Estimating particle number size distributions from multi-instrument observations with Kalman Filtering

Atmospheric aerosol particles have several important effects on the environment and human society. The exact impact of aerosol particles is largely determined by their particle size distributions. However, no single instrument is able to measure the whole range of the particle size distribution. Estimating a particle size distribution from multiple simultaneous measurements remains a challenge in aerosol physical research. Current methods to combine different measurements require assumptions concerning the overlapping measurement ranges and have difficulties in accounting for measurement uncertainties. In this thesis, Extended Kalman Filter (EKF) is presented as a promising method to estimate particle number size distributions from multiple simultaneous measurements. The particle number size distribution estimated by EKF includes information from prior particle number size distributions as propagated by a dynamical model and is based on the reliabilities of the applied information sources. Known physical processes and dynamically evolving error covariances constrain the estimate both over time and particle size. The method was tested with measurements from Differential Mobility Particle Sizer (DMPS), Aerodynamic Particle Sizer (APS) and nephelometer. The particle number concentration was chosen as the state of interest. The initial EKF implementation presented here includes simplifications, yet the results are positive and the estimate successfully incorporated information from the chosen instruments. For particle sizes smaller than 4 micrometers, the estimate fits the available measurements and smooths the particle number size distribution over both time and particle diameter. The estimate has difficulties with particles larger than 4 micrometers due to issues with both measurements and the dynamical model in that particle size range. The EKF implementation appears to reduce the impact of measurement noise on the estimate, but has a delayed reaction to sudden large changes in size distribution.Aerosolit ovat kaasussa leijuvia hiukkasia, jotka koostuvat kiinteistä ja/tai nestemäisistä aineksista. Ilmakehässä olevat aerosolit vaikuttavat ympäristöön ja yhteiskuntaan monilla tärkeillä tavoilla. Aerosolien kokonaisvaikutus riippuu kuitenkin hiukkasten kokoriippuvaisista ominaisuuksista, joita kuvataan aerosolikokojakaumilla. Koska aerosolihiukkasten halkaisijat ovat muutamasta nanometreistä kymmeniin mikrometreihin, ei mikään yksittäinen mittalaite pysty mittamaan aerosolihiukkasia yli koko kokojakauman alueen. Aerosolikokojakaumien arvioiminen useista samanaikaisista mittauksista on ajankohtainen haaste aerosolifysikaalisessa tutkimuksessa, koska niiden matemaattinen yhdistäminen ei ole suoraviivaista ja sisältää monia haasteita. Nykyisin käytetyt menetelmät eri havaintojen yhdistämiseen vaativat päällekkäisten mittausten muokkausta, eivätkä ne huomioi kunnolla mittauksiin liittyviä epävarmuuksia. Tässä väitöksessä esitellään Laajennettu Kalman Filtteri (EKF) lupaavana menetelmänä arvioida aerosolien kokojakaumia lukuisista samanaikaisista mittauksista. Työssä on keskitytty lukumääräpitoisuuden kokojakaumaan. EKF:n arvioima lukumääräkokojakauma hyödyntää myös aikaisempiin kokojakaumiin ja tunnettuihin mikrofysikaalisiin prosesseihin perustuvaa ennustetta. Arvio perustuu eri tietolähteiden luotettavuuksiin. Virheiden väliset riippuvuudet rajaavat ennusteen jatkuvaksi yli sekä ajan että hiukkaskoon. Menetelmää testattiin mittauksilla hyvin erilaisista mittalaitteista, jotka perustuvat sähköiseen liikkuvuuteen, aerodynaamiseen liikkuvuuteen ja valon sirontaan. Väitöksessä esitelty EKF sovellus sisältää lukuisia yksinkertaistuksia, mutta tulokset ovat varsin positiivisia. Kokojakauma-arvio sopi suhteellisen hyvin kaikkiin valittuihin mittauksiin ja silotti kokojakaumaa yli ajan ja halkaisijan. Hiukkaskoot, jotka ovat isompia kuin 4 mikrometriä, aiheuttavat menetelmälle kuitenkin ongelmia johtuen mittauksiin ja dynaamisiin malleihin liittyvistä haasteista tuolla kokoalueella. EKF sovellus vaikuttaa vähentävän mittaushälyn vaikutusta arvioon verrattuna nykyisin käytettyihin matemaattisiin menetelmiin, mutta se reagoi viiveellä suuriin muutoksiin kokojakaumassa

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe

Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Model-calculated forecasts of soil organic carbon (SOC) are important for approximating global terrestrial carbon pools and assessing their change. However, the lack of detailed observations limits the reliability and applicability of these SOC projections. Here, we studied whether state data assimilation (SDA) can be used to continuously update the modeled state with available total carbon measurements in order to improve future SOC estimations. We chose six fallow test sites with measurement time series spanning 30 to 80 years for this initial test. In all cases, SDA improved future projections but to varying degrees. Furthermore, already including the first few measurements impacted the state enough to reduce the error in decades-long projections by at least 1 tCha(-1). Our results show the benefits of implementing SDA methods for forecasting SOC as well as highlight implementation aspects that need consideration and further research.Peer reviewe

Implementation and initial calibration of carbon-13 soil organic matter decomposition in the Yasso model

Soils account for the largest share of carbon found in terrestrial ecosystems, and their status is of considerable interest for the global carbon cycle budget and atmospheric carbon concentration. The decomposition of soil organic matter depends on environmental conditions and human activities, which raises the question of how permanent are these carbon storages under changing climate. One way to get insight into carbon decomposition processes is to analyse different carbon isotope concentrations in soil organic matter. In this paper we introduce a carbon-13-isotope-specific soil organic matter decomposition add-on into the Yasso soil carbon model and assess its functionality. The new C-13-dedicated decomposition is straightforward to implement and depends linearly on the default Yasso model parameters and the relative carbon isotope (C-13/C-12) concentration. The model modifications are based on the assumption that the heavier C-13 atoms are not as reactive as C-12. The new formulations were calibrated using fractionated C, C-13 and delta(13) measurements from litterbags containing pine needles and woody material, which were left to decompose in natural environment for 4 years. The introduced model modifications considerably improve the model behaviour in a 100-year-long simulation, where modelled delta(13) is compared against fractionated peat column carbon content. The work presented here is a proof of concept and enables C-13 to be used as a natural tracer to detect changes in the underlying soil organic matter decomposition.Peer reviewe

Silvicultural Interventions Drive the Changes in Soil Organic Carbon in Romanian Forests According to Two Model Simulations

We investigated the effects of forest management on the carbon (C) dynamics in Romanian forest soils, using two model simulations: CBM-CFS3 and Yasso15. Default parametrization of the models and harmonized litterfall simulated by CBM provided satisfactory results when compared to observed data from National Forest Inventory (NFI). We explored a stratification approach to investigate the improvement of soil C prediction. For stratification on forest types only, the NRMSE (i.e., normalized RMSE of simulated vs. NFI) was approximately 26%, for both models; the NRMSE values reduced to 13% when stratification was done based on climate only. Assuming the continuation of the current forest management practices for a period of 50 years, both models simulated a very small C sink during simulation period (0.05 MgC ha(-1) yr(-1)). Yet, a change towards extensive forest management practices would yield a constant, minor accumulation of soil C, while more intensive practices would yield a constant, minor loss of soil C. For the maximum wood supply scenario (entire volume increment is removed by silvicultural interventions during the simulated period) Yasso15 resulted in larger emissions (-0.3 MgC ha(-1) yr(-1)) than CBM (-0.1 MgC ha(-1) yr(-1)). Under 'no interventions' scenario, both models simulated a stable accumulation of C which was, nevertheless, larger in Yasso15 (0.35 MgC ha(-1) yr(-1)) compared to CBM-CSF (0.18 MgC ha(-1) yr(-1)). The simulation of C stock change showed a strong "start-up" effect during the first decade of the simulation, for both models, explained by the difference in litterfall applied to each scenario compared to the spinoff scenario. Stratification at regional scale based on climate and forest types, represented a reasonable spatial stratification, that improved the prediction of soil C stock and stock change.Peer reviewe

Cutting out the middleman: calibrating and validating a dynamic vegetation model (ED2-PROSPECT5) using remotely sensed surface reflectance

Ecosystem models are often calibrated and/or validated against derived remote sensing data products, such as MODIS leaf area index. However, these data products are generally based on their own models, whose assumptions may not be compatible with those of the ecosystem model in question, and whose uncertainties are usually not well quantified. Here, we develop an alternative approach whereby we modify an ecosystem model to predict full-range, high spectral resolution surface reflectance, which can then be compared directly against airborne and satellite data. Specifically, we coupled the two-stream representation of canopy radiative transfer in the Ecosystem Demography model (ED2) with a leaf radiative transfer model (PROSPECT 5) and a simple soil reflectance model. We then calibrated this model against reflectance observations from the NASA Airborne VIsible/InfraRed Imaging Spectrometer (AVIRIS) and survey data from 54 temperate forest plots in the northeastern United States. The calibration successfully constrained the posterior distributions of model parameters related to leaf biochemistry and morphology and canopy structure for five plant functional types. The calibrated model was able to accurately reproduce surface reflectance and leaf area index for sites with highly varied forest composition and structure, using a single common set of parameters across all sites. We conclude that having dynamic vegetation models directly predict surface reflectance is a promising avenue for model calibration and validation using remote sensing data.https://gmd.copernicus.org/preprints/gmd-2020-324/gmd-2020-324.pdfFirst author draf

Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH

We calibrated the JSBACH model with six different stomatal conductance formulations using measurements from 10 FLUXNET coniferous evergreen sites in the boreal zone. The parameter posterior distributions were generated by the adaptive population importance sampler (APIS); then the optimal values were estimated by a simple stochastic optimisation algorithm. The model was constrained with in situ observations of evapotranspiration (ET) and gross primary production (GPP). We identified the key parameters in the calibration process. These parameters control the soil moisture stress function and the overall rate of carbon fixation. The JSBACH model was also modified to use a delayed effect of temperature for photosynthetic activity in spring. This modification enabled the model to correctly reproduce the springtime increase in GPP for all conifer sites used in this study. Overall, the calibration and model modifications improved the coefficient of determination and the model bias for GPP with all stomatal conductance formulations. However, only the coefficient of determination was clearly improved for ET. The optimisation resulted in best performance by the Bethy, Ball-Berry, and the Friend and Kiang stomatal conductance models. We also optimised the model during a drought event at a Finnish Scots pine forest site. This optimisation improved the model behaviour but resulted in significant changes to the parameter values except for the unified stomatal optimisation model (USO). Interestingly, the USO demonstrated the best performance during this event.Peer reviewe

Carbon guide : An outlook on carbon in soil and on carbon cultivation

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Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century

Including soil alters the optimization of forestry with carbon sinks

We integrate a carbon net sink and stand-level wood production to analyze their simultaneous optimization as an economic problem. Carbon is included in living trees, wood products, and forest soil. Forestry is specified by a size-structured model for optimizing thinning timing and intensity, rotation period, and the optimal choice of rotation versus continuous cover forestry. The optimal inclusion of a carbon net sink increases the carbon pool mainly in living trees and forest soil, while the effect on the product carbon pool remains minor. With a 3% interest rate, increasing the CO2 price to euro40 per tCO2 increases the total steady-state carbon pool by 131% and the soil carbon accounts for ca. 60% of the increased carbon storage. Omitting soil carbon, as in previous studies, leads to underestimates of the carbon sink, significantly decreasing the optimal total CO2 net sink and achievable economic net gain from joint wood production and carbon management. The inclusion of soil carbon suggests that, in contrast to previous results, a higher CO2 price does not necessarily favor continuous cover forestry.Peer reviewe