Evaluation of the WRF-UCM mesoscale model and ECMWF global operational forecasts over the Paris region in the prospect of tracer atmospheric transport modeling

The quantification of CO2 emissions from cities using atmospheric measurements requires accurate knowledge of the atmospheric transport. Complex urban terrains significantly modify surface roughness, augment surface energy budgets, and create heat islands, all of which lead to lower horizontal winds and enhanced convection over urban areas. The question remains whether these processes should be included in atmospheric transport models that are used for city scale CO2 inversion, and whether they need to be tailored on a city basis. In this study, we use the WRF model over Paris to address the following research question: does WRF runs at a 3 km resolution, including urban effects and the assimilation of local weather data, perform better than ECMWF forecasts that give fields at 16 km resolution? The analysis of model performances focuses on three variables: air temperature, wind and the planetary boundary layer (PBL) height. The results show that the use of objective analysis and nudging tools are required to obtain good agreements between WRF simulated fields with observations. Surface temperature is well reproduced by both WRF and ECMWF forecasts, with correlation coefficients with hourly observations larger than 0.92 and MBEs within 1°C over one month. Wind speed correlations with hourly observations are similar for WRF (range 0.76~0.85 across stations) and ECMWF (0.79~0.84), but the associated RMSEs and MBEs are better for ECMWF. Conversely, WRF outperforms ECMWF forecasts for its description of wind direction, horizontal and vertical gradients. Sensitivity tests with different WRF physics schemes show that the wind speed and the PBL height are strongly influenced by PBL schemes. The marginal advantage of WRF over ECMWF for the desired application is sufficient to motive additional testing with prescribed CO2 flux maps for comparing modeled CO2 concentrations with available observations in an urban environment.

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The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-23681-2014 ◽

2014 ◽

Vol 14 (16) ◽

pp. 23681-23709

Author(s):

S. M. Miller ◽

I. Fung ◽

J. Liu ◽

M. N. Hayek ◽

A. E. Andrews

Keyword(s):

Boundary Layer ◽

Atmospheric Transport ◽

Regional Scale ◽

Co2 Flux ◽

Atmospheric Co2 ◽

Co2 Fluxes ◽

Atmospheric Measurements ◽

Meteorological Model ◽

Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

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WRF Model Experiments on the Antarctic Atmosphere in Winter

Monthly Weather Review ◽

10.1175/2010mwr3478.1 ◽

2011 ◽

Vol 139 (4) ◽

pp. 1279-1291 ◽

Cited By ~ 23

Author(s):

Esa-Matti Tastula ◽

Timo Vihma

Keyword(s):

Boundary Layer ◽

Surface Layer ◽

Wind Speed ◽

Surface Pressure ◽

Mesoscale Model ◽

Wrf Model ◽

Radiative Transfer Model ◽

Transfer Model ◽

Temperature Bias ◽

Radiation Parameterizations

Abstract The standard and polar versions 3.1.1 of the Weather Research and Forecasting (WRF) model, both initialized by the 40-yr ECMWF Re-Analysis (ERA-40), were run in Antarctica for July 1998. Four different boundary layer–surface layer–radiation scheme combinations were used in the standard WRF. The model results were validated against observations of the 2-m temperature, surface pressure, and 10-m wind speed at 9 coastal and 2 inland stations. The best choice for boundary layer and radiation parameterizations of the standard WRF turned out to be the Yonsei University boundary layer scheme in conjunction with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) surface layer scheme and the Rapid Radiative Transfer Model for longwave radiation. The respective temperature bias was on the order of 3°C less than the biases obtained with the other combinations. Increasing the minimum value for eddy diffusivity did, however, improve the performance of the asymmetric convective scheme by 0.8°C. Averaged over the 11 stations, the error growths in 24-h forecasts were almost identical for the standard and Polar WRF, but in 9-day forecasts Polar WRF gave a smaller 2-m temperature bias. For the Vostok station, however, the standard WRF gave a less positively biased 24-h temperature forecast. On average, the polar version gave the least biased surface pressure simulation. The wind speed simulation was characterized by low correlation values, especially under weak winds and for stations surrounded by complex topography.

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Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan

Atmosphere ◽

10.3390/atmos10110684 ◽

2019 ◽

Vol 10 (11) ◽

pp. 684 ◽

Cited By ~ 3

Author(s):

Chih-Chiang Wei

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Numerical Model ◽

Wind Velocity ◽

Mean Squared Error ◽

Weather Forecasting ◽

Global Analysis ◽

Wrf Model ◽

Weather Data ◽

Northern Taiwan

A scheme for wind-speed simulation during typhoons in Taiwan is highly desirable, considering the effects of the powerful winds accompanying the severe typhoons. The developed combination of deep learning (DL) algorithms with a weather-forecasting numerical model can be used to determine wind speed in a rapid simulation process. Here, the Weather Research and Forecasting (WRF) numerical model was employed as the numerical simulation-based model for precomputing solutions to determine the wind velocity at arbitrary positions where the wind cannot be measured. The deep neural network (DNN) was used for constructing the DL-based wind-velocity simulation model. The experimental area of Northern Taiwan was used for the simulation. Regarding the complex typhoon system, the collected data comprised the typhoon tracks, FNL (Final) Operational Global Analysis Data for the WRF model, typhoon characteristics, and ground weather data. This study included 47 typhoon events that occurred over 2000–2017. Three measures were used to analyze the models for identifying optimal performance levels: Mean absolute error, root mean squared error, and correlation coefficient. This study compared observations with the WRF numerical model and DNN model. The results revealed that (1) simulations by using the WRF-based models were satisfactorily consistent with the observed data and (2) simulations by using the DNN model were considerably consistent with those of the WRF-based model. Consequently, the proposed DNN combined with WRF model can be effectively used in simulations of wind velocity at arbitrary positions of study area.

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Analysis of WRF Model Wind Estimate Sensitivity to Physics Parameterization Choice and Terrain Representation in Andalusia (Southern Spain)

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0204.1 ◽

2013 ◽

Vol 52 (7) ◽

pp. 1592-1609 ◽

Cited By ~ 57

Author(s):

F. J. Santos-Alamillos ◽

D. Pozo-Vázquez ◽

J. A. Ruiz-Arias ◽

V. Lara-Fanego ◽

J. Tovar-Pescador

Keyword(s):

Wind Speed ◽

Spatial Resolution ◽

Mesoscale Model ◽

Wrf Model ◽

Longwave Radiation ◽

Ground Level ◽

Resource Assessment ◽

Southern Spain ◽

Terrain Representation ◽

Wind Resource

AbstractThis paper reports on an evaluation of the relative roles of choice of parameterization scheme and terrain representation in the Weather Research and Forecasting (WRF) mesoscale model, in the context of a regional wind resource assessment. As a first step, 32 configurations using two different schemes for microphysics, cumulus, planetary boundary layer (PBL), or shortwave and longwave radiation were evaluated. In a second step, wind estimates that were obtained from various experiments with different spatial resolution (1, 3, and 9 km) were assessed. Estimates were tested against data from four stations, located in southern Spain, that provided hourly wind speed and direction data at 40 m above ground level. Results from the first analysis showed that wind speed standard deviation (STD) and bias values were mainly sensitive to the PBL parameterization selection, with STD differences up to 10% and bias differences between −15% and 10%. The second analysis showed a weak influence of spatial resolution on the STD values. On the other hand, the bias was found to be highly sensitive to model spatial resolution. The sign of the bias depended on terrain morphology and the spatial resolution, but absolute values tended to be much higher with coarser spatial resolution. Physical configuration was found to have little impact on wind direction distribution estimates. In addition, these estimates proved to be more sensitive to the ability of WRF to represent the terrain morphology around the station than to the model spatial resolution itself.

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The Making of the New European Wind Atlas – Part 2: Production and evaluation

Geoscientific Model Development ◽

10.5194/gmd-13-5079-2020 ◽

2020 ◽

Vol 13 (10) ◽

pp. 5079-5102 ◽

Cited By ~ 2

Author(s):

Martin Dörenkämper ◽

Bjarke T. Olsen ◽

Björn Witha ◽

Andrea N. Hahmann ◽

Neil N. Davis ◽

...

Keyword(s):

Wind Speed ◽

Mesoscale Model ◽

Comprehensive Evaluation ◽

Positive Impact ◽

Wrf Model ◽

Added Value ◽

Large Set ◽

Mean Wind Speed ◽

The Mean ◽

Model Setup

Abstract. This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results.

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Impacts of Mesoscale Wind on Turbulent Flow and Ventilation in a Densely Built-up Urban Area

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0044.1 ◽

2015 ◽

Vol 54 (4) ◽

pp. 811-824 ◽

Cited By ~ 15

Author(s):

Seung-Bu Park ◽

Jong-Jin Baik ◽

Sang-Hyun Lee

Keyword(s):

Wind Speed ◽

Turbulent Flow ◽

Urban Areas ◽

Mesoscale Model ◽

Velocity Ratio ◽

Tall Buildings ◽

Streamwise Velocity ◽

Large Eddy Simulation Model ◽

Turbulence Structures ◽

Mean Wind Speed

AbstractTurbulent flow in a densely built-up area of Seoul, South Korea, for 0900–1500 LST 31 May 2008 is simulated using the parallelized large-eddy simulation model (PALM) coupled to a mesoscale model (Weather Research and Forecasting Model). Time-varying inflow that is composed of mesoscale wind and turbulent signals induces different mean flows and turbulence structures depending on time. Sweeps induced by upper flow are distinct for 0900–0910 LST, and strong ejections and weaker sweeps are dominant for 1450–1500 LST at height z = 200 m. To investigate pedestrian wind environment and ventilation, mean wind velocity and turbulent kinetic energy at 2.5 m above streets are analyzed. The reference mean wind speed at z = 600 m continuously increases after 1010 LST. The pedestrian mean streamwise velocity tends to decrease after 1100 LST, although the pedestrian mean wind speed tends to slowly increase. Whereas the temporal velocity variations related to mesoscale wind are distinct in a street canyon and an intersection, the variations induced by mesoscale wind disappear in a dense building area, indicating strong decoupling from mesoscale wind. The velocity ratio of the pedestrian mean wind speed to the reference mean wind speed, representing a measure of ventilation in urban areas, is high on broad streets and at intersections and is low in dense building areas. Vortices in street canyons and winding flows around tall buildings seem to induce high velocity ratio there. The velocity ratio is shown to be linearly proportional to the pedestrian mean streamwise velocity.

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Impacts of Anthropogenic Heat on Summertime Rainfall in Beijing

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0173.1 ◽

2017 ◽

Vol 18 (3) ◽

pp. 693-712 ◽

Cited By ~ 16

Author(s):

Wanshu Nie ◽

Benjamin F. Zaitchik ◽

Guangheng Ni ◽

Ting Sun

Keyword(s):

Land Surface ◽

Urban Areas ◽

Energy Demand ◽

Energy Transport ◽

Sensible Heat Flux ◽

Wrf Model ◽

Turbulent Energy ◽

Heat Emission ◽

Anthropogenic Heat ◽

Energy Budgets

Abstract Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL.

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US East Coast synthetic aperture radar wind atlas for offshore wind energy

10.5194/wes-2019-16 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tobias Ahsbahs ◽

Galen Maclaurin ◽

Caroline Draxl ◽

Christopher Jackson ◽

Frank Monaldo ◽

...

Keyword(s):

Wind Speed ◽

Synthetic Aperture Radar ◽

East Coast ◽

Mesoscale Model ◽

Wrf Model ◽

Offshore Wind ◽

Synthetic Aperture ◽

Wind Resources ◽

Us East Coast ◽

Aperture Radar

Abstract. We present the first synthetic aperture radar (SAR)-based offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from Radarsat-1, Envisat, Sentinel-1A, and Sentinel-1B are processed to wind maps using the Geophysical Model Function (GMF) CMOD5.N. Extensive comparisons with 6,008 collocated buoy observations revealed that biases of the individual system range from −0.8 to 0.6 m/s. Unbiased wind retrievals are crucial for producing an accurate wind atlas and intercalibration for correcting these biases by adjusting the normalized radar cross section is applied. The intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m/s, while at the same time improving the root mean squared error from 1.67 to 1.46 m/s. These intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas. Monthly averages are used to correct artefacts from seasonal biases. The SAR wind atlas is used as a reference to study wind resources derived from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation of wind resources and show that model results estimate lower coastal wind speed gradients than those from SAR. At sites designated for offshore wind development by the Bureau of Ocean Energy Management, mean wind speeds typically vary between 0.3 and 0.5 m/s for SAR and less than 0.2 m/s for the WRF model within each site. Findings indicate that wind speed gradients and variation might be underestimated in mesoscale model outputs along US East Coast.

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The Making of the New European Wind Atlas – Part 2: Production and Evaluation

10.5194/gmd-2020-23 ◽

2020 ◽

Cited By ~ 2

Author(s):

Martin Dörenkämper ◽

Bjarke T. Olsen ◽

Björn Witha ◽

Andrea N. Hahmann ◽

Neil N. Davis ◽

...

Keyword(s):

Wind Speed ◽

Mesoscale Model ◽

Comprehensive Evaluation ◽

Positive Impact ◽

Wrf Model ◽

Added Value ◽

Large Set ◽

Mean Wind Speed ◽

The Mean ◽

Model Setup

Abstract. This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from −1.50 ± 1.30 to 0.02 ± 0.78 ms−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearised flow model break down, both the mean bias and spread in wind speed are worse than the mesoscale results.

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Atmospheric transport and deposition of trace elements to lake erie from Urban areas

Water Science & Technology ◽

10.2166/wst.1996.0513 ◽

1996 ◽

Vol 33 (4-5) ◽

pp. 259-265

Author(s):

Gerald J. Keeler ◽

Nicola Pirrone

Keyword(s):

Trace Elements ◽

Urban Areas ◽

Lake Erie ◽

Atmospheric Transport ◽

Spatial And Temporal Variation ◽

Deposition Flux ◽

Emission Sources ◽

Dry Deposition Flux ◽

Ambient Concentration ◽

Ambient Concentrations

A hybrid receptor-deposition (HRD) modeling approach was used to determine the spatial and temporal variation in the ambient concentration and dry deposition flux of trace elements on fine (< 2.5 mm) and coarse (> 2.5 mm) particulate matter over Lake Erie. Upper-air observations from the National Weather Service (NWS) and ambient concentrations measured at two sampling sites downwind of major emission sources in the Lake Erie basin were input to the model. An evaluation of the deposition flux of size-segregated trace elements to the lake during the over-water transport was performed. The average total (fine + coarse) deposition flux was 9.6 ng/m2-h for V, 70 ng/m2-h for Mn, 3.2 ng/m2-h for As, 4.2 ng/m2-h for Se, 10 ng/m2-h for Cd, and 43.3 ng/m2-h for Pb.

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