scholarly journals Supplementary material to "The road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland"

2019 ◽  
Author(s):  
Erika Toivonen ◽  
Marjo Hippi ◽  
Hannele Korhonen ◽  
Ari Laaksonen ◽  
Markku Kangas ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Climate Model ◽  
Regional Climate ◽  
The Road ◽  
Weather Model ◽  
Climate Model Evaluation ◽  
Supplementary Material

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals Observational uncertainty and regional climate model evaluation: A pan-European perspective

2017 ◽  
Vol 39 (9) ◽  
pp. 3730-3749 ◽  
Author(s):  
Sven Kotlarski ◽  
Péter Szabó ◽  
Sixto Herrera ◽  
Olle Räty ◽  
Klaus Keuler ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Climate Model ◽  
Regional Climate ◽  
European Perspective ◽  
Climate Model Evaluation ◽  
Observational Uncertainty

scholarly journals The road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland

2019 ◽  
Author(s):  
Erika Toivonen ◽  
Marjo Hippi ◽  
Hannele Korhonen ◽  
Ari Laaksonen ◽  
Markku Kangas ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Input Data ◽  
Climate Model ◽  
Regional Climate ◽  
Weather Conditions ◽  
Road Surface ◽  
Road Maintenance ◽  
The Road ◽  
Surface Temperatures ◽  
Weather Model

Abstract. In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from a high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM) utilizing ALARO physics (HCLIM-ALARO). Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model precisely simulated road surface temperatures with a mean bias of −0.3 °C, RMSE of 2.1 °C, and Pearson's correlation coefficient of 0.93. The RoadSurf's output bias most probably stemmed from the lack of road maintenance operations in the model, such as snow ploughing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM-ALARO overestimated precipitation and had a warm bias in simulated air temperatures during the winter season. In turn, these input data biases seemed to result in a warm bias in simulated road surface temperatures. Furthermore, the lack of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions by forcing RoadSurf by future climate projections from RCMs, such as HCLIM.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Vol 11 (11) ◽  
pp. 4435-4449 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane E. Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland

2019 ◽  
Vol 12 (8) ◽  
pp. 3481-3501 ◽  
Author(s):  
Erika Toivonen ◽  
Marjo Hippi ◽  
Hannele Korhonen ◽  
Ari Laaksonen ◽  
Markku Kangas ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Input Data ◽  
Climate Model ◽  
Regional Climate ◽  
Weather Conditions ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
The Road ◽  
Weather Model

Abstract. In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from cycle 38 of the high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral boundaries. Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model simulated road surface temperatures with a mean monthly bias of −0.3 ∘C and mean absolute error of 0.9 ∘C. The RoadSurf's output bias most probably stemmed from the absence of road maintenance operations in the model, such as snow plowing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM38-ALARO overestimated precipitation and had a warm bias in near-surface air temperatures during the winter season. Moreover, the variability in the biases of air temperature was found to explain on average 57 % of the variability in the biases of road surface temperature. On the other hand, the absence of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions in Finland by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.

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