Maike SchumacherAalborg University · Department of Development and Planning
Maike Schumacher
Assistant Professor
About
50
Publications
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Introduction
Additional affiliations
December 2020 - present
September 2018 - November 2020
October 2016 - August 2018
Education
October 2012 - September 2016
October 2010 - September 2012
October 2007 - September 2010
Publications
Publications (50)
Ionospheric models are applied for computing the Total Electron Content (TEC) in ionosphere to reduce its effects on the Global Navigation Satellite System (GNSS)-based Standard Point Positioning (SPP) applications. However, the accuracy of these models is limited due to the simplified model structures and their dependency on the calibration period...
Global estimation of thermospheric neutral density (TND) on various altitudes is important for geodetic and space weather applications. This is typically provided by models, however, the quality of these models is limited due to their imperfect structure and the sensitivity of their parameters to the calibration period. Here, we present an ensemble...
A method for estimating complex soil permittivity (or moisture) and penetration depth based on SAR decomposition is presented. By combining model- and eigenbased decomposition techniques in a non-iterative way, SAR observations are separated into single scattering components (soil, vegetation). The proposed method incorporates a multilayer rough su...
The uncertainty in thermospheric neutral density (TND) estimates is one of the largest and persistent sources of uncertainty in orbit determination and prediction (OD/OP) of low Earth orbit space objects. The TNDs required for these applications are typically obtained from corresponding models. However, the simulation and forecasting skills of thes...
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 restri...
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between...
WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth.
Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has impr...
The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great p...
With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe at L-band (ROSE-L) and its combination with existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. To investigate the potential for estimating s...
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 o...
Climate variability and change along with anthropogenic water use have affected the (re)distribution of water storage and fluxes across the Contiguous United States (CONUS). Available hydrological models, however, do not represent recent changes in the water cycle. Therefore, in this study, a novel Bayesian Markov Chain Monte Carlo-based Data Assim...
WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has impr...
Observing global terrestrial water storage changes (TWSCs) from (inter-)seasonal to (multi-)decade timescales 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,...
Freshwater availability is of vital importance for humans, freshwater biota and ecosystem functions. In the past decades, global hydrological models (GHMs) were developed to improve understanding of the global freshwater situation in a globalized word, by filling gaps in observational coverage and assessing scenarios of the future under considerati...
We demonstrate a new approach to recover water mass changes from GRACE satellite data at a daily temporal resolution. Such a product can be beneficial in monitoring extreme weather events that last a few days and are missing by conventional monthly GRACE data. The determination of the distribution of these water mass sources over networks of juxtap...
Historically, hydrological models have been developed to represent land-atmosphere interactions by simulating water storage and water fluxes. These models, however, have their own unique characteristics (strength and weakness) in capturing different aspects of the water cycle, and their results are typically compared to or calibrated against in-sit...
Global Terrestrial Water Storage from 2003 to 2018, on 1° grid and smoothed by a Gaussian 400km half-width filter.
Glacial isostatic adjustment (GIA) is a crucial component in evaluating sea level change. The GIA process has been simulated globally from various physical forward models, and it can also be measured locally at some GPS stations. In this paper, we combine the physical model simulations and GPS measurements in a Bayesian hierarchical modeling framew...
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...
In order to reduce high frequency non-tidal mass changes, while inverting for the Earth’s time-variable gravity fields from the Gravity Recovery And Climate Experiment (GRACE) measurements, it is usual to apply the Atmospheric and Oceanic De-aliasing (AOD1B) products. However, limitations in these products count as a potential threat to the accurac...
Glacial isostatic adjustment (GIA) is the response of the solid Earth to past ice loading, primarily, since the Last Glacial Maximum, about 20 K yr BP. Modelling GIA is challenging because of large uncertainties in ice loading history and also the viscosity of the upper and lower mantle. GPS data contain the signature of GIA in their uplift rates b...
Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a “calibration” procedure. Optimization techniques are usually applied in the model calibration step, which assu...
We introduce a framework for updating large scale geospatial processes using a model-data synthesis method based on Bayesian hierarchical modelling. Two major challenges come from updating large-scale Gaussian process and modelling non-stationarity. To address the first, we adopt the SPDE approach that uses a sparse Gaussian Markov random fields (G...
We propose the globalization of the continent-scale Kalman Filtering (KF) previously developed by Ramillien et al. (2015) to produce time series of daily maps of surface mass variations by progressive integration of daily geopoten-tial variations measured by orbiting satellites. These geopotential variations can be determined from very accurate int...
Simulating hydrological processes within the (semi-)arid region of the Murray-Darling Basin (MDB), Australia, is very challenging specially during droughts. In this study, we investigate whether integrating remotely sensed terrestrial water storage changes (TWSC) from the Gravity Recovery And Climate Experiment (GRACE) mission into a global water r...
Assimilation of terrestrial water storage (TWS) information from the Gravity Recovery And Climate Experiment (GRACE) satellite mission can provide significant improvements in hydrological modeling. However, the rather coarse spatial resolution of GRACE TWS and its spatially correlated errors pose considerable challenges for achieving realistic assi...
The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation...
Previous studies indicate that water storage over a large part of the Middle East has been decreased over the last decade. Variability of the total (hydrological) water flux (TWF, i.e. precipitation minus evapotranspiration minus runoff) and water storage changes of the Tigris-Euphrates river basin and Iran's six major basins (Khazar, Persian, Urmi...
We propose a new method to produce time series of global maps of surface mass variations by integrating progressively daily geopotential changes measured by orbiting satellites. In the case of the Gravity Recovery And Climate Experiment (GRACE) mission, these geopotential variations can be determined from very accurate inter-satellite K-Band Range...
Recently, ensemble Kalman filters (EnKF) have found increasing application for merging hydrological models with total water storage anomaly (TWSA) fields from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Previous studies have disregarded the effect of spatially correlated errors of GRACE TWSA products in their investigatio...
Spatio-temporal patterns of hydrological droughts over the Greater Horn of Africa (GHA) are explored based on total water storage (TWS) changes derived from time-variable gravity field solutions of Gravity Recovery And Climate Experiment (GRACE, 2002–2014), together with those simulated by Modern Retrospective Analysis for Research Application (MER...
Climate extremes such as droughts and intense rainfall events are expected to strongly influence global/regional water resources in addition to the growing demands for freshwater. This study examines the impacts of precipitation extremes and human water usage on total water storage (TWS) over the Ganges-Brahmaputra-Meghna (GBM) River Basin in South...
Large-scale ocean-atmospheric phenomena like the El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) have significant influence on Australia's precipitation variability. In this study, multi-linear regression (MLR) and complex empirical orthogonal function (CEOF) analyses were applied to isolate (i) the continental precipitation varia...
The uploaded movie shows precipitation anomalies over Australia that are likely due to ENSO/IOD. Maps are generated by applying a Complex Orthogonal Function (CEOF) technique following Forootan et al. (2016-Remote Sensing of Environment, doi:10.1016/j.rse.2015.10.027). Before applying CEOF, a 5-month moving average filter was applied to the monthly...
An ensemble Kalman filter approach for improving the WaterGAP Global Hydrology Model (WGHM) has been developed, which assimilates Gravity Recovery And Climate Experiment (GRACE) data and calibrates the model parameters, simultaneously. The method uses the model-derived states and satellite measurements and their error information to determine updat...
We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pat...
There are two spurious jumps in the atmospheric part of the Gravity Recovery and Climate Experiment-
Atmosphere and Ocean De-aliasing level 1B (GRACEAOD1B) products, which occurred in January-February of the years 2006 and 2010, as a result of the vertical level and horizontal resolution changes in the ECMWFop (European Centre for Medium-Range Weat...
Temporal aliasing caused by incomplete reduction of background models is still a factor that affects the quality of the gravity field solutions derived from GRACE products. Our study addresses: (i) the computational aspect of the atmospheric de-aliasing (AD) products (de-aliasing aspect), and (ii) the biases caused by
existing jumps in the atmosphe...
Global hydrological models contribute to the understanding and
quantification of the global water cycle. However, large model
uncertainties persist due to insufficient model realism and climate
forcing data not being available with sufficient spatial/temporal
resolution on the global scale. The GRACE mission provides an
independent observation of w...
Global hydrological models contribute to the understanding and
quantification of the global water cycle. However, large model
uncertainties persist due to insufficient model realism and climate
forcing and anthropogenic data not being available with sufficient
spatial/temporal resolution on the global scale. The GRACE mission
provides an independen...
Projects
Projects (5)
Developing efficient multi-sensor Data Assimilation frameworks for integrating Earth ObservatioN Satellite data into Land Surface Models (DANSk-LSM)
FORSKNINGSPROJEKT 2 TEKNOLOGI OG PRODUKTION 2022
MODTAGEREhsan Forootan
Aalborg Universitet
BEVILGET BELØB
6.191.818 kr
Klimaændringerne har øget sandsynligheden for hydrologiske og vejrekstreme begivenheder, som tørke og oversvømmelser. Den meteorologiske verdensorganisation (WMO) har fornylig i en rapport advaret om, at en tredjedel af verdens befolkning ikke er tilstrækkeligt dækket af advarselssystemer, og at antallet af mennesker, der vil være i nød efter naturkatastrofer, kan stige med 50% i løbet af det næste årti. Derfor vil vi, med projektet DANSk-LSM, opbygge banebrydende ”tidlig varsling”-systemer med høj kapacitet, der kan håndtere en høj beregningsbelastning, en række forskellige satellitdata og udvikle kontinentale hydrologiske assimilative systemer med hidtil uset opløsning. For at opnå de bedste resultater, vil vi udvikle tre modeller og forskellige strategier for dataassimilering og kalibrering. For at øge nøjagtigheden af modellerne, vil der for første gang blive anvendt frit tilgængelige satellitdata om: Terrestrisk vandlagring (TWS) fra satellitgravitation, overfladebundfugt (SSM) og landoverfladetemperatur (LST) samt overfladevandændringer. Open-access produkter fra DANSk-LSM vil omfatte: Kontinentale (på 100 m og 1 km) og globale (på 5 km opløsning) tørkeindekser, oplysninger om sværhedsgraden af hydrologiske våde og tørre globale forhold og deres returperioder samt kort over hydrologiske tørke og oversvømmelsespotentialer såvel som videnskabelige værktøjer til satellit-dataassimilering og nedskalering.
In this project, we explore techniques to merge multi-sensor satellite data with Earth system models. The focus of our project is not only
1- on advancing the mathematical techniques, but also
2- on the application of data-model fusion techniques for real-world applications, such as developing continental drought/flood monitoring systems, as well as improving coupled land-surface-atmosphere models, and finally
3- on validation of data/models/techniques using independent sources.
We explore techniques to merge multi-sensor earth observations and models
