1. Introduction
Rapid urbanization globally has resulted in land-use and land-cover changes that have significantly affected climate (Jin et al. 2011; Bechtel et al. 2012). One of the most well-known urban climatic effects is the enhanced surface and air temperatures in cities, the urban heat island (UHI; Oke 1973; Kim and Baik 2005; Yow 2007). Many aspects of urban life are impacted (Oke 1995; Stone and Rodgers 2001; Weng et al. 2004) including human health and well-being (Basu and Samet 2002), the operations of city infrastructure (O’Malley et al. 2014), and ecological (Raich and Schlesinger 1992; Trumbore et al. 1996) and hydrological (Grimm et al. 2008; Kalma et al. 2008) processes. Thus, the ability to simulate high-resolution surface or near-ground air temperatures in cities is of considerable interest for a wide range of applications.
Satellite remote sensing has many advantages for monitoring land surface temperatures. Past studies have shown that the UHI derived from remote sensing responds to spatial patterns of surface characteristics (Roth et al. 1989; Gallo et al. 1993; Nichol et al. 2009). However, for many applications the value of remote sensing is limited by cloud cover, low temporal resolution of imagery, and/or coarse spatial resolution, although downscaling methods exist to address the latter (Xu et al. 2008; Bechtel et al. 2012; Bonafoni 2016).
Numerical simulations provide an effective way to forecast surface and/or air temperatures. Models are available for different surface types, including crops (Luo et al. 1992; Mihailović and Eitzinger 2007) and roads (Shao and Lister 1996; Diefenderfer et al. 2006). Methods underpinning these include obtaining the ground surface temperature by solving the surface energy balance equation (e.g., Best 1998; Herb et al. 2008); statistical downscaling from coarser-resolution models assuming stationary empirical relations (Winkler et al. 1997); multiobjective genetic-programming-based method to downscaling near-surface temperature by high-resolution information on land surface properties (Zerenner et al. 2016); and dynamic downscaling using a mesoscale model, for example, the Weather Research and Forecasting (WRF) Model (Pan et al. 2012).
An extensive comparison of 33 urban land surface models (ULSM) conducted as part of an international urban energy balance models comparison project (Grimmond et al. 2010) found, in general, simpler models perform just as well as more complex models (Grimmond et al. 2011; Best and Grimmond 2015). This finding is attributed, in part, to the ability of individual models to use the available input parameters and the difficulties of determining some of the parameters employed in more complex schemes. Moreover, complex models may require more computing resources (e.g., time, power). However, advances are being made with simple, practical models for cities (e.g., Wang et al. 2017; Lindberg et al. 2018).
This paper presents a novel simple model [the Surface Temperature and Near-Surface Air Temperature (at 2 m) Model (TsT2m)] for simulating high-spatial-resolution surface and near-surface air temperatures (2-m height). The model accounts for differences in surface cover proportion within a city (i.e., beyond coarse urban and rural differences) while using standard meteorological station data and easily obtained parameters. Critically, it runs rapidly and so permits operational high-resolution air temperature forecasts that allow heat- and cold-related urban climate and weather services to be provided. It considers both the surface energy budget and the effects of horizontal airflow. TsT2m is evaluated in Shanghai, a city with greater than 23 million inhabitants, more than 2.6 million automobiles, and more than 32 000 tall buildings (>30 m tall) in the year 2010 (Tan et al. 2015). Results are compared with 10-day weather forecast datasets derived from ECMWF model output (Molteni et al. 1996) and automatic weather station (AWS) observations distributed across the city of Shanghai.
2. TsT2m description
a. Model structure
TsT2m uses the surface energy balance to calculate surface temperature Ts (Ts submodel) and a dynamic energy balance of a column of air to diagnose the near-surface air temperature at 2-m height Ta (T2m submodel).
b. Surface temperature model (Ts submodel)
Land use (IGBP classes: numbers and color) and location of AWSs in the Shanghai region (province and constituent districts: gray lines) based on 2010 MODIS imagery (see text). The AWS national station codes are as follows: BS: Baoshan; CM: Chongming; FX: Fengxian; JD: Jiading; JS: Jinshan; MH: Minghang; SJ: Songjiang; PD: Pudong; XJH: Xujiahui; and QP: Qingpu. The eddy covariance (EC) tower is also at XJH, about 50 m from the AWS site. Simulated area is the same extent as this map (30.5°–32.0°N, 120.7°–122.2°E).
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
Normalized Taylor (2001) diagram for simulated incoming shortwave radiation assessed with observations from the XJH flux tower (Ao et al. 2016a) with changing combinations of transmission coefficients c1 and c2 (0.2 increment steps) for the period December 2012–August 2013 (30 min, at 0800 and 1400 LT, 712 samples). The ideal model performance is point O on the x axis. Points (red) are located based on the normalized standard deviation, correlation coefficient, and normalized RMSE. The best fit (point A) occurs with c1 = 0.4 and c2 = 0.8.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
The total input of energy in an urban environment (QF*=Q* + QF) requires the anthropogenic heat flux QF to be determined. Here, the Large-Scale Urban Consumption of Energy Model (LUCY) model (Allen et al. 2011; Gabey et al. 2018) with three sources of QF (traffic, building, and human metabolic emissions) is used. The traffic data are based on International Road Federation (IFR) World Road Statistics 2005, with the average traffic speed set to 48 km h−1. The population data are from the 2000 Global Rural Urban Mapping Project (GRUMP). For the average daily air temperature, the XJH station (September 2012–August 2013) data are used.
c. Near-surface air temperature model (T2m submodel)
3. Data and methods
a. Model parameter setting
The detailed land-cover classification used by the surface temperature model is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD12Q1 dataset (http://modis.gsfc.nasa.gov/data/dataprod/mod12.php) with 500-m resolution, based on the International Geosphere–Biosphere Programme (IGBP) land-cover classification (Cohen et al. 2006). In the study area (30.5°–32.0°N, 120.7°–122.2°E), the original 16 IGBP classes (Fig. 1) are reclassified into six types for the OHM model (Table 1). The barren or sparsely vegetated category refers to land with no plants, building or water cover, but usually porous ground. Pavement is impermeable, such as concrete or asphalt road surfaces. As the 500-m MODIS data do not provide land-cover fractions (e.g., proportions of pavement, building, vegetation) detailed analyses of both residential and commercial areas in Shanghai use similar methods to Ao et al. (2016a) for XJH. From this the urban and built-up land-cover class is assumed to be have the following characteristics: paved areas = 58%, buildings = 25%, grass = 14%, and trees = 3%. To explore the model performance at the national AWS stations, the surface-cover metadata [based on Shanghai Institute of Surveying and Mapping data; see Table 4 in Tan et al. (2015)] within 1 km of each is analyzed.
The surface temperature submodel of TsT2m [Eqs. (1)–(25)] is run with a spatial resolution of 0.015° and 1-h time step for the area of interest. It is initialized with a spatial dataset that includes surface temperature (bare soil), 2-m air temperature and humidity, 10-m wind direction and wind speed, and station pressure corrected to sea level. Data from the 10 AWS (Fig. 1) operated by the Shanghai Meteorological Service [see Tan et al. (2015) for more details about these stations] are interpolated by inverse distance weighting (IDW; Shepard 1968) to the model grid scale to provide model input. IDW is selected as it does not require other parameters (e.g., influence radius) and with the sites fixed in space the interpolation result is deterministic and results of adjacent grid points are continuous. Alternatives, such as nearest-neighbor (NN) interpolation may have adjacent grid values that change abruptly. IDW is advantageous for routine interpolation. It has smaller errors than kriging or NN methods and is recommended for local-scale patterns of air temperature (e.g., Honjo et al. 2015; Li et al. 2017).
To assess the impact of the IDW interpolation, a clear-sky case (24–25 July 2016; cloud cover around 10%) is analyzed with and without the XJH station. The initial surface temperature of the XJH station area is 31.2°C (30.2°C) with (without) XJH and the corresponding simulated surface temperature at 1400 LT are 59.0° (61.1°)C. The pattern of temperature distribution changes little (Fig. 3). For the investigation of the impact of the IDW interpolation method we have chosen to initiate the model with ECMWF run time after sunset at 2000 LT during which the temperature trends were not abruptly changing.
Interpolated 2000 LT 24 Jul surface temperature (a) without XJH, (b) with XJH, and the resulting simulated surface temperature at 1400 LT (c) without XJH and (d) with XJH. Gray lines delineate Shanghai and its districts.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
b. Evaluation method
The surface temperature submodel of TsT2m is evaluated for the period from 1 September 2012 to 31 August 2013. Simulated incoming shortwave and net all-wave radiation fluxes are compared against the XJH flux tower observations (Ao et al. 2016a) by season and weather type. Modeled surface temperatures are compared to twice daily (about 1100 and 1300 LT) MODIS land surface temperature products (MOD11C1), which have a resolution of 5 km (https://modis.gsfc.nasa.gov/data/dataprod/mod11.php).
The near-surface air temperature submodel of TsT2m is initialized with interpolated surface and air temperatures from the 10 AWS observations. The ECMWF forecast 10-m wind direction and wind speed, humidity, air pressure, total and lower cloud cover (resolution = 0.125°), and 850-hPa temperature (resolution = 0.25°) are used to force this submodel through interpolation. The new spatial resolution of 0.015° for the model grid is obtained by bilinear interpolation. The 1-h temporal resolution (from 3 h) is obtained by linear interpolation. Simulations are conducted from 1 May 2016 to 31 October 2016 using the next 24-h forecast (1200 UTC) with a 1-h time step. Simulated air temperatures are compared to the 3-h ECMWF forecast 2-m air temperature.
4. Results and discussion
a. Surface temperatures
The simulated hourly incoming shortwave flux and net all-wave radiation flux for clear days and cloudy days are compared with observations from the XJH tower (Fig. 4). Performance for both incoming shortwave and net all-wave radiation is better on clear days (R2 = 0.94 and 0.98) than on cloudy days (R2 = 0.68 and 0.70).
Simulated hourly (a),(b) incoming shortwave and (c),(d) net all-wave radiation flux compared with observed data at the XJH tower for (a),(c) clear days (N = 664) and (b),(d) cloudy days (N = 1478) during December 2012–August 2013. Regression slope (forced through the origin) and coefficients of determination R2 are given.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
Violin plots allow both the distribution and frequency of the errors to be seen (Fig. 5). On clear days simulated incoming shortwave radiation is higher than observed in spring and summer, but lower in autumn and winter. On cloudy days generally, simulated incoming shortwave radiation is higher than the observed data for all seasons. The hourly incoming shortwave flux MAE are 35, 23, 34, and 24 W m−2 in spring, summer, autumn, and winter, respectively (RMSE = 110, 123, 132, and 100 W m−2, respectively). The simulated transmissivity Tr on clear days are 0.70, 0.68, 0.61, and 0.66 on average in spring, summer, autumn, and winter, respectively, which follows the observed monthly median (1100–1300 LT) of 0.6–0.7 for clear conditions documented by Ao et al. (2016a). On cloudy days, values are 0.65, 0.58, 0.53, and 0.55 on average (spring, summer, autumn, and winter, respectively), which are higher than those derived from observations (0.59, 0.49, 0.48, and 0.47, respectively) (Ao et al. 2016a).
Violin plot showing maximum, median, and minimum whiskers and the kernel density of simulated bias for (a),(b) incoming shortwave radiation flux (K↓mod – K↓obs) and (c),(d) net all-wave radiation flux 
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
Following Leroyer et al. (2011) and Zhang et al. (2011), modeled surface temperatures are compared to the MODIS skin temperature. The simulation values reproduce the general pattern well and give more detailed patterns of the surface urban heat island than is evident from MODIS (Fig. 6). The high temperature in the center of city does not have the surrounding gradient evident in the MODIS skin temperature (Fig. 6a). Significantly lower temperatures near the Huangpu River in the city center are seen in the simulated temperatures (Fig. 6b). The Yangtze River and Chongming Island have similar temperature with the waters and land being hard to distinguish in Fig. 6a, but easy to distinguish in Fig. 6b. To evaluate the modeled results, the data are stratified by surface type (water, urban, short vegetation, trees and shrubs, and barren). The simulated surface temperature for urban areas (N = 16 600) are 10.3°C warmer on average than those derived from MODIS, while the others are more similar [water: −0.2°C (N = 36 936), short vegetation 3.1°C (N = 25 411), trees and shrubs 0.1°C (N = 12 203), and barren ground −0.3°C (N = 1158)]. The larger difference associated with the urban surfaces is not unexpected as the coarser MODIS data are unlikely to only have the warmer built surfaces but will typically include cooler surface covers (e.g., grass, trees) as well.
Surface temperature (°C) on 12 Aug 2013 derived from (a) MODIS (1040 LT) and (b) simulations (1100 LT). Black lines delimit Shanghai and its districts.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
Four cloud-free MODIS images are analyzed (1300 LT 3 June, 1300 LT 12 July, 1100 LT 11 August, and 1100 LT 12 August 2013) to investigate the performance of the surface temperature simulation for different land covers (Fig. 7). Observations of surface temperature are available at 10 national AWS sites. These are also used to evaluate the grids where they occur using the barren surface cover type. The barren surface temperatures’ MBE and RMSE are −2.8° and 4.8°C, while for the MODIS data they are −5.2° and 6.1°C, respectively (N = 36).
Variation in surface temperature on four clear days in summer (1300 LT 3 Jun, 1300 LT 12 Jul, 1100 LT 11 Aug, and 1100 LT 12 Aug 2013) determined from MODIS (MOD) and simulation (SIM) by surface type (see Table 1 for classes in domain). Boxplots show interquartile range (IQR), 1.5 × IQR whiskers, median (red line), and mean (black dashed line).
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
b. Near-surface air temperatures
To evaluate the near-surface air temperature of Shanghai, a day with high temperatures is selected (27 July 2016). A mix of cloud and sun occurred, with low wind speeds from inland (southwest) and near the sea from the southeast. The downscaled air temperature distribution is compared to air temperatures from 110 AWS (Fig. 8) and the ECMWF high-resolution 2-m temperature prediction with 3-h temporal resolution and 0.125° spatial resolution. The model is initialized at 2000 LT 26 July 2016 (i.e., the same time as the as ECMWF prediction) and temperatures simulated for 24 h. At 1400 LT 27 July 2016, the highest air temperature areas are in the downtown area of Shanghai for all three approaches: the observations are the warmest (~41°C), TsT2m is 39.8°C, and ECMWF (~37°C) the coolest. Both ECMWF and TsT2m simulate lower temperatures near the sea, because of a sea breeze flowing in from the cooler sea surface. ECMWF has a secondary hot spot in the southwest corner of Shanghai, but this is not evident in the AWS or the TsT2m results.
Air temperature at 2 m above ground level in Shanghai at 1400 LT 27 Jul 2016 at (a) AWS observations, (b) ECMWF (1200 UTC) forecast, and (c) TsT2m [initialized with 1200 UTC (2000 LT) 26 Jul 2016 ECMWF forecast].
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
For 1 May–31 October 2016, the near-surface air temperatures (Fig. 9) from ECMWF are mostly cooler than observed, except for some stations near the sea. In contrast, the TsT2m results are mostly warmer than observed, especially in the urban area. The RMSETsT2m are more spatially consistent than RMSEECMWF, as the latter are larger in the urban area than in the nonurban area. In August (Figs. 9c,d), the hottest month in Shanghai, the MBEECM is <−1.0°C for 77 stations, and for 15 stations RMSEECM is >2.0°C. Most of these are stations in the urban area. However, 96 stations have a MAETsT2m < 1.0°C. The four stations with RMSETsT2m > 2.0°C are located close to the coast.
Variation of MBE (color) and RMSE (dot size) for 2-m air temperature assessed with AWS data for (a),(b) May–October and (c),(d) August 2016 simulated by (a),(c) TsT2m and (b),(d) ECMWF.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
The average FA for the near-surface air temperature (Table 2) is better for FATsT2m than FAECM at almost all stations. In XJH and Chongming (CM) (Fig. 1), the full suite of TsT2m results are much better than those from the original ECMWF as both sites have a 20% higher FA. This also occurs in the MBE and RMSE (Fig. 10 shows XJH; others not shown). The FATsT2m is better than FAECM for every month, and much better in July (by 12.9%), August (24.8%) and September (11.4%). This indicates the TsT2m performance is much better on hot days (MBETsT2m < 1°C; RMSETsT2m < 3°C in each month) and better than the ECMWF forecast.
Air temperature (at 2 m) FA (%) of ECMWF (ECM) and the TsT2m (T2m) at each station (Fig. 1) by month (May–October 2016).
Boxplot [IQR, median (red line), mean (black dotted line), whiskers 1.5 × IQR] of hourly air temperature bias at XJH using TsT2m and ECMWF from May (5) to October (10).
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
To evaluate the effect of the surface cover, the fractions of building and pavement (%) for each station are determined (Fig. 11a, section 3a). FAECM has a negative correlation with impervious cover (Fig. 11b) (R2 = 0.45, t-test significance p = 0.002 for α = 0.05). However, there is little influence of urban fractional cover on FATsT2m. Thus, the ECMWF prediction of temperature in urban areas (e.g., Xujiahui, Minghang, Songjiang) is modified from that of nonurban area (e.g., Fengxian, Qingpu, Pudong). The new TsT2m 2-m air temperature, by accounting for urban land cover, results in much improved performance for areas with road and built-up cover.
Land-cover characteristics (a) within 1-km radius of the national stations (Fig. 1) and (b) urban percentage (paved + building) with FA of 2-m air temperature from May to October 2016.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
c. Application
A range of potential weather and climate service applications are possible with the new model. These include regular weather forecasts at higher spatial resolution and climate services mitigation applications.
Here the impact of changing land cover for heat mitigation is assessed by analyzing the hottest day in Shanghai in 2016 using the same forcing data as in section 4b. The air temperature is simulated with the original land cover (section 3a) to characterize urban grids (i.e., paved areas = 58%, buildings = 25%, grass = 14%, trees = 3%). This is used as a reference temperature. Two more sets of simulations are conducted that have decreasing paved proportions (58%–8%, 10% steps) that are replaced with (i) grass or (ii) trees. The maximum and minimum daily air temperature for each urban grid (T2m,max and T2m,min, respectively) are compared (simulation minus reference temperature simulation) to obtain the change (ΔT2m,max and ΔT2m,min, respectively) for each land-cover change (Fig. 12).
Impact of change in surface cover [section 4c; pavement replaced by grass (green) or tree (blue)] on simulated (by TsT2m) daily air temperature (27 Jul 2016) (a) maximum (ΔT2m,max) and (b) minimum (ΔT2m,min). Spatial difference boxplot for all urban grids with IQR, and 1.5 × IQR whiskers, median (red line), and mean (red diamond). Note y-scale differences.
Citation: Journal of Applied Meteorology and Climatology 57, 10; 10.1175/JAMC-D-17-0255.1
The mean T2m,max decreases with an increase in both grass and trees; therefore, ΔT2m,max becomes larger with an increase of grass or tree proportion. The reduction in T2m,max is on average greater for an increase in trees [0.45°C (10%)−1] than grass [0.37°C (10%)−1]. Similarly, the mean T2m,min decreases with an increase in both grass and trees (Fig. 12b). The decrease associated with the trees [0.063°C (10%)−1] is larger than with grass [0.026°C (10%)−1] but smaller than the impact on the maximum temperature (cf. Figs. 12a,b).
5. Discussion and conclusions
In this paper a new model (TsT2m) to downscale coarse NWP (e.g., ECMWF) forecast surface and air temperature based on underlying surface cover types is proposed. The approach provides higher temporal and spatial resolution outputs than traditional statistical downscaling. The model is assessed using AWS and MODIS data in Shanghai. For air temperature, the performance of the TsT2m is better than the coarser (spatially and temporally) ECMWF forecast, and reflects better the impact of the environment on the near-surface (2 m) temperature. In Shanghai, enhanced performance is most evident on hot days. From May to October, the ECMWF 2-m air temperature forecast in the urban area is systematically cooler, while the mean and median errors of TsT2m are both smaller. As TsT2m mostly uses conventional meteorological data (temperature, humidity, wind speed, air pressure, cloud cover, etc.), and requires limited computing resources, it has great potential to be translated and extended to other cities and applications.
TsT2m performs less well in winter than summer. This may result from the assumption that air near ground is well mixed, which it less likely under more stable conditions. Currently, TsT2m does not consider hydrological processes. As surface moisture processes are important, their inclusion is important in future improvements. Heterogeneity of urban morphology influences land surface temperature (Guo et al. 2016) and this also will be considered in future studies. As TsT2m is tested only in Shanghai, evaluation in other cities is important to establish the robustness of the approach and its broader utility.
Given a lack of high-resolution observations to initialize the model, it is necessary to interpolate data, which inevitably introduces biases in the model simulations. Here, IDW is chosen given the local scale and as it yields continuous results. Obviously, inclusion of more AWSs in the interpolation will result in a better spatial representation.
The storage heat flux is sometimes overestimated with the OHM mode (especially in the afternoon). This may lead to an overestimate of Ts. Given the simplicity of OHM. and the fact it does not account for impacts of surface moisture and wind speed variations (Grimmond and Oke 1999), the first-order results of this study suggest that assessing the new analytical OHM (AnOHM) model (Sun et al. 2017) is warranted to increase OHM coefficients in future simulations.
TsT2m has the potential to be used to study finer-scale urban processes and conditions, for example: thermodynamic conditions of local convection, the effects of which are too small scale to be captured by a NWP models. Thus, TsT2m provides a useful tool for evaluating the impact of urbanization on the thermal environment and to inform city planning and design. TsT2m is developed with the aim of improving climate and weather services for heat stress conditions, particularly for those most vulnerable. The improved spatial resolution, particularly in the densest urban areas, will enable improved predictions and interventions and aid long-term planning for extreme heat conditions.
Acknowledgments
This work is supported by The National Natural Science Foundation of China Grants 41775019 (J.G. Tan) and 41475040 (M.B. Du) and the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund (Grimmond, D.W. Liu, and J. Peng UK visit). Useful discussions with Ting Sun (Reading) are appreciated.There are no conflicts of interest.
APPENDIX A
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