Recent Publications

2026

Dehvari, M., Farzaneh. S., Forootan, E. (2026), Enhancing short-term Zenith Wet Delay prediction using a combined data assimilation and conditional GAN approach, Advances in Space Research, ISSN 0273-1177, https://doi.org/10.1016/j.asr.2026.04.077

Abstract

Accurate short-term prediction of the Zenith Wet Delay (ZWD) is essential for enhancing the precision of GNSS positioning, numerical weather prediction, and tropospheric water vapor monitoring. Recently, combined data assimilation (C/DA) approaches have shown promising results in improving short-term ZWD estimates by integrating GNSS-derived observations with numerical model outputs. However, their prediction accuracy tends to decrease rapidly at longer forecast horizons due to the limited temporal resolution of background models and the highly variable nature of atmospheric water vapor. To address this limitation, this study proposes an enhanced predictive framework that integrates a Conditional Generative Adversarial Network (cGAN) with the C/DA-derived ZWD maps. The cGAN model is trained using sequences of 24-h C/DA ZWD maps as inputs and the corresponding 24-h-ahead C/DA analysis maps (0-h predictions) as targets, enabling the model to capture the nonlinear spatiotemporal evolution of tropospheric delay with higher fidelity. The proposed framework was evaluated over the year 2022 and compared with C/DA, GTrop, and Global Forecast System (GFS) models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) metrics. Results demonstrate that the cGAN method consistently outperforms all reference models across all prediction horizons, achieving RMSE values between 0.95–4.5 cm and CC values between 0.99 and 0.86 for prediction from 1 to 24 h horizons. On average, the cGAN model improves RMSE by 33% and 38% compared to the C/DA and GFS models, respectively, and by approximately 50% compared to the GTrop model. These findings confirm that combining deep learning with data assimilation can significantly enhance the accuracy and stability of short-term ZWD forecasts, offering a robust and efficient solution for real-time atmospheric delay modeling and GNSS-based meteorological applications.

Garrido-Carretero M. S., De Lacy-Pérez de los Cobos M. C., Giménez-De Ory E., Retegui-Schiettekatte L. A. (2026), GNSS-CORS as water vapor sensors for local atmospheric monitoring: Comparing high-end geodetic-grade and low-cost stations in S Spain, Volume 41, Remote Sensing Applications: Society and Environment, 101880, ISSN 2352-9385,  https://doi.org/10.1016/j.rsase.2026.101880

Abstract

In order to study a possible densification of the regional GNSS network in S Spain, a local cost-effective GNSS network has been installed in the province of Jaén: JAENet. This network is the first one installed in Spain to analyze the GNSS-derived Precipitable Water Vapor (GNSS-PWV) and its time variations. JAE1 is the first low-cost GNSS Continuously Operating Reference Station (GNSS-CORS) setup. It is strategically located very close to UJAE, a high-end geodetic-grade GNSS-CORS, in order to investigate the GNSS-PWV at the same geographic location and under the same environmental and atmospheric conditions. This study aims to evaluate the performance of low-cost GNSS devices for atmospheric water vapor monitoring through an experimental design and the first comparison of data coming from low-cost and high-precision devices over a period of more than one year. Eighteen months divided into six seasonal periods is considered. The common GNSS data period for both GNSS-CORS has been processed using the Precise Point Positioning (PPP) method to estimate their coordinates and evaluate the Zenith Tropospheric Delay (ZTD) using open-source GNSS software. The results show a good agreement between JAE1 and UJAE ZTD time series, with differences ranging from −11.59 mm to 10.12 mm, a mean difference value at the 2-mm level and a remarkably high correlation equal to 0.99. The difference between the mean of GNSS-PWV at GNSS-CORS throughout the six periods analyzed is always under the 1-mm level. The results show that low-cost GNSS-CORS are promising as water vapor sensors for local atmospheric monitoring.
2025

Schumacher, M., van Dijk, A.I.J.M., Retegui-Schiettekatte, L. et al. (2025), Space-based natural and human-induced water storage change quantification. Sci Rep 15, 18484. https://doi.org/10.1038/s41598-025-01938-8

Abstract

Understanding water availability and its response to climate change and water extraction is crucial for sustainable water management in Australia’s Murray–Darling Basin. This study introduces a space-based method that quantifies the natural and human-induced impact on changes in terrestrial water storage. It reveals an impact of 17% due to water extraction for irrigation over the past two decades, with 84% of this extraction coming from surface water and 16% from groundwater. The human-induced impact varies spatially with higher values in the southern Murray (up to 5.6%) and smaller values in the northern Darling (down to 0.2%). Data-model fusion of the satellite-based water storage changes into a hydrological model, which does not simulate water extraction, man-made reservoirs and wetlands, improved the representation of water storage variability and intensified trends in drying and wetting periods. This study adds valuable findings to better understand natural and human-induced impacts on the regional water resources under changing climate and to better represent these impacts (80% and 20% respectively) within hydrological models after data-model fusion.

Retegui-Schiettekatte, L., Schumacher, M., Yang, F. et al. (2025), An ensemble Kalman filter with rescaling disaggregation for assimilating terrestrial water storage into hydrological models. Sci Rep 15, 28675. https://doi.org/10.1038/s41598-025-13602-2

Abstract

Assimilating satellite-based Terrestrial Water Storage (TWS) observations can improve the vertical summation of water storage states in hydrological models. However, it can degrade individual storage compartments or hydrological fluxes, limiting the applicability of TWS Data Assimilation (DA) for water management and flood monitoring. This issue arises from the ensemble-based TWS update disaggregation approach used by DA techniques like the Ensemble Kalman Filter (EnKF). Thus, this study makes two key contributions. First, we introduce a novel analysis method that provides quantitative and qualitative insights into how individual storage compartments are affected during TWS DA, by examining the sign and magnitude of the individual storage updates and their responses. Second, we propose a new disaggregation approach, EnKF-R, which “rescales” the individual storage of model compartments to match the updated TWS, avoiding the use of ensemble statistics within the disaggregation process. The EnKF-R approach was tested in two climatologically different river basins and validated against both synthetic and real independent data. Our results show that EnKF-R produces similar TWS estimates to the classical EnKF while reducing degradations in individual water storage compartments and with lower computational cost, making it a promising alternative. Limitations regarding spatial continuity and uncertainty estimation require further developments.

Yang, F., Schumacher, M., Retegui-Schiettekatte, L., van Dijk, A. I. J. M., and Forootan, E. (2025), PyGLDA: a fine-scale python-based global land data assimilation system for integrating satellite gravity data into hydrological models, Geosci. Model Dev., 18, 6195–6217, https://doi.org/10.5194/gmd-18-6195-2025

Abstract

Data assimilation (DA) of time-variable satellite gravity observations, such as those from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and future gravity missions, can be used to constrain simulations of the vertical sum of water storage in Global Hydrological Models (GHMs). However, current DA implementations of these Terrestrial Water Storage (TWS) changes are often performed at regional scales or, if applied globally, at low spatial resolutions. This limitation is primarily due to the high computational demands of DA and numerical challenges, such as instabilities in covariance matrix inversion. To fully exploit the potential of satellite gravity observations and the high spatial resolution of GHMs, we developed PyGLDA, an open-source Python-based system that enables fine-scale and computationally efficient global DA. The key innovations of PyGLDA include (1) a global patch-wise DA approach using domain localization and neighboring-weighted global aggregation and (2) seamless compatibility between basin-scale and grid-scale DA implementations. PyGLDA represents a significant functional improvement over previous DA systems, offering wide-ranging and flexible options for user-specific applications. The modular structure of the system allows users to customize water storage compartments, modify observation representations, and potentially select different GHMs. This paper provides a comprehensive description of PyGLDA and its application in a case study of the Danube River Basin, along with a demonstration of global DA, where experiments involve integrating monthly GRACE TWS fields (2002–2010) with the daily W3RA water balance model at 0.1° spatial resolution.

Çakan, Ç., Yılmaz, M. T., Dobslaw, H., Ince, E. S., Evrendilek, F., Förste, C., and Yagci, A. L. (2025), Evaluation of globally gridded precipitation data and satellite-based terrestrial water storage products using hydrological drought recovery time, Hydrol. Earth Syst. Sci., 29, 3359–3377, https://doi.org/10.5194/hess-29-3359-2025

Abstract

Accurate precipitation observations are crucial for understanding meteorological and hydrological processes. Most precipitation products rely on station-based observations, either directly or for bias-corrected satellite retrievals. To validate these station-based precipitation products, additional independent data sources are necessary. This study aims to assess the performance of the Global Precipitation Climatology Centre (GPCC) Full Data Monthly Product v2022 and Global Precipitation Climatology Project (GPCP) v3.2 Monthly Analysis Product by estimating the hydrological drought recovery time (DRT) from precipitation and the terrestrial water storage anomaly (TWSA) acquired from satellite gravimetry. This study also evaluates the drought monitoring performance of G3P and JPL mascon total water storage (TWS) monthly solutions from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The current study employed two methods to estimate DRT and evaluated the consistency of DRT estimates by calculating the time difference in DRT values derived from the two methods. Globally and across all climate zones, GPCC and GPCP showed comparable performance in hydrological applications with no significant differences in the mean DRT estimates. For the TWS products, DRT estimates using JPL mascon were, on average, 2.6 months longer than those using G3P. However, G3P showed approximately 5.0 % higher consistency than JPL mascon globally and across each climate zone, suggesting its better suitability for more precise drought-related analyses. These findings indicate that G3P outperforms JPL mascon in aligning with precipitation products and offers better consistency in DRT estimation. These results provide valuable insights into the accuracy of precipitation and TWSA products by utilizing hydrological drought characteristics, enhancing our understanding of meteorological and hydrological processes.

Mo, S., Schumacher, M., van Dijk, A., I., J., M., Shi, X., Wu, J., Forootan, E. (2025), Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts, Volume 52, e2024GL112677, ISSN 0094-8276,  https://doi.org/10.1029/2024GL112677

Abstract

Global terrestrial water storage anomaly (TWSA) products from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE/FO) have an approximately three-month latency, significantly limiting their operational use in water management and drought monitoring. To address this challenge, we develop a Bayesian convolutional neural network (BCNN) to predict TWSA fields with uncertainty estimates during the latency period. The results demonstrate that BCNN provides near-real-time TWSA estimates that closely match GRACE/FO observations, with median correlation coefficients of 0.92–0.95, Nash-Sutcliffe efficiencies of 0.81–0.89, and root mean squared errors of 1.79–2.26 cm for one- to three-month ahead predictions. More importantly, the model advances global hydrological drought monitoring by enabling detection up to three months before GRACE/FO data availability, with median characterization mismatches below 16.4%. This breakthrough in early warning capability addresses a fundamental constraint in satellite-based hydrological monitoring and offers water resource managers critical lead time to implement drought mitigation strategies.

Wu, Y., Yang, F., Liu, S., Forootan, E., (2025), PyHawk: An efficient gravity recovery solver for low–low satellite-to-satellite tracking gravity missions, Computers & Geosciences, Volume 201, 105934, ISSN 0098-3004,
https://doi.org/10.1016/j.cageo.2025.105934

Abstract

The low–low satellite-to-satellite tracking (LL-SST) gravity missions, such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO), provide an important space-based Essential Climate Variable (ECV) to measure changes in the Terrestrial Water Storage (TWS). Due to the high-precision Global Navigation Satellite System (GNSS) receiver, accelerometers, and inter-satellite ranging instrument, these LL-SST missions are able to sense extremely tiny perturbations on both the orbit and inter-satellite ranges, which can project into the Earth’s time-variable gravity fields. The measurement systems of these LL-SST missions are highly complex; therefore, a data processing chain is required to exploit the potential of their high-precision measurements, which challenges both general and expert users. In this study, we present an open-source, user-friendly, cross-platform and integrated toolbox “PyHawk”, which is the first Python-based software in relevant field, to address the complete data processing chain of LL-SST missions including GRACE, GRACE-FO and probably the future gravity missions. This toolbox provides non-expert users an easy access to the payload data pre-processing, background force modeling, orbit integration, ranging calibration, as well as the ability for temporal gravity field recovery using LL-SST measurements. In addition, a series of high-standard benchmark tests have been provided to evaluate PyHawk, confirming its performance to be comparable with those used to provide the official Level-2 time-variable gravity field solutions of GRACE. Researchers working with orbit determination and gravity field modeling can benefit from this toolbox.

Retegui-Schiettekatte, L., Schumacher, M., Madsen, H. & Forootan, E. (2025), Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin, Science of The Total Environment, Volume 975, 179181, ISSN 0048-9697,
https://doi.org/10.1016/j.scitotenv.2025.179181

Abstract

The integration of satellite-based observations into hydrological models contributes to achieving more precise simulations, thus supporting hazard mitigation and policy-making especially in poorly gauged basins. Sub-monthly Terrestrial Water Storage (TWS) observations derived from the Gravity Recovery and Climate Experiment (GRACE) mission have been shown to contain useful information for the prediction and monitoring of sub-monthly water storage anomalies such as floods. This study assesses, for the first time, the benefits and challenges of integrating sub-monthly TWS into a large-scale hydrological model during flood events. The experiment is carried out for the Brahmaputra River Basin and the integration is performed through the state-of-the-art of sequential Data Assimilation (DA) with the aim of improving model water storage estimates. The results indicate that the daily TWS DA, based on the Ensemble Kalman Filter (EnKF), successfully introduces the observed sub-monthly TWS variability into the model (differences below 10 mm with daily GRACE TWS). The daily TWS DA spatially and vertically downscales storage updates with precise timing and distribution. Especially, it modifies the river storage compartment, where sub-monthly variations are expected during floods. In contrast, the updates of monthly TWS DA, implemented through both an EnKF and an Ensemble Kalman Smoother (EnKS), introduce undesired peaks in the TWS time series. Choosing an adequate model covariance localization is found to be crucial for daily TWS DA. Finally, the statistical characteristics of the daily TWS DA and the translation of water storage updates into river discharge are investigated, and recommendations for future developments are provided.

ZHANG, W., Yang, F., WU, Y., LIU, H., Zhang, T., Luo, Z., & Forootan, E. (2025). HUST-CRA: A New Atmospheric De-aliasing Model for Satellite Gravimetry. ADVANCES IN ATMOSPHERIC SCIENCES, 42(2), 382–396. https://doi.org/10.1007/s00376-024-4045-6

Abstract

Atmospheric de-aliasing is one of the most important background models for recovering Earth’s temporal gravity field from gravity satellite missions. To meet the needs of China’s gravimetric satellite platform, an independent atmospheric de-aliasing model that relies on Chinese meteorological data needs to be developed. The release of CRA-40, as the first-generation Chinese atmospheric reanalysis, provides the opportunity. This study proposes a revised modeling method to calibrate CRA-40 and develops a new atmospheric de-aliasing model (HUST-CRA, 2002–20). Intensive assessments are made between HUST-CRA and the latest official de-aliasing product of the international gravity satellite mission. The tidal components of the two products demonstrate high consistency, e.g., the spatial correlation for the major tide S1 is 0.96. The non-tidal components of the two products are also equivalent: (1) the temporal correlation of low-degree terms is higher than 0.97, except for the term of S22 (0.93); (2) the spectral correlation of degree geoid height up to degree/order 100 is as high as 0.99; (3) the confidence interval of the spatial correlation (2002–20) is [0.971, 0.995] at a confidence level of 95%; and (4) the difference in KBRR (K-band range rate) residuals is less than 0.08 µm s−1, the difference in the derived temporal gravity field is less than 0.32 mm in terms of geoid height, and both are apparently beyond the ability of the current gravity satellite mission. This confirms that CRA-40 is of high quality and that the derived de-aliasing product, HUST-CRA, is accurate enough to be used in both Chinese and international gravity satellite missions.

Dehvari, M., Farzaneh, S., & Forootan, E. (2025). Forecasting rainfall events based on zenith wet delay time series utilizing eXtreme Gradient Boosting (XGBoost). Advances in Space Research, 75(3), 2584-2598. https://doi.org/10.1016/j.asr.2024.11.013

Abstract

Accurate rainfall prediction is vital for mitigating flood and storm disasters as well as for planning agricultural activities and water resources management. GNSS observations enable the estimation of atmospheric water vapor content through the Zenith Wet Delay (ZWD) value, where previous studies indicate a strong correlation between the ZWD-derived indicators and rainfall events. However, specifying these indicators is challenging due to the spatial variability of precipitation and the location of GNSS stations. While many studies have integrated meteorological parameters with GNSS-derived Zenith Total Delay (ZTD) values to enhance prediction accuracy, the scarcity of meteorological instruments at GNSS stations remains a limitation. In this study, we employed ZWD-derived features and utilized the eXtreme Gradient Boosting (XGBoost) classification method to predict rainfall events. Ten parameters (including station latitude, longitude, elevation, ZWD monthly anomaly, ZWD slope, ZWD maximum, maximum ZWD derivative, month, hour, and precipitation flag) were used as features in the input layer of the considered XGBoost model. For training, data from 40 GNSS stations spanning five consecutive years (2016 to 2020) in the eastern United States of America were analyzed to derive the required features from 4-hour ZWD time series. To evaluate the proposed method, estimated rainfall was compared with the observations of weather stations during 2021. Furthermore, the results of five GNSS stations (not included in the training) were compared with the regional rainfall events of 2016 to 2021. Our results indicate that the proposed method achieves a mean True Forecast Rate (TFR) and a mean False Forecast Rate (FFR) of approximately 0.75 and 0.15, respectively, demonstrating performance comparable to studies incorporating meteorological parameters.

Liu, S., Yang, F., & Forootan, E. (2025). SAGEA: A toolbox for comprehensive error assessment of GRACE and GRACE-FO based mass changes. Computers & Geosciences, 196, Article 105825. https://doi.org/10.1016/j.cageo.2024.105825

Abstract

The level-2 time-variable gravity fields obtained from Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission are widely used in multi-discipline geoscience studies. However, the post-processing of these gravity fields to obtain a desired signal is rather challenging for users who are not familiar with the level-2 products. In addition, the error assessment/quantification of these derived signals, which is of increasing demand in science application, is still a challenging issue even among professional GRACE(-FO) users. In this paper, we review the known steps of post-processing, along with their implementation strategies. We also make a comprehensive investigation into the error of GRACE(-FO) based mass changes, and for the first time, we define the so-called error into three independent categories. This work, including the post-processing steps and the assessment of each error, is integrated into an open-source Python toolbox called SAGEA (SAtellite Gravity Error Assessment). With diverse options, SAGEA provides flexibility to generate signals along with the full error from level-2 products. In addition, a novel in-depth optimization of our post-processing implementation gains a speed-up of ∼100 times better than traditional method. For verification, a number of case studies are carried out with SAGEA to obtain a comprehensive error assessment of GRACE(-FO) level-2 product at global and local scales.

van Dijk, A., Beck, H. E., Boergens, E., de Jeu, R. A. M., Dorigo, W. A., Edirisinghe, C., Forootan, E., Guo, E., Güntner, A., Hou, J., Mehrnegar, N., Mo, S., Preimesberger, W., Rahman, J., & Rozas Larraondo, P. (2025). Global Water Monitor 2024, Summary Report. http://www.globalwater.online. Acces to pdf document

Abstract

Our global water systems are under mounting pressure as climate change drives more extreme weather events and disrupts the water cycle. The year 2024 was a year of extremes but not an isolated occurrence. It fits with a worsening trend of more intense floods, prolonged droughts, and record-breaking extremes. These changes impact water availability and increase the risks to lives, infrastructure and ecosystems from water-related disasters. Reliable and timely information about water resources and hazards is more crucial than ever, yet traditional groundbased measurement networks continue to decline. Satellite observations now play a vital role, offering rapid and consistent global data on the atmosphere and Earth’s surface, but they should not replace networks on the ground. The Global Water Monitor Consortium unites public and private organisations to deliver open, actionable climate and water data. By integrating satellite and ground observations, we aim to provide timely updates on critical aspects of the water cycle. Our Global Water Monitor platform (www.globalwater.online) allows anyone to explore a wealth of climate and water data free of charge. This third annual report builds on the work of previous years, summarising the state of the global water cycle in 2024, identifying key trends, and analysing major hydrological events. It includes updated metrics on rainfall, temperature, air humidity, river flows and water stored in lakes, soil and underground. It also provides insights into extreme rainfall and
temperatures. This report reinforces a clear message: as the planet warms, water challenges are escalating, year after year. By trying to provide information on changes and events, we hope to support informed decision-making to protect communities, infrastructure, and ecosystems in an increasingly volatile future.
2024

Retegui, L. , Casas, D., Casalbore, D., Yenes, M., Nespereira, J., Estrada, F., Canari, A., Chiocci, F.L. , Idárraga-García, J., Teixeira, M., Ramos, J., López-Gonzalez, N. (2024). Size-frequency distribution of submarine mass movements on the Palomares continental slope (W Mediterranean). Marine Geology. Volume 477, 107411, ISSN 0025-3227,
https://doi.org/10.1016/j.margeo.2024.107411.

Abstract

In this work, over 3620 km2 from the Palomares continental slope, which is located in the W. Mediterranean Sea, was analysed to quantify the impact of recent mass movements on this margin. A total of 936 landslides were identified, mapped and characterised by defining several morphometric variables that outline the accumulated impact of landslides equivalent to 918 km2 and 10.34 km3 of eroded sediment on the continental slope. The smallest event area was 0.0014 km2, whereas the largest event area was 32.48 km2. Smaller scars with a higher headwall gradient tend to dominate when the environment is steeper, and major mass movements are located on open slopes and structural highs. However, the slight or null correlations between variables indicate that a wide range of sizes may occur on any slope gradient and at any depth. The Palomares continental slope is intensively affected by mass movements. Compared with other passive margins (e.g., the U.S. Atlantic continental margin), landslides mobilised a limited amount of sediment, although it is comparable to other Mediterranean areas where small- to moderate-sized events are characteristic. The cumulative size distribution can be defined by a power-law function that describes events larger than 0.7 km2 with an exponent of α = 1.269. These results are consistent with those of other published inventories, including onshore cases. This result allows us to assume that the scale-invariant properties of the events are mapped. Scale-invariant properties can be explained by different models; self-organised criticality (SOC) is probably the most assumed by the scientific community, although alternative models may be nominated. Each model has important implications in terms of the landslide distribution and long-term landslide history of any slope. Alternative scenarios, such as submarine slopes, with more precise landslide inventories may contribute to new hazard assessment models that consider scaling exponents derived from size–frequency distributions.

Höhle, J. Automated Generation of Urban Land Cover Maps and Their Enhancement and Regularization. PFG (2024). https://doi.org/10.1007/s41064-024-00316-9

Abstract

The aim of this article is to automatically generate and update Land Cover Map data for urban areas. The introduction examines the state of the art with a focus on enhancement of the classification results. The materials and methods of the processing are explained using a practical example. The applied classification method uses 18 features (normalized difference vegetation index, height above terrain and 16 attributes generated from four spectral bands) for each pixel of a digital true ortho-image. When training the classifier, only three small image patches per class were used. The enhancement of the classification results takes place in three steps. The first two steps create raster maps with smoothed outlines and generalized content. In the third step, straight, orthogonal, and parallel vectors are created for the outlines of buildings. The produced Land Cover Map of an urban area was checked for completeness and geometric accuracy. All buildings were detected, and the calculated standard deviations of building corner coordinates were σE = 1.0m and σN = 0.8m when the true ortho-image was used as reference. Possible improvements regarding source data, classification method, and enhancement are discussed. All processing can be done by open-source software, and a developed software package including documentation and examples can be downloaded from the Internet for own use. The results of this work can inspire both mapping organizations and amateurs to produce up-to-date thematic and topographic map data inexpensively and quickly.

Yang, F., Forootan, E., Liu, S., & Schumacher, M. (2024). A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies. Water Resources Research. https://doi.org/10.1029/2023WR036764

Abstract

Understanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.

Dehvari, M., Farzaneh, S., & Forootan, E. (2024). Ensemble based estimation of wet refractivity indices using a functional model approach. Earth and Space Science. https://doi.org/10.1029/2023EA003453

Abstract

The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real-time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill-posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch-wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B-spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study’s region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively.

Chen, W., Forootan, E., Shum, CK., Zhong, M., Feng, W., Xiong, Y., & Li, W. (2024). A novel dynamic scale factor designed for recovering global TWS changes. Journal of Hydrology, 637, Artikel 131364. https://doi.org/10.1016/j.jhydrol.2024.131364

Abstract

For time-variable satellite gravity solutions of GRACE and GRACE-FO in terms of spherical harmonics coefficients, the Scale Factor (SF) is often used to recover the close to “true” signal of Terrestrial Water Storage (TWS) anomalies. However, the conventional SF method has some limitations that may hinder its effectiveness, including: (1) their dependency on input hydrological models that may lead to divergent estimations of SFs; (2) the arbitrary choice of filter strength, which may not be representative in different regions; (3) limited consideration of SF in the temporal dynamics (the conventional SF was fixed value) for monthly varied TWS. Here, we propose a new Dynamic SF method to overcome these limitations and increase accuracy of the restored global TWS changes. This method involves: (1) the Bayesian Three-Cornered Hat (BTCH) method is applied to merge three sets of hydrological products into an optimal hydrological dataset to be used for estimating a unique SF, (2) the anisotropic DDK3 filter (found to be numerically optimal) is applied to suppress the correlated noise, and (3) an iterative Kalman filter process is formulated and implemented to estimate monthly Dynamic SF corresponding to monthly global TWS fields. The Dynamic SF outperformed an ordinary SF method in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), and the Signal to Noise Ratio (SNR), which were found to be improved by 30.8%, 32.2%, and 41.3%, respectively. Moreover, the recovered TWS using the Dynamic SF method showed a good agreement with the GRACE/GRACE-FO RL06 mascon solutions of the CSR and that of JPL regarding the long-term trend, seasonal, and interannual variations.

Yang, F., Liu, S. & Forootan, E. A spatial-varying non-isotropic Gaussian-based convolution filter for smoothing GRACE-like temporal gravity fields. J Geod 98, 66 (2024). https://doi.org/10.1007/s00190-024-01875-w

Abstract

The strong noise of satellite-based Time-Variable Gravity (TVG) field is often suppressed by applying the averaging filters. However, how to appropriately compromise the data blurring and de-noising remains as a challenge. In our hypothesis, the optimum spatial averaging filter expects to contain averaging kernels that capture the same amount of orbital samples everywhere, to avoid introducing excessive data blurring. To achieve the goal, we take advantages of the spherical convolution and introduce extra spatial constraints into a Gaussian kernel: (1) its half-width radius adapts to the global inhomogeneity of satellite orbit, and (2) the kernel is reshaped as an ellipsoid to adapt to the regional anisotropy. In this way, we designed optimal filters that contain a spatially-Varying non-isotropic Gaussian-based Convolution (VGC) kernel. The VGC-based filter is compared against three most popular filters through real TVG fields and another closed-loop simulation. In both scenarios, VGC-based filters retain more realistic secular trend and seasonal characteristics, in particular at high latitudes. The spatial correlation between the VGC estimates and the simulated ground truth is found to be 0.95 and 0.86 over Greenland and Antarctica, which is found to be 10% better than other tested filters. Temporal correlations with the ground truth are also found to be considerably better than the other filters over 90% of the globally distributed river basin. Besides, the VGC-based filters provide tolerable efficiency (3.5 s per month) and sufficient accuracy (integral error less than 3%). The method can be extended to the next generation gravity mission as well.

Dehvari, M., Farzaneh, S. & Forootan, E. Assessment of ZWD field predictions using the dynamic mode decomposition method. GPS Solut 28, 145 (2024). https://doi.org/10.1007/s10291-024-01692-w

Abstract

The existing water vapor present in the lower regions of the atmosphere plays a pivotal role in both weather forecasting and the propagation of signals in satellite-based observations. This parameter introduces a delay in GNSS observations, known as tropospheric wet delay. Accurately predicting the spatial distribution of this parameter can significantly enhance our ability to forecast rainfall and floods. It can also improve satellite-based positioning techniques. One mathematical technique that proves invaluable in modeling various temporal aspects of a signal is the Dynamic Mode Decomposition (DMD) method. To construct the necessary snapshot matrix in the DMD method, we have opted to employ B-spline coefficient time series, computed by assimilating GNSS-derived Zenith et Delay (ZWD) values into the GPT3w model as a reference, with the Ensemble Kalman Filter (EnKF) method serving as the core of the assimilation process. In the DMD procedure, we have utilized a dataset spanning approximately 30 consecutive days, with a temporal resolution of roughly 5 min, to predict B-spline coefficients representing the spatial distribution of ZWD values for a 24-h period ahead. This dataset comprises ZWD values collected from 241 GNSS stations located in Germany and nearby regions throughout the year 2018. Comparative analysis has been performed, including 10 excluded GNSS stations from the assimilation and DMD procedure and 10 existing radiosonde stations within the study region. The results of the analysis step demonstrate the superiority of the proposed method over the ERA5, GFS, and GPT3w models, showcasing the Root Mean Squared Error (RMSE) of approximately 0.8 cm. This performance marks a substantial improvement, being approximately 51%, 57%, and 74% lower than each respective
model. In the prediction phase, the proposed method outperforms the ERA5 and GFS models up to the 6-h and 24-h prediction windows in comparison with the GPT3w model.

Anke Fluhrer, Thomas Jagdhuber, Carsten Montzka, Maike Schumacher, Hamed Alemohammad, Alireza Tabatabaeenejad, Harald Kunstmann, Dara Entekhabi, Soil moisture profile estimation by combining P-band SAR polarimetry with hydrological and multi-layer scattering models, Remote Sensing of Environment, Volume 305, (2024), 114067, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2024.114067.

Abstract

An approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comparing these with hydrological simulations, soil moisture profiles from the soil surface until a soil depth of 30 cm (assumed average P-band penetration depth) are estimated. Here, the hydrological model HYDRUS-1D, as a representative of any soil hydrological model, is employed to simulate an ensemble of realistic soil moisture profiles, which are used for a multi-layer soil scattering model to obtain forward modeled soil scattering components. Compared to the decomposed SAR-based soil scattering components, the most appropriate soil moisture profile from the ensemble is estimated. The approach is able to provide physically (hydraulic) more meaningful soil moisture profile shapes than currently existing profile estimation approaches, like polynomial fitting to few measurements at discrete soil depths. Results are presented across eight in situ measuring stations in the U.S. within six test sites of NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission between 2013 and 2015. In-depth analyzes and validations with in situ measured soil moisture information demonstrate the feasibility of the proposed approach. Overall, estimated soil moisture profiles at the different sites match the varying local climate, vegetation cover, and soil conditions. Coefficients of determination between estimated and in situ measured soil moisture values vary between 0.48 and 0.92, while unbiased errors range from 1.4 vol% to 3.7 vol%, and Fréchet distances (analyzing the similarity of profile shapes) vary between 0.1 and 0.2 [−].

Kosary, M., Farzaneh, S., Schumacher, M., & Forootan, E. (2024). Assimilating space-based thermospheric neutral density (TND) data into the TIE-GCM coupled model during periods with low and high solar activity. Space Weather, 22, e2023SW003811. https://doi.org/10.1029/2023SW003811

Abstract

The global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space-based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space-based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm-C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm-B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE-GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.

Chen, W., Xiong, Y., Shum, C. K., Forootan, E., Zhong, M., Ran, J., … Shen, Y. (2024). A statistical collocation accuracy assessment of contemporary satellite temporal gravimetry data products. All Earth, 36(1), 1–17. https://doi.org/10.1080/27669645.2024.2399984

Abstract

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions have enabled consistent production of monthly gravity field solutions by international institutes, contributing to the International Centre for Global Earth Models. Each institute employs distinct processing strategies, yielding varied estimates of terrestrial water storage (TWS). In this study, we employ statistical collocation techniques (Total assessment ratio, TAR) to assess and compare the performance of GRACE TWS data products (2003.03 ~ 2014.03) and GRACE-FO TWS data (2018.06 ~ 2022.11). For GRACE TWS, the TAR values are as follows: COST-G (0.15), ITSG (0.83), APM-SYSU (0.85), CSR (0.91), JPL (0.93), GFZ (0.94), Tongji (0.96), HUST (1.08), SUST (1.18), CNES (1.37), and AIUB (1.41). Similarly, for GRACE-FO TWS, the TAR values are COST-G (0.15), JPL (0.81), ITSG (0.96), CSR (0.97), GFZ (1.06), and CNES (1.41). Furthermore, our comparison across basin sizes and climatic regions reveals that COST-G exhibits lower uncertainty and larger signal-to-noise ratios in TWS, making it particularly noteworthy for its utility. Conversely, other single solutions that depict long-term trends and annual amplitudes demonstrate comparable values across various basin sizes, climatic regions, and specific areas.

Forootan, E., Mehrnegar, N., Schumacher, M., Retegui-Schiettekatte, L. A., Jagdhuber, T., Farzaneh, S., van Dijk, A., Shamsudduha, M., Shum, C. K., (2024). Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model. Science of The Total Environment, Volume 912, 169476, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.169476.

Abstract

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo – Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5° standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003–2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

J. . -P. Montillet et al., “How Big Data Can Help to Monitor the Environment and to Mitigate Risks due to Climate Change: A review,” in IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 2, pp. 67-89, (June 2024), doi: 10.1109/MGRS.2024.3379108.

Abstract

Climate change triggers a wide range of hydrometeorological, glaciological and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing, geodetic and in situ instruments have been developed to effectively monitor and help comprehend the Earth’s system including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such Big Datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analysing. Big Datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems which necessitate low latency requirements. In this review, we delve into the monitoring and analysis of various climate change-related phenomena, including, but not limited to, droughts, floods, cyclones-induced storm surges, urban heat islands, ice mass balance, sea-level rise, and the modelling of the influence of solar variability on the Earth’s climate. By examining these phenomena, we explore some of the current and future trends in Big Data, aiming to encourage and speed-up the development of such techniques and promoting their benefits to timely monitor and towards achieving climate sustainability, thereby addressing its threat to humanity.

ZHANG, W., Yang, F., WU, Y., LIU, H., Zhang, T., Luo, Z., & Forootan, E. (2024). HUST-CRA: A New Atmospheric De-aliasing Model for Satellite Gravimetry. ADVANCES IN ATMOSPHERIC SCIENCES. https://doi.org/10.1007/s00376-024-4045-6

Abstract

Atmospheric de-aliasing is one of the most important background models for recovering Earth’s temporal gravity field from gravity satellite missions. To meet the needs of China’s gravimetric satellite platform, an independent atmospheric de-aliasing model that relies on Chinese meteorological data needs to be developed. The release of CRA-40, as the first-generation Chinese atmospheric reanalysis, provides the opportunity. This study proposes a revised modeling method to calibrate CRA-40 and develops a new atmospheric de-aliasing model (HUST-CRA, 2002–20). Intensive assessments are made between HUST-CRA and the latest official de-aliasing product of the international gravity satellite mission. The tidal components of the two products demonstrate high consistency, e.g., the spatial correlation for the major tide S1 is 0.96. The non-tidal components of the two products are also equivalent: (1) the temporal correlation of low-degree terms is higher than 0.97, except for the term of S22 (0.93); (2) the spectral correlation of degree geoid height up to degree/order 100 is as high as 0.99; (3) the confidence interval of the spatial correlation (2002–20) is [0.971, 0.995] at a confidence level of 95%; and (4) the difference in KBRR (K-band range rate) residuals is less than 0.08 µm s−1, the difference in the derived temporal gravity field is less than 0.32 mm in terms of geoid height, and both are apparently beyond the ability of the current gravity satellite mission. This confirms that CRA-40 is of high quality and that the derived de-aliasing product, HUST-CRA, is accurate enough to be used in both Chinese and international gravity satellite missions.

Mafi, S., Farzaneh, S., Sharifi, M. A., & Forootan, E. (2024). Spline retracker: a geometrical retracking algorithm for coastal and open ocean altimetry. Marine Geodesy, 47(2), 83-118. https://doi.org/10.1080/01490419.2023.2291772

Abstract

Satellite altimetry has enhanced the understanding of ocean dynamics through high-rate sampling and global coverage. However, land contamination and bad reflection effects limit its accuracy. We present a geometrical method for retracking altimetry waveforms in coastal areas. Our method follows a geometrical assumption related to the symmetrical reciprocal motion of the radar pulse. Based on this assumption, the altimetry waveform is modelled as a continuous and differentiable third-order spline function, and the symmetry point of this function is considered as the retracking gate. The spline retracking algorithm is validated against the tide gauges at Onsala, Halmstad, and Muscat stations in Sweden and Oman, and its performance is compared with existing retracking algorithms. Our results showed a remarkable reduction of 50-91% in the unbiased-Root-Mean-Squared-Error (ubRMSE) and an increase of at least 13% in correlation coefficients when compared with other algorithms in Swedish coast. This algorithm presented equivalent results with the threshold and improved threshold retrackings in Muscat station, based on Jason-2 measurements. However, along the Jason-3 pass, our spline method showed a considerable reduction of 80% in ubRMSE and the minimum increase of 42% in correlation coefficients than the empirical algorithms. This method also outperformed the ALES algorithm in most cases.

2023

Dehvari, M., Farzaneh, S., Forootan, E. (2023). Developing Iran’s empirical zenith wet delay model (IR-ZWD), Journal of Atmospheric and Solar-Terrestrial Physics, Volume 253, 106163, ISSN 1364-6826, https://doi.org/10.1016/j.jastp.2023.106163.

Abstract

The presence of water vapor in the lower atmosphere can introduce errors in satellite-based geodetic observations. Accurate modeling of this part of atmospheric delay is particularly challenging due to the considerable variations of water vapor. Therefore, constructing a reasonable model to predict Zenith Wet Delay (ZWD) can improve the accuracy of geodetic observations and positioning techniques. In this study, we aim at constructing a regional ZWD model for Iran and nearby regions (called the IR-ZWD model) using base functions with local support. The mode is based on the five-year outputs of the Empirical Reanalysis Fifth generation (ERA5) data with the spatial resolution of about 0.25° from 2017 to 2021. The B-spline base functions are used to effectively represent local spatial changes in the spectral domain and to decrease the number of unknown parameters. A B-spline model with the order and surface resolution of about 3 and 5 (scalar values) is found to be efficient, which has an equivalent spatial resolution of ∼0.5°. Temporal variations are accounted for by applying a constant term, along with periodic components with annual, semi-annual, 3-, and 4-monthly periods. Our results demonstrate that the proposed model has a mean Root Mean Squared Error (RMSE) of about 0.035 m within Iran, which represents an improvement of approximately 12.5% compared to the commonly used global empirical models such as GPT3w, GTrop, and HGPT2. The correlation coefficient value of 0.55 is found between IR-ZWD and ERA5 data, which is about 10% higher than that of, e.g., GPT3w and GTrop. The IR-ZWD model is also evaluated against five radiosonde stations and ZWD from the Jason-3 satellite mission. In both cases, the results indicate that IR-ZWD can reduce the RMSE and MAE values of about 10%, and it improves the correlation coefficient value about 9%.

Forootan, E., Dehvari, M., Farzaneh, S., & Karimi, S. (2023). Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique. Atmosphere, 14(1), Article 112. https://doi.org/10.3390/atmos14010112

Abstract

Constructing accurate models that provide information about water vapor content in the troposphere improves the reliability of numerical weather forecasts and the position accuracy of low-cost Global Navigation Satellite System (GNSS) receivers. However, developing models with high spatial-temporal resolution demands compact observational datasets in the regions of interest. Empirical models, such as the Global Pressure and Temperature 3 (GPT3w), have been constructed based on the monthly averaged outputs of numerical weather models. These models are based on the assimilation of existing measurements to provide estimations of atmospheric parameters. Therefore, their accuracy may be reduced over regions with a low resolution of radiosonde or continuous GNSS stations. By emerging and increasing the Low-Earth-Orbiting (LEO) satellites that measure atmospheric parameter profiles using the Radio Occultation (RO) technique, new opportunities have appeared to acquire high-resolution atmospheric observations at different altitudes. This study aims to apply these RO observations to improve the accuracy of the GPT3w model over Iran, which is sparse in terms of long-term GNSS and radiosonde measurements. The temperature, pressure, and water vapor pressure parameters from the GPT3w model have been used as the input layers of the Extremely Learning Machine (ELM) technique. The wet refractivity indices from the RO technique are considered target parameters in the output layer to train the ELM. The RO observations of 2007–2020 are applied for training, and those of 2020–2022 for evaluating the performance of the developed ELM. Our numerical results indicate that the developed ELM decreases the Root-Mean-Square Error (RMSE) values of the wet refractivity indices by about 17 percent, compared to the original GPT3w RMSE values. Additionally, the wet refractivity indices from ELM have revealed correlation coefficients of about 0.64, which is about 1.9 times those related to the original GPT3w model. The performance of ELM has also been examined by comparison with the data of six located radiosonde stations covering the year 2020. This comparison shows an improvement of about 14 percent in the average RMSE values of the estimated wet refractivity indices.

Vazquez-Ontiveros, J.R., Martinez-Felix, C.A., Melgarejo-Morales, A. et al. (2023). Assessing the quality of raw GNSS observations and 3D positioning performance using the Xiaomi Mi 8 dual-frequency smartphone in Northwest Mexico. Earth Sci Inform. https://doi.org/10.1007/s12145-023-01148-8

Abstract

GNSS observations from smartphones have gained popularity in recent years due to the high precision achieved in various applications. While most studies have focused on signal quality evaluation, few have explored static and kinematic positioning. Furthermore, the majority of these studies have primarily concentrated on European and Asian countries. Therefore, we present the first study conducted in Northwest Mexico, which evaluates the performance of static and kinematic positioning using code and phase observations obtained from the Xiaomi Mi 8 smartphone. In addition, we assess the signal quality of ~ 100 available GNSS satellites. This study proposes an alternative method for analyzing the observed Carrier-to-Noise Density Ratio (C/ N) of GNSS observations in relation to theoretical reference values. The results reveal that the average C/ N value of the GNSS satellites is approximately 18% lower than the reference values. Furthermore, the pseudorange observations indicate a significant multipath error, with magnitudes close to 200 cm for L1/E1 and less than 86 cm for L5/E5a, highlighting the susceptibility of the smartphones GNSS antenna to this type of error. The static experiment demonstrates RMS positioning errors of 0.7 cm, 1.2 cm, and 4.2 cm for the E, N, and U components, respectively. Moreover, the kinematic experiment exhibits discrepancies of 1.4 cm due to the circular trajectory of the smartphone. Finally, the results suggest that dual-frequency smartphones offer promising positioning capabilities, presenting opportunities for engineering applications, including structural health monitoring, among others.

Mehrnegar, N., Schumacher, M., Jagdhuber, T., Forootan, E. (2023). Making the Best Use of GRACE, GRACE-FO and SMAP Data Through a Constrained Bayesian Data-Model Integration. Water Resources Research. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR034544

Abstract

The Gravity Recovery and Climate Experiment (GRACE, 2003–2017) and its Follow-On mission GRACE-FO (2018-now) provide global estimates of the vertically integrated Terrestrial Water Storage Changes (TWSC). Since 2015, the Soil Moisture Active Passive (SMAP) radiometer observes global L-band brightness temperatures, which are sensitive to near-surface soil moisture. In this study, we introduce our newly developed Constrained Bayesian (ConBay) optimization approach to merge the TWSC of GRACE/GRACE-FO along with SMAP soil moisture data into the ∼10 km resolution W3RA water balance model. ConBay is formulated based on two hierarchical multivariate state-space models to (I) separate land hydrology compartments from GRACE/GRACE-FO TWSC, and (II) constrain the estimation of surface soil water storage based on the SMAP data. The numerical implementation is demonstrated over the High Plain (HP) aquifer in the United States between 2015 and 2021. The implementation of ConBay is compared with an unconstrained Bayesian formulation, and our validations are performed against in-situ USGS groundwater level observations and the European Space Agency (ESA)’s Climate Change Initiative (CCI) soil moisture data. Our results indicate that the single GRACE/GRACE-FO assimilation improves particularly the groundwater compartment. Adding SMAP data to the ConBay approach controls the updates assigned to the surface storage compartments. For example, correlation coefficients between the ESA CCI and the ConBay-derived surface soil water storage (0.8) that are considerably higher than those derived from the unconstrained experiment (−0.3) in the North HP. The percentage of updates introduced to the W3RA groundwater storage is also decreased from 64% to 57%.

Retegui-Schiettekatte, L., Garrido, S., M., de Lacy, C., M., (2023). Use of GNSS and ERA5 Precipitable Water Vapor based Standardized Precipitation Conversion Index for drought monitoring in the Mediterranean coast: a first case study in Southern Spain.
Advances in Space Research. https://doi.org/10.1016/j.asr.2023.08.030

Abstract

In this paper the Standardized Precipitation Conversion Index (SPCI), a PWV-based drought index, has been computed using GNSS and ERA5 PWV and its performance has been tested with respect to the Standardized Precipitation Evapotranspiration Index (SPEI) in Southern Spain. One of the climatic features of this area is the low correlation level between PWV and precipitation, in contrast with other areas in which SPCI has been previously tested. The GNSS-SPCI has been derived from validated ZTD time series estimated from local GNSS permanent stations’ data. All the needed meteorological values were derived from ERA5, excepting precipitation values and SPEI-SPI values which were extracted from a national high-resolution dataset.

The resulting SPCI time series have shown high correlation coefficients with respect to the SPEI. The use of longer SPCI time series allowed by ERA5 model has provided the most coherent results, suggesting that the ERA5-PWV data can be interesting to overcome problems caused by the short timespan of GNSS time series in SPCI computation. In general, high correlation coefficients have been obtained compared to global results from previous studies. This shows that, even for regions with low correlation levels between PWV and precipitation, the SPCI can have an interesting potential for drought monitoring. The SPCI was found to perform better on higher timescales (12 and 24 months). The performance of SPCI has also been compared that of the SPI: SPCI is able to outperform SPI for the 24-month timescale for a limited geographical region. This supports that the inclusion of PWV data in drought monitoring indices could be promising and is worth keeping to be investigated.

Forootan, E., Kosary, M., Farzaneh, S. et al., (2023). Empirical Data Assimilation for Merging Total Electron Content Data with Empirical and Physical Models. Surv Geophys. https://doi.org/10.1007/s10712-023-09788-7

Abstract

An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average 42.46% and 31.89% root mean squared error (RMSE) reduction during our test period, September 2017.

2022

Forootan, E., & Mehrnegar, N., (2022). A hierarchical Constrained Bayesian (ConBay) approach to jointly estimate water storage and Post-Glacial Rebound from GRACE(-FO) and GNSS data. All Earth, 34(1), 120-146. https://doi.org/10.1080/27669645.2022.2097768

Abstract

Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have become an indispensable tool in monitoring global mass variations. However, separating GRACE(-FO) signals into its individual Terrestrial Water Storage Changes (TWSC) and surface deformation contributors, i.e. Post-Glacial Rebound (PGR), is desirable for many hydro-climatic and geophysical applications. In this study, a hierarchical constrained Bayesian (ConBay) approach is formulated to apply GRACE(-FO) fields and the uplift rate measurements from the Global Navigation Satellite System (GNSS) stations to simultaneously estimate the contribution of TWSC and PGR. The proposed approach is formulated based on a hierarchical Markov Chain Monte Carlo optimisation algorithm within a dynamic multivariate state-space model, while accounting for the uncertainties of a priori information and observations. The numerical implementation is demonstrated over the Great Lakes area, covering 2003–2017, where the W3RA water balance and the ICE-5G(VM2) and ICE-6G-D(VM5a) GIA models are merged with GRACE and GNSS data. Validations are performed against independent measurements, which indicate that the average root-mean-squares-of-differences between the PGR estimates and independent measurements reduced by (Formula presented.) after merging observations with models through ConBay. The ConBay updates, introduced to the long-term trends, as well as the seasonal and inter-annual components, are found to be realistic.

Fluhrer, A., Jagdhuber, T., Tabatabaeenejad, A., Alemohammad, H., Montzka, C., Friedl, P., Forootan, E., & Kunstmann, H., (2022). Remote Sensing of Complex Permittivity and Penetration Depth of Soils Using P-Band SAR Polarimetry. Remote Sensing, 14(12), 2755. https://doi.org/10.3390/rs14122755

Abstract

A Pband SAR moisture estimation method is introduced for complex soil permittivity and penetration depth estimation using fully polarimetric P-band SAR signals. This method combines eigen- and model-based decomposition techniques for separation of the total backscattering signal into three scattering components (soil, dihedral, and volume). The incorporation of a soil scattering model allows for the first time the estimation of complex soil permittivity and permittivity-based penetration depth. The proposed method needs no prior assumptions on land cover characteristics and is applicable to a variety of vegetation types. The technique is demonstrated for airborne P-band SAR measurements acquired during the AirMOSS campaign (2012–2015). The estimated complex permittivity agrees well with climate and soil conditions at different monitoring sites. Based on frequency and permittivity, P-band penetration depths vary from 5 cm to 35 cm. This value range is in accordance with previous studies in the literature. Comparison of the results is challenging due to the sparsity of vertical soil in situ sampling. It was found that the disagreement between in situ measurements and SAR-based estimates originates from the discrepancy between the in situ measuring depth of the top-soil layer (0–5 cm) and the median penetration depth of the P-band waves (24.5–27 cm).

Forootan, E., (n.d.). ESA’s multi-level global thermosphere data products consistent with Swarm and GRACE (-FO). (2022). https://earth.esa.int/eogateway/activities/swarm-disc-pre-study-5-2

Overview

Swarm DISC Pre-Study 5.2

In this project, the possibility of using the space-based along-track Thermospheric Neutral Density (TND) estimates for generating the European Space Agency (ESA)’s Level 3 (L3) global multi-level TND data products is assessed. For this, the TND estimates along the CHAMP, GRACE, and Swarm satellites are used as observation within the sequential Calibration and Data Assimilation (C/DA) framework proposed by (Forootan et al., 2020 & 2022: doi: 10.1093/gji/ggaa507 & doi: 10.1038/s41598-022-05952-y).

The C/DA approach is applied to re-calibrate four key parameters of the NRLMSISE-00 model, which are most sensitive to the thermospheric neutral mass and thermospheric temperature. The model with re-calibrated parameters is called ‘C/DA-NRLMSISE-00’, whose outputs fit to the space-based TNDs. The C/DA-NRLMSISE-00 is able to forecast TNDs and individual neutral mass compositions at any predefined vertical level (i.e., the same vertical coverage as the NRLMSISE-00) and arbitrary spatial-temporal resolution. Therefore, the C/DA method is tested to produce level 3 (L3) TND data consistent with space-based TND estimates.

Seven periods (October 2003, July 2004, March 2008, April 2010, March 2015, September 2017, and September 2020), associated with relatively high geomagnetic activity, are selected for investigating the L3 products because most of available models represent difficulties to provide reasonable TND estimations. Independent comparisons (validations) are performed with the space-based TNDs that were not used within the C/DA framework (from CHAMP, GRACE, GOCE and Swarm missions), as well as with the outputs of other thermosphere models such as the Jacchia-Bowman 2008 (JB08) and the High Accuracy Satellite Drag Model (HASDM).

The Project Report is available for download.

Chen, W., Shum, CK., Forootan, E., Feng, W., Zhong, M., Jia, Y., Li, W., Guo, J., Wang, C., Li, Q., & Liang, L., (2022). Understanding Water Level Changes in the Great Lakes by an ICA-Based Merging of Multi-Mission Altimetry Measurements. Remote Sensing, 14(20), 5194. https://doi.org/10.3390/rs14205194

Abstract

Accurately monitoring spatio-temporal changes in lake water levels is important for studying the impacts of climate change on freshwater resources, and for predicting natural hazards. In this study, we applied multi-mission radar satellite altimetry data from the Laurentian Great Lakes, North America to optimally reconstruct multi-decadal lake-wide spatio-temporal changes of water level. We used the results to study physical processes such as teleconnections of El Niño and southern oscillation (ENSO) episodes over approximately the past three-and-a-half decades (1985–2018). First, we assessed three reconstruction methods, namely the standard empirical orthogonal function (EOF), complex EOF (CEOF), and complex independent component analysis (CICA), to model the lake-wide changes of water level. The performance of these techniques was evaluated using in-situ gauge data, after correcting the Glacial Isostatic Adjustment (GIA) process using a contemporary GIA forward model. While altimeter-measured water level was much less affected by GIA, the averaged gauge-measured water level was found to have increased up to 14 cm over the three decades. Our results indicate that the CICA-reconstructed 35-year lake level was more accurate than the other two techniques. The correlation coefficients between the CICA reconstruction and the in situ water-level data were 0.96, 0.99, 0.97, 0.97, and 0.95, for Lake Superior, Lake Michigan, Lake Huron, Lake Erie, and Lake Ontario, respectively; ~7% higher than the original altimetry data. The root mean squares of errors (RMSE) were 6.07 cm, 4.89 cm, 9.27 cm, 7.71 cm, and 9.88 cm, respectively, for each of the lakes, and ~44% less than differencing with the original altimetry data. Furthermore, the CICA results indicated that the water-level changes in the Great Lakes were significantly correlated with ENSO, with correlation coefficients of 0.5–0.8. The lake levels were ~25 cm higher (~30 cm lower) than normal during EI Niño (La Niña) events.

2021

Höhle, J. (2021). Automated mapping of buildings through classification of DSM-based ortho-images and cartographic enhancement. International Journal of Applied Earth Observation and Geoinformation, 95(3), Artikel 102237. https://doi.org/10.1016/j.jag.2020.102237

Abstract

Urban areas are changing rapidly. In order to document the urban realities in topographic databases and geographic information systems efficient methods are required. Vector data of buildings are of special importance. A methodology for an automated generation of cartographically enhanced data is presented and applied to two test sites at Vaihingen, Germany. The steps of the workflow are described in detail. The examples use imagery of a large-format aerial camera to map different types of buildings. First, land cover maps are generated by means of supervised classification using two sets of attributes (basic attributes and attribute profile, basic attributes and dispersion). After the enhancement of the extracted buildings their outlines have straight, orthogonal, and parallel line segments created by least squares adjustment. The assessment of the geometric accuracy used 264 well-defined building corners and two types of references (land cover map, ortho-image). The obtained average standard deviation of the coordinates was σ_x,y = 1.0 m. The additional use of an attribute profile did not improve upon the geometric accuracy that was obtained by means of five attributes (height above ground, normalized difference vegetation index, standard deviation of the elevations in the 5 × 5 pixels window, intensity value of the near-infrared band, and standard deviation of intensities in the 5 × 5 surrounding at a pixel of the near-infrared band). The experiences with the developed software reveal that a graphical output of intermediate results is helpful to obtain complete and reliable results at complex building structures.

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