Flight-Deck Automation for Trajectory-Based Surface Operations

To address anticipated growth in air traffic demand, the Surface Operation Automation Research (SOAR) is a collection of research activities designed with the common goal to explore and develop automation technologies for enhancing surface movement efficiency at major airports. The concept features a tower automation system that collaborates with a flight-deck automation system to jointly deliver highly efficient and safe surface operations. The tower automation counts on the availability of advanced surveillance data to plan timed surface operations. The time-based trajectories are communicated to the flight decks as 4- dimensional (4D) trajectory clearances via digital data link. The flight-deck automation counts on the availability of advanced navigation capabilities to execute the 4D trajectories with high timing precision. Several publications have documented the SOAR concept and initial feasibility studies of the tower and flight-deck automation systems based on early experimental software prototypes of the automation functions. This paper reports on the latest development of the flight-deck automation system, including its clearance handling capabilities, guidance and control functions, pilot interface, conflict and incursion monitoring functions, as well as plans for the assessment of an experimental prototype implementation

Proposal and extensive test of a calibration protocol for crop phenology models

Funding Information: Open Access funding enabled and organized by Projekt DEAL. This study was implemented as a co-operative project under the umbrella of the Agricultural Model Intercomparison and Improvement Project (AgMIP). This work was supported by the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project EFFI, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy - BonaRes”, project “BonaRes (Module B, Phase 3): BonaRes Centre for Soil Research, subproject B” (grant 031B1064B), the BonaRes project “I4S” (031B0513I) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 -390732324 EXC (PhenoRob), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797), the Agriculture and Agri-Food Canada’s Project J-002303 “Sustainable crop production in Canada under climate change” under the Interdepartmental Research Initiative in Agriculture, the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), and the INRAE CLIMAE meta-program and AgroEcoSystem department. The order in which the donors are listed is arbitrary. Publisher Copyright: © 2023, The Author(s).A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.Peer reviewe

How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

International audiencePredicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population