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

Li, Huoqing; Zhang, Hai-Liang; Mamtimin, Ali; Fan, Shuiyong; Ju, Chenxiang

doi:10.3390/atmos11040350

Cited by 23 publications

(18 citation statements)

References 27 publications

(29 reference statements)

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“…These results strengthen the need for the correct mapping of land cover classes, which is needed for impact studies and forecasts. Several recent studies among other regions in the world called for the same conclusion (see for example Cao et al, 2015;Bhati and Mohan, 2018;Li et al, 2020;López-Espinoza et al, 2020;Glotfelty et al, 2021;Golzio et al, 2021).…”

Section: Discussionmentioning

confidence: 61%

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Tölle

Churiulin

2021

Front. Earth Sci.

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Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

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Section: Discussionmentioning

confidence: 61%

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Tölle

Churiulin

2021

Front. Earth Sci.

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“…10/2017 2 ), this may affect the model's performance. Li et al (2020), for example, forced the WRF with a new land-use data set based on the latest Global Land Cover Data set (GLC2015) at 300 m resolution and observed an improvement in the simulation of heat fluxes, wind and temperature at the surface. Similar results were also obtained by Nahian et al (2020) when improving the land-use and topographical data set, highlighting the importance of considering land use as a physical driver of the biosphere atmosphere interaction rather than a static variable.…”

Section: Discussionmentioning

confidence: 99%

“…Li et al . (2020), for example, forced the WRF with a new land‐use data set based on the latest Global Land Cover Data set (GLC2015) at 300 m resolution and observed an improvement in the simulation of heat fluxes, wind and temperature at the surface. Similar results were also obtained by Nahian et al .…”

Section: Discussionmentioning

confidence: 99%

WRF wind field assessment under multiple forcings using spatialized aircraft data

Carotenuto

Gualtieri

Toscano

et al. 2020

Meteorological Applications

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The performances of limited area weather models are affected by the choice of core solvers, domain resolutions, and initial and boundary conditions. To understand the extent of such differences on simulated wind fields, weather research and forecast (WRF) simulations initialized by different forcings were extensively compared with an aircraft-derived high-resolution data set. The two used forcings were the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis and the National Centers for Environmental Predictions (NCEP) Climate Forecast System Reanalysis (CFSR). The model domain covered a large portion of central western Italy (including part of the Tyrrhenian coast) encompassing the aircraft track and allowed the characterization of their performance across the simulation domain rather than a small set of point-based observations. The WRF results show good agreement with the aircraft data across the whole flight track with both forcings (root mean square errors (RMSEs) < 2.3 m•s −1 and an average r 2 = 0.7). Orography and coasts show an effect on simulated wind fields. The presence of a strong orography (which is smoothed by the model internal terrain elevation model) is associated with increased errors. Distance from the coast is also associated with a variation in RMSE (even if in a non-straightforward manner) because of potential breeze effects. No forcing data set clearly outperforms the other, while the ECMWF has higher correlation coefficients when considering wind direction.

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“…Nevertheless, we used the global land cover map GLC2015 from the European Space Agency (ESA) climate change initiative project (http://maps.elie.ucl.ac.be/CCI/viewer/download. php) (accessed on 16 November 2020), which has a high accuracy classification beyond 74.58% and we applied it to a numerical weather forecast model [48,49]. The land cover data were used to analyze the feature of BBE distribution and land cover.…”

Section: Remote Sensing Datamentioning

confidence: 99%

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

Liu

Mamtimin

et al. 2021

Remote Sensing

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Broadband emissivity is a crucial parameter for calculating the radiation budget, still, it adopts a constant value in land surface models due to a lack of adequate observations. Arid regions have complex underlying surfaces and estimations of the broadband emissivity in such areas suffer from high spatial variation and uncertainty. Here, we propose a novel method for estimating broadband emissivity in the 8–14 µm range based on Fourier-transform infrared spectroscopy (FTIR) observations, moderate resolution imaging spectrometer (MODIS) emissivity, the leaf area index (LAI) and reflectance products. The proposed method exploits FTIR observations, MODIS single-channel emissivity, reflectance and the LAI to fit a linear regression of the broadband emissivity, so the optimal equation includes emissivity, reflectance and the LAI, with an R2 and root-mean-squared error of 0.942 and 0.08. Then we used the proposed method to generate a broadband emissivity map of Northwest of China, the broadband emissivity estimated by the method showed higher variations and finer distribution in arid areas and sparsely vegetated regions compared to data from the global land surface satellite and land model. An analysis of the relationship between the broadband emissivity, land-use type and soil moisture found an existing but not linear relationship, which indicated that the relationship was complicated under the inhomogeneous surface of wetness and vegetation. In conclusion, our results suggest that the proposed method can accurately estimate the broadband emissivity in arid regions. In future research, we will test the data in a land model.

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

Cited by 23 publications

References 27 publications

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

WRF wind field assessment under multiple forcings using spatialized aircraft data

A New Linear Relation for Estimating Surface Broadband Emissivity in Arid Regions Based on FTIR and MODIS Products

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