scholarly journals WRF Sensitivity Analysis in Wind and Temperature Fields Simulation for the Northern Sahara and the Mediterranean Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 259 ◽  
Author(s):  
Umberto Rizza ◽  
Enrico Mancinelli ◽  
Elisa Canepa ◽  
Jacques Piazzola ◽  
Tathy Missamou ◽  
...  
Keyword(s):  
Surface Layer ◽  
Wind Speed ◽  
Land Surface ◽  
Mediterranean Basin ◽  
Wrf Model ◽  
Land Surface Model ◽  
Temperature Fields ◽  
Surface Model ◽  
The Mediterranean ◽  
The Mediterranean Basin

Different configurations for the Weather Research and Forecasting (WRF) model were evaluated to improve wind and temperature fields predictions in the Northern Sahara and the Mediterranean basin. Eight setups, associated with different combinations of the surface layer physical parameters, the land surface model, and the grid nudging parameters, were considered. Numerical simulations covered the entire month of November 2017. Model results were compared with surface data from meteorological stations. The introduction of the grid nudging parameters leads to a general improvement of the modeled 10 m wind speed and 2 m temperature. In particular, nudging of wind speed parameter inside the planetary boundary layer (PBL) provides the most remarkable differences. In contrast, the nudging of temperature and relative humidity parameters inside the PBL may be switched off to reduce computational time and data storage. Furthermore, it was shown that the prediction of the 10 m wind speed and 2 m temperature is quite sensitive to the choice of the surface layer scheme and the land surface model. This paper provides useful suggestions to improve the setup of the WRF model in the Northern Sahara and the Mediterranean basin. These results are also relevant for topics related with the emission of mineral dust and sea spray within the Mediterranean region.

scholarly journals Analysis of WRF Model Performance over Subtropical Region of Delhi, India

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Manju Mohan ◽  
Shweta Bhati
Keyword(s):  
Surface Layer ◽  
Land Surface ◽  
Model Performance ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Subtropical Region ◽  
Statistical Measures ◽  
Current Availability

Model performance and sensitivity to model physics options are studied with the Weather Research and Forecasting model (version 3.1.1) over Delhi region in India for surface and upper air meteorological parameters in summer and winter seasons. A case study with the model has been performed with different configurations, and the best physics options suited for this region have been, determined. Comparison between estimated and observed data was carried out through standard statistical measures. Generally, the combination of Pleim-Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model has been found to produce better estimates of temperature and relative humidity for Delhi region. Wind speed and direction estimations were observed best for MM5 similarity surface layer along with Yonsei University boundary layer scheme. Nested domains with higher resolutions were not helpful in improving the simulation results as per the current availability of the data. Overall, the present case study shows that the model has performed reasonably well over the subtropical region of Delhi.

scholarly journals Performance evaluation of the WRF model in a tropical region: wind speed analysis at different sites

2021 ◽  
Author(s):  
Noéle Bissoli Perini Souza ◽  
Erick Giovani Sperandio Nascimento ◽  
Davidson Martins Moreira
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Weather Research And Forecasting ◽  
Tropical Region ◽  
Surface Model ◽  
Dry And Rainy Seasons

In this study, the performance of the mesoscale Weather Research and Forecasting (WRF) model is evaluated using combinations of three Planetary Boundary Layer (PBL) and three Land Surface Model (LSM) schemes, in order to identify the optimal parameters for the determination of wind speed in a tropical region. The state of Bahia in Brazil is selected as the location for the case study and simulations are performed over a period of eight months between 2015 and 2016. This is done to ensure that the dry and rainy seasons at the three different experimental sites—Esplanada, Mucuri, and Mucugê—are well separated from each other. The results of the simulations are compared with the observational data obtained from three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, strategically placed at each site. Overestimation of wind speed is observed in the simulations, despite similarities between the simulated and observed wind directions. In addition, the accuracies of simulations corresponding to sites that are closer to the ocean are observed to be lower—the most accurate wind speed estimates are obtained corresponding to Mucugê, which is located farthest from the ocean. Finally, analysis of the results obtained from each tower accounting for periods with higher and lower precipitation reveals that the combination of the PBL-YSU scheme with the LSM-RUC scheme yields the best results.

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

scholarly journals Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Heliyon ◽  
2019 ◽  
Vol 5 (9) ◽  
pp. e02469 ◽  
Author(s):  
Achenafi Teklay ◽  
Yihun T. Dile ◽  
Dereje H. Asfaw ◽  
Haimanote K. Bayabil ◽  
Kibruyesfa Sisay
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Lake Tana ◽  
Model Simulations ◽  
Lake Tana Basin

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

scholarly journals Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 815
Author(s):  
Marcelo Somos-Valenzuela ◽  
Francisco Manquehual-Cheuque
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Short Wave ◽  
Reanalysis Data ◽  
Wave Radiation ◽  
Surface Model ◽  
Nwp Model

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

scholarly journals Assessment of WRF Land Surface Model Performance over West Africa

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Ifeanyi C. Achugbu ◽  
Jimy Dudhia ◽  
Ayorinde A. Olufayo ◽  
Ifeoluwa A. Balogun ◽  
Elijah A. Adefisan ◽  
...  
Keyword(s):  
West Africa ◽  
Land Surface ◽  
Model Performance ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Reanalysis Dataset ◽  
The North ◽  
Temperature And Precipitation ◽  
Moisture Advection

Simulations with four land surface models (LSMs) (i.e., Noah, Noah-MP, Noah-MP with ground water GW option, and CLM4) using the Weather Research and Forecasting (WRF) model at 12 km horizontal grid resolution were carried out as two sets for 3 months (December–February 2011/2012 and July–September 2012) over West Africa. The objective is to assess the performance of WRF LSMs in simulating meteorological parameters over West Africa. The model precipitation was assessed against TRMM while surface temperature was compared with the ERA-Interim reanalysis dataset. Results show that the LSMs performed differently for different variables in different land-surface conditions. Based on precipitation and temperature, Noah-MP GW is overall the best for all the variables and seasons in combination, while Noah came last. Specifically, Noah-MP GW performed best for JAS temperature and precipitation; CLM4 was the best in simulating DJF precipitation, while Noah was the best in simulating DJF temperature. Noah-MP GW has the wettest Sahel while Noah has the driest one. The strength of the Tropical Easterly Jet (TEJ) is strongest in Noah-MP GW and Noah-MP compared with that in CLM4 and Noah. The core of the African Easterly Jet (AEJ) lies around 12°N in Noah and 15°N for Noah-MP GW. Noah-MP GW and Noah-MP simulations have stronger influx of moisture advection from the southwesterly monsoonal wind than the CLM4 and Noah with Noah showing the least influx. Also, analysis of the evaporative fraction shows sharp gradient for Noah-MP GW and Noah-MP with wetter Sahel further to the north and further to the south for Noah. Noah-MP-GW has the highest amount of soil moisture, while the CLM4 has the least for both the JAS and DJF seasons. The CLM4 has the highest LH for both DJF and JAS seasons but however has the least SH for both DJF and JAS seasons. The principal difference between the LSMs is in the vegetation representation, description, and parameterization of the soil water column; hence, improvement is recommended in this regard.

scholarly journals WRFv3.2-SPAv2: development and validation of a coupled ecosystem–atmosphere model, scaling from surface fluxes of CO<sub>2</sub> and energy to atmospheric profiles

2013 ◽  
Vol 6 (4) ◽  
pp. 1079-1093 ◽  
Author(s):  
T. L. Smallman ◽  
J. B. Moncrieff ◽  
M. Williams
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystem ◽  
Surface Fluxes ◽  
Surface Model ◽  
Terrestrial Ecosystem Model ◽  
Atmospheric Profiles

Abstract. The Weather Research and Forecasting meteorological (WRF) model has been coupled to the Soil–Plant–Atmosphere (SPA) terrestrial ecosystem model, to produce WRF-SPA. SPA generates realistic land–atmosphere exchanges through fully coupled hydrological, carbon and energy cycles. The addition of a~land surface model (SPA) capable of modelling biospheric CO2 exchange allows WRF-SPA to be used for investigating the feedbacks between biosphere carbon balance, meteorology, and land use and land cover change. We have extensively validated WRF-SPA using multi-annual observations of air temperature, turbulent fluxes, net radiation and net ecosystem exchange of CO2 at three sites, representing the dominant vegetation types in Scotland (forest, managed grassland and arable agriculture). For example air temperature is well simulated across all sites (forest R2 = 0.92, RMSE = 1.7 °C, bias = 0.88 °C; managed grassland R2 = 0.73, RMSE = 2.7 °C, bias = −0.30 °C; arable agriculture R2 = 0.82, RMSE = 2.2 °C, bias = 0.46 °C; RMSE, root mean square error). WRF-SPA generates more realistic seasonal behaviour at the site level compared to an unmodified version of WRF, such as improved simulation of seasonal transitions in latent heat flux in arable systems. WRF-SPA also generates realistic seasonal CO2 exchanges across all sites. WRF-SPA is also able to realistically model atmospheric profiles of CO2 over Scotland, spanning a 3 yr period (2004–2006), capturing both profile structure, indicating realistic transport, and magnitude (model–data residual

Response of the surface climate to different land surface models: WRF sensitivity to surface model options

2021 ◽  
Author(s):  
Daniela C.A. Lima ◽  
Rita M. Cardoso ◽  
Pedro M.M. Soares
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Models ◽  
Land Surface Processes ◽  
Surface Models

&lt;p&gt;The Weather Research and Forecasting (WRF) model version 4.2 includes different land surface schemes, allowing a better representation of the land surface processes. Four simulations with the WRF model differing in land surface models and options were investigated as a sensitivity study over the European domain. These experiments span from 2004-2006 with a one-month spin-up and were performed at 0.11&lt;sup&gt;o&lt;/sup&gt; horizontal resolution with 50 vertical levels, following the CORDEX guidelines. The lateral boundary conditions were driven by ERA5 reanalysis from European Centre for Medium-Range Weather Forecasts. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho &amp; Fan groundwater scheme. The physical parameterizations options are the same for all simulations. These experiments allow the analysis of the sensitivity of different land surface options and to understand how the representation of land surface processes impacts on the atmosphere properties. This study focusses on the investigation of land-atmosphere feedbacks trough the analysis of the soil moisture &amp;#8211; temperature and soil moisture &amp;#8211; precipitation interactions, latent and sensible heat fluxes, and moisture fluxes. The influence of different surface model options on atmospheric boundary layer is also explored.&lt;/p&gt;&lt;p&gt;Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 &amp;#8211; Instituto Dom Luiz.&lt;/p&gt;

scholarly journals A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface–Atmosphere Models

2016 ◽  
Vol 18 (1) ◽  
pp. 49-63 ◽  
Author(s):  
Kjetil Schanke Aas ◽  
Kjersti Gisnås ◽  
Sebastian Westermann ◽  
Terje Koren Berntsen
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Digital Camera ◽  
Cold Bias ◽  
Surface Model ◽  
Surface Energy Fluxes ◽  
Subgrid Scales ◽  
Moisture Fluxes

Abstract A mosaic approach to represent subgrid snow variation in a coupled atmosphere–land surface model (WRF–Noah) is introduced and tested. Solid precipitation is scaled in 10 subgrid tiles based on precalculated snow distributions, giving a consistent, explicit representation of variable snow cover and snow depth on subgrid scales. The method is tested in the Weather Research and Forecasting (WRF) Model for southern Norway at 3-km grid spacing, using the subgrid tiling for areas above the tree line. At a validation site in Finse, the modeled transition time from full snow cover to snow-free ground is increased from a few days with the default snow cover fraction formulation to more than 2 months with the tiling approach, which agrees with in situ observations from both digital camera images and surface temperature loggers. This in turn reduces a cold bias at this site by more than 2°C during the first half of July, with the noontime bias reduced from −5° to −1°C. The improved representation of subgrid snow variation also reduces a cold bias found in the reference simulation on regional scales by up to 0.8°C and increases surface energy fluxes (in particular the latent heat flux), and it resulted in up to 50% increase in monthly (June) precipitation in some of the most affected areas. By simulating individual soil properties for each tile, this approach also accounts for a number of secondary effects of uneven snow distribution resulting in different energy and moisture fluxes in different tiles also after the snow has disappeared.

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