Conference Agenda

The atmosphere is an important factor that affects the accuracy of remote sensing radiation at night. Effective atmospheric correction for night-light satellite data is a prerequisite for realizing the quantitative application of night-light remote sensing. The atmospheric correction method based on the radiative transfer model is widely used during the day because of its clear physical meaning and high accuracy. The transmission mechanism of atmospheric radiation at night is the same as that under daytime conditions. The main difference between day transmission and night
transmission is that the radiation source at night is the moon. The brightness of the moon will change due to the changes of the moon phase angle and the moon-earth distances, which will affect the upward and downward radiation during transmission. Therefore, the accurate calculation of lunar radiation is the prerequisite for the use of atmospheric radiative transfer model to carry out high-precision atmospheric correction at night. MODTRAN (MODerate resolution TRANsmission) is designed with night radiance mode, which can simulate the radiative transmission of lunar light at night, but this mode has certain defects, mainly including that the model does not consider the changes of moon-earth distances, replacing sun-moon distance with sun-earth distance, and the moon phase function does not consider wavelength correlation, etc. This may introduce a certain error to the MODTRAN night radiance mode and reduce the accuracy of atmospheric correction. To solve the problem, this research couples the MODTRAN model and MT2009 to simulate radiative transmission at night at a high precision. Based on this model, this paper proposes an atmospheric correction method for night-light remote sensing data and demonstrates its application.

Quantification and control of NOx and CO2 emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NOx and CO2 emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO2 columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO2 mixing ratio are employed. We quantified daily NOx and CO2 emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NOx and CO2 emissions are verified against bottom-up inventories with minor deviations (<3 % for the 2019 mean, ranging from −20 % to 48 % on a daily basis). Based on the estimated CO2 emissions, we also predicted daily CO2 column mixing ratio enhancements, which match well with OCO-2 observations (<5 % bias, within ±0.3 ppm). We capture the day-to-day variation of NOx and CO2 emissions from Wuhan in 2019–2020, which does not reveal a substantial “weekend reduction” but does show a clear “holiday reduction” in the NOx and CO2 emissions. Our method also quantifies the abrupt decrease and slow NOx and CO2 emissions rebound due to the Wuhan lockdown in early 2020. This work demonstrates the improved superposition model to be a promising new tool for the quantification of city NOx and CO2 emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments.

As the spatial resolution of space-borne imaging spectrometers is rapidly improving and moving towards sub-kilometre scale, three-dimensional (3D) cloud effects become more prominent in the retrieval of atmospheric trace gases. Currently in the Sentinel-5P (S5P) nitrogen dioxide (NO₂) product (3.6 km x 5.6 km resolution) the Fast Retrieval Scheme for Clouds from the Oxygen A band (FRESCO) algorithm is used to retrieve a one dimensional (1D) horizontal homogeneous Lambertian cloud layer for cloud correction. However, in reality clouds are 3D objects, they are not spatially homogeneous in brightness, and they can have effects on neighbouring clear-sky pixels by casting shadows on lower clouds or on the ground surface or by scattering light into the pixels.

In the S5P NO₂ retrieval algorithm the retrieved slant column density is translated to a vertical column density (VCD) by correcting for the light path using pre-calculated air-mass factors (AMF) from a 1D radiative transfer model (DAK), using surface and cloud parameters as input. When a cloud shadow is cast over a clear-sky pixel the downward light intensity is reduced, altering the average observed light path. This lowers the sensitivity of the measurement for the lower atmospheric layers and thus influences the AMF and the resulting NO₂ VCD.

We attempt to observe such cloud shadow effects in the AMF and VCD fields in the S5P NO₂ data with focus on hot-spot areas during winter when generally more clouds are present, and the cast cloud shadow surface areas are relatively high due to higher solar zenith angles. SUOMI-NPP VIIRS data can be used to identify the pixels affected by cloud shadows.

In addition, we use a vectorised 3D Monte Carlo radiative transfer model (MONKI), developed at KNMI, to simulate different cloud scenarios and calculate the 3D AMF and NO₂ VCDs. The 3D cloud effects on the NO₂ retrieval are then investigated and quantified by comparing the 3D results to their 1D counterparts.

Current satellite-based methods for measuring aerosols require a homogeneous surface and pre-assumed surface albedo or reflectance, which is not suitable for urban areas with highly inhomogeneous surfaces. However, with the development of high-resolution satellites, building shadows can be clearly identified in satellite images. A new algorithm has been proposed to retrieve aerosol optical depth and surface albedo by using building shadows and adjacent sun-shined bright pixels. The algorithm was validated using GF-2 satellite images with a spatial resolution of 4 meters and successfully retrieved AOD and surface albedo values for locations near the Beijing Olympic Center. The AOD derived from the shadow method were found to be in close agreement with those obtained from ground-based CIMEL sun-photometer measurements, with differences of less than±0.03. The results indicate that the shadow method can accurately retrieve aerosol data over megacities at a finer spatial resolution.

The Greenhouse gases Monitoring Instrument (GMI) on Chinese hyperspectral satellite GF5-02 can provide more abundant observations of global atmospheric CO₂ which plays an important role in climate research. CO₂ retrieval precision is the key to determine the application value of the GMI. In order to reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI's CO₂ retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of GMI and DPC, this paper designs the pointing registration method of the GMI based on the coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. With the combination of DPC which supplied the spectral data and aerosol product, the retrieval ability of the Coupled Bidirectional reflectance distribution function CO₂ Retrieval (CBCR) method developed for GMI CO₂ retrieval was improved as the retrieval efficiency of CO₂ products increased by 27% and the CO₂ retrieval precision increased from 3.3 ppm to 2.7 ppm. Meanwhile, the collaborative use not only guaranteed the GMI's ability to detect global and area CO₂ concentration distribution characteristics like the significant concentration differences between the northern and southern hemispheres in winter and high CO₂ concentration in urban agglomeration areas caused by human activities, but also extended GMI’s potential of monitoring anomalous events like Tonga volcanic eruption.

In the last years, several researchers demonstrated the capability of a Synthetic Aperture Radar (SAR) to estimate the so-called Atmospheric Phase Screen (APS) accurately. Amplitude images are loosely affected by atmospheric conditions in the path from the satellite to the ground. On the other hand, variations in the refractive index in the medium primarily affect the phase of a coherent radar system. In SAR Interferometry (InSAR), the atmosphere is seen as a disturbance for estimating ground deformation. Therefore, the APS is generally removed or mitigated using Numerical Weather Prediction Models (NWPM) or data-driven methods exploiting the spatiotemporal statistics of the atmospheric signal.

However, the definition of signal and noise depends on the application at hand. While geologists define the deformation as a signal and the APS as noise, it is the inverse for meteorologists. It has been proved that APS can be used as an input dataset to NWPM with measurements from radio-sonde, ground-based weather radars, Global Navigation Satellite Systems (GNSS), ground-based weather stations, and more. SAR data is beneficial when the other measurements are unavailable or unreliable to provide high-quality input to NWPM.

However, the APS estimated using a SAR system must be properly calibrated before the ingestion process into NWPM. In particular, one of the most dangerous aberrations is the one that springs from an error in the knowledge of the platform trajectory during image acquisition. Even a tiny deviation in the order of a few centimeters can generate large-scale trends in the derived APS. The trend can generally be modeled as a plane added to the true APS map, often called Orbital Phase Screen (OPS). Very low spatial frequency aberrations are the most dangerous in an NWPM. In fact, such systems are programmed to work on a continental scale at low resolutions, taking advantage of very large-scale signals that must be error-free. The further problem is that the filtering of OPS is not trivial at all. The atmospheric signal is often a large-scale trend, and when signal and noise share the same statistics, their separation is impossible. One could attempt to remove the OPS by fitting a 2D plane into the atmospheric map and remove it, with the risk of also removing part of the APS.

In this poster, we propose a solution to the problem encompassing the usage of a network of GNSS stations on the ground. The raw data from each station is processed to extract a GNSS-derived APS. Then, the SAR-derived APS is extracted on the spatial location of the GNSS stations. Such measurements are the sum of the true APS and the orbital error. We use the GNSS-derived APS as a ground truth, removing them from the SAR-derived estimates leading to a set of measurements of the pure orbital error. An inverse problem is solved, leading to two parameters characterizing the orbital error. The benefit of this inversion is double. First of all, the two estimated parameters can be used to provide a quality proxy for the trajectory. Second, the two parameters can be used to compute the forward model on the whole grid of the APS map (and not just on the set of GNSS stations as done before), leading to a calibration phase screen.

The procedure is tested using a dataset of more than 30 Sentinel-1 images and a network of GNSS stations in Sweden. The algorithm shows excellent performance. The validation process compares a set of independent GNSS stations with the SAR-derived APS before and after the calibration procedure. A second validation is carried out using a separate NWPM showing, once again, very good performances.

Multi-Platform NESZ Estimation over Land
Naomi Petrushevsky (1), Marco Manzoni (1), Andrea Virgilio Monti-Guarnieri (1), Stefano Tebaldini (1)
(1) Department of Electronics, Information and Bioengineering; 20133 - Politecnico di Milano, Milan, Italy.
As the space economy grows and new satellites are constantly launched, fast and robust performance measures are of great interest. For example, the expected launch of Sentinel-1C by the European Space Agency will require tools to validate the new sensor in short time. Noise Equivalent Sigma Zero (NESZ) is an important property of a Synthetic Aperture Radar (SAR) system related to its noise floor. It defines the equivalent backscatter coefficient which would produce the actual noise power in the focused data. Noise properties depend mainly on the sensor’s inner circuits and may change from Its nominal values. Thus, NESZ should be estimated directly from the data.
Standard techniques exploit the smoothness of water bodies, causing all the energy from the radar to deflect, allowing the measurement of noise levels directly. However, calm waters are very different from standard land scenarios in terms of received power and surface temperature, so the applicability of the estimate to the general case may be inaccurate. Also, the approach requires procuring special data, which is generally not of interest for land monitoring.
An alternative approach for NESZ estimation exploits an interferometric pair of images over land. Both images should be radiometrically calibrated beforehand, converting the received intensity to backscatter and correcting fluctuations due to the antenna pattern. The method is based on the inverse relation between coherence and the NESZ. From the 2D histogram w.r.t backscatter and coherence, it is possible to fit the model and obtain the noise level, but only if both master and slave images are acquired by the same satellite. If two different platforms are used, each may have different gains and thermal noise. In this case, we propose an extension to the method, given that one satellite is already in orbit for a long time, such that a stack of master images is available.
First, we use the stack to generate a set of interferograms with short temporal baselines. Each interferogram is used to measure the master’s NESZ, and all measures are averaged to improve robustness. After obtaining an accurate estimate for the master, we proceed to process the interferogram between the master and the new satellite. By repeating for each incidence angle, the NESZ profile of the new satellite can be characterized.
Estimating NESZ for a new sensor was validated and tested using a stack of Sentinel-1A (S1A) and Sentinel-1B (S1B) images. First, the noise level of S1B was obtained separately according to Equation (1). Then, S1B’s NESZ was estimated by the procedure described above, using the stack of S1A data. The comparison between the two results confirmed that the noise level of a new satellite could be characterized over land, with as little as one available product.

In this paper, we compare two techniques for estimating forest height and vertical structure using airborne synthetic aperture radar (SAR) data, namely SAR tomography (TomoSAR) and the phase histogram (PH) technique. Using multiple SAR images, TomoSAR allows for a direct imaging of the three-dimensional (3D) electromagnetic structure of the vegetation layer, from which biophysical parameters such as forest height and terrain topography can be extracted[1], [2]. The PH technique assigns each pixel in a SAR interferogram to a specific height bin based on the value of the interferometric phase, allowing for a local estimation of the vertical profile of forest scattering by accumulation of pixels fall within a given spatial window[3]–[5].

The aim of this paper is to study the connection between TomoSAR and the PH technique on an experimental ground by analyzing L-Band tomographic data from the ESA airborne campaign TomoSense, flown in 2020 at the Kermeter area in the Eifel Park, North-West Germany[6]. The data analyzed in this paper feature 30+30 overpasses acquired along two opposite flight headings, and provide a vertical resolution consistently better than 5 m on the whole area of interest.

Results indicate that the PH technique can only loosely approximate the vertical structure produced by SAR tomography, but it can be used to produce a fairly good estimate of forest height. In particular, TomoSAR and the PH technique are observed to produce an average root mean square error (RMSE) of 2.63 m and 4.35 m in NW flight data, and 1.84 m and 5.46m in SE flight data, respectively. The observed results are interpreted in light of a simple physical model to predict phase variations in the two cases where forest scattering is determined by the presence of a dominant scatterer at each resolution cell or by a multitude of elementary scatterers, leading to the conclusion that the PH technique is best fit for the case of high- or very high-resolution data at higher frequency bands. Overall, the analysis in this paper demonstrates, both theoretically and experimentally, that the PH technique cannot achieve the same performance as multi-baseline tomography when applied to lower frequency data at a resolution of few meters. Yet, even in these conditions we remark that the PH technique allows for the retrieval of forest height based on a single interferogram at a single polarization. This makes the PH technique extremely interesting in the context of spaceborne missions.

The introduction of SAR tomography has opened the way to a completely new approach to look at SAR data, providing evidence of the possibility to directly image the 3D structure of natural media such as forests, snow, and ice. As of today, the benefit of Tomographic imaging has been demonstrated experimentally based on airborne data in the context of different environmental applications, including estimation of forest height and Above Ground Biomass, retrieval of snowpack depth, density, and internal layering, and monitoring the internal structure of alpine glaciers and ice sheets. Despite the many successful experimental campaigns, spaceborne tomography is yet to come for what concerns of natural scenarios. This is largely due to the fact that that the vertical resolution provided by SAR tomography is inherently linked to the number of available viewpoints, which - for the case of a single satellite - corresponds to the number of orbits over a given area. It is then clear that the success of spaceborne TomoSAR is crucially linked to the possibility to fly multiple sensors at the same time. Concrete signs in this direction have appeared in recent years, since advances in electronics and antenna technologies have made SAR payload compatible with small satellites.

In this context, activities are being carried out at Politecnico di Milano to develop specific signal processing algorithms for the implementation of a tomographic demonstrator based on the use of a small fleet of Unmanned Aerial Vehicles (UAVs) carrying Radio-Frequency devices. Specifically, in this paper we introduce a novel approach to the problem of focusing SAR data in the presence of a poor knowledge of platform trajectories. This is especially the case of UAV-based systems, which often employ low-cost navigational units.

The proposed algorithm is a geometrical evolution of the well-known Phase Gradient Algorithm (PGA) [1]. PGA is an iterative algorithm that tries to estimate the gradient of the unknown phase error based on SAR data at selected points. Four processing steps are required to compensate for the phase errors, these are: circular shifting, Fourier Transform over a selected window, phase gradient estimation, iterative correction. The PGA approach is based on the intrinsic assumption that the same phase correction applies to any point in the imaged scene. While this hypothesis can be approximately retained for a spaceborne geometry, it is surely non valid for a low altitude platform, for which the variation of incidence and squint angle determines space-varying phase errors.

In our approach, the processing steps within the PGA are re-interpreted on a rigorous geometrical basis. Circular shifting and Fourier Transforming are replaced by a defocusing operator that allows to measure the phase history of selected points. Phase gradient estimation is replaced by a direct estimation of platform trajectory. Final image correction is then carried out by refocusing the data according to the estimated trajectory. In so-doing, the proposed algorithm is intended to achieve the accuracy and efficiency of the PGA, while granting a rigorous geometrical approach as in [2],[3].

The algorithm has been applied in two real-world cases: 1) bistatic L-Band data acquired by operating a fixed transmitted and flying a receiver onboard a UAV; 2) monostatic P-Band acquired during the ASI helicopter borne campaign in [4].

Results indicate that the proposed approach can successfully correct trajectory errors when present, while it does not produce further degradation in the case where navigational data are accurate.

[1] D.E. Wahl, P.H. Eichel, D.C. Ghiglia, and C.V. Jakowatz. Phase gradient autofocus a robust tool for high resolution sar phase correction. IEEE Transactions on Aerospace and Electronic Systems, 30(3):827–835, 1994

[2] Hubert M. J. Cantalloube and Carole E. Nahum. Multiscale local map drift driven multilateration sar autofocus using fast polar format image synthesis. In 8th European Conference on Synthetic Aperture Radar, pages 1–4, 2010.

[3] Jan Torgrimsson, Patrik Dammert, Hans Hellsten, and Lars M. H. Ulander. Sar processing without a motion measurement system. IEEE Transactions on Geoscience and Remote Sensing, 57(2):1025–1039, 2019.

[4] Stefano Perna et al. The asi integrated sounder-sar system operating in the uhf-vhf bands: First results of the 2018 helicopter-borne morocco desert campaign. Remote Sensing, 11(16), 2019.

The Paris Agreement, adopted in 2015, requires monitoring of anthropogenic greenhouse gas (GHG) emissions and assessment of collective climate mitigation efforts. Several space-based carbon dioxide (CO₂) monitoring measurement systems have become available since 2009, including Japan’s GOSAT and GOSAT-2 and NASA’s OCO-2 and OCO-3. China’s first CO₂ measurement satellite mission, TanSat, was launched in December 2016.

In this work, we analyze anthropogenic emissions signatures using TanSat as well as OCO-2/3 measuring systems. The space-based CO₂ observations are analyzed together with the European Copernicus Sentinel-5 Precursor (S5P) TROPOMI nitrogen dioxide (NO₂) measurements as nitrogen oxides are often co-emitted with CO₂. In the future, satellite constellation missions will focus on carbon dioxide released into the atmosphere specifically through human activity. The future missions include China’s TanSat-2, Japan’s GOSAT-GW and the Copernicus Carbon Dioxide Monitoring mission CO2M.

Greenhouse gas emissions from anthropogenic activities are the main driver of current global climate change. Emission monitoring is essential for the verification of emission reduction efforts and a feasible way for attaining global coverage are satellite observations. Recent developments in space-based hyperspectral cameras open up new possibilities for greenhouse gas emission monitoring also on a smaller scale.
Most of the anthropogenic greenhouse gas emissions originate from urban areas. Urban areas are also sources of co-emitted atmospheric aerosols, which decrease the local air quality and complicate the atmospheric radiative transfer. Even slight concentrations of atmospheric aerosols can cause considerable inaccuracies in space-based remote sensing observations of carbon dioxide (CO2).

In this work, we present a novel retrieval method for a co-emitted CO2 and aerosol emission plume content originating from a point source observed from a satellite. We plan to test the method for a joint CO2 and aerosol retrieval and emission rate estimation from satellite-based hyperspectral imaging data, such as imagery obtained using PRISMA or EMIT. The solar and viewing angle dependent radiative coupling of adjacent camera pixels and co-emission of aerosols are investigated as means to improve the CO2 retrieval process.

Additionally, the prospect of optimizing radiative transfer (RT) calculations by preliminary wavelength pruning is examined. The presented approach reduced the amount of needed wavelengths in the calculation by 15 – 45 % in the tested cases and generalizes to arbitrary spectral observations.

As part of this work, a space-based hyperspectral imaging simulator is developed. The GPU-based simulator outputs top-of-the-atmosphere radiances in near- to shortwave-infrared wavelengths and thus enables a rapid retrieval of atmospheric constituents in a 3D atmosphere.

In the summer of 2022, nearly half of mainland China experienced a heatwave with a severity not experienced since 1961, with temperatures reaching 45oC in some parts of the country. The accompanying widespread drought, the worst since 1954, caused some of China’s main rivers, including part of Yangtze river, to dry up. This led to reduced hydropower generation, interrupted shipping, reduced agriculture and factory outputs, and severely impacted drinking water supplies to millions of people, livestock and wildlife. This nationwide drought will have likely caused widespread disturbances to carbon balance. Reduced hydropower generation resulted in higher GHG emissions from thermal power plants to meet energy demands. Low soil moisture and heat stress will have impacted carbon sequestration from the land biosphere. These impacts have not yet been quantified but are of great interest to the wider public because they illustrate how Chinese GHG emissions might change in the future as extreme climate events becomes more frequent.

Satellites developed in the last decade, such as the Japanese GOSAT and the NASA OCO-2, provide continuous monitoring of atmospheric greenhouse gases at the global scale, with unprecedented precision. We interpret those data to infer geographical distributions of CO2 and methane fluxes over mainland China. We put the fluxes inferred for 2022 during the extreme drought into context of fluxes in recent years.

Maritime transport plays a vital role in national trade, and the improvement of ship transport capacity, while boosting China's economic development, has also exacerbated air pollution in ports, coastal, river and surrounding areas. Due to the large number of domestic inland river vessels, limited legislation for emission control and no monitoring infrastructure, information on inland river vessel emissions is very limited. Taking the Yangtze River in the region of Nanjing as research area, the STEAM algorithm is used to calculate the emissions of inland vessels in Nanjing area one by one according to the real-time information received by the Automatic Vessel Identification System (AIS), the relevant basic data of ships provided by the China Classification Society (CCS) database and the relevant data of field research. The temporal and spatial characteristics of inland ship emissions are analyzed. Combined with the hourly meteorological data of Nanjing meteorological station, the estimated ship emissions were compared with MAX-DOAS data to explore the contribution of inland river ship emissions to air pollution. Using this comparison, we analyzed the relative effects of ship emissions on densely populated areas around rivers.

During the Research on the Simulation and Mechanism of the impacts of Black Carbon on Climate and Environment atmospheric measurement campaign carried out near Nanjing, China in June 2018, a lightweight, accurate nitrogen dioxide (NO₂) sensor was attached to a quadcopter to measure vertical profiles of NO₂. Between 1 and 14 June 2018, ∼50 vertical NO₂ profiles were measured inside the planetary boundary layer up to an altitude of 900-1300 meters during 13 subsequent measurement days. Six NO₂ soundings were conducted on a daily basis at approximately 8 AM (morning), 12 & 4 PM (afternoon), 8 PM (evening) and 12 & 4 AM (night). The NO₂ measurements were calibrated using a scaling factor derived from a side-by-side inter comparison with a commercial NO₂ analyzer operated by NUIST prior to the start of the campaign. These measurements clearly demonstrate the diurnal cycle of NO₂, including the emergence of elevated concentrations close to the surface during the night and early morning and the mixing of the boundary layer from sunrise onward resulting in flat NO₂ vertical profile shapes with lower concentrations. The in-situ NO₂ vertical profile shapes were compared to NO₂ profile information retrieved from nearby MAX-DOAS observations as well as computed using the CHIMERE chemistry-transport model. This comparison demonstrates that in-situ quadcopter measurements could play an important role in the validation of future geostationary satellites since the diurnal cycle of NO₂ will have an impact on the accuracy of the satellite retrievals and is not always flawlessly captured by commonly used measurement techniques and models.

In this paper, continuously altimetric satellite sea surface height measurements from Cryo-TEMPO between 2011 and 2020 are used to illustrate that the NPO plays a significant role in connecting the Beaufort Sea level to the Pacific Ocean. It is found that summertime NPO has a significant negative connection with sea surface heights in the Beaufort Sea. A negative NPO phase tends to be associated to an intensified Beaufort High paired with anomalous anticyclonic circulations over the Arctic, contributing to positive SSH anomalies locally because of increasing more freshwater entering the Beaufort Sea from the Chukchi Sean through Bering Strait. CESM2-LE is used to examine the connection between North Pacific teleconnection and the Beaufort Sea level change for longer time spans. It is suggested that the remarkable relationship between SSH in the Beaufort Sea and NPO is reproduced during 2011–2020, 2000–2020 and 1990–2020. In addition, the pre-winter SST may be a predictor for SSH in the Beaufort Sea. These findings highlight that the impacts of the teleconnection and SST anomalies in North Pacific on the Arctic sea level are of great importance and need to be taken into consideration when evaluating future climate predictions and projections.

The increasing influence of the Atlantic Water (AW) in the Arctic, known as “Atlantification”, has been an important topic of scientific interest for several years. Recent studies reiterated the need to have a better understanding of AW transformation in Nordic Seas (NS) to understand and predict the ocean’s role in ongoing and future Arctic climate change (Asbjørnsen et al., 2020). A “missing puzzle” yet to be studied in detail is the role of mesoscale eddies on the Atlantification. Eddies generated from instabilities of the mean-flow (Stammer and Wunsch, 1999) are ubiquitous features in the NS (e.g., Raj et al., 2016) whereby mean kinetic energy is transformed to eddy kinetic energy with subsequent reduction in the mean northward flow of the AW. Eddies can also capture and trap heat and salt from the mean AW flow (Bolenenko et al., 2020), thereby cooling the AW poleward heat transport (Isachsen et al., 2012). In regards to the Atlantification in the Arctic Ocean, the question is therefore related to the occurrences of eddies in the NS over the last decades; has the number of eddies changed or is it stable?

In this poster we present the results of two distinct analysis of 11 years (2011-2022) of satellite sensed data (interpolated to 25 km spatial resolution at monthly to seasonal timescales) combined with mesoscale eddy tracking to advance the insight of mesoscale eddy activity and upper ocean circulation in the NS. First, the Multiparameter Eddy Significance Index (MESI) proposed by Roman-Stork et al.(2023) is estimated. The index combines sea level anomaly, sea surface temperature and salinity fields, chlorophyll distribution and eddy kinetic energy for all the eddies in the NS. Second, climatologies of the singularity exponents for the satellite-based sea surface temperature and salinity values are provided. The singularity exponent is expected to reveal mixture of horizontal transport and dispersion processes of the upper ocean circulation with particular focus on the impact of mesoscale eddies.

In this presentation we will highlight the findings and results in relation to: (i) observed changes in the annual number of eddies in the NS from altimetry; (ii) assessment of the number of eddies based on the MESI approach; and (iii) consistency between the climatology of singularity exponents and MESI. The relevance of the results, in turn, will also be discussed in relation to the Atlantification of the Arctic Ocean.

High-resolution soil moisture data are essential for studying the complex interactions between the water, energy, and carbon cycles from local to global scales. For agricultural and hydrological applications, a 1 km global-scale soil moisture product is of great interest to the community. In this study, we propose a new dual-polarization algorithm (DPA) for soil moisture retrieval using C-band synthetic aperture radar (SAR) observations. Based on this algorithm, a Sentinel-1-based global-scale soil moisture dataset with a spatial resolution of 1 km (S1-DPA) was generated. Specifically, using optical data as a proxy of vegetation water content, a semi-empirical forward model from soil moisture to backscattering was constructed and calibrated based on the relationship between Sentinel-1 SAR backscatter and SMAP (Soil Moisture Active and Passive) soil moisture product under different vegetation and soil texture conditions. With the calibrated forward model, soil moisture was estimated using the backscatter coefficients on VV and VH polarizations observed by Sentinel-1 C-band SAR in ascending and descending orbits. The S1-DPA soil moisture data product has the same temporal resolution as Sentinel-1, of 3-6 days for Europe and 6-12 days for other regions. It covers the global land surface and spans the period from 2016 to 2020, utilizing both daily ascending and descending data. The S1-DPA product was validated using ground measurements from the International Soil Moisture Network (ISMN). The results show that the S1-DPA product captures the spatial and temporal characteristics of in-situ soil moisture reasonably, with an overall median Pearson correlation of 0.372, bias of -0.003 m³/m³, RMSD (root mean squared difference with respect to in-situ measurements) of 0.105 m³/m³, and ubRMSD (unbiased root mean squared difference) of 0.076 m³/m³. The generated global 1 km soil moisture product has the potential to promote the application of high-resolution soil moisture data in the fields of hydrology, ecology, and meteorology.

The Tibetan Plateau is a climate change sensitive and ecologically fragile area. Global climate change is prone to have an higher impact on the region's local climate than other regions, and therefore have a potential that impose a significant influence on local ecological environment. Therefore, this study aims at evaluating the climate change in the Tibetan Plateau and its impact on vegetation in the plateau in the past up to the end of 21th century in the future. First of all, this study uses CN05.1 meteorological data to first conduct trend analysis, mutation analysis, and periodic analysis on precipitation and temperature in the Tibetan Plateau region over the past 40 years (1979-2017). Then, combined with 11 GCM model data and CN05.1 data from the CMIP6, the ability of a single climate model, a full model set (MME) and a better model set (BMME) to simulate precipitation and temperature in the Tibetan Plateau was evaluated using Taylor chart, interannual variability assessment index and rank scoring method (RS method). A set of optimal models with good simulation capabilities of precipitation and temperature under three future climate scenarios (SSP126, SSP245, and SSP585) was selected, and the Delta method was used for bias correction. After that, by utilizing CN05.1 data, GIMMS NDVI data, and 1:1000000 vegetation distribution map data in China, methods such as linear regression analysis, Sen's slope, Hurst index, partial correlation coefficient, and residual analysis were applied to explore the dynamic changes of existing vegetation in the Tibetan Plateau and its response to climate factors. Finally, based on the corrected CMIP6 climate model data, the CSCS model and land use transfer matrix were used to analyze the potential vegetation distribution and changes in the Tibetan Plateau under three different climate change scenarios in the early 21st century (2021-2040), mid 21st century (2041-2060), and late 21st century (2081-2100).

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000–2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simulations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HELT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapotranspiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

Accurate calculation of evapotranspiration at the basin scale can provide the information for dynamic analysis of the hydrological cycle within the basin. In this study, The Qinghai-Tibet Plateau (TP) , consisting of 12 watersheds, was used as the study area. The process of realization of the medium-scale evapotranspiration calculation by the REOF-3T model can be generalized as follows. Each watershed was divided into several subregions based on the analysis results of the rotated empirical orthogonal function (REOF) method for 10a downward shortwave radiation. The modified 3T model was used to calculate the evaporation in the subregions, thus realizing the distributed calculation of the 3T model. To validate the accuracy of the model, site observations and other remote sensing products were compared to the calculated ET series. The results showed that the REOF-3T model has a significant correlation with the average ET in 8 days of six eddy covariance flux stations over the TP. The Pearson’s correlation coefficient (R) of EC observed sites ranged from 0.6 to 0.78 (P<0.01), the root-mean square error (RMSE) ranged from 1.006 mm/d to 1.408 mm/d. The estimated ET (REOF-3T model) also displayed a good consistency with the observed ET (water evaporation) in 93 meteorological stations during 2008 – 2018. More than 93% of sites have R-values over 0.6. The average annual R in 93 stations exceeded 0.9, except for 2008, 2016, and 2018. There is an increasing trend of ET in the southwestern of TP, especially in the upper Yangtze River basin. While the north and northwest are on a downward trend.

Although global land-atmosphere energy and carbon fluxes is a key driver of Earth’s climate system, global continuous high resolution fluxes datasets are still limited. In this study, we used the STEMMUS-SCOPE simulations at 170 FLUXNET sites as the training dataset, enabling the physics-informed Machine Learning (PIML) to generate a global, long-term, spatially continuous high resolution dataset of fluxes. STEMMUS-SCOPE model is a process-based model simulating water, carbon, and energy fluxes, along with predicting leaf to canopy photosynthesis, reflectance and fluorescence spectra, as well as subsoil moisture and temperature dynamics. Results show that PIML can estimate fluxes with Pearson Correlation Coefficient score (r score) 0.99 for latent heat (LE), and 0.99 for sensible heat (H), and the root mean square error (RMSE score) are 12.89 W/m² and 18.6 W/m² respectively. It can also predict net radiation (Rn) with r score 0.99 and RMSE 7.54 W/m², and root zone soil moisture (RZSM) with r score 0.99 and RMSE 0.0045 cm³/cm³. With solar induced chlorophyll fluorescence (SIF), the r score is 0.99 and RMSE lower than 0.03 W/m²/μm/sr. Incoming shortwave radiation, surface soil moisture, and air temperature are the main predictor variables that determine the prediction performance, followed by incoming longwave radiation and wind speed etc.

Soil moisture is an essential variable in the hydrological cycle and exhibits a strong connection to weather and climate change. The comprehensive understanding of the physical mechanism underlying brightness temperature enables more accurate estimation of soil moisture. The integration of process-based understanding into machine learning models has the potential to leverage the advantages of both methods. This research aims to develop an emulator using machine learning algorithms to conduct a forward simulation of ELBARA-III brightness temperature at L-band. A combination of meteorological data, in-situ soil moisture and soil temperature data and vegetation parameters was used for training. A total of four years’ data, encompassing various combinations, is employed for training purposes, resulting in the construction of 64 models each for horizontal and vertical polarizations. The best-performing model exhibits a correlation coefficient of R = 0.995 for horizontal polarization and R = 0.998 for vertical polarization. Notably, there was a significant enhancement in performance after incorporating the observed data for model training. The primary objective of this research is to investigate the underlying physical mechanisms involved in the emission process and explore the potential of employing machine learning algorithms for simulating microwave signals across extensive spatial and temporal domains. These findings suggest that while random forest regression and support vector regression can capture the general variation trend observed in brightness temperature, some challenges remain. During specific time periods, such as the transition season of October and November, the models' predictions appear smoother and fail to fully capture all signal fluctuations.

Three-dimensional (3-D) radiative transfer effects of clouds on trace gases and aerosols have been studied extensively using satellite products and model simulations. In the vicinity of clouds, satellite measured reflectances are higher than the cloud-free scenes at the bright side of clouds and lower in the shadows. In order to understand the 3-D effects of clouds, we have developed a 3-D Monte Carlo radiative transfer model at KNMI (called MONKI). MONKI has been used to simulate TROPOMI measurements at UV wavelengths with polarization.

TROPOMI is a satellite spectrometer with a spatial resolution of 3.5 km x 5.5 km. The objective of TROPOMI is to provide accurate atmospheric composition products. We have used MONKI to simulate the TROPOMI NO₂ airmass factors and reflectances at 340 and 380 nm at different cloudy scenes. Various cloud optical thickness, cloud heights, and surface albedo values are specified in the simulations. Then Absorbing Aerosol Index (AAI) values are calculated for the simulated scenes using TROPOMI AAI algorithm. Based on the AAI features in the simulated scenes, we re-analysed the AAI data in the TROPOMI product in the shadows. For the NO₂ products, we simulated the NO₂ airmass factors using MONKI and compared with NO₂ airmass factor calculated using 1-D model simulations. Finally we analysed the TROPOMI NO₂ products in the shadowed pixels and in the cloud-free, shadow-free pixels to quantify the impacts of shadows on the NO₂ product.

Shadows from clouds and buildings present in high spatial resolution satellite imagery are typically filtered out in image processing. However, the shadows can be used to retrieve aerosol and surface properties simultaneously. In a new retrieval algorithm, the aerosol optical thickness is retrieved using the contrast between shadowed pixels and bright pixels and compared with AERONET data.

In the presentation we will report the progresses on the 3-D model simulations of AAI, NO₂ AMFs, impacts of shadows on NO₂ products, and the aerosol retrievals using shadows.

Northern Ireland's contribution to the UK's fifth carbon budget mandates a reduction in emissions of at least 35% by 2030 compared to the 1990 level. In comparison to the rest of the United Kingdom, Northern Ireland has relatively high percentages per capita emission in the agricultural, transportation, residential, LULUCF (land use, land use change, and forestry) and power sector. The electricity generated by the renewable energy is increasing since 2003 significantly. The increasing rate is nearly three times for the N. Ireland than the UK. In the year 2021, the electricity generated by the wind has increased to 47% (Figure 1). In this project we have conducted investigations into the current status of carbon emission in Northern Ireland (NI) along with the electricity generation situation through the renewable energy like wind and solar energy applications. Further more the types of renewable energy sources have been analysed. As comparison, the CO2 emission distribution in the NI has been observed by the GHGSat and a program has been developed to carry on analysis with the CO2 emission data collected during the past ten years. This developed tool will help us to study the effect of using the renewable energy for the power generation with the CO2 distribution in the atmosphere in the N.Ireland. It is also, the analysis will help us to understand the influence of different types of renewable energy to the CO2 reduction.

Figure 1 shows the total electricity energy consumption in N. Ireland along with the increased portion of electricity generated by the renewable energy since 2008. The electricity consumption in the past 15 year is reducing while the percentage of the electricity generated by the renewable energy is continuing increased from 6% in 2008 to 47% in 2022.

The Figure 2 shows the renewable energy applications since 1990 to 2022 with capacity up to 48MW. The installation of the renewable energy sites is continuing over time. It is aiming to find out the effect of CO2 reduction with the geo-distribution of the renewable applications.

Figure 3 shows the mirror image of the CO2 distribution in the N. Ireland in the past ten years.

EMPAC addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical influences. Satellite and ground-based remote sensing together with detailed in situ measurements provide complimentary information on the contributions from different sources and processes affecting AQ, with scales varying from the whole of China to local studies and from the surface to the top of the boundary layer and above. Different species contributing to air quality are studied, i.e. aerosols, in AQ studies often represented as PM2.5, trace gases such as NO2, NH3, Volatile Organic Compounds (VOCs) and O3. The primary source of information in these studies is the use of a variety of satellite-based instruments providing data on atmospheric composition using different techniques. However, satellite observations provide column-integrated quantities, rather than near-surface concentrations. The relation between column-integrated and near-surface quantities depends on various processes. This relationship and the implications for the application of satellite observations in AQ studies are the focus of the EMPAC project. Initial results of detailed process studies using ground/based in situ measurements, instrumented towers, as well as remote sensing using lidar and Max-DOAS will be presented. A unique source of information on the vertical variation of NO2, O3, PM2.5 and BC is obtained from the use of an instrumented drone.
The results of the third year will be presented, including trend studies of air pollutants, algorithm development for aerosol retrieval, and analyses of NOx emissions from Sentinel 5p over the YRD.

This project is focused on multi-orbits applications of SAR imaging, and is intended to support use of multi-pass data stacks from: the upcoming P-Band mission BIOMASS; future L-Band missions, such as the SAOCOM constellation, the upcoming Chinese L-Band bistatic Mission Lu-Tan1, and potentially Tandem-L and Rose-L; the C-Band Sentinel Missions.

The main results until now are summarized into 4 contributions:

1. A detailed experimental analysis was carried out to compare two techniques for estimating forest height and vertical structure using airborne synthetic aperture radar (SAR) data, namely SAR tomography (TomoSAR) and the phase histogram (PH) technique. Using multiple SAR images, TomoSAR allows for a direct imaging of the three-dimensional (3D) electromagnetic structure of the vegetation layer, from which biophysical parameters such as forest height and underlying terrain topography can be extracted. The PH technique assigns each pixel in a SAR interferogram to a specific height bin based on the value of the interferometric phase, allowing for a local estimation of the vertical profile of forest scattering by accumulation of pixels fall within a given spatial window.

Results indicate that the PH technique can only loosely approximate the vertical structure produced by SAR tomography, but it can be used to produce a fairly good estimate of forest height. In particular, using the datasets collected by the TomoSense campaign, TomoSAR and PH techniques are observed to produce an average root mean square error (RMSE) of 2.63 m and 4.72 m in NW flight data, and 1.86 m and 5.26m in SE flight data, respectively. The observed results are interpreted in light of a simple physical model to predict phase variations in the two cases where forest scattering is determined by the presence of a dominant scatterer at each resolution cell or by a multitude of elementary scatterers, leading to the conclusion that the PH technique is best fit for the case of high- or very high-resolution data at higher frequency bands. Overall, the analysis in this paper demonstrates, both theoretically and experimentally, that the PH technique cannot achieve the same performance as multi-baseline tomography when applied to lower frequency data at a resolution of few meters. Yet, even in these conditions we remark that the PH technique allows for the retrieval of forest height based on a single interferogram at a single polarization. This makes the PH technique extremely interesting in the context of spaceborne missions.

2. A solution is proposed to the problem of atmospheric estimation using SAR data and a network of GNSS stations on the ground. The raw data from each station is processed to extract a GNSS-derived APS. Then, the SAR-derived APS is extracted on the spatial location of the GNSS stations. Such measurements are the sum of the true APS and the orbital error. We use the GNSS-derived APS as a ground truth, removing them from the SAR-derived estimates leading to a set of measurements of the pure orbital error. An inverse problem is solved, leading to two parameters characterizing the orbital error. The benefit of this inversion is double. First of all, the two estimated parameters can be used to provide a quality proxy for the trajectory. Second, the two parameters can be used to compute the forward model on the whole grid of the APS map (and not just on the set of GNSS stations as done before), leading to a calibration phase screen.

The procedure is tested using a dataset of more than 30 Sentinel-1 images and a network of GNSS stations in Sweden. The algorithm shows excellent performance. The validation process compares a set of independent GNSS stations with the SAR-derived APS before and after the calibration procedure. A second validation is carried out using a separate NWPM showing, once again, very good performances.

3) An alternative approach is proposed for NESZ estimation that exploits an interferometric pair of images over land. The method is based on the relation between coherence and noise. we use the stack to generate a set of interferograms with short temporal baselines. Each interferogram is fitted with a coherence model, and all measures are averaged to improve robustness. By repeating for each incidence angle, the NESZ profile of the new satellite can be characterized. The procedure was validated and tested using a stack of Sentinel-1A (S1A) and Sentinel-1B (S1B) images. First, the noise level of S1B was obtained separately according to Equation (1). Then, S1B’s NESZ was estimated by the procedure described above, using the stack of S1A data. The comparison between the two results confirmed that the noise level of a new satellite could be characterized over land, with as little as one available product.

4) InSAR has been widely recognized as an effective tool for landslide investigation. However, its measurement accuracy is largely limited by the complex atmospheric delay distortion in alpine valley regions, resulting in poor performance of landslide detection and monitoring. Particularly, the spatial atmospheric heterogeneity over wide areas cannot be accurately reflected by conventional empirical phase-elevation models or external data-based methods. Here we proposed a multi-temporal moving-window linear model (MMLM) to correct the tropospheric delay for wide-area landslide investigation. This is a linear regression model based on the elevation-phase relationship for modeling multi-temporal phases within a sliding local window. It mitigates the influence of local turbulent phase, local landslide deformation, and phase unwrapping error on parameter estimation, providing precise heterogeneous InSAR atmospheric corrections for wide-area landslide identification and deformation monitoring. Experimental results with descending and ascending Sentinel-1 data over the reservoir area of the Lianghekou hydropower station clearly demonstrated that the proposed MMLM model outperforms modern APS correction approaches including the ERA5, GACOS, spatial-temporal filtering, and traditional linear model.

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. The lack of available global CO₂ and CH₄ measurements makes it difficult to estimate their emissions accurately. Satellite measurements would be very helpful for understanding the global CO₂ and CH₄ flux distribution if CO₂ and CH₄ column-averaged dry air mole fractions (XCO₂ and XCH₄) could be measured with a precision of better than 2 ppm. To this point, the main objectives of this research project in Dragon 5 is to use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval inter-comparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. In this presentation, we will introduce our newly progress on CO₂ and CH₄ concentration measurement from in-flight and future satellite, and the CO₂ and CH₄ flux inversed from satellite and ground base measurement. The next generation of TanSat mission kicked-off in last two years, the new design of TanSat mission will provide wilder measurement in the swath to cover the global in daily observation. The preliminary OSSE on global and regional scale introduce the error reduction efficiency of TanSat-2 mission, and we also develop a new method to separate the ecosystem and anthropogenic emission which will be helpful for atmospheric inversion method toward the Global Stocktake. The TanSat mission has been used in city carbon emission signature investigation, which proof the TanSat capability on the anthropogenic emission signal Identify. We also developed UAV and ground-based CO2 measurement network, e.g. CHACOON for the carbon monitoring system build, and validations for satellite.

The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen5-02 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The method about the on-board calibration and authenticity verification method are designed and the results are analyzed, and the results illustrate that the specifications meet the mission’s requirements. The on-board calibration results showed that the calibration coefficient range of the O2 channel is 1.05–1.15, the mean value is 1.10 and the standard deviation is 2.72%; the calibration coefficient of the CO2-1 channel is 1.05–1.13, the mean value is 1.09 and the standard deviation is 2.64%; the calibration coefficient of the CH4 channel is 1.08–1.10, the mean value is 1.11 and the standard deviation is 2.73%; the calibration coefficient of the CO2-2 channel is 1.09–1.14, the mean value is 1.12 and the standard deviation is 2.93%. The above results show that the radiation performance of each channel of the GMI shows no significant attenuation during this period, that the site calibration coefficient has no significant fluctuation and that the in-orbit operation state is stable. The authenticity verification results showed that the CO2 column concentration deviation of the satellite ground synchronization inversion was about 1.5 ppm, and the CH4 column concentration deviation was about 11.3 ppb, which verified the on-orbit detection accuracy of the GMI, and laid a foundation for the subsequent satellite inversion algorithm optimization and systematic error correction.

A large number of studies explored the mechanisms of synoptic-scale forming of ozone mini holes/highs: far-range meridional transport of air masses from regions with different climatological ozone mixing ratios (called “mechanism A”) and adiabatic vertical displacement of isentropes (mechanism B). In the paper, we investigate ozone mini holes/highs events over the North China Plain in winter during 1979-2019. The analysis shows that most ozone mini holes/highs events conform to the mechanism A and B and two typical weather change processes accompanying with these events: rapid cooling weather processes accompanies with ozone minihighs, while abnormal rapid warming weather processes accompanies with ozone miniholes. However, we also find a significant proportion of anomalous events do not conform to this rule: rapid cooling processes accompanies with ozone minihighs, while rapid warming processes accompanies with ozone minihighs. Behind these abnormal phenomena, there may exists a new ozone mini holes/highs forming mechanism: rapid cooling weather processes accompanies with ozone miniholes, while abnormal warming weather processes accompanies with ozone minihighs. The new mechanism may be related to the land and sea position of the North China Plain in the east and its landform features in the west.

ID: 59376/DRAGON 5
Title: Pacific modulation of the Sea level variability in the Arctic Ocean and Nordic Seas.

It is crucial to monitor and understand regional sea-level changes that can differ from Global Mean Sea Level (GMSL) both in terms of magnitude as well as governing forcing and mechanisms (Stammer et al., 2013). For instance, while changes in salinity can have significant distinct impact on regional sea level change, such as in the Arctic Ocean, it has minor effect on GMSL. Quantifying the natural variability in the regional sea level change is also urgent in order to distinguish it from a potentially forced (anthropogenic) signal. Furthermore, the role of remote impact of climate variability in one region on the other needs to be well-understood. Climate change in the Pacific can, for instance, impact Arctic warming and the sea ice (Li et al., 2015; Svendsen et al., 2018; Yang et al., 2020). How this translates to sea level change remains unclear. The aim of this study is to examine and relate the sea level variability of the Beaufort Gyre (BG) in the Arctic Ocean to natural climate variability of the Pacific Ocean.

In so doing, results of three distinct analyses are reported here: (i) The variability of the BG as estimated using the state-of-the-art ESA Cryo-TEMPO altimeter data, while freshwater content estimates are derived from in-situ observations, ocean reanalysis and satellite sea surface salinity, satellite altimeter and gradiometer data; (ii) The benefits of the reprocessed altimetry dataset at 5 Hz with augmented signal resolution to study the mesoscale-based sea level variability of the Arctic and Nordic Seas; (iii) The usefulness of estimating a Multiparameter Eddy Significant Index in the Nordic Seas; and (iv) The remarkable role of North Pacific Oscillation in the Beaufort Sea level change.

龙计划5 ID：59376
题目：太平洋气候系统对北冰洋和北欧海海平面变化的影响

监测和理解区域海平面变化极为重要。区域海平面变化的幅度以及影响因子和机制方面均有别于全球海平面的变化（Stammer等，2013）。例如，盐度变化在北冰洋海平面的变化中具有重要作用，但其对全球海平面变化的影响则很小。定量研究区域海平面变化对自然变率和人为强迫的响应具有重要意义。此外，一个地区的气候变化对另一个地区的远距离影响需要深入探究。例如，太平洋气候系统变化可以影响北极地区的气候变化（Li等，2015；Svendsen等，2018；Yang等，2020）。然而，太平洋气候变化如何影响北冰洋的海平面变化尚不明晰。本研究旨在探究波弗特海的海平面变化与太平洋气候系统之间的联系。

本次报告主要介绍四项相关的研究内容：（1）利用先进的ESA Cryo-TEMPO高度计数据估算波弗特海平面的变化，其中数据来自于实地观测、海洋再分析、卫星海表盐度、卫星高度计和梯度计；（2）重新处理的z具有增强信号分辨率的5 Hz下的高度计数据，用于研究北冰洋和北欧海域的中尺度海平面变化；（3）在北欧海域估算多参数涡度显著指数的作用；（4）北太平洋涛动模态可以显著影响波弗特海平面变化

Successful monitoring and modelling climate change in water, energy and carbon cycles in the Pan-Third Pole Environment (CLIMATE-Pan-TPE) demands improved understanding of the interaction between the Asian monsoon, the Tibetan Plateau surface, and the plateau atmosphere in terms of the water and energy budget. CLIMATE-Pan-TPE aims to verify or falsify recent hypotheses, which include the links between plateau heating and monsoon circulation, snow cover and monsoon strength, soil moisture and timing of monsoon. We analysed the mechanistic bases for projections of the changes of glaciers and permafrost in relation to surface and tropospheric heating on the Tibetan Plateau, and their impacts on water resources in South East Asia. More specifically we will report the following results: (1) a long-term (2005-2016) hourly dataset of the integrated land-atmosphere interaction observations from six field stations over the Tibetan Plateau; (2) monthly actual evapotranspiration and its spatial distribution on the TP (2001-2018) using the Surface Energy Balance System (SEBS) model with satellite products and meteorological reanalysis data as input; (3) hourly land surface heat fluxes and evapotranspiration estimated based on multisource remote sensing data; (4) a monthly 0.01° terrestrial evapotranspiration product for the TP (1982-2018) using the MOD16-STM equation; (5) methods for estimating surface soil moisture, monitoring and predicting freeze-thaw states and quantifying soil ice content with microwave remote sensing data, (6) estimation of the total annual evaporation amounts over the entire TP lakes as 51.7±2.1 km³ year^-1, with a plausible hypothesis of near-zero heat storage during ice-free season and near-constant ice sublimation during winter; and (7) the water vapor channel of the Yarlung Zangbo Grand Canyon (YGC) in the southeastern TP was investigated by establishing a three-dimensional comprehensive observation system of mountain land-air interaction, water vapor transport, cloud cover, and rainfall activity. The observation datasets will benefit future research on mountain meteorology.

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