Sökresultat

Selection of the number and which of multisource precipitation datasets is crucially important for precipitation fusion. Considering the effects of different inputs, this study proposes a new framework based on the Bayesian model averaging (BMA) algorithm to integrate precipitation information from gauge-based analysis CPC, reanalysis-derived dataset ERA5, and satellite-retrieval products IMERG-E

Reliability in Ricean multiple-input-multiple-output (MIMO) channels is crucial for safety related vehicle-tovehicle (V2V) applications. Due to the time variability of vehicular communication channel, it is very significant to study the temporal evolution of channel characterization criteria. We present evaluation results of the temporal evolution of the spectral efficiency from channel sounder me

The COST2100 channel model is a reference channel model which provides dynamic multiple-input multiple-output channel responses for radio system simulations. In this paper, channels created by the COST2100 model were compared to channel measurements in order to understand behaviours of the model. Model parameters of the COST2100 model were derived by dynamic double-directional channel measurements

Clouds are essential elements within Earth's atmosphere, posing a challenge for cloud-resolving models in understanding the creation of new cloud ice particles from existing ice and liquid phases. Such ice initiation determines cloud microphysical and radiative properties, influencing cloud phase, precipitation and cloud extent/properties. To address this challenge effectively, it proves beneficia

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrolo

We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and prop

Despite the fact that the response of tropical hydroclimate to North Atlantic cooling events during the Heinrich Stadial 1 (HS1) has been extensively studied in African, South American and Indonesia, the nature of such responses remains debated. Here we investigate the tropical hydroclimate pattern over the Indo-Asian-Australian monsoon region during the HS1 by integrating hydroclimatic records, a

Modern embedded software includes complex functionalities and routines, often implemented by splitting the code across different tasks. Such tasks communicate their partial computations to their successors, forming a task chain. Traditionally, this architecture relies on the assumption of hard deadlines and timely communication. However, in actual implementations, tasks may miss their deadlines, t

The tropical Pacific Ocean plays a significant role in climate change, and the El Niño-Southern Oscillation (ENSO) is considered to be closely related to extreme climate phenomenon worldwide. However, the evolution of the ENSO-like patterns in the tropical Pacific during the Pleistocene glacial cycles remains controversial. In this study, we present geochemical indices and a transient model simula

Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ obser

With the open and big data movement in full swing, data sharing becomes more ubiquitous and more often crosses sectoral boundaries. The promise of data to help address societal issues and foster innovation requires public organizations to work together with businesses and researchers. Data collaboratives whereby actors collaborate to share and use data for public good gain increasing interest. Mos

We extend the generalized information criteria for high-dimensional penalizedmodels to sparse statistical jump models, a new class of statistically robust and computationally efficient alternatives to hidden Markov models. In a simulation study, we demonstrate that the new generalized information criteria selects the correct hyperparameters with high probability. Finally, providing an empirical ap

Two conspicuous wet shifts in the peat stratigraphy of Store Mosse in southern Sweden, associated with bog-wide changes in vegetation and degree of peat decomposition, were analysed at high resolution. The bog-surface wetness (BSW) proxy data (organic matter bulk density, C/N ratio, plant macrofossils and testate amoebae) highlight the importance of interactions between vegetation composition, mic

Building on the base of the existing telescopes of the Event Horizon Telescope (EHT) and ALMA, the next-generation EHT (ngEHT) aspires to deploy ∼10 more stations. The ngEHT targets an angular resolution of ∼15 microarcseconds. This resolution is achieved using Very Long Baseline Interferometry (VLBI) at the shortest radio wavelengths ∼1 mm. The Submillimeter Array (SMA) is both a standalone radio

Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework

Accurately representing and predicting hydrological processes in terrestrial ecosystems is crucial for understanding the impacts of global environmental change, particularly climate change. This doctoral thesis aims to advance the understanding of vegetation-hydrology interactions and provides new modelling approaches to enhance hydrological prediction abilities. The thesis is grounded in the eval

Tropical oceans are the main global water vapor and latent heat sources, but their responses to radiative forcing remain unclear. Here, we investigate oceanic moisture dynamics of the western tropical Pacific (WTP) over the past 210,000 years through an approach of planktonic foraminiferal triple oxygen isotope (Δ′17O). The Δ′17O record is dominated by the precession cycles (~23,000 years), with l

For decades, the environmental sciences have engaged in measuring and modelling natural systems to increase our understanding and prediction of climate change. This thesis advances these efforts by analyzing and applying in-situ instruments, remote sensing, and modelling approaches. The main focus lies in utilizing a state-of-the-art global Earth System Model (ESM) EC-Earth3, which is used to simu