Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model

Jiménez‐Esteve, Bernat; Udina, Mireia; Soler, M. R.; Pepin, Nick; Miró, Josep Ramon

doi:10.1016/j.atmosres.2017.11.012

Cited by 57 publications

(38 citation statements)

References 32 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, RAMS produces warmer temperatures than observed in this case. Relative humidity is related to air temperature, and cold air can hold less water vapor than warm air (Jiménez-Esteve et al, 2018). An overestimation of the 2-m minimum temperature, such as that found over BRX under synoptic conditions, increases the air capacity to contain water vapor and would lead to the observed underestimation of the simulated night-time 2-m relative humidity.…”

Section: Temperature and Moisturementioning

confidence: 97%

Improved meteorology and surface fluxes in mesoscale modelling using adjusted initial vertical soil moisture profiles

Gómez

Caselles

Estrela

et al. 2018

Atmospheric Research

View full text Add to dashboard Cite

The Regional Atmospheric Modeling System (RAMS) is being used for different and diverse purposes, ranging from atmospheric and dispersion of pollutants forecasting to agricultural meteorology and ecological modelling as well as for hydrological purposes, among others. The current paper presents a comprehensive assessment of the RAMS forecasts, comparing the results not only with observed standard surface meteorological variables, measured at FLUXNET stations and other portable and permanent weather stations located over the region of study, but also with non-standard observed variables, such as the surface energy fluxes, with the aim of evaluating the surface energy budget and its relation with a proper representation of standard observations and key physical processes for a wide range of applications. In this regard, RAMS is assessed against in-situ surface observations during a selected period within July 2011 over Eastern Spain. In addition, the simulation results are also compared with different surface remote sensing data derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) (MSG-SEVIRI) as well as the uncoupled Land Surface Models (LSM) Global Land Data Assimilation System (GLDAS). Both datasets complement the available in-situ observations and are used in the current study as the reference or ground truth when no observations are available on a selected location. Several sensitivity tests have been performed involving the initial soil moisture content, by adjusting this parameter in the vertical soil profile ranging from the most superficial soil layers to those located deeper underground. A refined adjustment of this parameter in the initialization of the model has shown to better represent the observed surface energy fluxes. The results obtained also show an improvement in the model forecasts found in previous studies in relation to standard observations, such as the air temperature and the moisture fields. Therefore, the application of a drier or wetter soil in distinct soil layers within the whole vertical soil profile has been found to be crucial in order to produce a better agreement between the simulation and the observations, thus reiterating the determining role of the initial soil moisture field in mesoscale modelling, but in this case considering the variation of this parameter vertically.

show abstract

Section: Temperature and Moisturementioning

confidence: 97%

Improved meteorology and surface fluxes in mesoscale modelling using adjusted initial vertical soil moisture profiles

Gómez

Caselles

Estrela

et al. 2018

Atmospheric Research

View full text Add to dashboard Cite

show abstract

“…Such data describe the properties of different types of land, including land-use categories characterized by six key physical parameters (e.g., albedo α, emissivity ε, roughness z 0m , soil heat capacity C, surface thermal inertia λ and soil moisture availability M); each of these parameters plays an important role in land-atmosphere interactions [1,2]. These parameters regulate the exchanges of heat, moisture and momentum between the soil and the air, which in numerical models determine the calculations of meteorological variations (e.g., temperature, humidity) near the surface [3]. Land-use data are necessary for the WRF (Weather Research and Forecasting) model.…”

Section: Introductionmentioning

confidence: 99%

A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model

Zhang

Mamtimin

et al. 2020

Atmosphere

View full text Add to dashboard Cite

The USGS (United States Geological Survey) land-use data used in the Weather Research and Forecasting (WRF) model have become obsolete as they are unable to accurately represent actual underlying surface features. Therefore, this study developed a new multi-satellite remote-sensing land-use dataset based on the latest GLC2015 (Global Land Cover, 2015) land-use data, which had 300 m spatial resolution. The new data were used to update the default USGS land-use dataset. Based on observational data from national meteorological observing stations in Xinjiang, northwest China, a comparison of the old USGS and new GLC2015 land-use datasets in the WRF model was performed for July 2018, where the simulated variables included the sensible heat flux (SHF), latent heat flux (LHF), surface skin temperature (Tsk), two-meter air temperature (T2), wind speed (Winds), specific humidity (Q2) and relative humidity (RH). The results indicated that there were significant differences between the two datasets. For example, our statistical verification results found via in situ observations made by the MET (model evaluation tools) illustrated that the bias of T2 decreased by 2.54%, the root mean square error (RMSE) decreased by 1.48%, the bias of Winds decreased by 10.46%, and the RMSE decreased by 6.77% when using the new dataset, and the new parameter values performed a net positive effect on land–atmosphere interactions. These results suggested that the GLC2015 land-use dataset developed in this study was useful in terms of improving the performance of the WRF model in the summer months.

show abstract

“…However, they often are outdated and have a coarser resolution, which fails to depict land surface features due to dramatic disturbance caused by human activities (Boucher et al, 2004; Findell et al, 2007; Schubert & Grossman‐Clarke, 2013). For example, the default 30‐arc second USGS land cover dataset (hereafter referred to as USGS_LC) was updated in 1992, which underestimates urban and built‐up land (hereafter referred to as UBL) proportion (Schicker et al, 2016) and leads to considerable misclassifications for the forest, cropland, and grassland in certain regions (Jimenez‐Esteve et al, 2018; Thomas et al, 2018). In terms of the newest MODIS land cover dataset (hereafter referred to as MODIS_LC), underestimation for UBL and misrepresentation for forest are also existing on a large scale.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Impacts of Land Cover and Soil Texture Changes on Simulated Surface Wind and Temperature

Liu

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

In this study, Chinese Academy of Sciences land cover dataset (CAS_LC) and soil texture dataset (CAS_ST) as well as Tsinghua University land cover dataset (TU_LC) were incorporated into the Weather Research and Forecasting (WRF) model to evaluate the impacts of land cover and soil texture changes on the surface wind and air temperature as compared with outdated default datasets. Six modeling scenarios including single updating for the three new datasets, combined updating of new datasets (CAS_LC + CAS_ST, TU_LC + CAS_ST), and default datasets are designed. WRF simulations were conducted with three resolutions (9, 3, and 1 km) for the period of 14 July to 31 September 2017 over Harbin. Results of the 10-m wind speed, 10-m wind direction, and 2-m air temperature (T2) were evaluated against WRF default datasets (WLS). Results show that TU_LC-related simulations with lower mean absolute error (MAE) demonstrated the usefulness of this dataset in the wind speed simulation, while the changing soil texture exerted a limited effect on modeled wind. CAS_LC improved simulated wind direction and T2. Significant changes in the three variables were frequently induced by urban and built-up land or water bodies related transformations. The 3-km grid resolution was sufficient to reasonably simulate the surface winds and air temperature. The roughness length was a main factor disturbing the wind speed, while the land cover transformation was responsible for the changes in surface temperature and heat fluxes, thereby affecting T2 simulation.

show abstract

Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model

Cited by 57 publications

References 32 publications

Improved meteorology and surface fluxes in mesoscale modelling using adjusted initial vertical soil moisture profiles

Improved meteorology and surface fluxes in mesoscale modelling using adjusted initial vertical soil moisture profiles

A New Land-Use Dataset for the Weather Research and Forecasting (WRF) Model

Evaluating the Impacts of Land Cover and Soil Texture Changes on Simulated Surface Wind and Temperature

Contact Info

Product

Resources

About