Search Results (13)

China has experienced significant urbanization during the past 40 years, which exerts impacts on regional climates through changing land surface properties. Previous studies mainly focused on the Pearl River Delta, the Yangtze River Delta, and the Beijing-Tianjin-Hebei areas, while less attention has been paid to central China. In this paper, the regional climate effects of urbanization around the greater Wuhan area were investigated using the WRF model. High resolution, satellite-derived, impervious datasets were used to generate two realistic scenarios representing urban surface states of the years 1986 and 2018. By comparing the simulation results of two sensitivity experiments from 1 July 2015 to 12 July 2015, the spatial and diurnal changes in surface air temperature, surface skin temperature, and surface energy budget were analyzed. Our results reveal that urban expansion leads to 2 m air temperature and surface skin temperature increases by approximate 0.63 °C and 0.83 °C, respectively. Surface sensible heat flux increases, while latent heat flux decreases, with much greater effects in daytime than nighttime. The planetary boundary layer height (PBLH) increases with its maximum value over 100 m, and a 2 m water vapor mixing ratio decreases with a peak value around −2 g/kg. These findings provide knowledge to improve the understanding of land–atmospheric interactions and pave the way to studying urban expansion effects under future climate change scenarios. Full article

Observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard polar-orbiting satellites are considered to be the most effective satellite data in terms of obviously reducing operational prediction errors. However, there are still significant difficulties in the application of AMSU-A low-level channel data assimilation over land. One of them is the inaccurate surface skin temperature (SKT) of the background on land areas, which leads to significant uncertainty in the accuracy of simulating brightness temperature (BT) in these channels. Therefore, improving the accuracy of SKT in the background field is a direct way to improve the assimilation effect of these low-level channel data over land. In this study, both high-spatio-temporal-resolution automatic weather station (AWS) observation data from China in September 2021 and the AMSU-A observation data from NOAA-15/18/19 and MetOp-A were used. Based on the Advanced Research version of the Weather Research and Forecast model (WRF-ARW) and Gridpoint Statistical Interpolation (GSI) assimilation system, we first analyzed the differences in SKT between AWS observations and model simulations and then attempted to directly replace the simulated SKT with the observation data. On this basis, the differences in BT simulation effects over the land area of Southwest China before and after replacement were meticulously analyzed and compared. In addition, the impacts of SKT replacement in areas with different terrain elevations and in cloudy areas were also evaluated. The results indicate that the SKTs of background fields were generally lower than the surface observations, whereas the diurnal variation in SKT was not well simulated. After replacing the SKT of the background field with station observations, the BT differences between the observation and background (O–B, observation minus background) were remarkably reduced, especially for channels 3–5 and 15 of the AMSU-A. The volume of data passing the GSI quality control significantly increased, and the standard deviation of O–B decreased. Further analysis showed that the improvement effect was better in areas at an elevation above 1600 m. Moreover, introducing SKT observations leads to a significant and stable improvement over BT simulations in cloudy areas over land. Full article

Based on morphological methods, MODIS satellite remote sensing data were used to establish a dataset of the local roughness length (Z₀) of vegetation-covered surfaces in Guangdong Province. The local Z₀ was used to update the mesoscale Weather Research and Forecasting (WRF) model in order to quantitatively evaluate its impact on the thermodynamic environment of vegetation-covered surfaces. The specific results are as follows: evergreen broad-leaved forests showed the largest average Z₀ values at 1.27 m (spring), 1.15 m (summer), 1.03 m (autumn), and 1.15 m (winter); the average Z₀ values of mixed forests ranged from 0.90 to 1.20 m; and those for cropland-covered surfaces ranged from 0.17 to 0.20 m. The Z₀ values of individual vegetation coverage types all exhibited relatively high values in spring and low values in autumn, and the default Z₀ corresponding to specific vegetation-covered surfaces was significantly underestimated in the WRF model. Modifying the default Z₀ of surfaces underlying evergreen broad-leaved forests, mixed forests, and croplands in the model induced only relatively small changes (<1%) in their 2 m temperature, relative humidity, skin surface temperature, and the planetary boundary layer height. However, the average daily wind speed of surfaces covered by evergreen broad-leaved forests, mixed forests, and croplands was reduced by 0.48 m/s, 0.43 m/s, and 0.26 m/s, respectively, accounting for changes of 12.0%, 11.1%, and 6.5%, respectively. Full article

In this study, localised and non-uniform urban morphology (UM) and urban fraction (UF) parameters are implemented in a single-layer urban canopy scheme in the Weather Research and Forecasting (WRF) mesoscale meteorological model. The purpose of this research is to evaluate the effect of the refined parameterisation scheme on the simulation of dynamic and thermal fields in the urban canopy of the Guangzhou metropolitan area. The results showed that, compared with the default urban canopy parameters of the WRF model, using the localised UM parameters resulted in the most significant improvement in the 10 m wind speed simulation. In urban districts, the mean bias between the observed and simulated 10 m wind speed was reduced significantly by 59% from 2.63 m/s to 1.09 m/s during the daytime. For the thermal environment simulation during the daytime, higher UF and UM values resulted in lower surface albedos and generated narrower street canyons compared with the default modelling setting, which caused more heat to be trapped in the urban canopy and ultimately led to an increase in the surface skin temperature (TSK) and a largely increased ground heat flux (GRD). As a result, at night, more heat was transferred from the ground to the surface, producing a higher TSK. The effect of the localised UF on the sensible heat flux (HFX) was closely related to the near-surface temperature gradient. The UM caused the HFX to increase during the daytime, which was related to the near-surface heat exchange coefficient in the lower model layers. As the high-resolution UM significantly altered the urban geometry, the dynamic environment simulation resulted in a large increase in friction velocity and a decrease in wind speed. Full article

The current study evaluates the ability of the Weather Research and Forecasting Model (WRF) to forecast surface energy fluxes over a region in Eastern Spain. Focusing on the sensitivity of the model to Land Surface Model (LSM) parameterizations, we compare the simulations provided by the original Noah LSM and the Noah LSM with multiple physics options (Noah-MP). Furthermore, we assess the WRF sensitivity to different Noah-MP physics schemes, namely the calculation of canopy stomatal resistance (OPT_CRS), the soil moisture factor for stomatal resistance (OPT_BTR), and the surface layer drag coefficient (OPT_SFC). It has been found that these physics options strongly affect the energy partitioning at the land surface in short-time scale simulations. Aside from in situ observations, we use the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor to assess the Land Surface Temperature (LST) field simulated by WRF. Regarding multiple options in Noah-MP, WRF has been configured using three distinct soil moisture factors to control stomatal resistance (β factor) available in Noah-MP (Noah, CLM, and SSiB-types), two canopy stomatal resistance (Ball–Berry and Jarvis), and two options for surface layer drag coefficients (Monin–Obukhov and Chen97 scheme). Considering the β factor schemes, CLM and SSiB-type β factors simulate very low values of the latent heat flux while increasing the sensible heat flux. This result has been obtained independently of the canopy stomatal resistance scheme used. Additionally, the surface skin temperature simulated by Noah-MP is colder than that obtained by the original Noah LSM. This result is also highlighted when the simulated surface skin temperature is compared to the MSG-SEVIRI LST product. The largest differences between the satellite data and the mesoscale simulations are produced using the Noah-MP configurations run with the Monin–Obukhov parameterization for surface layer drag coefficients. In contrast, the Chen97 scheme shows larger surface skin temperatures than Monin–Obukhov, but at the expense of a decrease in the simulated sensible heat fluxes. In this regard, the ground heat flux and the net radiation play a key role in the simulation results. Full article

This work aims to compare the performance of the single‑(SLUCM) and multilayer (BEP-Building effect parameterization) urban canopy models (UCMs) coupled with the Weather Research and Forecasting model (WRF), along with the application of two urban heat island (UHI) identification methods. The identification methods are: (1) the “classic method”, based on the temperature difference between urban and rural areas; (2) the “local method” based on the temperature difference at each urban location when the model land use is considered urban, and when it is replaced by the dominant rural land use category of the urban surroundings. The study is performed as a case study for the city of Lisbon, Portugal, during the record-breaking August 2003 heatwave event. Two main differences were found in the UHI intensity (UHII) and spatial distribution between the identification methods: a reduction by half in the UHII during nighttime when using the local method; and a dipole signal in the daytime and nighttime UHI spatial pattern when using the classic method, associated with the sheltering effect provided by the high topography in the northern part of the city, that reduces the advective cooling in the lower areas under prevalent northern wind conditions. These results highlight the importance of using the local method in UHI modeling studies to fully isolate urban canopy and regional geographic contributions to the UHII and distribution. Considerable improvements were obtained in the near‑surface temperature representation by coupling WRF with the UCMs but better with SLUCM. The nighttime UHII over the most densely urbanized areas is lower in BEP, which can be linked to its larger nocturnal turbulent kinetic energy (TKE) near the surface and negative sensible heat (SH) fluxes. The latter may be associated with the lower surface skin temperature found in BEP, possibly owing to larger turbulent SH fluxes near the surface. Due to its higher urban TKE, BEP significantly overestimates the planetary boundary layer height compared with SLUCM and observations from soundings. The comparison with a previous study for the city of Lisbon shows that BEP model simulation results heavily rely on the number and distribution of vertical levels within the urban canopy. Full article

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRF_HR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRF_HR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRF_HR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R² amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRF_HR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R², as well as the small RMSE and BIAS amounts, indicate that ET_WRF_HR has achieved a very good performance. Full article

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. Full article

Pronounced spatial non-uniformity has been obtained of daytime sea surface temperature (SST) of the Dead Sea and of land surface temperature (LST) over areas adjacent to the Dead Sea. This non-uniformity was observed in the summer months, under uniform solar radiation. Our findings are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data (2002–2016) on board the Terra and Aqua satellites. MODIS data showed that, on average for the 15-year study period, daytime SST over the eastern part of the lake (Te) exceeded by 5 °C that over the western part (Tw). This SST non-uniformity (observed in the absence of surface heat flow from land to sea at the eastern side) was accompanied by spatial non-uniform distribution of land surface temperature (LST) over areas adjacent to the Dead Sea. Specifically, LST over areas adjacent to the eastern side exceeded by 10 °C that over areas adjacent to the western side. Our findings of spatial non-uniformity of SST/LST based on MODIS data were supported by Meteosat Second Generation LST records. Regional atmospheric warming led to a decrease in spatial non-uniformity of SST during the study period. Temperature difference between Te and Tw steadily decreased at the rate of 0.32 °C decade⁻¹, based on MODIS/Terra data, and 0.54 °C decade⁻¹, based on MODIS/Aqua data. Our simulations of monthly skin temperature distribution over the Dead Sea by the Weather Forecast and Research (WRF) model contradict satellite observations. The application to modeling of the observed SST/LST spatial non-uniformity will advance our knowledge of atmospheric dynamics over hypersaline lakes. Full article

The building environment parameterization scheme (BEP) is a built-in “urban physics” scheme in the weather research and forecasting (WRF) model. The urbanized College Park (CP) in Maryland state (MD) in the United States (US) covers an approximate land area of 14.8 km² and has a population of 32,000 (reported by The United States Census Bureau, as of 2017). This study was an effort to validate and improve the BEP urban physics scheme for a small urban setting, College Park, MD. Comparing the WRF/BEP-simulated two-meter air temperatures with the local rooftop WeatherBug® observations and with the airport observations, systemic deficiencies in BEP for urban heat island effect simulation are evident. Specifically, WRF/BEP overestimates the two-meter air temperature by about 10 °F during clear summer nights and slightly underestimates it during noon of the same days by about 1–3 °F. Similar deficiencies in skin temperature simulations are also evident in WRF/BEP. Modification by adding an anthropogenic heat flux term resulted in better estimates for both skin and two-meter air temperatures on diurnal and seasonal scales. Full article

Recent studies have shown the unique value of satellite-observed land surface thermal infrared (TIR) information (e.g., skin temperature) and the feasibility of assimilating land surface temperature (LST) into land surface models (LSMs) to improve the simulation of land-atmosphere water and energy exchanges. In this study, two different types of LST assimilation techniques are implemented and the benefits from the techniques are compared. One of the techniques is to directly assimilate LST using ensemble Kalman filter (EnKF) data assimilation (DA) utilities. The other is to use the Atmosphere-Land Exchange Inversion model (ALEXI) as an “observation operator” that converts LST retrievals into the soil moisture (SM) proxy based on the ratio of actual to potential evapotranspiration (f_PET), which is then assimilated into an LSM. While most current studies have shown some success in both directly the assimilating LST and assimilating ALEXI SM proxy into offline LSMs, the potential impact of the assimilation of TIR information through coupled numerical weather prediction (NWP) models is unclear. In this study, a semi-coupled Land Information System (LIS) and Weather Research and Forecast (WRF) system is employed to assess the impact of the two different techniques for assimilating the TIR observations from NOAA GOES satellites on WRF model forecasts. The NASA LIS, equipped with a variety of LSMs and advanced data assimilation tools (e.g., the ensemble Kalman Filter (EnKF)), takes atmospheric forcing data from the WRF model run, generates updated initial land surface conditions with the assimilation of either LST- or TIR-based SM and returns them to WRF for initializing the forecasts. The WRF forecasts using the daily updated initializations with the TIR data assimilation are evaluated against ground weather observations and re-analysis products. It is found that WRF forecasts with the LST-based SM assimilation have better agreement with the ground weather observations than those with the direct LST assimilation or without the land TIR data assimilation. Full article

The urban heat island effect (UHI) for inner land regions was investigated using satellite data, ground observations, and simulations with an Single-Layer Urban Canopy Parameterization (SLUCP) coupled into the regional Weather Research Forecasting model (WRF, http://wrf-model.org/index.php). Specifically, using the satellite-observed surface skin temperatures (T_skin), the intensity of the UHI was first compared for two inland cities (Xi’an City, China, and Oklahoma City (OKC)), which have different city populations and building densities. The larger population density and larger building density in Xi’an lead to a stronger skin-level UHI by 2 °C. However, the ground observed 2 m surface air temperature (T_air) observations showed an urban cooling island effect (UCI) over the downtown region in OKC during the daytime of 19 July 2003, from a DOE field campaign (Joint Urban 2003). To understand this contrast between satellite-based T_skin and ground-based T_air, a sensitivity study using WRF/SLUCP was analyzed. The model reproduced a UCI in OKC. Furthermore, WRF/Noah/SLUCM simulations were also compared with the Joint Urban 2003 ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLUCM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions, and energy fluxes reasonably well. Full article

In this study we examine the relationship between remotely sensed, in situ and modelled land surface temperature (LST) over a heterogeneous land-cover (LC) enclosed in alpine terrain. This relationship can help to understand to what extent the remotely sensed data can be used to improve model simulations of land surface parameters such as LST in mountainous areas. LST from the MODerate resolution Imaging Spectro-radiometer (MODIS), the modelled surface skin temperature by the Weather Research and Forecasting (WRF) mesoscale numerical model and the in situ measurements of surface temperature are used in the analysis. The test-site is located in a mountain valley in the Southern Alps of New Zealand. Geospatial analysis in GIS is used to relate pixels, grid-cells and points from the MODIS LST, model simulations and the in situ data, respectively. Differences between LST from MODIS, the WRF model and the in situ data are presented with respect to surface LC at different times of day. Initial results from regression analysis of the three datasets showed a goodness of fit R2 coefficient of 0:77 for the model simulations and 0:35 for the MODIS LST. These values improved significantly when time-lags were considered and the few outliers were removed, giving R2 values of 0:80 for the model and 0:73 for the MODIS LST. These results show that the WRF model correlates better with the in situ measurements over various LC types in this region compared with the MODIS LST. Longer time-series, however, are required to draw more robust conclusions about the applicability of the MODIS LST product for improving WRF simulations over alpine complex terrain. Full article