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Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests., Global Change Biology, 28 (23), ISSN:1354-1013, ISSN:1365-2486

By using various skill scores and spatial characteristics of spatial verification methods and traditional techniques of the model evaluation tool, the gridded precipitation observation, known as Climate Prediction Center Morphing Technique, gauge observation and three datasets that were derived from local, Shanghai, and Grapes models, respectively, were conducted to assess the 3 lead day rainfall forecast with 0.5 day intervals during the summer of 2020 over Central East China. Results have shown that the local model generally outperforms the other two for the most skill scores but usually with relatively larger uncertainties than the Shanghai model, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of the Grapes model has been heavily underestimated and is accompanied with a large displacement error. Both the local and Shanghai model can effectively forecast the large-scale convection and rainstorms but over forecast the local convection, while the local model likely over forecasts the local rainstorms. In addition, the Shanghai model slightly favors over forecasting on a broad scale range and a broad threshold range, and the local model slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended when considering reasonable classification of thresholds if the accuracy is highly demanding. In addition, most spatial methods are suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the verification metrics including spatial characteristic difference information should be recommended to emphasize rewarding the severe events forecast under a global warming background.

Mitigation of adverse effects of global warming relies on accurate flow projections under climate change. These projections usually focus on changes in hydrological signatures, such as 100-year floods, which are estimated through statistical analyses of simulated flows under baseline and future conditions. However, models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistics of hydrological signatures. Here, we consider this dichotomy by testing whether performance indicators (e.g. Nash-Sutcliffe coefficient) are informative about model ability to reproduce distributions and trends in the signatures. Results of streamflow simulations in 50 high-latitude catchments with the 3DNet-Catch model show that high model performances according to traditional indicators do not provide assurance that distributions or trends in hydrological signatures are well reproduced. We therefore suggest that performance in reproducing distributions and trends in hydrological signatures should be included in the process of model selection for climate change impact studies.

Item response theory models and applications are affected by many sources of variability, including errors associated with item parameter estimation. Metric stability analysis (MSA) is one method to evaluate the effects of item parameter standard errors that quantifies how well a model determines the latent trait metric. This paper describes how to evaluate MSA in dichotomous and polytomous data and describes a Bayesian implementation of MSA that does not require a positive definite variance–covariance matrix among item parameters. MSA analyses are illustrated in the context of an oral-health-related quality of life measure administered before and after prosthodontic treatment. The R code to implement the methods described in this paper is provided.

In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration framework called WeDoWind was developed in recent work. The main innovation of this framework is the way it creates tangible incentives to motivate and empower different types of people from all over the world to share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA-data-based wind turbine fault detection models are investigated by carrying out a new case study, the “WinJi Gearbox Fault Detection Challenge”, based on the WeDoWind framework. A total of six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to exhibit high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. The guidelines can be used by researchers in the sector in order to improve model evaluation and data sharing in the future.

Asphaltenes are known to cause problems related to flocculation, precipitation, and plugging, either in the formation, production lines, and processing equipment. Different models have been proposed to predict the thermodynamic conditions under which asphaltenes precipitate over the past years. This work analyses the performance of various models on their capability to match the literature experimental data of precipitated asphaltene mass fractions. Twenty-five different models based on equation-of-state (EoS), polymer solution, and thermodynamic-colloidal theories were identified. The performance/test datasets were collected and classified according to their pressure/temperature conditions, CO2, n-C5/n-C7 gas, and liquid titrations. Statistical analysis, including residuals, parity plots, and average absolute relative deviation (AARD, %), were used to compare the adequacy of selected models. Results confirmed the need for further model development for general applications over wide pressure, temperature, and composition intervals.

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Digital twin is an important emerging technology for digital transformation and intelligent upgrading. Digital twin models are the prerequisite for digital twin applications, and their quality directly affects the quality of digital twin services in monitoring, simulation, prediction, optimization, and other areas. However, researchers have paid insufficient attention to the quality control of digital twin models, thus hindering their effective application. To effectively control model construction and optimize model quality in the design process, this study developed digital twin model quality optimization and control methods based on workflow management. First, a workflow process model integrating digital twin model evaluation was constructed, which integrated the design process and model evaluation methods into workflow management. Then, digital twin model quality control and optimization in different stages were achieved at the macro and micro levels. Thus, the digital twin model quality was effectively controlled during the design process, and targeted design resources were selected to optimize model quality. Finally, the validity of the proposed method of model quality optimization and control was verified using the digital twin models of a practical teaching platform and a multifunctional lift-and-slide experimental line. All evaluation indexes of the model achieved good values, and the target quality optimization of the model could be performed during the design process. The results indicate that the proposed method can effectively control and optimize the model quality, which has excellent feasibility and enables the effective application of the digital twin.

Semi-empirical kernel-driven models have been widely used to characterize anisotropic reflectance due to their simple form and physically meaningful approximation. Recently, several kernel-driven models have been coupled with topographic effects to improve the fitting of bidirectional reflectance over rugged terrains. However, extensive evaluations of the various models’ performances are required before their subsequent application in remote sensing. Three typical kernel-driven BRDF models over snow-free rugged terrains such as the RTLSR, TCKD, and the KDST-adjusted TCKD (KDST-TCKD) were investigated in this paper using simulated and observed BRFs. Against simulated data, the fitting error (NIR/Red RMSE) of the RTLSR gradually increases from 0.0358/0.0342 to 0.0471/0.0516 with mean slopes (α) increases from 9.13° to 33.40°. However, the TCKD and KDST-TCKD models perform an overall better fitting accuracy: the fitting errors of TCKD gradually decreased from 0.0366/0.0337 to 0.0252/0.0292, and the best fit from the KDST-TCDK model with NIR/Red RMSE decreased from 0.0192/0.0269 to 0.0169/0.0180. When compared to the sandbox data (α from 8.4° to 30.36°), the NIR/Red RMSE of the RTLSR model ranges from 0.0147/0.0085 to 0.0346/0.0165, for the TCKD model from 0.0144/0.0086 to 0.0298/0.0154, and for the KDST-TCKD model from 0.0137/0.0082 to 0.0234/0.0149. Using MODIS data, the TCKD and KDST-TCKD models show more significant improvements compared to the RTLSR model in rugged terrains. Their RMSE differences are within 0.003 over a relatively flat terrain (α < 10°). When α is large (20°–30° and >30°), the RMSE of the TCKD model has a decrease of around 0.01 compared to that of the RTLSR; for KDST-TCKD, it is approximately 0.02, and can even reach 0.0334 in the savannas. Therefore, the TCKD and KDST-TCKD models have an overall better performance than the RTLSR model in rugged terrains, especially in the case of large mean slopes. Among them, the KDST-TCKD model performs the best due to its consideration of topographic effects, geotropic growth, and component spectra.

Plastic trash can be found anywhere, around the marina, beaches, and coastal areas in recent times. This study proposes a trash dataset called HAIDA and a trash detector that uses a YOLOv4-based object detection algorithm to monitor coastal trash pollution efficiently. Model selection, model evaluation, and hyperparameter tuning were applied to obtain the best model for the lowest generalization error in the real world. Comparison of the state-of-the-art object detectors based on YOLOv3, YOLOv4, and Scaled-YOLOv4 that used hyperparameter tuning, the three-way holdout method, and k-fold cross-validation have been presented. An unmanned aerial vehicle (UAV) was also employed to detect trash in coastal areas using the proposed method. The performance on image classification was satisfactory.

Local, short-duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio-temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub-kilometre (750?m) HARMONIE-Climate model simulation driven by ERA5 reanalysis data. Three convection-permitting models at 750?m, 3 and 5?km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event-focused. Precipitation events are analysed at both hourly and sub-hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub-kilometre scale simulation seems limited compared to the convection-permitting models at 3 and 5?km.

The effectiveness of adaptive measures tackling the effects of climate change is dependent on robust climate projections. This becomes even more important in the face of intensifying extreme events. One example of these events is flooding, which embodies a major threat to highly vulnerable coastal urban areas. This includes eastern Asia, where multiple coastal megacities are located, e.g. Shanghai and Shenzhen. While the ability of general circulation models (GCMs) and regional climate models (RCMs) to project atmospheric changes associated with these events has improved, systematic errors (biases) remain. This study therefore assess capabilities of improving the quality of regional climate projections for eastern Asia. This is performed by evaluating an ensemble consisting of bias adjustment methods, GCM-RCM model runs and future emission scenarios based on representative concentration pathways (RCP) obtained from EAS-CORDEX. We show that bias adjustment significantly improves the quality of model output and best results are obtained by applying quantile delta mapping. Based on these results we evaluate potential future changes in crucial hydrometeorological predictors, univariate extreme events and compound extreme events, focusing on high wind speeds and extreme precipitation. Key findings include an increase in daily maximum temperature of 1.5 to nearly 4 °C, depending on the scenario, as well as increased levels of precipitation under RCP 8.5. Furthermore, a distinct intensification of extreme events including high temperatures and heavy precipitation is detected and this increase exceeds the increase of the overall mean of these predictors. The annual number of compound events including heavy precipitation and extreme wind speeds shows a significant increase of up to 50% for RCP 8.5 in the South China Sea as well as the adjacent coastal areas.

Most strategic and operational forest management decisions are taken based on stand-level information, and quantitative models of forest dynamics are key for developing sustainable management strategies. However, data on forest stands for the initialisation of such models that are representative at large spatial scales, e.g., countries or ecoregions, are often lacking. National Forest Inventories (NFIs) provide forest data from small sample plots at large spatial scales, yet deriving full stand information based on such data is challenging. Here, we evaluate seven methods of varying complexity for deriving quantitative stand descriptions based on sample data as provided by the Swiss NFI. We selected 271 extensively measured Swiss forests stands with unimodal diameter distributions, classified them as beech- vs. spruce-dominated in five development stages and randomly placed a small sized sample plot in each stand using the Swiss NFI sampling design (i.e., a circular plot of 500 m2). Seven modelling approaches were used to derive diameter distributions and species-specific stem numbers (i.e., tree species composition) from the sample data that are representative for a particular stand (local scale) and for stand types in general (generalised scale). The prediction performance of the modelling approaches was evaluated using 100 random samples per stand to calculate prediction errors. Generalised even-aged diameter distributions were best predicted by the simultaneous parameter prediction method (PPM), i.e. a combined three-step regression approach, with on average 1.3 to 2.5 times lower prediction errors compared to the simple pooling of diameter samples. However, uneven-aged diameter distributions were best predicted by pooling. At the local scale, the simultaneous PPM performed best for data from sample plots with fewer than 17 to 19 trees across all development stages. Prediction performance of the PPMs increased for structurally and spatially diverse local stands with positively skewed diameter distributions. A Random Forest approach was most suitable for predicting species composition at both the generalised and the local scale. Our study evaluates the strengths and weaknesses of methods to model stands based on data from small sample plots. We emphasise terminological pitfalls by consequently distinguishing local accuracy and generalised representativity of the stand descriptions. We demonstrate the feasibility of deriving locally accurate stands using data from small forest sample plots and evaluate the derivation of generalised stands representative at large regions. At both scales, our developments contribute to an improved initialisation of forest models and thus to a more realistic modelling of forest development under future boundary conditions., Forest Ecology and Management, 544, ISSN:0378-1127, ISSN:1872-7042

The valley wind system belongs to the most characteristic circulations of the at- mospheric boundary layer in mountainous terrain. It dominates surface winds at the valley bottom, contributes to the vertical air mass exchange with the free tropo- sphere, and plays a crucial role in the horizontal air mass exchange with neighboring plains and valleys. The accurate prediction of the valley wind system is therefore crucial for forecasting the weather in complex terrain. In this study, we assess the ability of the ICON model to resolve along-valley winds in the Inn Valley, by comparing the simulation to measurements from a char- acteristic valley wind day during the CROSSINN campaign (CROSSINN = Cross- valley flow in the Inn Valley). The three-dimensional observations allow us to eval- uate the simulation beyond classical statistical validation techniques and to further investigate the underlying processes (process-based evaluation). Particularly, the thermal forcing is investigated using radiosounding-derived valley-plain tempera- ture and pressure gradients. Besides the model evaluation, we provide a compre- hensive and detailed overview of the observed development of the valley boundary layer during the selected day (13 September 2019, the 8th intensive observational period (IOP8) of CROSSINN). At 1 km horizontal grid spacing, the ICON model can simulate the daytime up- valley flow well, including its strength, timing, and vertical structure (for the Inn Valley with ridge-to-ridge distance of 10 km or 10 dx). However, during the night the model is not able to correctly represent valley-plain temperature and pressure contrasts, thus deviating from the observed nighttime down-valley flow. We identify three main challenges for the simulation of IOP 8. The first challenge is the observed westerly down-valley flow during the night and morning of IOP 8, which may in- volve a scale interaction with the synoptic westerly flow. The second challenge is the complex interplay between the up-valley wind, the slope circulation, and the cross-valley vortex, which affects the detachment of the up-valley wind from the surface and its transition to higher altitudes.The third challenge pertains to the sta- ble boundary layer and valley cold air pool during the night after IOP 8. The model limitations in representing these processes result in the failure to simulate thermal forcing and the connected down-valley flow in the second night. The main limitations of the model include the mis-representation of synoptic flow (limited predictability), smaller-scale thermally-driven flows (limited resolu- tion), and cold air pools (stable boundary layer issues). In summary, this study contributes to a better understanding of along-valley winds under non-idealized conditions, and provides ICON’s first process-based model evaluation on thermally driven flows.

The purpose of this research is to develop and evaluate a scientific information exchange model at digital library on Internet of things. In order to attain the objective, the model’s components are extracted first. This study is a developmental study using mixed method consisting of documentary, focus group, data modeling, and quantitative evaluation methods. The study population in the documentary section includes the study of information resources retrieved in related subjects. The study population in the focus group section consists of 9 experts in “Internet of things” and “digital library”. Data gathering procedure was done by applying a checklist, and Edraw Max, Protégé, evaluation tab of Protégé software is applied for the analysis. Based on the findings of the documentary method (examining 10 core models and ontologies in the field of Internet of Things, physical and virtual objects), 44 key concepts, 108 sub-concepts, and 62 attributes constitute the primary core elements of scientific information exchange model at digital library in the context of internet of things. The findings showed that the 9 main classes, namely “end-user”, “librarian”, “microcomputer”, “digital library server”, “automated information services”, “physical resources”, “virtual resources”, “information resources on the digital library server”, and “security” in general model of scientific information exchange are very contributive. In general, 27 sub-classes and 38 attributes are identified for the main classes for this purpose. In this model, the way the classes communicate and interact with one another is illustrated to justify this theme. The evaluation of the model showed that the hierarchical structure of the concepts is not dense and some types of property and property restriction have not been considered. To make use of Internet of Things in libraries, the modeling of all the essential elements and properties of the internet of things in libraries must be incorporated into a model that constitutes the logical structure of the digital library scientific information exchange database system. This model can serve as a database map for database professionals and designers.

Background Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. Methods We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. Results Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. Conclusion Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

International audience; Several modeling strategies have been proposed to study Land Use and Land Cover changes (LUCC). However, substantial discrepancies have been noted between different models for the same problem, questioning their overall reliability and reproducibility. To address this challenge, we elaborate a generic, formally correct, theoretical framework for pattern-based LUCC modeling, which is implemented in our own software, CLUMPY (Comprehensive Land Use [and cover] Modeling in PYthon).The present work focuses on calibration. We devise a kernel density calibration-estimation method (Bayes-eKDE) that is shown on synthetic artificial data to be both accurate and algorithmically efficient. We also introduce a generic evaluation method that allows us to compare the calibration efficiency of existing models. The gain in precision and computational time of our calibration method is precisely quantified in this way.

Air pollution continues to be of concern for Bulgarian cities, mainly due to particulate matter of aerodynamic diameter smaller than 10 μm (PM10). There is public and expert interest in the improvement of two operational air quality modeling systems: the Bulgarian Chemical Weather Forecast System (BgCWFS) and the Local Air Quality Management System (LAQMS) for the city of Plovdiv. The aim of the study is to investigate the effects of satellite data assimilation in BgCWFS on surface concentrations over Bulgaria (resolution 9 km), to downscale BgCWFS output to LAQMS (resolution 250 m), and to examine effects on PM10 in Plovdiv. Data from the Global Ozone Monitoring Experiment-2 (GOME-2) (MetOP satellites) for aerosols, nitrogen dioxide (NO2), and sulfur dioxide (SO2) were assimilated in BgCWFS using objective analysis. Simulation experiments with and without satellite data were conducted for a summer and a winter month. The comparison to surface observations in the country showed improvement of results when using satellite data, especially in the summer due to mineral dust events captured by satellites. The decrease in the normalized mean bias (NMB) over the two months was 43% (PM10) and 73% (SO2). The LAQMS estimated background contributions to PM10 in the city as 32%. The absolute NMB by LAQMS decreased by 38%.

This study presents a comparison of the usual statistical methods used for crop model assessment. A case study was conducted using a data set from observations of the total dry weight in diploid potato crop, and six simulated data sets derived from the observations aimed to predict the measured data. Statistical indices such as the coefficient of determination, the root mean squared error, the relative root mean squared error, mean error, index of agreement, modified index of agreement, revised index of agreement, modeling efficiency, and revised modeling efficiency were compared. The results showed that the coefficient of determination is not a useful statistical index for model evaluation. The root mean squared error together with the relative root mean squared error offer an excellent notion of how deviated the simulations are in the same unit of the variable and percentage terms, and they leave no doubt when evaluating the quality of the simulations of a model. RESUMEN Este artículo presenta una comparación de los métodos estadísticos habituales que se utilizan para la evaluación de modelos de cultivos. Se realizó un estudio de caso utilizando un conjunto de datos observados del peso seco total en un cultivo de papa diploide y seis conjuntos de datos simulados destinados a predecir las observaciones. Los parámetros estadísticos evaluados fueron el coeficiente de determinación, la raíz cuadrada del cuadrado medio del error, la raíz cuadrada del cuadrado medio del error relativo, el error medio, el índice de concordancia, el índice de concordancia modificado, el índice de concordancia revisado, el índice de eficiencia y el índice de eficiencia revisado. Los resultados mostraron que el coeficiente de determinación no es un índice estadístico útil para la evaluación de modelos de cultivo. La raíz cuadrada del cuadrado medio del error junto a la raíz cuadrada del cuadrado medio del error relativo, ofrecen una excelente idea de cuánto están desviadas las simulaciones en la misma unidad de medida de la variable y en términos porcentuales. La raíz cuadrada del cuadrado medio del error y la raíz cuadrada del cuadrado medio del error relativo no dejan dudas al evaluar la calidad de las simulaciones de un modelo respecto a las observaciones

This presentation describes the functions and capabilities of the Earth System Model Evaluation Tool (ESMValTool) that aims to facilitate the analysis of climate date by providing a standard format for scientific analysis workflow a.k.a. "recipes". ESMValGroup, which is an international community of scientists and software engineers developing the ESMValTool is introduced, selected publications with results from the tool are presented, and the computing clusters providing access to the tool are listed.

Socio‐economic scenarios such as the Shared Socioeconomic Pathways (SSPs) have been widely used to analyze global change impacts, but representing their diversity is a challenge for the analytical tools applied to them. Taking Great Britain as an example, we represent a set of stakeholder‐elaborated UK‐SSP scenarios, linked to climate change scenarios (Representative Concentration Pathways), in a globally‐embedded agent‐based modeling framework. We find that distinct model components are required to account for divergent behavioral, social and societal conditions in the SSPs, and that these have dramatic impacts on land system outcomes. From strong social networks and environmental sustainability in SSP1 to land consolidation and technological intensification in SSP5, scenario‐specific model designs vary widely from one another and from present‐day conditions. Changes in social and human capitals reflecting social cohesion, equality, health and education can generate impacts larger than those of technological and economic change, and comparable to those of modeled climate change. We develop an open‐access, transferrable model framework and provide UK‐SSP projections to 2080 at 1 km2 resolution, revealing large differences in land management intensities, provision of a range of ecosystem services, and the knowledge and motivations underlying land manager decision‐making. These differences suggest the existence of large but underappreciated areas of scenario space, within which novel options for land system sustainability could occur., Key Points: A national‐scale agent‐based model is developed to represent paired climatic and socio‐economic scenarios in the land system. Key scenario characteristics relate to forms of human behavior, interactions and societal preferences. Large differences emerge between scenarios in terms of land management intensities, ecosystem service provision and land sparing., Helmholtz Association http://dx.doi.org/10.13039/501100009318, Natural Environment Research Council http://dx.doi.org/10.13039/501100000270, Climate Resilience Programme, Forestry Commission UK Forestry Commission http://dx.doi.org/10.13039/100017497, UKRI, Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266, Global Food Security Programme, DAAD, German Academic Exchange Service London http://dx.doi.org/10.13039/501100001654, Government of the United Kingdom http://dx.doi.org/10.13039/100013986, Deutscher Akademischer Austauschdienst http://dx.doi.org/10.13039/501100001655, Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347, Karlsruhe Institute of Technology http://dx.doi.org/10.13039/100009133, Leibniz‐Gemeinschaft http://dx.doi.org/10.13039/501100001664, https://landchange.earth/CRAFTY, https://doi.org/10.17605/OSF.IO/CY8WE

With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (>20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.

Socio-economic scenarios such as the Shared Socioeconomic Pathways (SSPs) have been widely used to analyze global change impacts, but representing their diversity is a challenge for the analytical tools applied to them. Taking Great Britain as an example, we represent a set of stakeholder-elaborated UK-SSP scenarios, linked to climate change scenarios (Representative Concentration Pathways), in a globally-embedded agent-based modeling framework. We find that distinct model components are required to account for divergent behavioral, social and societal conditions in the SSPs, and that these have dramatic impacts on land system outcomes. From strong social networks and environmental sustainability in SSP1 to land consolidation and technological intensification in SSP5, scenario-specific model designs vary widely from one another and from present-day conditions. Changes in social and human capitals reflecting social cohesion, equality, health and education can generate impacts larger than those of technological and economic change, and comparable to those of modeled climate change. We develop an open-access, transferrable model framework and provide UK-SSP projections to 2080 at 1 km2 resolution, revealing large differences in land management intensities, provision of a range of ecosystem services, and the knowledge and motivations underlying land manager decision-making. These differences suggest the existence of large but underappreciated areas of scenario space, within which novel options for land system sustainability could occur.

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

Accurately predicting the exchange rate prices helps investors to obtain maximum profit from their exchange rate investments as well as to help firms conducting business with exchange rates to manage their trading based on these predictions. Therefore, the prediction of exchange rate prices is crucial for both investors and companies engaged in exchange rates. In this study, ARMA (Autoregressive Moving Average) models combined with various GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models are applied to model exchange rate prices. For this purpose, ARMA-GARCH (M) models are investigated in order to determine the effect of volatility on exchange rate prices as well as ARMA-GARCH models in which the errors are distributed both symmetrically and skewed. It is concluded that the best fitted model which is determined based on the goodness of fit and estimation accuracy performance criteria, is firstly ARMA-NAGARCH model which can model asymmetric and non-linear structures and secondly ARMA-GJRGARCH model. However, since the performance of ARMA-GARCH (M) models is lower than ARMA-GARCH models, the effect of volatility on exchange rate prices is found to be weak., Döviz kuru fiyatlarının doğru bir şekilde tahmin edilmesi, yatırımcıların döviz kuru yatırımlarından maksimum kazanç elde etmelerine ve döviz kurları ile iş yapan firmaların bu tahminlere göre ticaretlerini yönetmelerine yardımcı olur. Bu nedenle döviz kuru fiyatlarının tahmini, hem yatırımcılar hem de döviz kurlarıyla iş yapan şirketler için çok önemlidir. Bu çalışmada, döviz kuru fiyatlarını modellemek için çeşitli GARCH (Genelleştirilmiş Otoregresif Koşullu Değişken Varyans) modelleri ile birleştirilmiş ARMA (Otoregresif Hareketli Ortalama) modelleri uygulanmıştır. Bu amaçla, volatilitenin döviz kuru fiyatları üzerindeki etkisini belirlemek için hataların hem simetrik hem de çarpık olarak dağıldığı ARMA-GARCH (M) modelleri ve ARMA-GARCH modelleri incelenmiştir. Uyum iyiliği ve tahmin doğruluğu performans kriterlerine göre belirlenen en uygun modelin, ilk olarak asimetrik ve doğrusal olmayan yapıları modelleyebilen ARMA-NAGARCH modeli ve ikinci olarak ise ARMA-GJRGARCH modeli olduğu sonucuna varılmıştır. Bununla birlikte ARMA-GARCH (M) modellerinin performansı, ARMA-GARCH modellerine göre daha düşük olması nedeniyle volatilitenin döviz kuru fiyatlarına etkisi zayıf bulunmuştur.

Transpiration makes up the bulk of total evaporation in forested environments yet remains challenging to predict at landscape-to-global scales. We harnessed independent estimates of daily transpiration derived from co-located sap flow and eddy-covariance measurement systems and applied the triple collocation technique to evaluate predictions from big leaf models requiring no calibration. In total, four models in 608 unique configurations were evaluated at 21 forested sites spanning a wide diversity of biophysical attributes and environmental backgrounds. We found that simpler models that neither explicitly represented aerodynamic forcing nor canopy conductance achieved higher accuracy and signal-to-noise levels when optimally configured (rRMSE = 20%; R-2 = 0.89). Irrespective of model type, optimal configurations were those making use of key plant functional type dependent parameters, daily LAI, and constraints based on atmospheric moisture demand over soil moisture supply. Our findings have implications for more informed water resource management based on hydrological modeling and remote sensing.

The enormous amounts of data make it progressively difficult for a human to assign meaning to individual data points. For example, if several users are active on different devices in a computer network, all kinds of connections are registered: browsing websites, scrolling through social media, video calling with friends, and so on. However, these `human' labels are usually not stored by the computer. And, simply due to time restrictions, it is not possible for a cyber expert to give an interpretation to all the traffic. Fortunately, over the past decades, the field of data science has grown at lightning speed. Often Machine Learning (ML) is used to extract information from all kinds of data. ML includes mathematical algorithms that learn patterns in data and that can make predictions about it. Since a computer can perform calculations extremely much faster, huge datasets can be interpreted in considerably less time. Typically ML has difficulty recognising patterns of data that is has not been able to learn from before, though. People, however, are good at assigning meaning to deviating data points by using domain knowledge. Therefore, we investigate how we can combine human knowledge and computer power to arrive at better and understandable predictions. This thesis focuses on molding the symbiosis between human and machine. We consider the following three themes: Anomaly Detection, Model Evaluation and Active Learning. Anomaly detection algorithms are widely used within network intrusion detection, the field that deals with finding cyber attacks in network data. In Chapters 2 and 3, we apply algorithms to detect malicious points in, respectively, real cyber data and data with online aeroplane bookings that contain fraudulent reservations. First, we remove redundant or uninformative data features and create new variables by using domain knowledge and by combining or standardising other features. In addition, we demonstrate how we deal with unlabelled and partially labelled data. Finally, we involve human experts by having them evaluate the results. By having the experts assess data points in a targeted manner, we efficiently obtain an indication of the predictive power of ML algorithms. Moreover, malicious data can be found faster. It is difficult to determine the predictive power of an ML model when fully labelled evaluation data is insufficiently available. Therefore, in Chapter 4 we develop a new evaluation metric that provides a good and robust estimate of the predictive power when no negative labels are available. In Chapter 5, we consider Model Evaluation more fundamentally using a common problem that arises when evaluating an algorithm. Often an evaluation metric is used to determine predictive power. For example, a metric score of 80% sounds good, but on what is this feeling based? Therefore, we introduce the Dutch Draw as a method to generate general, simple, but informative baselines. In the Active Learning (AL) paradigm, the human expert becomes an integral part of the methodology as a labelling source. AL is important in fields where labelled data is relatively scarce. In Chapters 6 and 7, we develop new AL methods for network intrusion detection. The first method, Jasmine, has a dynamic selection procedure built in. This means that unlabelled data is given meaning in a more effective manner, allowing malicious network traffic to be discovered at an earlier stage. In Chapter 7, we improve the Jasmine methodology and extend it to Plusmine. We refine the dynamic selection procedure and we include Automatic Classification (AC) that increases the speed of labelling. An important advantage of our method is that it works relatively simply and therefore requires little extra computational resources.

The enormous amounts of data make it progressively difficult for a human to assign meaning to individual data points. For example, if several users are active on different devices in a computer network, all kinds of connections are registered: browsing websites, scrolling through social media, video calling with friends, and so on. However, these `human' labels are usually not stored by the computer. And, simply due to time restrictions, it is not possible for a cyber expert to give an interpretation to all the traffic. Fortunately, over the past decades, the field of data science has grown at lightning speed. Often Machine Learning (ML) is used to extract information from all kinds of data. ML includes mathematical algorithms that learn patterns in data and that can make predictions about it. Since a computer can perform calculations extremely much faster, huge datasets can be interpreted in considerably less time. Typically ML has difficulty recognising patterns of data that is has not been able to learn from before, though. People, however, are good at assigning meaning to deviating data points by using domain knowledge. Therefore, we investigate how we can combine human knowledge and computer power to arrive at better and understandable predictions. This thesis focuses on molding the symbiosis between human and machine. We consider the following three themes: Anomaly Detection, Model Evaluation and Active Learning. Anomaly detection algorithms are widely used within network intrusion detection, the field that deals with finding cyber attacks in network data. In Chapters 2 and 3, we apply algorithms to detect malicious points in, respectively, real cyber data and data with online aeroplane bookings that contain fraudulent reservations. First, we remove redundant or uninformative data features and create new variables by using domain knowledge and by combining or standardising other features. In addition, we demonstrate how we deal with unlabelled and partially labelled data. Finally, we involve human experts by having them evaluate the results. By having the experts assess data points in a targeted manner, we efficiently obtain an indication of the predictive power of ML algorithms. Moreover, malicious data can be found faster. It is difficult to determine the predictive power of an ML model when fully labelled evaluation data is insufficiently available. Therefore, in Chapter 4 we develop a new evaluation metric that provides a good and robust estimate of the predictive power when no negative labels are available. In Chapter 5, we consider Model Evaluation more fundamentally using a common problem that arises when evaluating an algorithm. Often an evaluation metric is used to determine predictive power. For example, a metric score of 80% sounds good, but on what is this feeling based? Therefore, we introduce the Dutch Draw as a method to generate general, simple, but informative baselines. In the Active Learning (AL) paradigm, the human expert becomes an integral part of the methodology as a labelling source. AL is important in fields where labelled data is relatively scarce. In Chapters 6 and 7, we develop new AL methods for network intrusion detection. The first method, Jasmine, has a dynamic selection procedure built in. This means that unlabelled data is given meaning in a more effective manner, allowing malicious network traffic to be discovered at an earlier stage. In Chapter 7, we improve the Jasmine methodology and extend it to Plusmine. We refine the dynamic selection procedure and we include Automatic Classification (AC) that increases the speed of labelling. An important advantage of our method is that it works relatively simply and therefore requires little extra computational resources.

An adequate representation of the relationship between effective rainfall and rainfall losses is required in hydrological rainfall–runoff models to reduce the uncertainty of the modelling results. This study evaluates the performance of several hydrological rainfall loss methods using the experimental data obtained from a laboratory small-scale physical landslide model with variable slope inclination, homogenous material and no vegetation effects. Three different experiments were selected and five rainfall loss methods were tested to evaluate their performance in reproducing the experimental results from the perspective of the surface runoff formation on the experimental slope. Initial and calibrated parameters were used to test the performance of these hydrological rainfall loss methods. The results indicate that the initial parameters of the rainfall loss model can satisfactorily reproduce the experimental results in some cases. Despite the fact that the slope material characteristics used in the laboratory experiments were relatively homogenous, some well-known methods yielded inaccurate results. Hence, calibration of the rainfall loss model proved to be essential. It should also be noted that, in some cases, the calibrated model parameters were relatively different from the initial model parameters estimated from the literature. None of the tested hydrological rainfall loss methods proved to be superior to the others. Therefore, in the case of natural environments with heterogeneous soil characteristics, multiple rainfall loss methods should be tested and the most suitable method should be selected only after cross-validation or a similar evaluation of the tested methods.

The receiver operating characteristic (ROC) and precision–recall (PR) plots have been widely used to evaluate the performance of species distribution models. Plotting the ROC/PR curves requires a traditional test set with both presence and absence data (namely PA approach), but species absence data are usually not available in reality. Plotting the ROC/PR curves from presence‐only data while treating background data as pseudo absence data (namely PO approach) may provide misleading results.In this study, we propose a new approach to calibrate the ROC/PR curves from presence and background data with user‐provided information on a constant c, namely PB approach. Here, c defines the probability that species occurrence is detected (labeled), and an estimate of c can also be derived from the PB‐based ROC/PR plots given that a model with good ability of discrimination is available. We used five virtual species and a real aerial photography to test the effectiveness of the proposed PB‐based ROC/PR plots. Different models (or classifiers) were trained from presence and background data with various sample sizes. The ROC/PR curves plotted by PA approach were used to benchmark the curves plotted by PO and PB approaches.Experimental results show that the curves and areas under curves by PB approach are more similar to that by PA approach as compared with PO approach. The PB‐based ROC/PR plots also provide highly accurate estimations of c in our experiment.We conclude that the proposed PB‐based ROC/PR plots can provide valuable complements to the existing model assessment methods, and they also provide an additional way to estimate the constant c (or species prevalence) from presence and background data., The receiver operating characteristic (ROC) and precision–recall (PR) plots have been widely used to evaluate the performances of species distribution models. We propose a new approach to calibrate the ROC/PR curves from presence and background data with user‐provided information on a constant c. An estimate of c can also be derived from the ROC/PR plots given that a model with good ability of discrimination is available.

The fungus Sclerotinia sclerotiorum causes serious losses to several agricultural crops worldwide. By using systems analysis, we retrieved the available knowledge concerning S. sclerotiorum from the literature and then analyzed and synthesized the data to develop a mechanistic, dynamic, weather-driven model for the prediction of epidemics on different crops. The model accounts for i) the production and survival of apothecia; ii) the production, dispersal, and survival of ascospores; iii) infection by ascospores; and iv) lesion onset. The ability of the model to predict the occurrence of apothecia was evaluated for epidemics observed with different climates, soil types, and host crops (soybean, white bean, and carrot) using independent data obtained from trials conducted in Ontario (Canada) in 1981, 1982, and from 1999 to 2002; in Michigan (U.S.A.) in 2015 and 2016; and in Wisconsin (U.S.A.) in 2016. The model showed 0.82 accuracy and 0.73 specificity in predicting the presence of apothecia, with a posterior probability of correctly predicting apothecia to be present or absent of 0.804 and 0.876, respectively. The model was also validated for its ability to predict disease progress on soybean and sunflower in Ontario in 1981 and 1982, in Manitoba (Canada) in 2001 and 2002, and in Michigan in 2015 and 2016. Comparison of model output with observations showed a concordance correlation coefficient of 0.948, and a root mean square error of 0.122. The model represents an improvement of previous S. sclerotiorum models and could be useful for making decisions on disease control.

Q9 is widely used in industries handling flammable fluids and is central to explosion risk assessment (ERA). Q9 transforms complex flammable clouds from pressurised releases to simple cuboids with uniform stoichiometric concentration, drastically reducing the time and resources needed by ERAs. Q9 is commonly believed in the industry to be conservative but two studies on Q9 gave conflicting conclusions. This efficacy issue is important as impacts of Q9 have real life consequences, such as inadequate engineering design and risk management, risk underestimation, etc. This paper reviews published data and described additional assessment on Q9 using the large-scale experimental dataset from Blast and Fire for Topside Structure joint industry (BFTSS) Phase 3B project which was designed to address this type of scenario. The results in this paper showed that Q9 systematically underpredicts this dataset. Following recognised model evaluation protocol would have avoided confusion and misinterpretation in previous studies. It is recommended that the modelling concept of Equivalent Stoichiometric Cloud behind Q9 should be put on a sound scientific footing. Meanwhile, Q9 should be used with caution, users should take full account of its bias and variance.

In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number of potentially (co)integrated variables. By extending the classical single-equation error correction model, SPECS enables the researcher to model large cointegrated datasets without necessitating any form of pre-testing for the order of integration or cointegrating rank. Under an asymptotic regime in which both the number of parameters and time series observations jointly diverge to infinity, we show that SPECS is able to consistently estimate an appropriate linear combination of the cointegrating vectors that may occur in the underlying DGP. In addition, SPECS is shown to enable the correct recovery of sparsity patterns in the parameter space and to possess the same limiting distribution as the OLS oracle procedure. A simulation study shows strong selective capabilities, as well as superior predictive performance in the context of nowcasting compared to high-dimensional models that ignore cointegration. An empirical application to nowcasting Dutch unemployment rates using Google Trends confirms the strong practical performance of our procedure. (c) 2020 Elsevier B.V. All rights reserved.

Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR.

The aim of the study was to investigate students’ views of model evaluation through the lens of personal epistemology. We developed an integrated analytical framework by combining a developmental framework, including absolutist, multiplist, and evaluatist, with a multi-dimensional framework, including limits of knowing, certainty of knowing, and criteria of knowing. Furthermore, we examined the potential influence of the question contexts and the students’ grade levels. A total of 188 secondary school students were surveyed. Students answered two sets of model evaluation questions based on two scientific contexts. After reading the information about the two models, the students had to choose from three epistemic assumptions and then provide written justifications explaining their choice of assumptions. Quantitative and qualitative analyses were conducted for the multiple-choice questions and the written responses. In both contexts there were higher percentages of 11th-grade students choosing the evaluatist assumptions than the eighth-grade students. For students choosing multiplist and evaluatist assumptions, the 11th-grade students were more likely than the eighth-grade students to think in terms of pragmatic and evidential criteria as the criteria of knowing. Different contexts of the questions evoked different views of model evaluation particularly regarding the limits of knowing. Four additional categories of epistemic levels also emerged from the data. This study provides a new framework for understanding students’ thinking about model evaluation. Implications and suggestions for future research are provided.

The satellite signal deteriorates as it propagates from the source to the ground antenna on the surface of the earth. While some losses involving the amplitude and energy of the signal are unavoidably recovered due to the long slant path through the atmosphere, the impairment due to precipitation, especially rain, can be mitigated. Site diversity is one of the ways to obtain a less rain-attenuated signal, simply by preparing the other site to receive a link similar to that of the main site. Dual site diversity is common in practice with a site separation distance of at least a rain cell extent. This article presents an overview of the site diversity concept and a description of all parameters involved regarding the factor that contributed to the gain, which has been used as a performance metric to measure the effectiveness of a site diversity scheme. A detailed assessment of the capability of the gain prediction model is presented using site diversity experimental data conducted in two different climatic groups, namely, Greece in a temperate region and India in a tropical climatic region. The models involved in the evaluation were the ITU-R, Hodge, and Panagopoulos models, which were validated using temperate regional data, and the Semire and Yeo models, which were validated using tropical regional data. The observations of model behavior revealed that the Semire and Yeo models of the tropics were consistent with the measured gain obtained from India and Greece, respectively; thus, this demonstrated that the direction of future prediction models should consider both climates in the validation process.

[Introduction] To address the issue of decisions under nuclear emergency and nuclear accident consequence assessment of common concern at home and abroad, in this paper. [Method] Tracer experiment data set KINCAID and model validation and evaluation method MVK both used widely were reviewed. Model ARTM was evaluated using MVK. [Result] Statistical results show that: the simulation results of ARTM model are basically reliable. [Conclusion] The forecasts in 3 km indicate a tendency of underestimate, while the forecasts out of 5 km indicate a tendency of overestimate.

Stem rust (or black rust) of wheat, caused by Puccinia graminis f. sp. tritici (Pgt), is a re-emerging, major threat to wheat production worldwide. Here, we retrieved, analyzed, and synthetized the available information about Pgt to develop a mechanistic, weather-driven model for predicting stem rust epidemics caused by uredospores. The ability of the model to predict the first infections in a season was evaluated using field data collected in three wheat-growing areas of Italy (Emilia-Romagna, Apulia, and Sardinia) from 2016 to 2021. The model showed good accuracy, with a posterior probability to correctly predict infections of 0.78 and a probability that there was no infection when not predicted of 0.96. The model’s ability to predict disease progress during the growing season was also evaluated by using published data obtained from trials in Minnesota, United States, in 1968, 1978, and 1979, and in Pennsylvania, United States, in 1986. Comparison of observed versus predicted data generated a concordance correlation coefficient of 0.96 and an average distance between real data and the fitted line of 0.09. The model could therefore be considered accurate and reliable for predicting epidemics of wheat stem rust and could be tested for its ability to support risk-based control of the disease.

Land surface processes can significantly influence weather and climate. The Common Land Model version 2005 (CoLM2005) has been coupled to the Global Forecast System of the Global/Regional Assimilation and Prediction System (GRAPES_GFS), which is independently developed by the China Meteorological Administration. Since a new version of CoLM has been developed (CoLM2014) with updated soil basic data and parts of hydrological processes, we coupled CoLM2014 with GRAPES_GFS to investigate whether the land surface model can help to improve the prediction skill of the weather forecast model. The forecast results were evaluated against global validation datasets at different forecasting lengths and over various regions. The results demonstrate that GRAPES_GFS coupled with CoLM2005 and CoLM2014 can both well reproduce the spatial patterns and magnitude of atmospheric variables, and the effective predictable lengths of time are up to 3 days on the global scale and even up to 6 days on regional scales. Moreover, the GRAPES_GFS coupled with CoLM2014 outperforms the original one in predicting atmospheric variables. In addition, GRAPES_GFS coupled with both versions of CoLM reproduce acceptably accurate spatial distribution and magnitude of land variables. GRAPES_GFS coupled with CoLM2014 significantly improves the forecast of land surface state variables compared to the one coupled with CoLM2005, and the improvement signal is more notable than that in atmospheric variables. Overall, this study shows that CoLM is suitable for coupling with GRAPES_GFS, and the improvement of the land surface model in a weather forecast model can significantly improve the prediction skill of both atmospheric and land variables.

Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) model with spectral bin microphysics scheme (BIN) and two-moment seven-class bulk parameterization scheme (BULK). The simulations were then separated into three different typhoon landfall periods (i.e., pre-landfall, landfall, and post-landfall). It was found that typhoon intensity prediction is sensitive to microphysical schemes regardless of landfall periods, while typhoon track prediction tends to be more (less) sensitive to microphysical schemes after (before) typhoon landfall. Moreover, significant differences exist between BIN and BULK schemes in simulating the storm intensity, track, and rainfall distribution. BIN scheme simulates stronger (weaker) typhoon intensity than BULK scheme after (before) landfall, while BULK scheme simulates typhoon moving faster (slower) than BIN scheme before (after) landfall. BIN scheme produces much more extensive and homogeneous typhoon rainbands than BULK scheme, whereas BULK scheme produces stronger (weaker) rainfall in the typhoon inner (outer) rainbands. The possible reasons for such differences are discussed. At present, the ability of WRF and other mesoscale models to accurately simulate the typhoon precipitation hydrometeors is still limited. To evaluate the performances of BIN and BULK schemes of WRF model in simulating the condensed water in Typhoon In-Fa, the observed microwave brightness temperature and radar reflectivity from the core observatory of Global Precipitation Mission (GPM) satellite are directly used for validation with the help of a satellite simulator. It is suggested that BIN scheme has better performance in estimating the spatial structure, overall amplitude, and precise location of the condensed water in typhoons before landfall. During typhoon landfall, the performance of BIN scheme in simulating the structure and location of the condensate is close to that of BULK scheme, but the condensate intensity prediction by BIN scheme is still better; BULK scheme performs even better than BIN scheme in the prediction of condensate structure and location after typhoon landfall. Both schemes seem to have poorer performances in simulating the spatial structure of precipitation hydrometeors during typhoon landfall than before/after typhoon landfall. Moreover, BIN scheme simulates more (less) realistic warm (cold) rain processes than BULK scheme, especially after typhoon landfall. BULK scheme simulates more cloud water and larger convective updraft than BIN scheme, and this is also reported in many model studies comparing BIN and BULK schemes.

Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

International audience; The use of spatially explicit land use and land cover (LULC) change models is widespread in environmental sciences and of interest in public decision-help. However, it appears that these models suffer from significant biases and shortcomings, the sources of which can be mathematical, conceptual or algorithmic. We formalize a modeling environment that distinguishes a calibration-estimation module and an allocation module. We propose an accurate calibration-estimation method based on kernel density estimation and detail an unbiased allocation algorithm. Moreover, a method of evaluation of LULC change models is presented and allows us to compare them on various fronts (accuracy, biases, computational efficiency). A case study based on a real land use map but with known (enforced) transition probabilities is used. It appears that the estimation error of the methods we propose is substantially improved over the best existing software. Moreover, these methods require the specification of very few parameters by the user, and are numerically efficient. This article presents an overview of our LULC change modeling framework; its various formal and algorithmic constituents will be detailed in forthcoming papers.

One of the weaknesses of current line-source models for predicting downwind concentrations from mobile sources is accounting for the dispersion of effluents. Most of the investigators in the field have taken different approaches over the last 50 years, ranging from the use of Pasquill–Gifford (P-G) dispersion curves to the use of equations based on atmospheric turbulence for point source dispersion. Madiraju and Kumar (2021) proposed a three-phase turbulence (TPT) model using the key features of mobile source dispersion that appear in the existing literature. This paper examines the performance of line-source models using an updated TPT model. The generic dispersion equations were considered from the SLINE 1.1, CALINE 4, ADMS, and SLSM models. Multiple air quality field data sets collected by other investigators near the roadways were used during this study. These include field data collected from the Idaho Falls Tracer Experiment 2008 (used as the dataset to compare with the initial model), the CALTRANS Highway 99 Tracer experiment, and the Raleigh 2006 experiment. The predicted concentrations were grouped under unstable and stable atmospheric conditions. The evaluation of the model was performed using several statistical parameters such as FB, NMSE, R2, MG, VG, MSLE, and MAPE. The results indicate that the ADMS and SLINE 1.1 models perform better than CALINE4 and SLSM. SLINE 1.1 tends to overpredict for stable atmospheric conditions and underpredict for unstable atmospheric conditions. A trial test was performed to implement the TPT model in the basic line-source model (SLSM). The results indicate that the majority (FB, NMSE, R2, and MSLE) of the indicators have improved and are in the satisfactory range of a good model performance level.

OHEJP Project: DiSCoVeR

Surface albedo is an essential parameter in many solar radiation applications. Although several models are available, it remains debatable whether they are applicable to other locations. Using long-term daily measurements of radiation acquired by observational networks in China, this study examined the applicability of six existing albedo models: Ineichen model (IeM), Gueymard model (GM), Dong model (DeM), Iziomon-Mayer model (IMM), Morton model (MM), and Zhou model (ZeM). The evaluation results of model performance through statistical analysis showed that among the available ground albedo models, ZeM had the best overall performance at 12 selected stations, IeM was shown to provide acceptable estimations for locations where albedo records are readily available. The statistical results of individual station have shownthat the number of input parameters is not the only key factor for determining the predictive performance of ground albedo models. In other words, a simple model has potential for accurate estimation of ground albedo with appropriate model parameters. Therefore, the simple two-parameter DeM was selected to re-calibrate with in-situ radiometric measurements, which can be adopted as a surrogate for ZeM to predict surface albedo in China.

The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin (“Fast Spectral Bin Microphysics”, FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions.