98,582 research outputs found
Evaluating probability forecasts
Probability forecasts of events are routinely used in climate predictions, in
forecasting default probabilities on bank loans or in estimating the
probability of a patient's positive response to treatment. Scoring rules have
long been used to assess the efficacy of the forecast probabilities after
observing the occurrence, or nonoccurrence, of the predicted events. We develop
herein a statistical theory for scoring rules and propose an alternative
approach to the evaluation of probability forecasts. This approach uses loss
functions relating the predicted to the actual probabilities of the events and
applies martingale theory to exploit the temporal structure between the
forecast and the subsequent occurrence or nonoccurrence of the event.Comment: Published in at http://dx.doi.org/10.1214/11-AOS902 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Inclusion of window opening habits in a window model based on activity and occupancy patterns
The occupantsâ window opening behaviour can have a substantial influence on the indoor climate and the energy use in low energy dwellings. In literature, most window opening models are based on outdoor and/or indoor climate variables. However a study of Verbruggen et al. [1] revealed that these models are not able to predict the window opening behaviour accurately in wintertime, which may be attributed to the presence of window opening habits. The occupants perform the habits not according to a fixed time step but rather to the performance of a reoccurring activity or an occupancy change. Therefore, a window opening model is generated based on the occupancy and activity patterns of the inhabitants. The model links certain behaviours to specific activities or moments in an occupantâs day without relating it to an exact time-step or specific weather conditions. Data on these habits and the links with occupancy are acquired from a survey conducted in a NZEB case-study project in Belgium. This paper includes the results of the habit-survey and explains how the window use model based on habits is generated. Based on the answers from the survey the window use in bedrooms and bathrooms could be fully defined for 93% of the households, only in the living room no complete window use profile could be defined. The developed model is able to predict the window use in a more realistic way compared to weather-models, with window opening actions linked to specific moments in the occupantâs day
DADA: data assimilation for the detection and attribution of weather and climate-related events
A new nudging method for data assimilation, delayâcoordinate nudging, is presented. Delayâcoordinate nudging makes explicit use of present and past observations in the formulation of the forcing driving the model evolution at each time step. Numerical experiments with a lowâorder chaotic system show that the new method systematically outperforms standard nudging in different model and observational scenarios, also when using an unoptimized formulation of the delayânudging coefficients. A connection between the optimal delay and the dominant Lyapunov exponent of the dynamics is found based on heuristic arguments and is confirmed by the numerical results, providing a guideline for the practical implementation of the algorithm. Delayâcoordinate nudging preserves the easiness of implementation, the intuitive functioning and the reduced computational cost of the standard nudging, making it a potential alternative especially in the field of seasonalâtoâdecadal predictions with large Earth system models that limit the use of more sophisticated data assimilation procedures
Evaluating probabilistic forecasts with scoringRules
Probabilistic forecasts in the form of probability distributions over future
events have become popular in several fields including meteorology, hydrology,
economics, and demography. In typical applications, many alternative
statistical models and data sources can be used to produce probabilistic
forecasts. Hence, evaluating and selecting among competing methods is an
important task. The scoringRules package for R provides functionality for
comparative evaluation of probabilistic models based on proper scoring rules,
covering a wide range of situations in applied work. This paper discusses
implementation and usage details, presents case studies from meteorology and
economics, and points to the relevant background literature
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Weather, climate, and hydrologic forecasting for the US Southwest: A survey
As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users
Multimodal 3D Object Detection from Simulated Pretraining
The need for simulated data in autonomous driving applications has become
increasingly important, both for validation of pretrained models and for
training new models. In order for these models to generalize to real-world
applications, it is critical that the underlying dataset contains a variety of
driving scenarios and that simulated sensor readings closely mimics real-world
sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a
novel tool for generating training data from the CARLA simulator to be used in
autonomous driving research. The tool is able to export high-quality,
synchronized LIDAR and camera data with object annotations, and offers
configuration to accurately reflect a real-life sensor array. Furthermore, we
use this tool to generate a dataset consisting of 10 000 samples and use this
dataset in order to train the 3D object detection network AVOD-FPN, with
finetuning on the KITTI dataset in order to evaluate the potential for
effective pretraining. We also present two novel LIDAR feature map
configurations in Bird's Eye View for use with AVOD-FPN that can be easily
modified. These configurations are tested on the KITTI and CADET datasets in
order to evaluate their performance as well as the usability of the simulated
dataset for pretraining. Although insufficient to fully replace the use of real
world data, and generally not able to exceed the performance of systems fully
trained on real data, our results indicate that simulated data can considerably
reduce the amount of training on real data required to achieve satisfactory
levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu
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Snow model verification using ensemble prediction and operational benchmarks
Hydrologic model evaluations have traditionally focused on measuring how closely the model can simulate various characteristics of historical observations. Although advancing hydrologic forecasting is an often-stated goal of numerous modeling studies, testing in a forecasting mode is seldom undertaken, limiting information derived from these analyses. One can overcome this limitation through generation, and subsequent analysis, of ensemble hindcasts. In this study, long-range ensemble hindcasts are generated for the available period of record for a basin in southwestern Idaho for the purpose of evaluating the Snow-Atmosphere-Soil Transfer (SAST) model against the current operational benchmark, the National Weather Service's (NWS) snow accumulation and ablation model SNOW17. Both snow models were coupled with the NWS operational rainfall runoff model and ensembles of seasonal discharge and weekly snow water equivalent (SWE) were evaluated. Ensemble predictions from both the SAST and SNOW17 models were better than climatology forecasts, for the period studied. In most cases, the accuracy of the SAST-generated predictions was similar to the SNOW17-generated predictions, except during periods of significant melting. Differences in model performance are partially attributed to initial condition errors. After updating the SWE state in the snow models with the observed SWE, the forecasts were improved during the first 2-4 weeks of the forecast window and the skills were essentially equal in both forecasting systems for the study watershed. Climate dominated the forecast uncertainty in the latter part of the forecast window while initial conditions controlled the forecast skill in the first 3-4 weeks of the forecast. The use of hindcasting in the snow model analysis revealed that, given the dominance of the initial conditions on forecast skill, streamflow predictions will be most improved through the use of state updating. © 2008 American Meteorological Society
Agricultural Production and Externalities Simulator (APES) prototype to be used in Prototype 1 of SEAMLESS-IF
Production Economics,
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