25,945 research outputs found
Sensor-Driven, Spatially Explicit Agent-Based Models
Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting
Data assimilation in slow-fast systems using homogenized climate models
A deterministic multiscale toy model is studied in which a chaotic fast
subsystem triggers rare transitions between slow regimes, akin to weather or
climate regimes. Using homogenization techniques, a reduced stochastic
parametrization model is derived for the slow dynamics. The reliability of this
reduced climate model in reproducing the statistics of the slow dynamics of the
full deterministic model for finite values of the time scale separation is
numerically established. The statistics however is sensitive to uncertainties
in the parameters of the stochastic model. It is investigated whether the
stochastic climate model can be beneficial as a forecast model in an ensemble
data assimilation setting, in particular in the realistic setting when
observations are only available for the slow variables. The main result is that
reduced stochastic models can indeed improve the analysis skill, when used as
forecast models instead of the perfect full deterministic model. The stochastic
climate model is far superior at detecting transitions between regimes. The
observation intervals for which skill improvement can be obtained are related
to the characteristic time scales involved. The reason why stochastic climate
models are capable of producing superior skill in an ensemble setting is due to
the finite ensemble size; ensembles obtained from the perfect deterministic
forecast model lacks sufficient spread even for moderate ensemble sizes.
Stochastic climate models provide a natural way to provide sufficient ensemble
spread to detect transitions between regimes. This is corroborated with
numerical simulations. The conclusion is that stochastic parametrizations are
attractive for data assimilation despite their sensitivity to uncertainties in
the parameters.Comment: Accepted for publication in Journal of the Atmospheric Science
Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation
The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.Comment: 32 pages, 3 figure
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