Application of satellite observations to study the changes of hypoxic conditions in near-bottom water in the western part of Peter the Great Bay (the Sea of Japan)

In this paper we explore the possibilities of applying satellite ocean colour (OC) observations and SST to study the changes in the conditions of hypoxia in the near-bottom water in the western part of Peter the Great Bay. Near-bottom water hypoxia occurs in water bodies with increased organic matter influx when the dissolved oxygen (DO) consumed at its oxidation is not restored. Consumption of most DO is usually attributed to the oxidation of organic matter formed as a result of increased algae growth during water eutrophication. Satellite data on indicators of phytoplankton (chlorophyll-a concentration (Chl) and fluorescence (FLH)) allow to analyze the spatial-temporal changes of this substation. Coloured dissolved organic matter (CDOM), non-algal particles (NAP) influence on satellite Chl estimates and also on near-bottom water hypoxia formation. This study analyzes daily, seasonal, and inter-annual changes in the distributions of indicators (Chl, FLH, the coefficients of light absorption by coloured detrital matter (a_CDM) and light backscattering by suspended particles (b_bp)), based on the instant satellite OC data from MODIS-Aqua. Data on the Chl, the sea surface temperature (SST) from the MODIS-Aqua, the precipitation from the TRMM satellite and the hydrometeorological stations (HMSs), the wind speed and direction from HMS “Vladivostok” are used to study the influence of hydrometeorological conditions on the Chl values. These distributions were compared with the literary information based on field observations of the hypoxia cases in the same area and with the changes in the vertical DO, Chl, temperature, salinity distribution obtained by coastal expeditions in October-November 2010 and February-March 2011. Significant interrelations within 95% confidence level between the satellite Chl, FLH values calculated at the MUMM atmospheric correction and in situ Chl values obtained in the autumn of 2010 were reached separately for the cases with winds of northern and southern directions with the correlation coefficients of 0.71, 0.48 and 0.49, 0.71, respectively. Significant dependences of Chl on SST and Chl on wind speed explained by the influence of continental runoff and water ventilation were obtained. Therefore, the changes of Chl reflect the changes of hypoxic conditions in the near-bottom water. In Amursky Bay the onset of hypoxia was at the Chl and SST values equal to 4 mg m^?3 and 13 °C (↑ – at increasing SST); near Furugelm Island it was at 1.6 mg m^?3 and 25 °C (↑), 1 mg m^?3 and 21 °C (↓). The difference in the Chl values was reflected in the hypoxia onset timings that were the beginning of June (2011), August (2013), and September (2014), respectively. The water flow from the eastern coast of Amursky Bay in early August of 2013 recorded from the OC and SST satellite imagers appeared in an additional hypoxic zone. Decreased OC characteristics in the runoff of the Razdolnaya River in August-September of 2014 were a sign of hypoxia at its mouth. Near Furugelm Island the hypoxia destruction (increase in the DO level from 1 to 4.5 ml L^?1) was observed at the Chl of 0.9 mg m^?3 and SST = 18 °C (↓). At the autumn maximum of Chl equal to 1.7 mg m^?3 and SST = 4 °C (↓) in mid-November the DO level here increased to 8 ml L^?1. In Amursky Bay, short-term destructions/weakening of hypoxia manifested themselves in sharp increases of Chl. At that, the ratio between the Chl value and the approximation level was equal to 2 and higher for SST equal to 22–25 °C (↑), to 0.9 and higher for SST equal to 5–13 °C (↓). With the water stratification destruction in temperature and the noticeable weakening of the stratification in salinity (mid-November), the hypoxia destructed (the DO level increased from 2 to 6 ml L^?1). In this case, Chl and SST were about 3 mg m^?3 and 5 °C (↓).     

A novel method for destriping of OCM-2 data and radiometric performance analysis for improved ocean color data products

Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products.     

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.     

Quantifying barystatic sea-level change from satellite altimetry,GRACE and Argo observations over 2005–2016

Time-varying spherical harmonic coefficients determined from the Gravity Recovery and Climate Experiment (GRACE) data provide a valuable source of information about the water mass exchange that is the main contributor to the Earth’s gravity field changes within a period of less than several hundred years. Moreover, by measuring seawater temperature and salinity at different layers of ocean depth, Argo floats help to measure the steric component of global mean sea level (GMSL). In this study, we quantify the rate of barystatic sea-level change using both GRACE RL05 and RL06 monthly gravity field models and compare the results with estimates achieved from a GMSL budget closure approach. Our satellite altimetry-based results show a trend of 3.90 ± 0.14 mm yr⁻¹ for the GMSL rise. About 35% or 1.29 ± 0.07 mm yr⁻¹ of this rate is caused by the thermosteric contribution, while the remainder is mainly due to the barystatic contribution. Our results confirm that the choice of decorrelation filters does not play a significant role in quantifying the global barystatic sea-level change, and spatial filtering may not be needed. GRACE RL05 and RL06 solutions result in the barystatic sea-level change trends of 2.19 ± 0.13 mm yr⁻¹ and 2.25 ± 0.16 mm yr⁻¹, respectively. Accordingly, the residual trend, defined as the difference between the altimetry-derived GMSL and sum of the steric and barystatic components, amounts to 0.51 ± 0.51 and 0.45 ± 0.44 mm yr⁻¹ for RL05 and RL06-based barystatic sea-level changes, respectively, over January 2005 to December 2016. The exclusion of the halosteric component results in a lower residual trend of about 0.36 ± 0.46 mm yr⁻¹ over the same period, which suggests a sea-level budget closed within the uncertainty. This could be a confirmation on a high level of salinity bias particularly after about 2015. Moreover, considering the assumption that the GRACE-based barystatic component includes all mass change signals, the rather large residual trend could be attributed to an additional contribution from the deep ocean, where salinity and temperature cannot be monitored by the current observing systems. The errors from various sources, including the model-based Glacial Isostatic Adjustment signal, independent estimation of geocenter motion that are not quantified in the GRACE solutions, as well as the uncertainty of the second degree of zonal spherical harmonic coefficients, are other possible contributors to the residual trend.     

Seasonal-to-interannual variability of chlorophyll-a bloom timing associated with physical forcing in the Gulf of Cádiz

Seasonal-to-interannual variability of the winter-spring bloom in the Gulf of Cádiz, eastern North Atlantic, has been investigated using chlorophyll-a remote sensing (CHL). These data have been obtained from the GlobColour project; the temporal coverage extends from September 1997 to December 2010. In this study we develop a generic quantitative approach for describing the temporal variability in the shape of the winter-spring bloom within a region. Variability in both the timing and magnitude of the bloom in the basin has been evaluated as a function of physical properties in the water column such as Mixed Layer Depth (MLD, GODAS model), sea surface temperature (SST, from AVHRR radiometers), photosynthetically-active radiation (PAR, from ocean color data) and euphotic depth (Z_eu, from ocean color data). The analysis indicated that the timing, size and duration of the phytoplankton bloom in this area are largely controlled by both meteorological and oceanographic conditions at different scales; this means that it is likely to vary widely from one year to another.     

Comparative evaluation of five global gravity models over a part of the Bay of Bengal

Several global gravity models (GGMs) are freely available in the public domain, which can be utilised to study the earth's gravity field in almost every part of the globe. The present study compared the free-air gravity anomalies calculated from the five GGMs EGM2008, EIGEN6C4, GECO, XGM2019e_2159, and SGG-UGM-2 archived by the International Centre for Global Earth Models (ICGEM) with respect to shipborne gravity in the Bay of Bengal. The average correlation coefficient and covariance are ～ 96 % and ～ 450mGal². The mean difference between the shipborne and the modelled gravity is ? 5 mGal. Relatively higher amplitude gravity differences observed at the continental-oceanic transition, the 85°E and Ninetyeast ridges, and the western basin are possibly due to high gradient, dominant density contrasts, and rugged topography. The average standard deviation and root-mean-square-error (RMSE) of the differences are ～ 6.5 mGal and ～ 7.5 mGal. A significantly lower standard deviation and RMSE found for the models generated at higher degree/order compared to lower degree/order is due to diminishing omission error of the GGMs with increasing degrees of truncation. The spectral analysis and coherence estimation of the modelled gravity demonstrate excellent correspondence for anomalies wider than ～ 25 km. The agreement between anomaly amplitudes and shapes and calculated statistics indicates that the accuracy and resolution of the modelled gravity data are certainly good enough for regional-scale studies aiming to map Moho topography and mantle structures. However, the delineation of shorter wavelength features originating from the shallow-level basement/sedimentary might be uncertain and requires further validations. The present study confirms that all five models show promising results in terms of their accuracy and resolution. Moreover, the SGG-UGM-2 and XGM2019e_2159 models compare favourably with respect to the GECO, EIGEN6C4 and EGM2008 models in the Bay of Bengal.     

Assessing the relationship between solar activity and some large scale climatic phenomena

The nature of the climatic response to solar variability is assessed over a long-time scale. The wavelet analysis applied to paleoclimatic proxy data of large scale atmospheric phenomena (North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and Southern Oscillation Index) has revealed coherence between the climatic oscillations and the solar phenomena (the cosmogenic isotope ¹⁰Be and the Total Solar Irradiance) preferentially with periods of Schwabe, Hale and Yoshimura–Gleissberg cycles that may reflect a modulation of solar activity.     

Satellite evidence of harmful algal blooms and related oceanographic features in the Bohai Sea during autumn 1998

Harmful algal blooms (HABs) are truly global marine phenomena of increasing significance. Some HAB occurrences are different to observe because of their high spatial and temporal variability and their advection, once formed, by surface currents. A serious HAB occurred in the Bohai Sea during autumn 1998, causing the largest fisheries economic loss. The present study analyzes the formation, distribution, and advection of HAB using satellite SeaWiFS ocean color data and other oceanographic data. The results show that the bloom originated in the western coastal waters of the Bohai Sea in early September, and developed southeastward when sea surface temperature (SST) increased to 25–26 °C. The bloom with a high Chl-a concentration (6.5 mg m⁻³) in center portion covered an area of 60 × 65 km². At the end of September, the bloom decayed when SST decreased to 22–23 °C. The HAB may have been initiated by a combination of the river discharge nutrients in the west coastal waters and the increase of SST; afterwards it may have been transported eastward by the local circulation that was enhanced by northwesterly winds in late September and early October.     

Aerosol measurements from earth orbiting spacecraft

Since the fall of 1978, two Earth-orbiting spacecraft sensors, SAM II, for Stratospheric Aerosol Measurement II, and SAGE, for Stratospheric Aerosol and Gas Experiment have been monitoring the global stratospheric aerosol. These experiments use the Sun as a source to make Earth-limb extinction measurements during each spacecraft sunrise and sunset. This paper describes the global aerosol data base (climatology) that is evolving. Seasonal and hemispheric variations such as the springtime layer expansion with warming temperatures and the local wintertime polar stratospheric clouds (PSC's) will be described. The PSC's enhance extinction by up to two orders of magnitude and optical depths by as much as an order of magnitude over the background 1000 nm values of about 1.2 × 10^?4 km^?1 and 1.3 × 10^?3, respectively. The detection and tracking of a number of volcanoes whose effluents penetrated the tropopause are also described. The mass of new aerosol injected into the stratosphere from each volcano is estimated. The May 1980 eruption of Mount St. Helens, for example, produced about 0.32 × 10⁹ kg of new stratospheric aerosol enhancing the Northern Hemispheric aerosol by more than 100 percent.     

Performance assessment of RTPPP positioning with SSR corrections and PPP-AR positioning with FCB for multi-GNSS from MADOCA products

The state-space representation (SSR) product of satellite orbit and clock is one of the most essential corrections for real-time precise point positioning (RTPPP). When it comes to PPP ambiguity resolution (PPP-AR), the fractional cycle bias (FCB) matters. The Japan Aerospace Exploration Agency (JAXA) has developed a multi-GNSS (i.e., global navigation satellite system) advanced demonstration tool for orbit and clock analysis (MADOCA), providing free and precise orbit and clock products. Because of the shortage of relevant studies on performance evaluation, this paper focuses on the performance assessment of RTPPP and PPP-AR by real-time and offline MADOCA products. To begin with, the real-time MADOCA products are evaluated by comparing orbit and clock with JAXA final products, which gives an objective impression of the correction. Second, PPP tests in static and simulated kinematic mode are conducted to further verify the quality of real-time MADOCA products. Finally, the offline MADOCA products are assessed by PPP and PPP-AR comparisons. The results are as follows: (1) Orbit comparisons produced an average error of about 0.04–0.13 m for the global positioning system (GPS), 0.14–0.16 m for the global navigation satellite system (GLONASS), and 0.07–0.08 m for the quasi-zenith satellite system (QZSS). The G15 satellite had the most accurate orbit, with a difference of 0.04 m between the JAXA orbit products and MADOCA’s counterpart, while the R07 satellite had the least accurate orbit with a difference of 0.16 m. Clock products had an accuracy of 0.4–1.3 ns for GPS, 1.4–1.6 ns for GLONASS, and 0.7–0.8 ns for QZSS in general. The G15 satellite had the most accurate clock with a difference of only 0.40 ns between the JAXA clock products and MADOCA products, and the R07 satellite had the least accurate clock with a difference of 1.55 ns. The orbit and clock products for GLONASS performed worse than those of GPS and QZSS. (2) After convergence, the positioning accuracy was 3.0–8.1 cm for static PPP and 8.1–13.7 cm for kinematic PPP when using multi-GNSS observations and precise orbit and clock products. The PFRR station performed the good performance both in static and kinematic mode with an accuracy of 2.99 cm and 8.08 cm, respectively, whereas the CPNM station produced the worst static performance with an error of 8.09 cm, and the ANMG station produced the worst kinematic performance with a counterpart of 13.69 cm. (3) The PPP-AR solution was superior to the PPP solution, given that, with respect to PPP, post-processing PPP-AR improved the positioning accuracy and convergence time by 13–32 % (3–89 %) in GPS-only mode by 2–15 % (5–60 %) in GPS/QZSS mode. Thus, we conclude that the current MADOCA products can provide SSR corrections and FCB products with positioning accuracy at the decimeter or even centimeter level, which could meet the demands of the RTPPP and PPP-AR solutions.     

High-performance altimeter Doppler processing for measuring sea level height under varying sea state conditions

This paper describes an innovative method for processing nadir altimeter data acquired in Synthetic Aperture Radar (SAR) mode, enhancing the system performances over open ocean. Similarly to the current SAR data processing scheme, the so-called LR-RMC (Low Resolution with Range Migration Correction) method, originally designed by Phalippou and Demeester (2011), includes Doppler beam forming, Doppler shift correction and range correction. In LR-RMC, however, an alternative and less complex averaging (stacking) operation is used so that all the Doppler beams produced in a radar cycle (4 bursts of 64 beams for the open-burst Sentinel-3-mode altimeter) are incoherently combined to form a multi-beam echo. In that manner, contrarily to the narrow-band SAR technique, the LR-RMC processing enlarges the effective footprint to average out the effects of surface waves and particularly those from small sub-mesoscale structures (<1 km) that are known to impact SAR-mode performances. On the other hand, the number of averaged beams is as high as in current SAR-mode processing, thus providing a noise reduction at least equally good. The LR-RMC method has the added benefit of reducing the incoherent integration time with respect to the SAR-mode processing (50 ms compared to 2.5 s) limiting possible surface movement effects. By processing one year of Sentinel-3A SRAL SAR-mode data using the LR-RMC method, it is shown that the swell impact on the SAR altimeter performances is totally removed and that an improvement of 10–50% is obtained in the measurement noise of the sea surface height and significant wave height with respect to SAR mode. Additionally, observational capabilities over the middle scales are enhanced potentially allowing the ocean mesoscale features to be retrieved and observations assimilated more usefully in ocean models.     

Models for estimating daily photosynthetically active radiation in oceanic and mediterranean climates and their improvement by site adaptation techniques

In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G₀), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates.According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed.On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use.The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique.     

The evaluation of damped Newton variational ocean wind retrieval method

This paper researches on Synthetic Aperture Radar (SAR) ocean wind retrieval approach, evaluates the method of Damped Newton VARiational method (DNVAR) and the direct method (DIRECT) using two years’ real SAR data and different background winds. Experiments are carried out using SENTINEL-1 SAR information as observation, National Data Buoy Center (NDBC) buoy data as in-situ data, Global Forecast System (GFS), Advanced SCATterometer (ASCAT), WindSAT, and Cross-Calibrated Multi-Platform (CCMP) wind products as background data. Thousands of match-ups are extracted. Results show that DNVAR can improve the wind field accuracy when the background data is ASCAT and CCMP. Especially, DNVAR wind speed results are much better than the official product of SENTINEL-1 wind data. The background data is crucial to the wind field retrieval results, so high accuracy of background data could lead to high accuracy results.     

Towards development of a consistent orbit series for TOPEX,Jason-1, and Jason-2

The TOPEX/Poseidon, Jason-1 and Jason-2 set of altimeter data now provide a time series of synoptic observations of the ocean that span nearly 17 years from the launch of TOPEX in 1992. The analysis of the altimeter data including the use of altimetry to monitor the global change in mean sea level requires a stable, accurate, and consistent orbit reference over the entire time span. In this paper, we describe the recomputation of a time series of orbits that rely on a consistent set of reference frames and geophysical models. The recomputed orbits adhere to the IERS 2003 standards for ocean and earth tides, use updates to the ITRF2005 reference frame for both the SLR and DORIS stations, apply GRACE-derived models for modeling of the static and time-variable gravity, implement the University College London (UCL) radiation pressure model for Jason-1, use improved troposphere modeling for the DORIS data, and apply the GOT4.7 ocean tide model for both dynamical ocean tide modeling and for ocean loading. The new TOPEX orbits have a mean SLR fit of 1.79 cm compared to 2.21 cm for the MGDR-B orbits. These new TOPEX orbits agree radially with independent SLR/crossover orbits at 0.70 cm RMS, and the orbit accuracy is estimated at 1.5–2.0 cm RMS over the entire TOPEX time series. The recomputed Jason-1 orbits agree radially with the Jason-1 GDR-C orbits at 1.08 cm RMS. The GSFC SLR/DORIS dynamic and reduced-dynamic orbits for Jason-2 agree radially with independent orbits from the CNES and JPL at 0.70–1.06 cm RMS. Applying these new orbits, and using the latest altimeter corrections for TOPEX, Jason-1, and Jason-2 from September 1992 to May 2009, we find a global rate in mean sea level of 3.0 ± 0.4 mm/yr.     

Ground and satellite-based observations of ionospheric plasma bubbles and blobs at 5.65° latitude in the Brazilian sector

This investigation uses simultaneous observations from all-sky imager system and an ionosonde collocated at Araguatins (5.65° S, 48.07° W and dip-latitude of 4.17° S), a near-equatorial region in Brazil. These simultaneous observations were used to investigate the occurrence of plasma bubbles and blobs in the field of the imaging system and their association with atypical range Spread-F signature in ionograms. Also, in-situ observation of plasma density from Swarm satellites were used to support the ground-based observations. Using a few cases, a methodology will be established to identify in the plasma blobs (atypical ESF) in the ionograms when there is the simultaneous observation of plasma bubbles and blobs in the field of view of the ionosonde. For this purpose, simultaneous sequence of OI 630.0 nm nightglow images and ionograms are presented for different case studies; 1. when there is the absence of a plasma bubble or blob, 2. when there is only the occurrence of plasma bubbles and 3. when there is the occurrence of plasma bubbles and blobs, in order to compare traces in the ionogram in all these case studies. With these we can cover all kinds of signatures in the ionograms corresponding to no irregularities, plasma bubbles only and plasma bubbles-blobs. These OI 630.0 nm nightglow and ionograms recorded simultaneously make it possible to establish a novel methodology to recognize in ionograms cases when there is the occurrence of Spread-F signature associated with bubble-blob in the FOV of the ionosonde.     

Determination of the mean dynamic ocean topography model through combining multi-source gravity data and DTU15 MSS around China's coast

Mean dynamic ocean topography (or MDT) is closely related to ocean circulation and global climate change. It has important scientific significance and application value for the development and utilization of marine resources in China's coastal areas. Based on the terrain gravity, marine gravity, and SRTM 3?s data, an algorithm to reduce the problem of gravity data gaps between land and sea is proposed. A consistent land-sea gravity model is established based on point-mass fusion method. Then geoid model, which accuracy was estimated to be 8.5?cm through the verification of 348 GNSS/level data from the coastal provinces, of China's coastal areas was calculated through remove-restore technique. Connecting the above geoid model with DTU15 MSS model to establish a MDT model in China's coastal areas using the direct method in space domain. The effect of gravity field model, dominant factors of sea surface topography, and low pass filter are analyzed. Taking Bohai Sea and Yellow Sea as an example, and comparing MDT with the two international models CNES_2013_MDT and DTU15_MDT. The results show that the MDT has the potential to construct a vertical datum of the ocean and carry out related scientific research and application.     

Novel method for reconstruction of hyperspectral resolution images from multispectral data for complex coastal and inland waters

Hyperspectral resolution image products of a synthetic sensor featuring the high spatial resolution of the space-borne sensor can offer cost-effective means for enhancing our current capabilities in terms of providing an array of images in lieu of designing an expensive system for image acquisition, which can serve the expanding needs of the scientific and user communities for various critical water color applications. Despite several studies on enhancing the capability of land remote sensing sensors, full spectrum reconstruction of water color images with varying spectral bands is hampered by the lack of methods and accurate atmospheric correction procedures. In the present work, a novel method is developed for reconstruction of hyperspectral resolution images from high spatial-resolution Sentinel 2 Multispectral Instrument (MSI) data representative of many complex waters in coastal and inland zones. This method uses a deep neural network (DNN) with multiple blocks of deconvolution and dense layers. The spectral reconstruction of hyperspectral resolution images from multispectral data was based on rigorous training data from the atmospherically-corrected and validated HICO normalized water-leaving radiance products (with spectral resolution 438-868 nm sampled at 5.7 nm) of diverse water types. The generalizability and versatility of the DNN method was tested and evaluated systematically by means of various qualitative and quantitative analyses using concurrent space-borne (MSI and HICO) and in-situ measurements from different regional waters. Reconstructed hyperspectral resolution radiances obtained from the MSI images closely matched with independent HICO and MSI measurements within the desired accuracy. Successful reconstruction and validation of the hyperspectral radiances indicate that the proposed state-of-the-art method provides possible future directions for enhancing our current capabilities of space-borne sensors for various research purposes and societal applications at local, regional and global scales.     

First calibration measurements with the dust impact detector DIDSY-IPM

The IPM detector consists of two separate impact ionization detectors, one of them covered by a 2.5 μm thick plastic film and a piezoelectric sensor mounted to the back of the joint impact plate. First impact tests, with iron projectiles in the mass range 10^?15 to 10^?9 g and in the speed range 1 km/s to 70 km/s, were performed with the calibration (FS) and the flight (F) model of this detector. The charge yield at 69 km/s impact speed (flyby speed of GIOTTO) has been extrapolated from the data and amounts to 400 Coulombs per gram. This corresponds to a preliminary sensitivity threshold for the impact ionization detector of about 6×10^?17 g. The penetration limit introduced by the plastic film is about 10^?14 g for iron particles. Only the biggest particles used for the test produced signals at the piezoelectric sensor. If one assumes an energy dependence of the piozoelectric signal, a preliminary sensitivity threshold of about 10^?13 g at 69 km/s can be established.     

Prediction of geosynchronous electron fluxes using an artificial neural network driven by solar wind parameters

There are hundreds of satellites operating at the geosynchronous (GEO) orbit where relativistic electrons can cause severe damage. Thus, predicting relativistic electron fluxes is significant for spacecraft safety. In this study, using GOES satellite data during 2011–2020, we propose two neural network models with two hidden layers to predict geosynchronous relativistic electron fluxes at two energy channels (>0.8 MeV and > 2 MeV). The number of input neurons of the two channels (>0.8 MeV and > 2 MeV) are determined to be 36 and 44, respectively. The > 0.8 MeV model has 22 and 9 neurons in the hidden layers, while the > 2 MeV model has 25 and 15 neurons in the hidden layers. The input parameters include the north–south component of the interplanetary magnetic field, solar wind speed, solar wind dynamic pressure and solar wind proton density. Through the analysis of different time delays, we determine that the optimal time delays of two energy channels (>0.8 MeV and > 2 MeV) are 8 days and 10 days, respectively. The training set and validation set (Jan 2011-Dec 2018) are divided by the 10-fold cross-validation method, and the remaining data (Jan 2019-Feb 2020) is used to analyze the model performance as a test set. The prediction results of both energy channels show good agreement with satellite observations indicated by low RMSE (～0.3 cm^-2sr^-1s^?1), high PE (～0.8) and CC (～0.9). These results suggest that only using solar wind parameters is capable of obtaining reasonable predictions of geosynchronous relativistic electron fluxes.     

GGTm-Ts: A global grid model of weighted mean temperature (Tm) based on surface temperature (Ts) with two modes

With the development of Global Navigation Satellite System (GNSS), the detection of precipitable water vapor (PWV) using the GNSS atmospheric sounding technique becomes a research interest in GNSS meteorology. In the conversion of zenith tropospheric delay (ZTD) to PWV, the weighted mean temperature (Tm) plays a crucial role. Generally, the Tm estimated by the linear regression models based on surface temperature (Ts) cannot meet the requirement for global use, and the accuracy of Tm derived from the empirical models is limited. In this study, a new Tm model, named GGTm-Ts model, was developed using the global geodetic observing system (GGOS) atmosphere Tm data and European Centre for Medium-Range Weather Forecasts (ECMWF) data from 2011 to 2015. Resting upon a global 2.5°*2° grid of coefficients of Tm-Ts linear function, the new model can provide Tm at any site in two modes, one for the case with measured Ts provided, i.e., the accurate mode, the other for the case that Ts provided by a subroutine, i.e., the normal mode. The performance of GGTm-Ts model was assessed against the Bevis formula, GPT2w and GPT2wh model using different data sources in 2016-the GGOS atmosphere and radiosonde data. The results show that the GGTm-Ts model in accurate mode achieves best performance with an improvement of 46.9 %/15.3 %, 37.8 %/19.5 % and 34.4 %/14.2 % over other three models in the GGOS atmosphere/radiosonde comparison. For the normal mode, the GGTm-Ts model outperforms the GPT2w model and achieves equivalence results with the GPT2wh model. Moreover, the impact of Tm on GNSS-PWV was analyzed to validate the performance of the GGTm-Ts model.