ORCID Profile
0000-0003-3055-041X
Current Organisations
Robert Bosch
,
Aalborg Universitet
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Publisher: Elsevier BV
Date: 12-2014
Publisher: Springer Science and Business Media LLC
Date: 20-02-2018
Publisher: Springer Science and Business Media LLC
Date: 12-2016
Publisher: Elsevier BV
Date: 09-2012
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-8310
Abstract: & & The aerodynamic drag depending on the neutral density of the thermosphere is the largest non-gravitational force that decelerates Low Earth Orbiting (LEO) satellites with altitudes lower than 1000 km.& Consequently, the knowledge of the thermospheric neutral density is of crucial importance for many applications in geo-scientific investigations, such as precise orbit determination (POD), re-entry prediction, manoeuvre planning or satellite lifetime predictions. The accuracy of existing thermosphere models depends on observation data of the thermosphere, which are quite sparse. Evaluations of different thermosphere models indicate considerable differences, especially for time epochs of severe space weather events. Hence, an improvement of thermosphere models is absolutely necessary.& & & & In this study, discrepancies between the empirical thermosphere model NRLMSISE-00 and the results of two geodetic observation techniques are discussed. For this purpose, two approaches are applied to calculate scale factors between the modelled density from the NRLMSISE-00 model and those from geodetic techniques. The first approach applies the POD of LEO satellites to estimate scale factors with a time resolution of 12 hours derived from Satellite Laser Ranging (SLR) tracking measurements. The SLR missions used here include the spherical satellites Starlette, Westpac, Blits, Stella and Larets. As our second approach, scale factors are computed by evaluating the aerodynamic acceleration using the on-board accelerometer data of the Challenging Mini-satellite Payload (CHAMP) mission and the Gravity Recovery and Climate Experiment (GRACE) mission. Here, the time resolution of scale factors is fixed to be 12 hours to be comparable with the first approach. Finally, we investigate the resulting scale factors from the above mentioned satellites at various altitudes, e.g. 960 km for Starlette and 400 km for GRACE. Especially, the temporal variation as well as the altitude dependency of the scale factors will be discussed.& &
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-12394
Abstract: The climate change along with anthropogenic water use have been affecting the (re)distribution of water storage and water flux across the Continental United States (CONUS). Understanding these changes requires tools that provide a big picture of the processes that drive these changes. These processes are implemented in this study by combining remote sensing data with available modeling techniques, where the data contains a s le of hydro-climatological signals and models reflect our understanding of these processes. Time series of Terrestrial Water Storage Changes (TWSC) from the Gravity Recovery and Climate Experiment (GRACE, 2003-2017) and its Follow-on (GRACE-FO, 2018-2021) are integrated into the W3RA water balance model within CONUS, where the model is run at 9-km resolution using ERA5 forcing data. The gap in the GRACE and GRACE-FO TWSC products is filled following 0.3390/rs12101639. To achieve the best possible statistical combination, the Markov Chain Monte Carlo-based Data Assimilation (MCMC-DA) approach (0.1016/j.scitotenv.2020.143579) is applied to use GRACE and GRACE-FO TWSC as a vertical integration constraint to update W3RA's in idual water storage estimates. This approach rigorously accounts for the uncertainties of model and observations.The outputs of MCMC-DA are evaluated using in-situ USGS groundwater level data and the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product. The results indicate changes in trend and seasonality of water storage variations, for ex le, in southwestern (California and Nevada), southeastern (including Florida, South and North Carolina, Virginia, and Georgia), and south-central CONUS (including Texas, New Mexico, Colorado, Kansas, and Oklahoma). MCMC-DA improves the estimation of soil water in regions with high forest intensity, where ESA CCI and models reveal difficulties in capturing the soil-vegetation-atmosphere continuum. The representation of El Nino Southern Oscillation (ENSO)-related variability in water storage are found to be considerably improved after integrating GRACE(-FO) into W3RA. This new hybrid approach is found efficient for understanding the linkage between the climate variability and large-scale hydrological processes.Keywords: CONU Data Assimilation Bayesian Method MCMC GRACE(-FO) W3RA groundwater storage soil water storage USGS ESA CCI.
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4190
Abstract: & & The development of space-geodetic observation techniques has brought out a wide range of applications such as positioning and navigation, where the Global Navigation Satellite System (GNSS) is the main tools to provide surveying measurements in these applications. Though GNSS signals enable the calculation of receiver's position, some errors restrict their accuracy. Among these errors, the ionospheric delay is considered as an important error source in the Standard Point Positioning (SPP) applications. Empirical ionospheric models such as Klobuchar, International Reference Ionosphere (IRI), and NeQuick are often applied for computing the Total Electron Content (TEC) within ionosphere and its equivalent delays. However the simulation and forecasting skills of these models are limited due to the simplified model structures and model sensitivity to the calibration period. In this study, we present a novel sequential Calibration approach based on the Ensemble Kalman Filter (C-EnKF) to improve the performance of TEC estimations for SPP applications. To demonstrate the results, the IRI model is used as our basis and the TEC estimates from 56 IGS stations in Europe are applied as observation. The C-EnKF is applied to calibrate some selected model parameter so that IRI can be tuned over Europe. The numerical assessments are performed against the TEC estimates from dual frequency GNSS measurements and against the final IONEX products (that are available with 11 days delays). Based on the forecasting results (during September 2017), we show that the accuracy of TEC estimates from the C-EnKF is improved in the range of 3.7-64.87% compared to IRI.& & strong& Keywords: & /strong& Ionosphere, Sequential Calibration, Ensemble Kalman Filter (EnKF), IRI, Total Electron Content (TEC), Standard Point Positioning (SPP), GNSS& &
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 11-2009
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-20224
Abstract: & & With the climate change, drought events likely become more frequent and severe in Australia, where the worst droughts were recorded during the 21st century. Particularly, in the South-East of the country, the so called & quot Millennium Drought& quot showed below average annual precipitation for an entire decade. The precipitation record was then increased by extreme precipitation events generated from the La Ni& #241 a events in 2010 and 2011. Afterwards, dry conditions began again to develop. The climate-driven events and anthropogenic adaptions to the circumstances resulted in strong impacts on the hydrological resources and agricultural production. In fact, simulating hydrological processes within the (semi-)arid region of South-East Australia is very challenging especially during extreme events. In previous studies, we found a strong underestimation of the decline of total terrestrial water storage (TWS) and of groundwater in comparison to remote sensing data and in-situ station networks. Thus, we successfully calibrated the W3RA water balance model and simultaneously assimilated TWS anomalies obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission to improve the model's skill during extreme meteorological conditions. In this presentation, we focus on the comparison of remote sensing observations and W3RA simulations after implementing the calibration and data assimilation with existing data records on anthropogenic intervention into the water cycle, as well as on agricultural production. Our results indicate high correlations between meteorological, hydrological and agricultural variables, and we observe strong similarities in the long-term trends and break points.& &
Publisher: Elsevier BV
Date: 11-2014
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-20304
Abstract: & & Space weather means a very up-to-date and interdisciplinary field of research. It describes physical processes in space mainly caused by the Sun& #8217 s radiation of energy. The manifestations of space weather are multiple, for instance, the variations of the Earth& #8217 s magnetic field or the changing states of the upper atmosphere, in particular the ionosphere and the thermosphere.& & & & The main objectives of the Focus Area on Geodetic Space Weather Research (FA GSWR) are (1) the development of improved ionosphere models, (2) the development of improved thermosphere models and (3) the study of the coupled processes between magnetosphere, ionosphere and thermosphere (MIT).& & & & & Objective (1) aims at the high-precision and the high-resolution (spatial and temporal) modelling of the electron density. This allows to compute a signal propagation delay, which will be used in many geodetic applications, in particular in positioning, navigation and timing (PNT). Moreover, it is also important for other techniques using electromagnetic waves, such as satellite- or radio-communications. Concerning objective (2), satellite geodesy will obviously benefit when working on Precise Orbit Determination (POD), but there are further technical matters like collision analysis or re-entry calculation, which will become more reliable when using high-precision and high-resolution thermospheric drag models. Objective (3) links the magnetosphere with the first two objectives by introducing physical laws and principles such as continuity, energy and momentum equations and solving partial differential equations.& & & & & & & & & To arrive at the above described objectives of the FA GSWR one new Joint Study Groups (JSG) and three Joint Working Groups (JWG) have been installed recently. In detail, these groups are titled as JSG 1: Coupling processes between magnetosphere, thermosphere and ionosphere, JWG 1: Electron density modelling, JWG 2: Improvement of thermosphere models, and JWG 3: Improved understanding of space weather events and their monitoring by satellite missions. Other implemented IAG Study and Working Groups within the IAG programme 2019 to 2023 will provide valuable input for the FA GSWR. In this presentation we provide the latest investigations and results from the above mentioned Joint Study and Working Groups JSG 1, JWG 1, JWG 2 and JWG 3 of the FA GSWR.& &
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-19070
Abstract: & & Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.& & & & The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For ex le, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.& & & & & strong& Keywords:& /strong& USA Data Assimilation Bayesian Method Kalman Filtering MCMC GRACE W3RA groundwater storage soil water storage USGS ESA CCI.& & & & & & &
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2015
Publisher: Informa UK Limited
Date: 02-09-2014
Publisher: Elsevier BV
Date: 09-2012
Publisher: Elsevier BV
Date: 09-2018
DOI: 10.1016/J.SCITOTENV.2018.04.159
Abstract: Africa, a continent endowed with huge water resources that sustain its agricultural activities is increasingly coming under threat from impacts of climate extremes (droughts and floods), which puts the very precious water resource into jeopardy. Understanding the relationship between climate variability and water storage over the continent, therefore, is paramount in order to inform future water management strategies. This study employs Gravity Recovery And Climate Experiment (GRACE) satellite data and the higher order (fourth order cumulant) statistical independent component analysis (ICA) method to study the relationship between terrestrial water storage (TWS) changes and five global climate-teleconnection indices El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Madden-Julian Oscillation (MJO), Quasi-Biennial Oscillation (QBO) and the Indian Ocean Dipole (IOD) over Africa for the period 2003-2014. Pearson correlation analysis is applied to extract the connections between these climate indices (CIs) and TWS, from which some known strong CI-rainfall relationships (e.g., over equatorial eastern Africa) are found. Results indicate unique linear-relationships and regions that exhibit strong linkages between CIs and TWS. Moreover, unique regions having strong CI-TWS connections that are completely different from the typical ENSO-rainfall connections over eastern and southern Africa are also identified. Furthermore, the results indicate that the first dominant independent components (IC) of the CIs are linked to NAO, and are characterized by significant reductions of TWS over southern Africa. The second dominant ICs are associated with IOD and are characterized by significant increases in TWS over equatorial eastern Africa, while the combined ENSO and MJO are apparently linked to the third ICs, which are also associated with significant increase in TWS changes over both southern Africa, as well as equatorial eastern Africa.
Publisher: Copernicus GmbH
Date: 28-01-2019
Publisher: Copernicus GmbH
Date: 24-08-2022
DOI: 10.5194/IAG-COMM4-2022-44
Abstract: & & An accurate estimation of the Thermospheric Neutral Density (TND) is important for predicting the orbit of satellites and objects, for ex le, those with the altitude of less than 1000 km. Models are often used to simulate TNDs but their accuracy is limited due to modelling restrictions and sensitivity to the calibration period. Satellite missions such as CHAMP, GRACE, GOCE, Swarm, and GRACE-FO are equipped with on-board accelerometer sensors to measure drag forces, which can be used to estimate along-track TNDs. However, spatial and temporal coverage of these space borne TNDs is restricted to the mission design. To make the best use of the modelling tools and measurements, we applied these along-track TND measurements within the sequential Calibration and Data Assimilation (C/DA) framework proposed by (Forootan et al., 2022, doi:10.1038/s41598-022-05952-y). The C/DA is used to re-calibrate the NRLMSISE00 model, which is called & #8220 C/DA-NRLMSISE00& #8221 , whose outputs fit well to the introduced space-borne TNDs. The C/DA-NRLMSISE00 is applicable for forecasting TNDs and in idual neutral mass compositions at any predefined vertical level (between ~100 and ~600 km) with user-defined spatial-temporal s ling. Nine time periods (October 2003, July 2004, March 2008, April 2010, March 2015, September 2017, August 2018, September 2020 and October 2021) associated with space weather storms are selected for our investigations because most of the available models lack accuracy to provide reasonable TND simulations. Independent comparisons are performed with the space-borne TNDs that were not used within the C/DA framework, as well as with the outputs of other thermospheric models such as Jacchia-Bowman 2008 (JB2008) and the High Accuracy Satellite Drag Model (HASDM) database. The numerical results indicate improvements in the Root Mean Squared Errors (RMSE) of the C/DA-NRLMSISE00's TND forecasts compared to NRLMSISE-00, JB2008 and HASDM along-track of the LEO missions. The percentage reductions are found to be: 51%, 8% and 8 % along GRACE (2003, average altitude 490 km), 25%, 20% and 48% along GOCE (2010, average altitude 270 km), 46%, 37% and 35% along Swarm B (2015, average altitude 520 km), 54%, 12% and 5 % along Swarm B (2017, average altitude 514 km), and 41% and 64% along GRACE (FO) (2021, average altitude 504 km), respectively.& &
Publisher: Copernicus GmbH
Date: 24-08-2022
DOI: 10.5194/IAG-COMM4-2022-46
Abstract: & & Space weather means a very up-to-date and interdisciplinary field of research. It describes physical processes in space mainly caused by the Sun& #8217 s radiation of energy. The manifestations of space weather are multiple, for instance, the variations of the Earth& #8217 s magnetic field or the changing states of the upper atmosphere, in particular the ionosphere and the thermosphere.& & & & The main objectives of the Focus Area on Geodetic Space Weather Research (FA GSWR) are (1) the development of improved ionosphere models, (2) the development of improved thermosphere models, (3) the study of the coupled processes between magnetosphere, ionosphere and thermosphere (MIT) and (4) the improved understanding of space weather events and their monitoring by space observations (geodetic and non-geodetic).& & & & Objective (1) aims at the high-precision and the high-resolution (spatial and temporal) modelling of the electron density. This allows to compute a signal propagation delay, which will be used in many geodetic applications, in particular in positioning, navigation and timing (PNT). Moreover, it is also important for other techniques using electromagnetic waves, such as satellite- or radio-communications. Concerning objective (2), satellite geodesy will obviously benefit when working on Precise Orbit Determination (POD), but there are further technical matters like collision analysis or re-entry calculation, which will become more reliable when using high-precision and high-resolution thermospheric drag models. Objective (3) links the magnetosphere with the first two objectives by introducing physical laws and principles such as continuity, energy and momentum equations and solving partial differential equations. Objective (4) finally connects the improved understanding to the monitoring techniques and vice versa. & & & & & & & & & To arrive at the above described objectives of the FA GSWR one Joint Study Group (JSG) and three Joint Working Groups (JWG) have been installed. In detail, these groups are titled as JSG 1: Coupling processes between magnetosphere, thermosphere and ionosphere, JWG 1: Electron density modelling, JWG 2: Improvement of thermosphere models, and JWG 3: Improved understanding of space weather events and their monitoring by satellite missions. Other implemented IAG Study and Working Groups within the IAG programme 2019 to 2023 will provide valuable input for the FA GSWR. In this presentation we provide an overview about the FA GSWR.& &
Publisher: Wiley
Date: 24-12-2021
Publisher: Oxford University Press (OUP)
Date: 04-2019
DOI: 10.1093/GJI/GGZ163
Publisher: Wiley
Date: 04-08-2016
DOI: 10.1002/JOC.4842
Publisher: MDPI AG
Date: 17-10-2022
DOI: 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.
Publisher: Oxford University Press (OUP)
Date: 07-07-2015
DOI: 10.1093/GJI/GGV240
Publisher: MDPI AG
Date: 07-02-2021
Abstract: In this study, we present a data processing framework to apply measurements of the Global Navigation Satellite System (GNSS) technique for analyzing and predicting the movements of civil structures such as bridges. The proposed approach reduces the noise level of GNSS measurements using the Kalman Filter (KF) approach and enables the estimation of static, semi-static, and dynamic components of the bridge’s movements using a series of analyses such as the temporal filtering and the Least Squares Harmonic Estimation (LS-HE). The numerical results indicate that by using a RTK-GNSS system the semi-static component is extracted with a Standard Deviation (STD) of 0.032, 0.048, and 0.06 m in the North, East, and Up (NEU) directions, while that of the dynamic component is 0.004, 0.003, and 0.01 m, respectively. Comparing the dominant frequencies of the bridge movements from LS-HE with those of the permanent stations provides information about the bridge’s stability. To predict its deflection, the Neural Network (NN) technique is tested to simulate the time-varying components, which are then compared with the safety limits, known by its design, to assess the structural health under usual load.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-5804
Abstract: An accurate estimation of the Thermospheric Neutral Density (TND) is important for predicting the orbit of satellites and objects, for ex le, those with the altitude of less than 1000 km. Models are often used to simulate TNDs but their accuracy is limited due to uncertainties. Satellite missions such as CHAMP, GRACE, GOCE, Swarm, and GRACE-FO or the Satellite Laser Ranging (SLR) missions can be used to estimate along-track TNDs. However, spatial and temporal coverage of these space borne TNDs is restricted to the mission design. To make the best use of the modelling tools and measurements, we applied these along-track TND measurements within the sequential Calibration and Data Assimilation (C/DA) framework proposed by (Forootan et al., 2022, doi:10.1038/s41598-022-05952-y). The C/DA is used to re-calibrate the NRLMSISE00 model, which is called & #8220 C/DA-NRLMSISE00& #8221 , whose outputs fit well to the introduced space-borne TNDs. The C/DA-NRLMSISE00 is applicable for forecasting TNDs and in idual neutral mass compositions at any predefined vertical level (between ~100 and ~600 km) with user-defined spatial-temporal s ling. Seven periods (between 2003 - 2020) with considerable geomagnetic activity are selected for our investigations because most of the available models lack accuracy to provide reasonable TND simulations. Independent comparisons are performed with the space-borne TNDs that were not used within the C/DA framework, as well as with the outputs of other thermospheric models such as Jacchia-Bowman 2008 (JB2008) and the High Accuracy Satellite Drag Model (HASDM) database. The numerical results indicate that indeed the new model is suitable for producing multi-level global thermospheric neutral density fields.
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 07-11-2017
Publisher: Wiley
Date: 27-11-2014
DOI: 10.1002/JOC.4198
Publisher: Elsevier BV
Date: 10-2017
Publisher: Copernicus GmbH
Date: 14-09-2015
Abstract: Abstract. Poor reliability of radiosonde observational networks across South Asia imposes serious challenges in understanding climate variability and thermodynamic structure of the upper-tropospheric and lower-stratospheric (UTLS) region. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 have overcome many observational limitations that are inherent in conventional atmospheric sounding instruments. This study investigated the interannual variability of the UTLS region over the Ganges–Brahmaputra–Meghna (GBM) basin based on COSMIC radio occultation (RO) data from August 2006 to December 2013. Detailed comparisons were also made with various different radiosonde types and Numerical Weather Prediction (NWP) products. The results indicated that Indian Meteorological Department (IMD) radiosondes performed poorly despite upgrading to newer techniques. ShangE (of China) sonde showed the best agreement with COSMIC RO data with a mean temperature difference of −0.06 °C and a standard deviation of 1.44 °C while the older version (ShangM) indicated a cold bias of 0.61 °C in the UTLS region. The inter-annual variability of temperature in the UTLS region based on COSMIC RO data indicated a clear pattern of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) while the stratospheric temperature anomalies reflected all three major Sudden Stratospheric Warming (SSW) events since 2007. The mean tropopause temperature varied from −70 to −80 °C, with an average height of about 15.5 to 16.3 km from winter to summer, indicating a pronounced annual cycle. The annual litudes of tropopause were found to be in the order of 0–6 °C and 0–1.5 km from tropical south to subtropical north. The anomalies of tropopause temperature and height exhibited the patterns of ENSO and IOD exceptionally well with a correlation of 0.65 and −0.52, respectively. The temperature data from Modern-Era Retrospective Analysis for Research Application (MERRA) agreed very well with COSMIC RO data.
Publisher: Copernicus GmbH
Date: 15-04-2016
Abstract: Abstract. Poor reliability of radiosonde records across South Asia imposes serious challenges in understanding the structure of upper-tropospheric and lower-stratospheric (UTLS) region. The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched in April 2006 has overcome many observational limitations inherent in conventional atmospheric sounding instruments. This study examines the interannual variability of UTLS temperature over the Ganges–Brahmaputra–Meghna (GBM) river basin in South Asia using monthly averaged COSMIC radio occultation (RO) data, together with two global reanalyses. Comparisons between August 2006 and December 2013 indicate that MERRA (Modern-Era Retrospective Analysis for Research Application) and ERA-Interim (European Centre for Medium-Range Weather Forecasts reanalysis) are warmer than COSMIC RO data by 2 °C between 200 and 50 hPa levels. These warm biases with respect to COSMIC RO data are found to be consistent over time. The UTLS temperature show considerable interannual variability from 2006 to 2013 in addition to warming (cooling) trends in the troposphere (stratosphere). The cold (warm) anomalies in the upper troposphere (tropopause region) are found to be associated with warm ENSO (El Niño–Southern Oscillation) phase, while quasi-biennial oscillation (QBO) is negatively (positively) correlated with temperature anomalies at 70 hPa (50 hPa) level. PCA (principal component analysis) decomposition of tropopause temperatures and heights over the basin indicate that ENSO accounts for 73 % of the interannual (non-seasonal) variability with a correlation of 0.77 with Niño3.4 index whereas the QBO explains about 10 % of the variability. The largest tropopause anomaly associated with ENSO occurs during the winter, when ENSO reaches its peak. The tropopause temperature (height) increased (decreased) by about 1.5 °C (300 m) during the last major El Niño event of 2009/2010. In general, we find decreasing (increasing) trend in tropopause temperature (height) between 2006 and 2013.
Publisher: MDPI AG
Date: 20-05-2020
DOI: 10.3390/RS12101639
Abstract: Observing global terrestrial water storage changes (TWSCs) from (inter-)seasonal to (multi-)decade time-scales is very important to understand the Earth as a system under natural and anthropogenic climate change. The primary goal of the Gravity Recovery And Climate Experiment (GRACE) satellite mission (2002–2017) and its follow-on mission (GRACE-FO, 2018–onward) is to provide time-variable gravity fields, which can be converted to TWSCs with ∼ 300 km spatial resolution however, the one year data gap between GRACE and GRACE-FO represents a critical discontinuity, which cannot be replaced by alternative data or model with the same quality. To fill this gap, we applied time-variable gravity fields (2013–onward) from the Swarm Earth explorer mission with low spatial resolution of ∼ 1500 km. A novel iterative reconstruction approach was formulated based on the independent component analysis (ICA) that combines the GRACE and Swarm fields. The reconstructed TWSC fields of 2003–2018 were compared with a commonly applied reconstruction technique and GRACE-FO TWSC fields, whose results indicate a considerable noise reduction and long-term consistency improvement of the iterative ICA reconstruction technique. They were applied to evaluate trends and seasonal mass changes (of 2003–2018) within the world’s 33 largest river basins.
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 06-2018
DOI: 10.1016/J.SCITOTENV.2017.12.289
Abstract: Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year.
Publisher: Elsevier BV
Date: 2016
Publisher: American Geophysical Union (AGU)
Date: 08-08-2016
DOI: 10.1002/2016GL069538
Publisher: MDPI AG
Date: 29-10-2020
DOI: 10.3390/RS12213545
Abstract: A new approach is presented to improve the spatial and temporal resolution of the Vertical Total Electron Content (VTEC) estimates for regional positioning applications. The proposed technique utilises a priori information from the Global Ionosphere Maps (GIMs) of the Center for Orbit Determination in Europe (CODE), provided in terms of Spherical Harmonic (SH) coefficients of up to degree and order 15. Then, it updates the VTEC estimates using a new set of base-functions (with better resolution than SHs) while using the measurements of a regional GNSS network. To achieve the highest accuracy possible, our implementation is based on a transformation of the GIM/CODE VTECs to their equivalent coefficients in terms of (spherical) Slepian functions. These functions are band-limited and reflect the majority of signal energy inside an arbitrarily defined region, yet their orthogonal property is remained. Then, new dual-frequency GNSS measurements are introduced to a Least Squares (LS) updating step that modifies the Slepian VTEC coefficients within the region of interest. Numerical application of this study is demonstrated using a synthetic ex le and ground-based GPS data in South America. The results are also validated against the VTEC estimations derived from independent GPS stations (that are not used in the modelling), and the VTEC products of international centres. Our results indicate that, by using 62 GPS stations in South America, the ionospheric delay estimation can be considerably improved. For ex le, using the new VTEC estimates in a Precise Point Positioning (PPP) experiment improved the positioning accuracy compared to the usage of GIM/CODE and Klobuchar models. The reductions in the root mean squared of errors were ∼23% and 25% for a day with moderate solar activity while 26% and ∼35% for a day with high solar activity, respectively.
Publisher: American Geophysical Union (AGU)
Date: 09-2022
DOI: 10.1029/2022WR031997
Abstract: Hydrological droughts are events of prolonged water scarcity and cause many devastating impacts. It is, therefore, extremely crucial to understand their spatiotemporal evolution to guide prevention and mitigation policies. The Gravity Recovery and Climate Experiment (GRACE, April 2002–June 2017) and GRACE Follow‐On (GRACE‐FO, June 2018 until the present) missions have been used to study large‐scale droughts of almost two decades. But characterizing droughts during the between missions gap period of 2017 and 2018 has not been well addressed and will be covered here. To bridge the gap, an innovative Bayesian convolutional neural network is developed to reconstruct the missing signals from hydroclimatic inputs. The reconstruction fields and existing signals are then used to explore regions that have experienced consecutive water storage deficits during the 2017–2018 gap. We found many regions of the northern midlatitudes exhibiting moderate to exceptional droughts in terms of water storage deficits, among which parts of Pakistan and Afghanistan, and Iberian Peninsula experienced exceptional droughts lasting for more than 1 year with the maximum deficits (−4.4 ± 0.8 and −7.2 ± 1.1 cm, respectively) being over 50% of the seasonal storage variations. Comparisons with climate indicators show that the identified droughts are predominantly caused by continuous below‐normal precipitation. The recovery process correlates generally well with the accumulation rate of precipitation surpluses (the correlation coefficient ( R ) can be up to 0.92). Besides, the reconstructed signals, which have R 0.7 with the testing GRACE(‐FO) data in over 90% of the globe, reliably maintain the data continuity and therefore they are recommended for hydro‐climatological studies.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 17-08-2018
DOI: 10.1093/GJI/GGY340
Publisher: Elsevier BV
Date: 02-2019
DOI: 10.1016/J.SCITOTENV.2018.09.231
Abstract: Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.
Publisher: American Geophysical Union (AGU)
Date: 08-2017
DOI: 10.1002/2016JB013633
Publisher: Copernicus GmbH
Date: 25-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-282
Abstract: & & An accurate simulation global thermospheric neutral density (TND) on various altitudes is important for geodetic and space weather applications. In addition, this is essential for designing the low-Earth-orbit (LEO) missions, predict their missions& #8217 lifetime and performing a reliable attitude control. Although empirical and physics-based models typically simulate TND variations, the quality of these models is limited due to various structural simplifications and the uncertainty of inputs. Here, we present an ensemble Kalman filter (EnKF)-based calibration and data assimilation (C/DA) technique that updates the model's states and simultaneously calibrates its key parameters. The proposed approach provides the opportunity to improve the now-cast and forecast skills of the NRLMISISE-00 and NRMSIS-2.0 models through re-calibrating the model& #8217 s key parameters including those controlling the influence of solar radiation and geomagnetic activity as well as those related to the calculation of exospheric temperature.& & & & In this research, TND estimates from on-board accelerometer measurements of GRACE, GRACE-FO and Swarm are ingested as observations into the NRLMSISE-00 and NRLMSIS-2.0 models based on the C/DA. The newly calibrated model, called here & #8216 C/DA-NRLMSISE& #8217 , is then used to simulate global maps of TND as well as in idual neutral mass densities covering the altitudes of 300-600 km. Various investigations are performed to test the temporal and vertical consistency of the TND outputs from C/DA-NRLMSISE.& &
Publisher: American Geophysical Union (AGU)
Date: 07-2018
DOI: 10.1029/2018WR022854
Publisher: American Geophysical Union (AGU)
Date: 05-2013
DOI: 10.1002/JGRB.50160
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 04-2013
Publisher: Oxford University Press (OUP)
Date: 22-10-2020
DOI: 10.1093/GJI/GGAA507
Abstract: Improving thermospheric neutral density (TND) estimates is important for computing drag forces acting on low-Earth-orbit (LEO) satellites and debris. Empirical thermospheric models are often used to compute TNDs for the precise orbit determination experiments. However, it is known that simulating TNDs are of limited accuracy due to simplification of model structure, coarse s ling of model inputs and dependencies to the calibration period. Here, we apply TND estimates from accelerometer measurements of the Challenging Minisatellite Payload (CHAMP) and the Gravity Recovery and Climate Experiment (GRACE) missions as observations to improve the NRLMSISE-00 model, which belongs to the mass spectrometer and incoherent scatter family of models. For this, a novel simultaneous calibration and data assimilation (C/DA) technique is implemented that uses the ensemble Kalman filter and the ensemble square-root Kalman filter as merger. The application of C/DA is unique because it modifies both model-derived TNDs, as well as the selected model parameters. The calibrated parameters derived from C/DA are then used to predict TNDs in locations that are not covered by CHAMP and GRACE orbits, and forecasting TNDs of the next day. The C/DA is implemented using daily CHAMP- and/or GRACE-TNDs, for which compared to the original model, we find 27 per cent and 62 per cent reduction of misfit between model and observations in terms of root mean square error and Nash coefficient, respectively. These validations are performed using the observations along the orbital track of the other satellite that is not used in the C/DA during 2003 with various solar activity. Comparisons with another empirical model, that is, Jacchia-Bowman, indicate that the C/DA results improve these quality measurements on an average range of 50 per cent and 60 per cent, respectively.
Publisher: Copernicus GmbH
Date: 18-05-2018
DOI: 10.5194/ANGEO-36-761-2018
Abstract: Abstract. Ultra-sensitive space-borne accelerometers on board of low Earth orbit (LEO) satellites are used to measure non-gravitational forces acting on the surface of these satellites. These forces consist of the Earth radiation pressure, the solar radiation pressure and the atmospheric drag, where the first two are caused by the radiation emitted from the Earth and the Sun, respectively, and the latter is related to the thermospheric density. On-board accelerometer measurements contain systematic errors, which need to be mitigated by applying a calibration before their use in gravity recovery or thermospheric neutral density estimations. Therefore, we improve, apply and compare three calibration procedures: (1) a multi-step numerical estimation approach, which is based on the numerical differentiation of the kinematic orbits of LEO satellites (2) a calibration of accelerometer observations within the dynamic precise orbit determination procedure and (3) a comparison of observed to modeled forces acting on the surface of LEO satellites. Here, accelerometer measurements obtained by the Gravity Recovery And Climate Experiment (GRACE) are used. Time series of bias and scale factor derived from the three calibration procedures are found to be different in timescales of a few days to months. Results are more similar (statistically significant) when considering longer timescales, from which the results of approach (1) and (2) show better agreement to those of approach (3) during medium and high solar activity. Calibrated accelerometer observations are then applied to estimate thermospheric neutral densities. Differences between accelerometer-based density estimations and those from empirical neutral density models, e.g., NRLMSISE-00, are observed to be significant during quiet periods, on average 22 % of the simulated densities (during low solar activity), and up to 28 % during high solar activity. Therefore, daily corrections are estimated for neutral densities derived from NRLMSISE-00. Our results indicate that these corrections improve model-based density simulations in order to provide density estimates at locations outside the vicinity of the GRACE satellites, in particular during the period of high solar/magnetic activity, e.g., during the St. Patrick's Day storm on 17 March 2015.
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-3577
Abstract: & & Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) provide time-variable Earth's gravity fields that contain signals related to different processes such as non-steric sea level changes, Terrestrial (surface and sub-surface) Water Storage Changes (TWSC), ice sheet melting, and Post Glacial Rebound (PGR). Although the GRACE(-FO) data represent an accurate superposition of these anomalies, separating them into in idual storage and surface deformation contributors is desirable for many geodynamic and hydro-climatic applications. Particularly, for hydrological applications, the PGR is often removed as a linear trend, during the post-processing, using the output of Glacial Isostatic Adjustment (GIA) models. As a result, estimating trends in TWSC depends on the accuracy of GIA models, which has considerable uncertainties. 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 optimization algorithm to update available information within a dynamic multivariate state-space model, while accounting for the uncertainties of models and observations. To evaluate the proposed approach, its numerical implementation is demonstrated over the Great Lakes area in North America on grids with 0.5-degree spatial resolution, covering 2003-2017, where the W3RA water balance model and the ICE-5G(VM2) GIA model are used as a & em& riori& /em& information of in idual water storage changes and PGR rates. Validations are done against independent measurements, i.e., in-situ USGS groundwater level observations, as well as independent GNSS measurements (not used in the optimization procedure). The results indicate that the bias between GIA model output and the in-situ GNSS observation reduced by 72% (from 7.8 to 2.2 mm/yr) and the root-mean-square-of-differences between USGS measurements and model-derived groundwater changes is reduced by 36%, 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.& & & & & & & & & keywords: Bayesian Signal Separation GRACE(-FO) GNSS Terrestrial Water Storage Changes Post Glacial Rebound & & &
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.SCITOTENV.2018.08.032
Abstract: With a growing number of available datasets especially from satellite remote sensing, there is a great opportunity to improve our knowledge of the state of the hydrological processes via data assimilation. Observations can be assimilated into numerical models using dynamics and data-driven approaches. The present study aims to assess these assimilation frameworks for integrating different sets of satellite measurements in a hydrological context. To this end, we implement a traditional data assimilation system based on the Square Root Analysis (SQRA) filtering scheme and the newly developed data-driven Kalman-Takens technique to update the water components of a hydrological model with the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS), and soil moisture products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) in a 5-day temporal scale. While SQRA relies on a physical model for forecasting, the Kalman-Takens only requires a trajectory of the system based on past data. We are particularly interested in testing both methods for assimilating different combination of the satellite data. In most of the cases, simultaneous assimilation of the satellite data by either standard SQRA or Kalman-Takens achieves the largest improvements in the hydrological state, in terms of the agreement with independent in-situ measurements. Furthermore, the Kalman-Takens approach performs comparably well to dynamical method at a fraction of the computational cost.
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-9517
Abstract: & & During geomagnetic storms, communication and navigation instruments can be dramatically affected by the rapid changes that occur in the upper atmosphere. The assimilation of data in physics-based models such as the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model through and ensemble Kalman filter, can improve the representation of the thermosphere-ionosphere (TI) system. Due to the coupled nature of the TI system, the ionosphere is affected by, among others, changes in the neutral atmosphere. In this study, we investigate the capability of the CTIPe model to provide better estimates of the ionosphere by improving its specification of the thermosphere via data assimilation. Here, we assimilate thermospheric mass density (TMD) observations from the Swarm mission normalized to 400 km altitude during the 2015 St. Patrick& #8217 s Day storm. The changes that occur in the ionosphere due to assimilation of TMD data are measured by means of the difference between the model results with and without assimilation. To measure the improvement gained with data assimilation, we compare with independent measurements of electron density along the orbit of GRACE (Gravity Recovery and Climate Experiment) satellite, that shows a reduction in the root mean square error (RMSE) by a 22% with respect to the non-assimilation run. The impact on the global scale is measured by comparing the CTIPe model results with the corresponding output of the 3D B-Spline electron density model. The results illustrate that the electron density equatorial region is the most affected region by assimilation of TMD, with an average RMSE reduction of 25% at the assimilation altitude of 400 km.& &
Publisher: Springer Science and Business Media LLC
Date: 20-12-2017
Publisher: Elsevier BV
Date: 2018
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-282
Abstract: & & The Global Ionosphere Maps (GIMs) are generated on a daily basis at the Center for Orbit Determination in Europe (CODE) using the observations from about 200 Global Positioning System (GPS)/GLONASS sites of the International GNSS Service (IGS) and other institutions. These maps contain Vertical Total Electron Content (VTEC) values, which are estimated in a solar-geomagnetic reference frame using a spherical harmonics expansion up to degree and order 15. Although these maps have wide applications, their relatively low spatial resolution limits the accuracy of many geodetic applications such as those related to Precise Point Positioning (PPP) and navigation. In this study, a novel Bayesian approach is proposed to improve the spatial resolution of VTEC estimations in regional and global scales. The proposed technique utilises GIMs as a prior information and updates the VTEC estimates using a new set of base-functions (with better resolution than that of spherical harmonics) and the GNSS measurements that are not included in the network of GIMs. To achieve the highest accuracy possible, our implementation is based on a transformation of spherical harmonics to the Slepian base-functions, where the latter is a set of bandlimited functions that reflect the majority of signal energy inside an arbitrarily defined region, yet they remain orthogonal within this region. The new GNSS measurements are considered in a Bayesian update estimation to modify those of GIMs. Numerical application of this study is demonstrated using the ground-based GPS data over South America. The results are also validated against the VTEC estimations derived from independent GPS stations.& & & & & strong& Key words:& /strong& Spherical Slepian Base-Functions, Spherical Harmonics, Ionospheric modelling, Vertical Total Electron Content (VTEC)& &
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-284
Abstract: & & Increasing the quality of ionosphere modeling is crucial and remains a challenge for many geodetic applications such as GNSS Precise Point Positioning (PPP) and navigation. Ionosphere models are the main tool to provide an estimation of Total Electron Content (TEC) to be corrected from GNSS career phase and pseudorange measurements. Skills of these models are however limited due to the simplifications in model equations and the imperfect knowledge of model parameters. In this study, an ionosphere reconstruction approach is presented, where global estimations of geodetic-based TEC measurements are combined with an ionospheric background model. This is achieved here through a novel simultaneous Calibration and Data Assimilation (C/DA) technique that works based on the sequential Ensemble Kalman Filter (EnKF). The C/DA method ingests the actual ionospheric measurements (derived from global GNSS measurements) into the IRI (International Reference Ionosphere) model. It also calibrates those parameters that control the F2 layer& #8217 s characteristics such as selected important CCIR (Comit& #233 Consultatif International des Radiocommunicationsand) URSI (International Union of Radio Science) coefficients.& The calibrated parameters derived from the C/DA are then replaced in the IRI to simulate TEC values in locations, where less GNSS ground-station infrastructure exists, as well as to enhance the prediction of TEC when the observations are not available or their usage is cautious due to low quality. Our numerical assessments indicate the advantage of the C/DA to improve the IRI& #8217 s performance. Values of the TEC-Root Mean Square of Error (RMSE) are found to be decreased by up to 30% globally, compared to the original IRI simulations. The importance of the new TEC estimations is demonstrated for PPP applications, whose results show improvements in navigation applications.& & & & & strong& Keywords: & /strong& Ionosphere, Calibration and Data Assimilation (C/DA), IRI, Total Electron Content (TEC), Precise Point Positioning (PPP), GNSS& &
Publisher: Elsevier BV
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 07-02-2014
Publisher: American Geophysical Union (AGU)
Date: 09-2021
DOI: 10.1029/2021JB021926
Abstract: The current state‐of‐the‐art of satellite gravity data processing makes use of de‐aliasing products to reduce high‐frequency mass anomalies. For ex le, the most recent official Atmosphere and Ocean De‐aliasing products (AOD1B‐RL06) are applied for the Gravity Recovery and Climate Experiment (GRACE) and GRACE‐Follow On (GRACE‐FO) missions. The temporal resolution of AOD1B‐RL06 is 3 h, and spectrally, they are computed up to degree and order 180. In this study, we explore a refined, e.g., geometrically, physically, and numerically improved, mass integration approach that is important for computing the atmosphere part of these products. Besides, the newly available ERA5 climate data are used to produce a new set of non‐tidal atmosphere de‐aliasing product (HUST‐ERA5) that is computed hourly up to degree and order 100, covering 2002 onwards. Despite an overall agreement with AOD1B‐RL06 (correlation 0.99), considerable discrepancies still exist between HUST‐ERA5 and AOD1B‐RL06. The possible reasons are therefore analyzed, and we find the input climate data, s ling rate and integration method may result in a product difference of ∼0.3, ∼0.15 and ∼0.05 millimeter geoid height, respectively. The total differences between HUST‐ERA5 and AOD1B‐RL06 can lead to a mean variation of ∼7.34 on the laser ranging interferometry (LRI) range‐rate residuals, for ex le, during January 2019, which is already close to the LRI precision. This impact is invisible for the GRACE (‐FO) gravity inversion because of the less accurate on‐board KBR (K‐band ranging) instrument, however, it will be non‐negligible and should be considered when the LRI completely replaces the KBR in the future gravity missions.
Publisher: Elsevier BV
Date: 12-2018
DOI: 10.1016/J.SCITOTENV.2018.07.212
Abstract: With the construction of the largest dam in Africa, the Grand Ethiopian Renaissance Dam (GERD) along the Blue Nile, the Nile is back in the news. This, combined with Bujagali Dam on the White Nile are expected to bring ramification to the downstream countries. A comprehensive analysis of the Nile's waters (surface, soil moisture and groundwater) is, therefore, essential to inform its management. Owing to its shear size, however, obtaining in-situ data from "boots on the ground" is practically impossible, paving way to the use of satellite remotely sensed and models' products. The present study employs multi-mission satellites and surface models' products to provide, for the first time, a comprehensive analysis of the changes in Nile's stored waters' compartments surface, soil moisture and groundwater, and their association to climate variability (El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) over the period 1992-2016. In this regard, remotely sensed altimetry data from TOPEX/Poseidon (T/P), Jason-1, and Jason-2 satellites along with the Gravity Recovery And Climate Experiment (GRACE) mission, and the Tropical Rainfall Measuring Mission Project (TRMM) rainfall products are applied to analyze the compartmental changes over the Nile River Basin (NRB). This is achieved through the creation of 62 virtual gauge stations distributed throughout the Nile River that generate water levels, which are used to compute surface water storage changes. Using GRACE total water storage (TWS), soil moisture data from multi-models based on the Triple Collocation Analysis (TCA) method, and altimetry derived surface water storage, Nile basin's groundwater variations are estimated. The impacts of climate variability on the compartmental changes are examined using TRMM precipitation and large-scale ocean-atmosphere ENSO and IOD indices. The results indicate a strong correlation between the river level variations and precipitation changes in the central part of the basin (0.77 on average) in comparison to the northern (0.64 on average) and southern parts (0.72 on average). Larger water storages and rainfall variations are observed in the Upper Nile in contrast to the Lower Nile. A negative groundwater trend is also found over the Lower Nile, which could be attributed to a significantly lower amount of rainfall in the last decade and extensive irrigation over the region.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-369
Abstract: & & An accurate estimation of the Thermospheric Neutral Density (TND) is important to compute drag forces acting on Low-Earth-Orbit (LEO) satellites and debris. Empirical thermospheric models are often used to compute TNDs (along-track of LEO satellites) for the Precise Orbit Determination (POD) experiments. However, recent studies indicate that the TNDs of available models do not perfectly reproduce TNDs derived from accelerometer observations. In this study, we use TND estimates from the Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) missions and merge them with the NRLMSISE00 from the Mass Spectrometer and Incoherent Scatter family. The integration is implemented by applying a simultaneous Calibration and Data Assimilation (C/DA) technique. The application of C/DA is advantageous since it uses model equation to interpolate and extrapolate TNDs that are not covered by CHAMP and GRACE. It also modifies the model's selected parameters to simulate TNDs that are closer to those of CHAMP and GRACE. The C/DA of this study is implemented daily using CHAMP- and/or GRACE-TNDs, while using the Ensemble Kalman Filter (EnKF) and Ensemble Square-Root Kalman Filter (EnSRF) as merger. Compared to the original model, on average, we found 27% (in the range of 2% to 56%) improvements in the estimation of TNDs. In addition, the results of the C/DA are compared with the TND outputs of the JB2008 model along the CHAMP and GRACE orbits, whose results indicate that the daily C/DA outputs are 60% closer to the observed TNDs (that are not used for the C/DA). Overall, our assessment indicates that EnSRF results in more realistic TND simulation and prediction compared to those derived from EnKF. We show that the improved TND estimates of this study will be beneficial for Precise Orbit Determination (POD) studies. & & & & & & strong& Keywords: & /strong& Thermosphere, Calibration and Data Assimilation (C/DA), NRLMSISE00, Ensemble Kalman Filter (EnKF), Ensemble Square-Root Kalman Filter (EnSRF)& &
Publisher: Springer Science and Business Media LLC
Date: 22-11-2011
Publisher: Elsevier BV
Date: 10-2013
Publisher: Copernicus GmbH
Date: 26-02-2023
DOI: 10.5194/EGUSPHERE-EGU23-11591
Abstract: & & The upper part of the Earth& #8217 s atmosphere is important for the ground-based and satellite radio communications and navigation. It consists of free electrons and ions, mainly ionized by solar radiation. The free electrons in the ionosphere strongly impact the propagation of radio waves. Therefore, modelling and predicting ionosphere are important for many space weather and geodetic applications. In this study, we apply geometry-free multi-GNSS approach enabling the simultaneous retrieval of vertical total electron content (VTEC). Unlike previous studies, multiple frequency GNSS receivers of smartphones and more accurate geodetic receivers are used to understand how various code hase observations could be combined. The smartphone observations are typically less precise and affected by multipath and they contain frequent cycle slips. Various combinations of the ionospheric observations, using both code and phase measurements, are tested to understand how to mitigate the challenges and to account for the measurements& #8217 quality. Finally, the advantages and disadvantages of these combinations are explained.& & & & & strong& Keywords: & /strong& Smartphone, Vertical Total Electron Content& strong& , & /strong& Multi-frequency GNSS& &
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-9644
Abstract: & & Atmospheric drag has a direct relationship with the thermospheric neutral density (TND) and represents a considerable impact on the precise orbit determination (POD) and prediction of low Earth orbit (LEO) satellites, for ex le those with the altitude of less than 1000 km, as well as space debris. The distribution of TND combined with the solar and geomagnetic activity has a direct impact on the electron distribution in the ionosphere. The latter is important for medium- and long-range high frequency communication, positioning, and over-the-horizon radar systems. New capabilities to understand, model and predict thermosphere variables are typically provided by models however, the quality of them is limited due to their imperfect structure and uncertainty of their inputs. In this study, we present various data assimilation frameworks to take advantage of freely available accelerometer derived TNDs from GRACE, GRACE-FO and Swarm missions. This is realized by (1) formulating ensemble Kalman filter (EnKF)-based calibration and data assimilation (C/DA) procedures to update the model's states (and simultaneously calibrates its key parameters if needed) and (2) an empirical decomposition-based data assimilation is applied to merge satellite derived along-track estimates with global model derived TND simulations. The results of these two frameworks are then evaluated against independent measurements. The technical challenges and benefits are discussed in detail.& &
Publisher: American Geophysical Union (AGU)
Date: 05-2020
DOI: 10.1029/2019WR026551
Publisher: Springer Science and Business Media LLC
Date: 04-2022
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10552
Abstract: & & The response of the Ionosphere - Thermosphere (IT) system to severe storm conditions is of great importance to fully understand its coupling mechanisms. The challenge to represent the governing processes of the upper atmosphere depends, to a large extent, on an accurate representation of the true state of the IT system, that we obtain by assimilating relevant measurements into physics-based models. Thermospheric Mass Density (TMD) is the summation of total neutral mass within the atmosphere that is derived from accelerometer measurements of satellite missions such as CHAMP, GOCE, GRACE(-FO) and Swarm. TMD estimates can be assimilated into physics-based models to modify the state of the processes within the IT system. Previous studies have shown that this modification can potentially improve the simulations and predictions of the ionospheric electron density. These differences could also be interpreted as an indicator of the ionosphere-thermosphere interaction. The research presented here, aims to quantify the impact of data satellite based TMD assimilation on numerical model results.& & & & Subject of this study is the Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe) physics-based model in combination with the recently developed Thermosphere-Ionosphere Data Assimilation (TIDA) scheme. TMD estimates from the ESA& #8217 s Swarm mission are assimilated in CTIPe-TIDA during the 16 to the 20 of March 2015. This period was characterized by a strong geomagnetic storm that triggered significant changes in the IT system, the so-called St. Patrick day storm 2015. To assess the changes in the IT system during storm conditions due to data assimilation, the model results from assimilating SWARM mass density normalized to the altitude of 400 km are compared to independent thermospheric estimates like GRACE-TMDS. In order to evaluate the impact of the data assimilation on the ionosphere, the corresponding output of electron density is compared to high-quality electron density estimates derived from data-driven model of the DGFI-TUM.& &
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2017
Publisher: Springer Science and Business Media LLC
Date: 04-01-2015
Publisher: Wiley
Date: 31-10-2014
Publisher: Springer Science and Business Media LLC
Date: 08-09-2012
Publisher: Informa UK Limited
Date: 22-07-2022
Publisher: American Geophysical Union (AGU)
Date: 12-2013
DOI: 10.1002/2013WR014350
Publisher: American Geophysical Union (AGU)
Date: 31-08-2023
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 ex le, 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%.
Publisher: Springer Science and Business Media LLC
Date: 25-05-2014
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 18-04-2017
Publisher: American Geophysical Union (AGU)
Date: 26-01-2016
DOI: 10.1002/2015JD023808
Publisher: Elsevier BV
Date: 03-2013
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-4901
Abstract: & & & strong& Abstract:& /strong& & & & & Global ionosphere maps (GIM) are generated on daily basis at the Center for Orbit Determination in Europe (CODE) using data from about 200 GNSS sites of the International GNSS Service (IGS) and other institutions. These measurement are used to numerically model the vertical total electron content (VTEC) in a solar-geomagnetic reference frame using a spherical harmonics expansion up to degree and order 15. In this study, an efficient method is developed and applied to densify the GIM model in a region of interest using the TEC measurements of& local networks. Our approach follows a Bayesian updating scheme, where the GIM data are utilized as a prior information in the form of Slepian-coefficients in the region of interest. These coefficients are then updated by the GNSS measurements in a Bayesian framework that considers both the uncertainty of a priori information and the new measurements. Numerical application is demonstrated using a GNSS network in South America. Our results indicate that by using 62 GNSS stations in South America, the ionospheric delay estimation can be considerably improved. For ex le, using the Bayesian-derived VTEC estimates in a Standard Point Positioning (SPP) experiment improved the positioning accuracy compared to the usage of GIM/CODE and Klobuchar models. The reductions in the root mean squared of errors were found to be & #8764 % and 25% for a day with moderate solar activity while 26% and & #8764 % for a day with high solar activity, respectively.& & & & & strong& Key words:& /strong& Bayesian densification, Slepian Functions, Spherical Harmonics, Ionospheric modelling, VTEC, SPPs& &
Publisher: Wiley
Date: 26-06-2013
DOI: 10.1002/JOC.3763
Location: United Kingdom of Great Britain and Northern Ireland
Location: Netherlands
Location: Iran (Islamic Republic of)
No related grants have been discovered for Ehsan Forootan.