The Weather Research and Forecasting Model (WRF): development, research and applications


The Weather Research and Forecasting Model (WRF): development, research and applications
Convener: Gert-Jan Steeneveld | Co-conveners: Hugo Hartmann, Arianna Valmassoi
Lightning talks
| Tue, 07 Sep, 11:00–12:30 (CEST)

Lightning talks: Tue, 7 Sep

Chairpersons: Gert-Jan Steeneveld, Arianna Valmassoi, Hugo Hartmann
Alfredo Peña and Jeffrey Mirocha

Mesoscale models, such as the Weather Research and Forecasting (WRF) model, are now commonly used to predict wind resources, and in recent years their outputs are being used as inputs to wake models for the prediction of the production of wind farms. Also, wind farm parametrizations have been implemented in the mesoscale models but their accuracy to reproduce wind speeds and turbulent kinetic energy fields within and around wind farms is yet unknown. This is partly because they have been evaluated against wind farm power measurements directly and, generally, a lack of high-quality observations of the wind field around large wind farms. Here, we evaluate the in-built wind farm parametrization of the WRF model, the so-called Fitch scheme that works together with the MYNN2 planetary boundary layer (PBL) scheme against large-eddy simulations (LES) of wakes using a generalized actuator disk model, which was also implemented within the same WRF version. After setting both types of simulations as similar as possible so that the inflow conditions are nearly identical, preliminary results show that the velocity deficits can differ up to 50% within the same area (determined by the resolution of the mesoscale run) where the turbine is placed. In contrast, within that same area, the turbine-generated TKE is nearly identical in both simulations. We also prepare an analysis of the sensitivity of the results to the inflow wind conditions, horizontal grid resolution of both the LES and the PBL run, number of turbines within the mesoscale grid cells, surface roughness, inversion strength, and boundary-layer height.

How to cite: Peña, A. and Mirocha, J.: Evaluation of the the wind farm wake parametrization with large-eddy simulations of wakes in WRF, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-180, https://doi.org/10.5194/ems2021-180, 2021.

Ivan R. Gelpi, Xabier Pedruzo, Aurelio Diaz de Arcaya, Jose Daniel Gomez de Segura, and Santiago Gaztelumendi

The sustainability of economic development and living conditions depends to a large extent on our ability to manage the risks associated with extreme events. In that sense, many practical problems require knowledge of the behaviour of extreme meteo-climatic variables at a high level of spatial and temporal detail. This is particularly true around highly populated areas where most part of the socio-economic activity takes place. Those metropolitan areas seem to be more vulnerable to extreme meteo-climatic conditions in the coming decades.


In this paper, we present some results of the implementation of operational high-resolution tools (down to 100m) for temperature and wind analysis at local level. Here we focus on different systems we have developed for its applications at local level in the Basque Country based on WRF and CALMET but we also include a brief descriptive analysis of some others available tools for thermal and wind analysis at high spatial resolution.

In order to test their operational capabilities, the behaviour of the different systems is analysed in diverse experiments corresponding to high impact weather scenarios, affecting the three largest metropolitan areas of the Basque Country, i.e. the metropolitan areas of Bilbao, Donostia-San Sebastian and Vitoria-Gasteiz, where more than half of the Basque population lives.  

The aim of this study is to evaluate and provide plausible tools and methodologies for very high resolution meteo-climatic analysis in the Basque Country area, focusing on wind and temperature extremes. With the final objective of identifying the benefit of hyperlocal modelling, finding under which conditions and spatio-temporal resolutions these highly computation demanding tools for wind and thermal characterisation are fully useful and provide sufficient added value. It is important to note that some results and main conclusions are of a general nature and could be extrapolated to other areas.

How to cite: R. Gelpi, I., Pedruzo, X., Diaz de Arcaya, A., Gomez de Segura, J. D., and Gaztelumendi, S.: Hyper-local extreme temperature and wind modeling in the Basque Country, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-235, https://doi.org/10.5194/ems2021-235, 2021.

Yu Cheng Chen, Fang Yi Cheng, Cheng Pei Yang, and Tzu Ping Lin

The climate in Taiwan is hot and humid, and urban show high-density development. The dense urban development has increased the heat storage of the ground and buildings. The compact arrangement of tall buildings causes the narrowness of the urban space to block the sky's view and hinders the relatively smooth airflow, which will cause problems such as poor ventilation in the city and cause high thermal risk in the city. In the past, when obtaining climate data, if only the climate stations set by the Central Meteorological Bureau were used, the distance between the stations was too far, and the coverage of the ground around the distribution area was almost the same, resulting in significant differences between the predicted results and the actual climate conditions. Therefore, this research established a microclimate measurement network to obtain climate data. For the urban environment information, the urban built environment data, such as digital surface model, and building information, were regulated data or required to purchase. Therefore, this study uses the Local Climate Zone (LCZ) which can consider land use and land cover simultaneously and can be freely produced by satellite images. The typology classification method can be used to view the city by the height and density of obstacles. LCZ can solve the inaccuracy of estimation caused by the mixed land-use in Taiwan and assign various types of related data from the scheme such as heat capacity, albedo, and roughness through classification results. This study herein applies LCZ combined with a mesoscale climate prediction model Weather Research and Forecasting (WRF) to predict the climate conditions within the study's scope and compare them with actual measured values. It can be used for urban climate assessment. The research results preliminarily show that by applying the LCZ classification and its corresponding factors to WRF with Multi-layer urban canopy model which can consider vertical surfaces such as building volume facades horizontal surfaces such as streets and roofs. The predicted temperature and actual temperature will be slightly underestimated, and it can be approximately between 1.5°C to 2.5°C in the urban area at night and 0.5°C to 1°C during the day. This phenomenon may be due to the relative ratio of buildings and road width in Taiwan, making the actual night. The heat dissipation effect is poor, and it is easy to cause heat accumulation in the urban area.

How to cite: Chen, Y. C., Cheng, F. Y., Yang, C. P., and Lin, T. P.: The combination use of microclimate measurement network, urban type classification and mesoscale climate prediction model to estimate the thermal condition distribution, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-3, https://doi.org/10.5194/ems2021-3, 2021.

Pubali Mukherjee and Balaji Ramakrishnan

Accurate estimation of meteorological parameters is crucial for the successful implementation of any operational oceanographic service. Surface wind information serves as a primary forcing in circulation models. Despite being an important aspect of climate studies, there remains a serious scarcity of extended in-situ observations, especially along the Indian coastline. Space-based observation of surface winds is limited by their spatial and temporal resolutions. In such a scenario simulation-based studies can be considered as the missing link between the satellite information and scarce in-situ observations. The present study attempts to understand the spatial variation of 10m winds simulated by the WRF (v-4.0) model against that Scatsat-1 scatterometer for the period of 20th  -29th June 2019. The entire study was conducted for the Arabian Sea region comprising the west coast of India. WRF-ARW was forced with two different initial conditions, NCEP-FNL  and GFS . NCEP-FNL initial conditions have a spatial resolution of 1° × 1° and a temporal resolution of 6 hours. GFS data have a spatial resolution of 0.25° × 0.25° and it is available at 3 hourly intervals. The model is simulated for the entire month of June 2019. Comparative analysis is carried out with 10m wind speed of Scatsat-1 level-4 analyzed winds from 20th June to 29th June as the satellite information was available only for that period. The analysis is carried out for the inner nested domain of 14km. The model simulated 10 m wind speed is spatially interpolated onto the size of the Scatsat-1 grid of 25km for spatial comparison. Spatial comparison 10 m wind speed of model simulation with NCEP-FNL initial conditions with that of Scatsat-1 revealed a high spatial correlation (0.6-0.7) between in the open ocean region but lower spatial correlation near the coastline. Similar analysis of model simulation initialized with GFS data showed a reasonably good spatial correlation in the open ocean but very low correlation along the nearshore region. This discrepancy can be attributed to the error in wind speed estimates of the satellite observations due to higher backscatter near the coastline. This indicates the inability of the model to represent the complicated topography of the study area coastline. The two different initial conditions reflected different patterns in spatial correlation due to the slight difference in the mode of generation of the two datasets. NCEP-FNL data is known to ingest 10% of more observational datasets than GFS, which might have reflected in the analysis. The entire analysis was conducted at a spatial resolution of 25km which again can be considered as a limiting factor. Hence it is expected that simulating the model at a high spatial resolution will resolve the complex topography of the nearshore region with improved accuracy.






How to cite: Mukherjee, P. and Ramakrishnan, B.: Comparative analysis of WRF simulated surface winds with satellite observations over the Arabian Sea, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-199, https://doi.org/10.5194/ems2021-199, 2021.

Jordi Mercader Carbó, Manel Bravo Blanco, Jordi Moré Pratdesaba, and Abdelmalik Sairouni Afif

The WRF-ARW has been the flagship mesoscale model in the Meteorological Service of Catalonia (SMC) since 2012. Several operational runs are performed daily (initialised at 00 and 12 UTC), using both the GFS and the IFS model for initial and boundary conditions, to account for uncertainties in the synoptic evolution. To provide more accurate forecasts to end-users, a convection-allowing simulation with a grid spacing of 1,5 km was added to the operational chain, starting in the summer of 2019. 

However, the verification results show that the improvement over its mother domain (a 3 km simulation with parameterised convection) is irregular because it does not happen for all the variables. For instance, the 2 m temperature forecasts are more reliable for the highest resolution domain but the wind speed at 10 m has a comparable skill. Regarding the precipitation, there is a very slight improvement only for high daily precipitation rates (50 or 80 mm) during some seasons; nevertheless, the results are worse in forecasting the occurrence of precipitation (that is, when considering low daily precipitation quantities). The comparison of the verification results among different model configurations (with various resolutions and initial conditions) can be easily performed by using a skill score table. This table and its design will also be presented in this session. 

Certainly, these results help to conceive strategies to enhance the skill of the 1,5 km simulations for some of the variables that arise as more inaccurate. For instance, it is evaluated to what extent using alternative static fields (changing the model topography or the land category) improves the forecasts of temperature, humidity or wind near the surface. Furthermore, the sensitivity of precipitation forecasts to several physics schemes is tested, seeking an enhancement of their skill. 

How to cite: Mercader Carbó, J., Bravo Blanco, M., Moré Pratdesaba, J., and Sairouni Afif, A.: Lessons learned after two years of operational high-resolution (1,5 km) WRF simulations in Catalonia and a plan to increase their skill, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-236, https://doi.org/10.5194/ems2021-236, 2021.

Michael Matějka, Kamil Láska, Klára Jeklová, and Jiří Hošek

The Antarctic Peninsula experiences a strong climate variability which is well reflected in glacier mass balance and state of the other cryospheric components. A better insight into the interactions of the atmosphere and the cryosphere can be obtained using numerical atmospheric models. In the presented work, the Weather Research and Forecasting (WRF) model at 700 m horizontal resolution was validated in the northern part of James Ross Island, Antarctic Peninsula. The model topography was based on the Reference Elevation Model of Antarctica. Sea ice cover was updated daily using high–resolution satellite observations. The WRF output was compared with air temperature, wind speed and wind direction observations from multiple automatic weather stations located in a complex topography and a mosaic of land cover types of Ulu Peninsula. Two periods in 2019/2020 representing contrasting seasons (summer and winter) were selected to identify possible seasonal effects on model accuracy. The three WRF boundary layer schemes (MYJ, MYNN, QNSE) were tested and the spatial and seasonal variability in their performance was evaluated. Simulated air temperatures were in very good agreement with measurements (mean bias –1.7 °C to 1.4 °C). The model was within 2 °C of observations in 47–72 % of the winter period and in 66–79 % of the summer period. An exception was a strong air temperature inversion at two winter days when the model accuracy decreased at low–altitude sites. Additional analysis of the WRF output revealed a good skill in simulating near–surface wind speed with higher correlation coefficients in winter (0.81–0.93) than in summer (0.41–0.59). Wind speed mean bias was mostly lower than 2.5 m·s–1, but higher wind speed overestimation was found at a coastal site during the winter validation period. The model successfully captured wind direction, showing only small differences to the observed values. Finally, the model accuracy at coastal and low–altitude sites was found to be more sensitive to the strength of synoptic–scale wind than at higher–altitude sites.  

How to cite: Matějka, M., Láska, K., Jeklová, K., and Hošek, J.: Assessment of the WRF model performance in air temperature and wind speed simulation over a complex Antarctic topography, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-274, https://doi.org/10.5194/ems2021-274, 2021.

Carlos Román-Cascón, Roberto Mulero-Martínez, Miguel Bruno, Carlos Yagüe, Marie Lothon, Fabienne Lohou, Oscar Álvarez, Jesus Gómez-Enri, Alfredo Izquierdo, Rafael Mañanes, and Jose Antonio Adame

Sea breezes are common and recurrent thermally-driven winds formed in coastal areas under conditions of weak synoptic forcing, due to the differential heat capacity of the sea and the land. Their accurate forecast is key because of the impacts on maxima near-surface temperatures, humidity (and then thermal comfort), pollutants distribution, convective-systems formation, etc., being crucial for the wind energy sector and because they develop in areas that are normally densely populated.

Some studies have investigated the impacts of the surface conditions in coastal breezes in different regions around the world. Their findings are diverse, mostly attributed to differences in the marine boundary layer stability, which can favour or inhibit the vertical mixing. This is needed to vertically distribute thermal changes in the land or the sea surfaces to deeper atmospheric layers, and thus to modify the horizontal surface pressure gradients. In this work, we use the Weather Research and Forecasting (WRF) model to investigate how the coastal breezes are affected by changes in the surface representation in the Gulf of Cádiz, in the Atlantic coast of the Southwestern Iberian Peninsula. We focus on artificial and realistic changes in land use, soil moisture and sea surface temperature. The analysis is performed for a case study of 20 days in August 2020, characterised by many coastal-breeze events in the area analysed and by a gradual decrease in the sea surface temperature. The model is evaluated with observational data at different coast locations, inland and on the ocean, as well as using wind speed transects from satellite altimetry.

How to cite: Román-Cascón, C., Mulero-Martínez, R., Bruno, M., Yagüe, C., Lothon, M., Lohou, F., Álvarez, O., Gómez-Enri, J., Izquierdo, A., Mañanes, R., and Adame, J. A.: How do the sea and the land conditions affect the coastal breezes? 20 days analysed from WRF simulations in the Gulf of Cádiz (Iberian Peninsula), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-344, https://doi.org/10.5194/ems2021-344, 2021.

Akriti Masoom, Panagiotis Kosmopolous, and Ankit Bansal

Poor resolution of solar irradiance ground data demonstrates the necessity and provides an opportunity for satellite data-based solar irradiance modeling. The study is focused on India due to its tropical climate that provides varied as well as extreme conditions for solar energy research. For solar energy monitoring in near real-time, the Indian Solar Irradiance Operational System (INSIOS) was developed using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate, respectively. It had high accuracy under clear-sky conditions for global horizontal irradiance (GHI) and direct normal irradiance (DNI) that were evaluated for a year at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The presented methodology could effectively support the penetration of photovoltaic installation as estimations were reliable during high solar energy potential conditions with median BSRN and INSIOS difference varying from 93 to 49 W/m2 for GHI.

Further, an operational day-ahead solar irradiance forecasting system (WRF-CAMS) is presented that ingests CAMS aerosol optical depth (AOD) into the WRF model to better quantify the aerosol impact on solar energy long-term forecasts, and validation was done against ground-based measurements from BSRN stations. The analysis was carried out for forecast horizons varying from 24 h to 36 h for different seasons, varying solar zenith angles, and different cloud cover classifications based on calculated clearness index. The correlation coefficient was improved from 0.93 to 0.95 for GHI and 0.75 to 0.82 for DNI after the ingestion of CAMS AOD as compared to WRF default aerosol scheme. The annual root mean square error was observed to vary from 10 to 130 W/m2 and 50 to 190 W/m2 for GHI and DNI, respectively. This system is hoped to provide more accurate forecasts for better solar plant energy planning and improve day-to-day electricity exchange market supporting solar energy producers and distribution system operators.

In the final analysis, INSIOS and WRF-CAMS models were used for forecasting dust impact on solar irradiance during an extreme dust event using Aeronet measurements, satellite observations (MODIS and CALIPSO), and ModIs Dust AeroSol (MIDAS) dust database. WRF-CAMS model was used to examine dust impact on solar irradiance for a high-intensity dust storm with AOD and dust optical depth values reaching up to 2. The observed average decrease in GHI and DNI due to the dust plume was 76 W/m2 and 275 W/m2, respectively, and a maximum reduction of 100 W/m2 (10%) and 400 W/m2 (40%), respectively. The proposed methodology can support solar energy producers, for optimum energy production forecasting, management, and maintenance as well as transmission and distribution system operators, as dust events of this extent significantly reduce solar irradiance and affect energy exploitation capacity due to solar aerosol-related extinction.

How to cite: Masoom, A., Kosmopolous, P., and Bansal, A.: Solar Irradiance Assessment and Forecasting in Tropical Climates using Satellite Remote Sensing and Physical Modelling, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-352, https://doi.org/10.5194/ems2021-352, 2021.


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