Surface representation impacts on turbulent heat fluxes in the Weather Research and Forecasting (WRF) model (v.4.1.3)

Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface lead to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with three widely used land surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach; 30 m sub-grid surface information was used to calculate the final grid fluxes based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests due to the large flux errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary-Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for coniferous forest. The mosaic approach produced fluxes more similar to reality and served to particularly improve the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.

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Surface representation impacts on turbulent heat ﬂuxes in WRF (v.4.1.3)

10.5194/gmd-2020-371 ◽

2021 ◽

Author(s):

Carlos Román-Cascón ◽

Marie Lothon ◽

Fabienne Lohou ◽

Oscar Hartogensis ◽

Jordi Vila-Guerau de Arellano ◽

...

Keyword(s):

Vegetation Type ◽

Coniferous Forest ◽

Grid Cell ◽

Heat Fluxes ◽

Turbulent Heat ◽

Surface Representation ◽

Surface Parameters ◽

Surface Information ◽

High Dependency ◽

Mosaic Approach

Abstract. The water and energy transfers at the interface between the Earth's surface and the atmosphere should be correctly simulated in numerical weather and climate models. This implies the need for a realistic and accurate representation of land cover (LC), including appropriate parameters for each vegetation type. In some cases, the lack of information and crude representation of the surface leads to errors in the simulation of soil and atmospheric variables. This work investigates the ability of the Weather Research and Forecasting (WRF) model to simulate surface heat fluxes in a heterogeneous area of southern France, using several possibilities for the surface representation. In the control experiments, we used the default LC database in WRF, which differed significantly from the actual LC. In addition, sub-grid variability was not taken into account since the model uses, by default, only the surface information from the dominant LC category in each pixel (dominant approach). To improve this surface simplification, we designed three new interconnected numerical experiments with four widely-used land-surface models (LSMs) in WRF. The first one consisted of using a more realistic and higher-resolution LC dataset over the area of analysis. The second experiment aimed at investigating the effect of using a mosaic approach, where 30-m sub-grid surface information was used to calculate the final grid fluxes, based on weighted averages from values obtained for each LC category. Finally, in the third experiment, we increased the model stomatal conductance for conifer forests, due to the large fluxes errors associated with this vegetation type in some LSMs. The simulations were evaluated with gridded area-averaged fluxes calculated from five tower measurements obtained during the Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST) field campaign. The results from the experiments differed depending on the LSM and displayed a high dependency of the simulated fluxes on the specific LC definition within the grid cell, an effect that was enhanced with the dominant approach. The simulation of the fluxes improved using the more realistic LC dataset except for the LSMs that included extreme surface parameters for the coniferous forest. The mosaic approach produced fluxes more similar to reality and served to improve, especially, the latent heat flux simulation of each grid cell. Therefore, our findings stress the need to include an accurate surface representation in the model, including soil and vegetation sub-grid information with updated surface parameters for some vegetation types, as well as seasonal and man-made changes. This will improve the modelled heat fluxes and ultimately yield more realistic atmospheric processes in the model.

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Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

Geoscientific Model Development ◽

10.5194/gmd-10-4229-2017 ◽

2017 ◽

Vol 10 (11) ◽

pp. 4229-4244 ◽

Cited By ~ 20

Author(s):

Joseph C. Y. Lee ◽

Julie K. Lundquist

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Statistical Significance ◽

Wrf Model ◽

Grid Cell ◽

Power Production ◽

Weather Research And Forecasting ◽

Model Version ◽

The Impact ◽

Weather Research

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

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The efficiency of the Weather Research and Forecasting (WRF) model for simulating typhoons

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-2179-2014 ◽

2014 ◽

Vol 14 (8) ◽

pp. 2179-2187 ◽

Cited By ~ 4

Author(s):

T. Haghroosta ◽

W. R. Ismail ◽

P. Ghafarian ◽

S. M. Barekati

Keyword(s):

Heat Flux ◽

Wind Speed ◽

Model Simulation ◽

Sensible Heat Flux ◽

Wrf Model ◽

Heat Fluxes ◽

Weather Research And Forecasting ◽

Data Set ◽

Wind Speed Prediction ◽

Weather Research

Abstract. The Weather Research and Forecasting (WRF) model includes various configuration options related to physics parameters, which can affect the performance of the model. In this study, numerical experiments were conducted to determine the best combination of physics parameterization schemes for the simulation of sea surface temperatures, latent heat flux, sensible heat flux, precipitation rate, and wind speed that characterized typhoons. Through these experiments, several physics parameterization options within the Weather Research and Forecasting (WRF) model were exhaustively tested for typhoon Noul, which originated in the South China Sea in November 2008. The model domain consisted of one coarse domain and one nested domain. The resolution of the coarse domain was 30 km, and that of the nested domain was 10 km. In this study, model simulation results were compared with the Climate Forecast System Reanalysis (CFSR) data set. Comparisons between predicted and control data were made through the use of standard statistical measurements. The results facilitated the determination of the best combination of options suitable for predicting each physics parameter. Then, the suggested best combinations were examined for seven other typhoons and the solutions were confirmed. Finally, the best combination was compared with other introduced combinations for wind-speed prediction for typhoon Washi in 2011. The contribution of this study is to have attention to the heat fluxes besides the other parameters. The outcomes showed that the suggested combinations are comparable with the ones in the literature.

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Diagnosing Model Errors from Time-Averaged Tendencies in the Weather Research and Forecasting (WRF) Model

Monthly Weather Review ◽

10.1175/mwr-d-15-0120.1 ◽

2016 ◽

Vol 144 (2) ◽

pp. 759-779 ◽

Cited By ~ 6

Author(s):

Steven M. Cavallo ◽

Judith Berner ◽

Chris Snyder

Keyword(s):

Global Analysis ◽

Wrf Model ◽

Ensemble Member ◽

Heat Fluxes ◽

Weather Research And Forecasting ◽

Limited Area ◽

Model Errors ◽

Short Term ◽

Wide Range ◽

Weather Research

Abstract Accurate predictions in numerical weather models depend on the ability to accurately represent physical processes across a wide range of scales. This paper evaluates the utility of model time tendencies, averaged over many forecasts at a given lead time, to diagnose systematic forecast biases in the Advanced Research version of the Weather Research and Forecasting (WRF) Model during the 2010 North Atlantic hurricane season using continuously cycled ensemble data assimilation (DA). Erroneously strong low-level heating originates from the planetary boundary layer parameterization as a consequence of using fixed sea surface temperatures, impacting the upward surface sensible heat fluxes. Warm temperature bias is observed with a magnitude 0.5 K in a deep tropospheric layer centered 700 hPa, originating primarily from the Kain–Fritsch convective parameterization. This study is the first to diagnose systematic forecast bias in a limited-area mesoscale model using its forecast tendencies. Unlike global models where relatively fewer time steps typically encompass a DA cycling period, averaging all short-term forecast tendencies can require potentially large data. It is shown that 30-min averaging intervals can sufficiently represent the systematic model bias in this modeling configuration when initializing forecasts from an ensemble member that is generated using a DA system with an identical model configuration. However, the number of time steps before model error begins to dominate initial condition (IC) errors may vary between modeling configurations. Model and IC error are indistinguishable in short-term forecasts when initialized from the ensemble mean, a global analysis from a different model, and an ensemble member using a different parameterization.

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Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes

Boundary-Layer Meteorology ◽

10.1007/s10546-021-00617-1 ◽

2021 ◽

Author(s):

Alessio Golzio ◽

Silvia Ferrarese ◽

Claudio Cassardo ◽

Gugliemina Adele Diolaiuti ◽

Manuela Pelfini

Keyword(s):

Boundary Layer ◽

Land Use ◽

Land Cover ◽

Mixing Layer ◽

Local Area ◽

Heat Fluxes ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Mountainous Terrain ◽

Weather Research

AbstractWeather forecasts over mountainous terrain are challenging due to the complex topography that is necessarily smoothed by actual local-area models. As complex mountainous territories represent 20% of the Earth’s surface, accurate forecasts and the numerical resolution of the interaction between the surface and the atmospheric boundary layer are crucial. We present an assessment of the Weather Research and Forecasting model with two different grid spacings (1 km and 0.5 km), using two topography datasets (NASA Shuttle Radar Topography Mission and Global Multi-resolution Terrain Elevation Data 2010, digital elevation models) and four land-cover-description datasets (Corine Land Cover, U.S. Geological Survey land-use, MODIS30 and MODIS15, Moderate Resolution Imaging Spectroradiometer land-use). We investigate the Ortles Cevadale region in the Rhaetian Alps (central Italian Alps), focusing on the upper Forni Glacier proglacial area, where a micrometeorological station operated from 28 August to 11 September 2017. The simulation outputs are compared with observations at this micrometeorological station and four other weather stations distributed around the Forni Glacier with respect to the latent heat, sensible heat and ground heat fluxes, mixing-layer height, soil moisture, 2-m air temperature, and 10-m wind speed. The different model runs make it possible to isolate the contributions of land use, topography, grid spacing, and boundary-layer parametrizations. Among the considered factors, land use proves to have the most significant impact on results.

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