Satellite Observations

The advent of modern geostationary satellite infrared radiance observations has noticeably improved numerical weather forecasts and analyses.However,compared to midlatitude weather systems and tropical cyclones,research into using infrared radiance observations for numerically predicting and analyzing tropical mesoscale convective systems remain mostly fallow.Since tropical mesoscale convective systems play a crucial role in regional and global weather,this deficit should be addressed.This study is the first of its kind to examine the potential impacts of assimilating all-sky upper tropospheric infrared radiance observations on the prediction of a tropical squall line.Even though these all-sky infrared radiance observations are not directly affected by lower-tropospheric winds,the high-frequency assimilation of these all-sky infrared radiance observations improved the analyses of the tropical squall line’s outflow position.Aside from that,the assimilation of all-sky infrared radiance observations improved the analyses and prediction of the squall line’s cloud field.Finally,reducing the frequency of assimilating these all-sky infrared radiance observations weakened these improvements to the analyzed outflow position,as well as the analyses and predictions of cloud fields.

Comments on "Direct Radiative Forcing of Anthropogenic Aerosols over Oceans from Satellite Observation"

Previous observational studies have estimated anthropogenic aerosol direct radiative forcing over oceans without due consideration of cloudy-sky aerosols. However, when interaction between clouds and aerosols located below or above the cloud level is taken into account, the aerosol direct radiative forcing is larger by as much as 5 W m-2 in most mid-latitude regions in the Northern Hemisphere.

Recent Progress of Earth Observation Satellites in China

Currently,China has 32 Earth observation satellites in orbit.The satellites can provide various data such as optical,multispectral,infrared,and radar.The spatial resolution of China Earth observation satellites ranges from low to medium to high.The satellites possess the capability to observe across multiple spectral bands,under all weather conditions,and at all times.The data of China Earth observation satellites has been widely used in fields such as natural resource detection,environmental monitoring and protection,disaster prevention and reduction,urban planning and mapping,agricultural and forestry surveys,land survey and geological prospecting,and ocean forecasting,achieving huge social benefits.This article introduces the recent progress of Earth observation satellites in China since 2022,especially the satellite operation,data archiving,data distribution and data coverage.

Global land 1° mapping dataset of XCO_(2) from satellite observations of GOSAT and OCO-2 from 2009 to 2020

A global mapping data of atmospheric carbon dioxide(CO_(2))concen-trations can help us to better understand the spatiotemporal varia-tions of CO_(2) and the driving factors of the variations to support the actions for emissions reduction and control.Greenhouse gases satel-lites that measure atmospheric CO_(2),such as the Greenhouse Gases Observing Satellite(GOSAT)and Orbiting Carbon Observatory(OCO-2),have been providing global observations of the column averaged dry-air mole fractions of CO_(2)(XCO_(2))since 2009.However,these XCO_(2) retrievals are irregular in space and time with many gaps.In this paper,we mapped a global spatiotemporally continuous XCO_(2) data-set(Mapping-XCO_(2))using the XCO_(2) retrievals from GOSAT and OCO-2 during the period from April 2009 to December 2020 based on a geostatistical approach that fills those data gaps.The dataset covers a geographic range from 56°S to 65°N and 169°W to 180°E for a 1°grid interval in space and 3-day time interval.The uncer-tainties of the mapped XCO_(2) values are generally less than 1.5 parts per million(ppm).The spatiotemporal characteristics of global XCO_(2) that are revealed by the Mapping-XCO_(2) are similar to the model data obtained from CarbonTracker.Compared to the ground observa-tions,the overall standard bias is 1.13 ppm.The results indicate that this long-term Mapping-XCO_(2) dataset can be used to investigate the spatiotemporal variations of global atmospheric XCO_(2) and can support studies related to the carbon cycle and anthropogenic CO_(2) emissions.The dataset is available at http://www.doi.org/10.7910/DVN/4WDTD8 and https://www.scidb.cn/en/detail?dataSetId=c2c3111b421043fc8d9b163c39e6f56e.

Three-dimensional thermohaline structure estimation derived from HY-2 satellite data over the Maritime Silk Road and its applications

Estimated ocean subsurface fields derived from satellite observations provide potential data sources for operational marine environmental monitoring and prediction systems.This study employs a statistic regression reconstruction method,in combination with domestic autonomous sea surface height and sea surface temperature observations from the Haiyang-2(HY-2)satellite fusion data,to establish an operational quasi-realtime three-dimensional(3D)temperature and salinity products over the Maritime Silk Road.These products feature a daily temporal resolution and a spatial resolution of 0.25°×0.25°and exhibit stability and continuity.We have demonstrated the accuracy of the reconstructed thermohaline fields in capturing the 3D thermohaline variations through comprehensive statistical evaluations,after comparing them against Argo observations and ocean analysis data from 2022.The results illustrate that the reconstructed fields effectively represent seasonal variations in oceanic subsurface structures,along with structural changes resulting from mesoscale processes,and the upper ocean’s responses to tropical cyclones.Furthermore,the incorporation of HY-2 satellite observations notably enhances the accuracy of temperature and salinity reconstructions in the Northwest Pacific Ocean and marginally improves salinity reconstruction accuracy in the North Indian Ocean when compared to the World Ocean Atlas 2018 monthly climatology thermohaline fields.As a result,the reconstructed product holds promise for providing quasi-real-time 3D temperature and salinity field information to facilitate fast decisionmaking during emergencies,and also offers foundational thermohaline fields for operational ocean reanalysis and forecasting systems.These contributions enhance the safety and stability of ocean subsurface activities and navigation.

Revisiting the seasonal wave height variability in the South China Sea with merged satellite altimetry observations

The seasonal variability of the significant wave height（SWH） in the South China Sea（SCS） is investigated using the most up-to-date gridded daily altimeter data for the period of September 2009 to August 2015. The results indicate that the SWH shows a uniform seasonal variation in the whole SCS, with its maxima occurring in December/January and minima in May. Throughout the year, the SWH in the SCS is the largest around Luzon Strait（LS） and then gradually decreases southward across the basin. The surface wind speed has a similar seasonal variation, but with different spatial distributions in most months of the year. Further analysis indicates that the observed SWH variations are dominated by swell. The wind sea height, however, is much smaller. It is the the largest in two regions southwest of Taiwan Island and southeast of Vietnam Coast during the northeasterly monsoon, while the largest in the central/southern SCS during the southwesterly monsoon. The extreme wave condition also experiences a significant seasonal variation. In most regions of the northern and central SCS, the maxima of the 99 th percentile SWH that are larger than the SWH theoretically calculated with the wind speed for the fully developed seas mainly appear in August–November, closely related to strong tropical cyclone activities.Compared with previous studies, it is also implied that the wave climate in the Pacific Ocean plays an important role in the wave climate variations in the SCS.

Spring snow-albedo feedback from satellite observation,reanalysis and model simulations over the Northern Hemisphere

The snow-albedo feedback(SAF)is one of the key processes that lead to rapid temperature increases at high latitude and altitude regions.Here,we examine the strength of SAF in the Northern Hemisphere(NH,30°-90°N)from satellite-based observation,climate reanalysis(i.e.,ERA5)and climate model projections(i.e.,CMIP5 and CMIP6).Using satellite-derived estimates as an observational reference,our results show that the strength of SAF is considerably overestimated by both climate reanalysis and climate models throughout the NH.This positive bias is mainly caused by a much higher month-to-month changing rate of surface albedo simulated by climate reanalysis and climate models,compared with satellite-based observation.In addition,we find that the strength of SAF is primarily controlled by changes in snow cover fraction(SNC)while the snow metamorphosis effect only plays a secondary role at the hemispheric scale and the regional scales.Finally,we demonstrate that there exists an emergent relationship between historical SAF and future SAF in climate model projections,which is then used to constrain modeled SAF in both CMIP5 and CMIP6 under various climate change scenarios.After constraining,the magnitude of modeled SAF is reduced to be more consistent with satellite-based estimates and the uncertainties of the modeled SAF are reduced by 10-50%for 13 sub-regions of the NH.Our results highlight the need for improving the representation of surface albedo and snow cover fraction in climate reanalysis and models and the advantage of combining observation and model predictions such that to achieve a more realistic quantification of SAF and a better understanding of the impacts of SAF on the climate system.

Development of Integrated and Intelligent Geodetic and Photogrammetry Satellites with Corresponding Key Technologies

Aerospace surveying and mapping has become the main method of global earth observation.It can be divided into the geodetic observation satellites and the topographic surveying satellites according to the disciplines.In this paper,the geodetic satellites and photographic satellites are introduced respectively.Then,the existing problems in Chinese earth observation satellites are analyzed,and the comprehensive satellite with integrated payloads,the intensive microsatellite constellation and the intelligent observation satellite are proposed as three different development ideas for the future earth observation satellites.The possibility of the three ideas is discussed in detail,as well as the related key technologies.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Comparison of Satellite and Ship Observations for Total Cloud Amount

The data series of monthly clouldiness over global ocean from COADS was compared with that of from satellite Nimbus-7 during April 1979 to March 1985. The correspondence between them is good. Both the two methods of observation can provide useful information of the distribution of cloudiness and the two data sets can be mutually complementary.

Ocean Observation from Haiyang Satellites

In 2018,China successfully launched three new Haiyang(which means ocean in Chinese,referred to as HY)satellites which are an ocean color observation satellite HY-1C(operational),an ocean dynamics environment satellite HY-2B(operational)and the China-France ocean satellite CFOSAT(experimental).In 2019,all the three satellites had finished their commissioning phases and were declared operational.HY-2A satellite continues to operate in-orbit,and its operational status is basically normal.So in 2020,China has 4 Haiyang satellites in-orbit,China’s ocean satellites enter into a new operational application phase.The operation of the ground application system of Chinese ocean satellites is stable.In 2019,Beijing,Hainan,Mudanjiang,and Hangzhou ocean satellite ground stations had received the data of HY-1C,HY-2A,HY-2B,and CFOSAT 5012 orbits and 26.46 TB data had been distributed to both domestic and international users.Chinese ocean satellite data has played an important role in marine disaster prevention and mitigation,development and management of marine resources,maintenance of marine rights and interests,marine environment protection,scientific researches,and blue economy development.

Dynamical and Chemical Features of a Cutoff Low over Northeast China in July 2007:Results from Satellite Measurements and Reanalysis

The European Centre for Medium-Range Weather orology and measurements from the Microwave Limb Forecasts Re-Analysis Interim （ERA-Interim） meteSounder, High Resolution Dynamics Limb Sounder, and Ozone Monitoring Instrument onboard the Earth Observing System Aura satellite were applied to analyze the dynamical and chemical features of a cutoff low （COL） event over northeast China in early July 2007. The results showed the polar stratospheric origin of an upper-level warm-core cyclone at 100 300 hPa, associated with a funnel-shaped tropopause intruding into the mid-troposphere just above the COL center. The impacts of the stratospheric intrusion on both column ozone and ozone profiles were investigated using satellite measurements. When the intensity of the COL peaked on 10 July 2007, the total column ozone （TCO） increase reached a maximum （40-70 DU）. This could be dynamically attributed to both the descent of the tropopause （~75%） and the downward transport of stratospheric ozone across the tropopause （~25%）. Analysis of the tropospheric ozone profiles provided evidence for irreversible transport/mixing of ozone-rich stratospheric air across the tropopause near the upper-level front region ahead of the COL center. This ozone intrusion underwent downstream transport by the upper tropospheric winds, leading to further increase in TCO by 12 16 DU over broad regions extending from east China toward the northern Japan Sea via South Korea. Meteorological analysis also showed the precedence of the stratospheric intrusion ahead of the development of cyclones in the middle and lower troposphere.

Arctic multiyear sea ice variability observed from satellites:a review

In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY)sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.)have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.

Chinese GF-1 and GF-3 satellites observed the giant iceberg calving off the Larsen C Ice Shelf

A massive iceberg, named A-68 by National Ice Center （NIC） officially, calved away from the Larsen C Ice Shelf in Antarctica on luly 12, 2017. The iceberg A-68 is about 5 800 km2, weighs more than a trillion tons and it is one of the biggest ever recorded icebergs. Chinese satellites Gaofen-1 （GF-1） and Gaofen-3 （GF-3） data was used to monitoring the propagation of the rift and the iceberg by National Satellite Ocean Application Service （NSOAS）.

Distributed cooperative task planning algorithm for multiple satellites in delayed communication environment

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.

Advanced land observing satellite data to identify ground vegetation in a juniper forest,northeast Iran

Juniperus excelsa subsp.polycarpos,(Persian juniper),is found in northeast Iran.In this study,the relationship between ground cover and vegetation indices have been investigated using remote sensing data for a Persian juniper forest.Multispectral data were analyzed based on the Advanced Visible and Near Infrared Radiometer type 2 and panchromatic data obtained by the Panchromatic Remote-sensing Instrument for Stereo Mapping sensors,both on board the advanced land observing satellite(ALOS).The ground cover was calculated using field survey data from 25 sub-sample plots and the vegetation indices were derived with 595 maximum filtering algorithm from ALOS data.R2 values were calculated for the normalized difference vegetation index(NDVI)and various soil-adjusted vegetation indices(SAVI)with soilbrightness-dependent correction factors equal to 1 and 0.5,a modified SAVI(MSAVI)and an optimized SAVI(OSAVI).R2 values for the NDVI,MSAVI,OSAVI,SAVI(1),and SAVI(0.5)were 0.566,0.545,0.619,0.603,and 0.607,respectively.Total ratio vegetation index for arid and semi-arid regions based on spectral wavelengths of ALOS data with an R2 value 0.633 was considered.Results of the current study will be useful for forest inventories in arid and semi-arid regions in addition to assisting decisionmaking for natural resource managers.

Preliminary results of CCD observations targeting Himalia acquired at Yunnan Observatories in 2015

In order to study the potential associated with high precision CCD astrometry of irregular satel- lites, we have acquired experimental observations of Himalia, the sixth and irregular satellite of Jupiter. A total of 185 CCD observations were obtained by using the 2.4m and 1 m telescopes administered by Yunnan Observatories over ten nights. Preliminary analysis of the observations were made, including geo- metric distortion, atmospheric refraction, and also the phase effect. All positions of Himalia are measured relative to the reference stars from the catalog UCAC4 in each CCD field of view. The theoretical positions of Himalia were retrieved from the Institute de M6chanique C61este et de Calcul des t^ph6m6rides, while the positions of Jupiter were obtained based on the planetary ephemeris INPOP13c. The results show that the means of observed minus computed （O - C） residuals are -0.004＂ and -0.002＂ in right ascension and declination, respectively. The standard deviations of （O - C） residuals are estimated to be about 0.04＂ in each direction.

On the turbulent heat fluxes:A comparison among satellite-based estimates,atmospheric reanalyses,and in-situ observations during the winter climate over Arctic sea ice

The surface energy budget is crucial for Arctic sea ice mass balance calculation and climate systems,among which turbulent heat fluxes significantly affect the airesea exchanges of heat and moisture in the atmospheric boundary layer.Satellite observations(e.g.CERES and APPX)and atmospheric reanalyses(e.g.,ERA5)are often used to represent components of the energy budget at regional and pan-Arctic scales.However,the uncertainties of the satellite-based turbulent heat fluxes are largely unknown,and cross-comparisons with reanalysis data and insitu observations are limited.In this study,satellite-based turbulent heat fluxes were assessed against in-situ observations from the N-ICE2015 drifting ice station(north of Svalbard,JanuaryeJune 2015)and ERA5 reanalysis.The turbulent heat fluxes were calculated by two approaches using the satellite-based ice surface temperature and radiative fluxes,surface atmospheric parameters from ERA5,and snow/sea ice thickness from the pan-Arctic Ice Ocean Modeling and Assimilation System(PIOMAS).We found that the bulk-aerodynamic formula based results could better capture the variations of turbulent heat fluxes,while the maximum entropy production based estimates are comparable with ERA5 in terms of root-mean-square error(RMSE).CERES-based estimates outperform the APP-X-based ones but ERA5 performs the best in all seasons(RMSE of 18 and 7 W m^(-2)for sensible and latent heat flux,respectively).The aireice temperature/humidity differences and the surface radiation budget were found the primary driving factors in the bulk-formula method and maximum entropy production(MEP)method,respectively.Furthermore,errors in the surface and near-surface temperature and humidity explain almost 50%of the uncertainties in the estimates based on the bulk-formula,whereas errors in the net radiative fluxes explain more than 50%of the uncertainties in the MEP-based results.