Browsing by Subject "Validation"

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A backscatter lidar forward operator for aerosol-representing atmospheric chemistry models
(2020) Geisinger, Armin; Wulfmeyer, Volker
State-of-the-art atmospheric chemistry models are capable of simulating the transport and evolution of aerosols and trace gases but there is a lack of reliable methods for model validation and data assimilation. Networks of automated ceilometer lidars (ACLs) could be used to fill this gap. These networks are already used for the detection of clouds and aerosols, providing a 3D dataset of atmospheric backscatter profiles. But as the aerosol number concentration cannot be obtained from the ACL data alone; one needs a backscatter-lidar forward model to simulate lidar profiles from the model variables. Such an operator allows then for a qualitative and quantitative model validation based on ACL data. In this work, a newly developed backscatter-lidar forward operator and the related sensitivity studies are presented and results of the forward operator applied on model output data are compared to measured ACL profiles in the frame of a case study. As case study, the eruption of the Icelandic volcano Eyjafjallajökull in 2010 was chosen and extensively analyzed. The Consortium for Small-scale Modeling - Aerosols and Reactive Trace gases (COSMO-ART) model of DWD (Deutscher Wetterdienst) was operated during this event for ash-transport simulations over Europe. For the forward model, the attenuated backscatter coefficient is used as lidar-independent variable, which only relies on the laser wavelength. To calculate the attenuated backscatter coefficient, the size-dependent aerosol number concentration and the scattering properties of each aerosol type and size have to be simulated. While the aerosol number concentration is a model output variable, the scattering properties were determined by extensive scattering calculations. As these scattering calculations require assumptions about the aerosol refractive indices and shapes, sensitivity studies were performed to estimate the uncertainties related to the particle properties as represented by the model system. An analysis of the particle shape effect for the extinction and backscatter coefficients resulted in huge differences of the scattering properties between spherical, ellipsoidal and cylindrical particle shapes. Due to a particle shape mixture in typical volcanic ash plumes, the application of non-spherical scattering calculation methods for estimating the effective optical properties requires more information related to the particle shape distribution (specifically: a particle size and shape distribution). As such information was not available for the present case study, it was necessary to assume spherical shaped volcanic ash particles but estimate the uncertainty related to this assumption within the frame of additional sensitivity studies. Finally, the forward modeled lidar profiles were compared to ACL measurements from stations of the German ACL network. The comparison required an extraction of common time and height intervals of the ACL and forward modeled COMSO-ART data as well as reshaping the datasets to the same vertical and temporal resolution. Significant differences between ACL profiles and the output of the forward operator applied to the COSMO-ART data were found. Some ash layer structures were at similar coordinates which is remarkable due to the uncertainties related to the model dynamics and the limited amount of measurement data that could be used for model validation. In detail, however, the major fraction of the compared time and height interval differed both in the relative signal intensity and the layer structures of the volcanic ash plume. Based on such quantitative comparison, a future data assimilation system could correct the model prediction of the forward modeled attenuated backscatter coefficient, the time of arrival, as well as the vertical structure of the volcanic ash plume. In summary, the continuous and distributed data stream provided by ACL stations was found to deliver valuable verification information for dispersion simulations of aerosol events. But major issues have been determined which limit current realizations of backscatter-lidar forward operators for aerosol transport simulations: First, it is suggested that the ACL systems improve their dynamic range and perform automatic calibration to increase the precision of ACL data and for calculating the measured attenuated backscatter coefficient with a minimum leftover of uncertainties. This will allow for the calculation of the attenuated backscatter coefficient in the presence of clouds as well as of faint aerosol signals. Second, the aerosols scattering properties have to be analyzed even more extensively which includes both the variety of aerosol sizes or types as well as the size distribution information. From the findings within this study, the particle size distribution was indentified to be a critical component when using monodisperse size classes.
Agent-based modeling of climate change adaptation in agriculture : a case study in the Central Swabian Jura
(2014) Troost, Christian; Berger, Thomas
Using the MPMAS multi-agent software, the present thesis implements an agro-economic agent-based model to analyze climate change adaptation of agricultural production in the Central Swabian Jura. It contributes to the DFG PAK 346 FOR 1695 research projects dedicated to improve the understanding of processes that shape structure and functions of agricultural landscapes in the context of climate change at regional scale. In the context of this example, this thesis discusses, develops and tests novel approaches to deal with four notorious challenges that have so far hampered the empirical use of agent-based models for applied economic analysis: data availability, process uncertainty, model validity and computational requirements. The model is used to examine climatic effects on agriculture, changes in agricultural price responses and biogas support and agri-environmental policies illustrating the applicability of the model to adaptation analysis. The first part of the thesis is dedicated to a methodological discussion of the use of mathematical programming-based multi-agent systems, such as MPMAS, for the analysis of agricultural adaptation to climate change. It synthesizes knowledge about the potential impacts of climate change and processes of farmer adaptation and reviews existing agent-based models for their potential contribution to adaptation analysis. The major focus of the first part is a discussion of available approaches to model validation, calibration and uncertainty analysis and their suitability for the use with mathematical programming-based agent-based models. This discussion is based on four principles required to ensure the validity of conclusions drawn from modeling studies: (i) a transparent model documentation, (ii) that the invariant elements of the model can really be expected to be invariant between scenarios assessed, (iii) that empirical calibration of the model is limited to the extent warranted by available observation and knowledge about the expected error distribution, and (iv) that the effect of process uncertainty on the conclusions is evaluated and communicated. Based on these conclusions, generic extensions of the MPMAS toolbox are developed to allow the application of suitable approaches for validation and uncertainty analysis. The second part of the thesis describes the application of the newly developed methodology in the construction and use of the Central Swabian Jura model. The model focuses on an endogenous representation of heterogeneity in agent behavior, an empirical parameterization of the model, and an incorporation of climate effects on possible crop rotations and suitable days for field work besides the expected effects on yields. It extends the demographic, investment and land market components of MPMAS to improve the simulation of structural change over time. The model was used to analyze potential effects of climate change adaptation on agricultural production and land use in the study area. The results show that besides effects on yields also other climate change-induced effects on the conditions of agricultural production may have important impacts on land use decisions of farmers and deserve more attention in climate change impact analysis. Potential impacts of changes in the time slots suitable for field work and an additional rotation option are predicted to be comparable to the impact of the changes in yields predicted by a crop growth model. Results point to an expansion of wheat and silage maize areas at the expense of barley areas. The partial crowding out of summer barley by wheat area held for current price relations and is less strong at higher relative prices for summer barley. Price response analysis indicated that winter wheat production enters into a substitutive relationship with summer barley production under climate change conditions, while competition with winter barley area diminishes. This leads also to a higher elasticity of the wheat area with respect to relative summer barley prices. The model was then used to analyze biogas support through the Renewable Energy Act (EEG) and the support for grassland extensification and crop rotation diversification through the MEKA scheme. Especially simulated participation in crop rotation diversification is strongly reduced in the climate change scenarios, while the investments in biogas plants are slightly increased. The conditions established by the latest EEG revision imply that further development of biogas capacity will crucially depend on the existence of demand for excess process heat, because the alternative option of using high manure shares seems to be rather unattractive for farmers in the area according to the simulation results.
Development of a robust sensor calibration for a commercially available rising platemeter to estimate herbage mass on temperate seminatural pastures
(2024) Werner, Jessica; Salazar‐Cubillas, Khaterine; Perdana-Decker, Sari; Obermeyer, Kilian; Velasco, Elizabeth; Hart, Leonie; Dickhöfer, Uta
Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.