scholarly journals Strong regulation of daily variations in nighttime surface urban heat islands by meteorological variables across global cities

2021 ◽  
Author(s):  
Yihang She ◽  
Zihan Liu ◽  
Wenfeng Zhan ◽  
Jiameng Lai ◽  
Fan Huang
Keyword(s):  
Soil Moisture ◽  
Wind Speed ◽  
Global Scale ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Specific Humidity ◽  
Meteorological Variables ◽  
Arid Zones ◽  
Near Surface ◽  
Urban Heat

Abstract Knowledge of the day-to-day dynamics of surface urban heat island (SUHI) as well as their underlying determinants is crucial to a better design of effective heat mitigation. However, there remains a lack of a globally comprehensive investigation of the responsiveness of SUHI variations to meteorological variables. Based on the MODIS LSTs and auxiliary data in 2017, here we investigated 10,000+ cities worldwide to reveal day-to-day SUHI intensity (SUHII) variations (termed as SUHIIdv) in response to meteorological variables using Google Earth Engine. We found that: (1) meteorological variables related to the thermal admittance, e.g., precipitation, specific humidity and soil moisture (represented by daily temperature range in rural area, DTRr), reveal a larger regulation on SUHIIdv than those related to the air conditions (e.g., wind speed and near-surface air temperature) over a global scale. (2) Meteorological regulations on SUHIIdv can differ greatly by background climates. The control of specific humidity on SUHIIdv is significantly strengthened in arid zones, while that of wind speed is weakened prominently in equatorial zones. SUHIIdv is more sensitive to soil moisture in cities with higher background temperatures. (3) All meteorological variables, except that related to soil moisture (DTRr), show larger impact on SUHIIdv with antecedent precipitation over the global scale. Precipitation is observed to mitigate the SUHIIdv globally, and such effects are even more pronounced in equatorial and arid zones. We consider that our findings should be helpful in enriching the knowledge of SUHI dynamics on multiple timescales.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

2017 ◽  
Vol 145 (12) ◽  
pp. 4997-5014 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Alejandro N. Flores ◽  
Satish Bastola ◽  
Rafael L. Bras
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Specific Humidity ◽  
Surface Model ◽  
Precipitation Data ◽  
Near Surface ◽  
Satellite Precipitation ◽  
Soil Moisture Data ◽  
Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

scholarly journals Development of an operational modelling system for urban heat islands: an application to Athens, Greece

2013 ◽  
Vol 1 (5) ◽  
pp. 4963-4996
Author(s):  
T. M. Giannaros ◽  
D. Melas ◽  
I. A. Daglis ◽  
I. Keramitsoglou
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Urban Climate ◽  
Mitigation Strategies ◽  
Urban Heat Islands ◽  
Heat Island ◽  
Meteorological Model ◽  
Near Surface ◽  
Urban Heat ◽  
Modelling System

Abstract. The urban heat island (UHI) effect is one prominent form of localized anthropogenic climate modification. It represents a significant urban climate problem since it occurs in that layer of the atmosphere where almost all daily human activities take place. This paper presents the development of a high-resolution modelling system that could be used for simulating the UHI effect in the context of operational weather forecasting activities. The modelling system is built around a state-of-the-art numerical weather prediction model, properly modified to allow for the better representation of the urban climate. The model performance in terms of simulating the near-surface air temperature and thermal comfort conditions over the complex urban area of Athens, Greece, is evaluated during a 1.5-month operational implementation in the summer of 2010. Results from this case study reveal an overall satisfactory performance of the modelling system. The discussion of the results highlights the important role that, given the necessary modifications, a meteorological model can play as a supporting tool for developing successful heat island mitigation strategies. This is further underlined through the operational character of the presented modelling system.

scholarly journals GEOSPATIAL ASSESSMENT AND MODELING OF URBAN HEAT ISLANDS IN QUEZON CITY, PHILIPPINES USING OLS AND GEOGRAPHICALLY WEIGHTED REGRESSION

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 85-92
Author(s):  
C. A. Alcantara ◽  
J. D. Escoto ◽  
A. C. Blanco ◽  
A. B. Baloloy ◽  
J. A. Santos ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Land Surface ◽  
Google Earth ◽  
Urban Heat Islands ◽  
Weighted Regression ◽  
View Factor ◽  
Landsat 8 ◽  
Quezon City ◽  
Heat Islands ◽  
Urban Heat

Abstract. Urbanization has played an important part in the development of the society, yet it is accompanied by environmental concerns including the increase of local temperature compared to its immediate surroundings. The latter is known as Urban Heat Islands (UHI). This research aims to model UHI in Quezon City based on Land Surface Temperature (LST) estimated from Landsat 8 data. Geospatial processing and analyses were performed using Google Earth Engine, ArcGIS, GeoDa, and SAGA GIS. Based on Urban Thermal Field Variance Index (UTFVI) and the normalized mean per barangay (village), areas with strong UHI intensities were mapped and characterized. high intensity UHIs are observed mostly in areas with high Normalized Difference Built-up Index (NDBI) like the residential regions while the weak intensity UHIs are noticed in areas with high Normalized Difference Vegetation Index (NDVI) near the La Mesa Reservoir. In the OLS regression model, around 69% of LST variability is explained by Surface Albedo (SA), Sky View Factor (SVF), Surface Area to Volume Ratio (SVR), Solar Radiation (SR), NDBI and NDVI. OLS yield relatively high residuals (RMSE = 1.67) and the residuals are not normally distributed. Since LST is non-stationary, Geographically Weighted Regression (GWR) regression was conducted, proving normally and randomly distributed residuals (average RMSE = 0.26).

scholarly journals Required sampling-density of ground-based soil moisture and brightness temperature observations for calibration/validation of L-band satellite observations based on a virtual reality

2019 ◽  
Author(s):  
Shaoning Lv ◽  
Bernd Schalge ◽  
Pablo Saavedra Garfias ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Confidence Level ◽  
Land Surface ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Microwave Emission ◽  
Near Surface ◽  
Temperature Observations ◽  
Better Than

Abstract. Microwave remote sensing is the most promising tool for monitoring global-scale near-surface soil moisture distributions. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are made to evaluate their soil moisture products via ground observations, forward microwave transfer simulation, and retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional terrestrial system model TerrSysMP and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify sampling distance required for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling resolution of better than 6 km is required to validate the targeted accuracy of 0.04 cm3/cm3 (70 % confidence level) in SMOS and SMAP over typical midlatitude European regions. The minimum sampling resolution depends on the land-surface inhomogeneity and the meteorological situation, which influence the soil moisture patterns, and ranges from about 7 km to 17 km for a 70 % confidence level for a typical year. At the minimum sampling resolution for a 70 % confidence level also the accuracy of footprint-averaged brightness temperature estimates is equal or better than 15 K/10 K for H/V polarization. Estimates strongly deteriorate with sparser sampling densities, e.g., at 3/9 km with 3/5 sampling sites the confidence level of derived footprint estimates can reach about 0.5–0.6 for soil moisture which is much less than the standard 0.7 requirements for ground measurements. The representativeness of ground-based soil moisture and brightness temperature observations – and thus their required minimum sampling densities – are only weakly correlated in space and time. This study provides a basis for a better understanding of sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

scholarly journals Radon-based technique for the analysis of atmospheric stability – a case study from Central Poland

Nukleonika ◽  
2018 ◽  
Vol 63 (2) ◽  
pp. 47-54 ◽  
Author(s):  
Agnieszka Podstawczyńska ◽  
Scott D. Chambers
Keyword(s):  
Wind Speed ◽  
Temperature Gradient ◽  
Urban Heat Island ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Vertical Temperature Gradient ◽  
Mixing State ◽  
Vertical Temperature ◽  
Near Surface ◽  
Urban Heat

Abstract An economical and easy-to-implement technique is outlined by which the mean nocturnal atmospheric mixing state (“stability”) can be assessed over a broad (city-scale) heterogeneous region solely based on near-surface (2 m above ground level [a.g.l.]) observations of the passive tracer radon-222. The results presented here are mainly based on summer data of hourly meteorological and radon observations near Łodź, Central Poland, from 4 years (2008–2011). Behaviour of the near-surface wind speed and vertical temperature gradient (the primary controls of the nocturnal atmospheric mixing state), as well as the urban heat island intensity, are investigated within each of the four radon-based nocturnal stability categories derived for this study (least stable, weakly stable, moderately stable, and stable). On average, the most (least) stable nights were characterized by vertical temperature gradient of 1.1 (0.5)°C·m−1, wind speed of ~0.4 (~1.0) m·s−1, and urban heat island intensity of 4.5 (0.5)°C. For sites more than 20 km inland from the coast, where soils are not completely saturated or frozen, radon-based nocturnal stability classification can significantly enhance and simplify a range of environmental research applications (e.g. urban climate studies, urban pollution studies, regulatory dispersion modelling, and evaluating the performance of regional climate and pollution models).

scholarly journals Changes in Wind Speed under Heat Waves Enhance Urban Heat Islands in the Beijing Metropolitan Area

2016 ◽  
Vol 55 (11) ◽  
pp. 2369-2375 ◽  
Author(s):  
Dan Li ◽  
Ting Sun ◽  
Maofeng Liu ◽  
Linlin Wang ◽  
Zhiqiu Gao
Keyword(s):  
Energy Balance ◽  
Wind Speed ◽  
Surface Energy ◽  
Metropolitan Area ◽  
Surface Energy Balance ◽  
Heat Waves ◽  
Urban Heat Islands ◽  
Positive Interaction ◽  
Heat Islands ◽  
Urban Heat

AbstractThe interaction between urban heat islands (UHIs) and heat waves (HWs) is studied using measurements collected at two towers in the Beijing, China, metropolitan area and an analytical model. Measurements show that 1) the positive interaction between UHIs and HWs not only exists at the surface but also persists to higher levels (up to ~70 m) and 2) the urban wind speed is enhanced by HWs during daytime but reduced during nighttime as compared with its rural counterpart. A steady-state advection–diffusion model coupled to the surface energy balance equation is then employed to understand the implication of changes in wind speed on UHIs, which reveals that the observed changes in wind speed positively contribute to the interaction between UHIs and HWs in both daytime and nighttime. The vertical structure of the positive interaction between UHIs and HWs is thus likely an outcome resulting from a combination of changes in the surface energy balance and wind profile.

Which hydro-meteorological variables control large-scale photosynthesis?

2020 ◽  
Author(s):  
Wantong Li ◽  
Mirco Migliavacca ◽  
Yunpeng Luo ◽  
René Orth
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Large Scale ◽  
Global Analysis ◽  
Spatial Scales ◽  
Meteorological Variables ◽  
Climate Projections ◽  
Net Radiation ◽  
Near Surface

<p>Vegetation dynamics are determined by a multitude of hydro-meteorological variables, and this interplay changes in space and time. Due to its complexity, it is still not fully understood at large spatial scales. This knowledge gap contributes to increased uncertainties in future climate projections because large-scale photosynthesis is influencing the exchange of energy and water between the land surface and the atmosphere, thereby potentially impacting near-surface weather. In this study, we explore the relative importance of several hydro-meteorological variables for vegetation dynamics. For this purpose, we infer the correlations of anomalies in temperature, precipitation, soil moisture, VPD, surface net radiation and surface downward solar radiation with respective anomalies of photosynthetic activity as inferred from Sun-Induced chlorophyll Fluorescence (SIF). To detect changing hydro-meteorological controls across different climate conditions, this global analysis distinguishes between climate regimes as determined by long-term mean aridity and temperature. The results show that soil moisture was the most critical driver with SIF in the simultaneous correlation with dry and warm conditions, while temperature and VPD was both influential on cold and wet regimes during the study period 2007-2018. We repeat our analysis by replacing the SIF data with NDVI, as a proxy for vegetation greenness, and find overall similar results, except for surface net radiation expanding controlled regions on cold and wet regimes. As the considered hydro-meteorological variables are inter-related, spurious correlations can occur. We test different approaches to investigate and account for this phenomenon. The results can provide new insight into mechanisms of vegetation-water-energy interactions and contribute to improve dynamic global vegetation models.</p>

scholarly journals Influence of Soil Moisture on Urban Microclimate and Surface-Layer Meteorology in Oklahoma City

2014 ◽  
Vol 53 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Syed Zahid Husain ◽  
Stéphane Bélair ◽  
Sylvie Leroyer
Keyword(s):  
Surface Layer ◽  
Soil Moisture ◽  
Wind Speed ◽  
Urban Soil ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Oklahoma City ◽  
Near Surface ◽  
Model Simulations ◽  
Urban Surface Layer

AbstractThe influence of soil moisture on the surface-layer atmosphere is examined in this paper by analyzing the outputs of model simulations for different initial soil moisture configurations, with particular emphasis on urban microclimate. In addition to a control case, four different soil moisture distributions within the urban and surrounding rural areas are considered in this study. Outputs from the Global Environmental Multiscale atmospheric model simulations are compared with observations from the Joint Urban 2003 experiment held in Oklahoma City, Oklahoma, and the relevant conclusions drawn in this paper are therefore valid for similar medium-size cities. In general, high soil moisture is found to be associated with colder near-surface temperature and lower near-surface wind speed, whereas drier soil resulted in warmer temperatures and enhanced low-level wind. Relative to urban soil moisture content, rural soil conditions are predicted to have larger impacts on both rural and urban surface-layer meteorological conditions. Dry rural and wet urban soil configurations are shown to have a strong influence on the urban–rural temperature contrast and resulted in city-induced secondary circulations that considerably affect the near-surface wind speed. Dry rural soil in particular is found to intensify the nocturnal low-level jet and significantly affect the thermal stability of nocturnal near-neutral urban surface layer by altering both thermal and mechanical generation of turbulence.

scholarly journals A statistical–dynamical downscaling methodology for the urban heat island applied to the EURO-CORDEX ensemble

2021 ◽  
Author(s):  
Benjamin Le Roy ◽  
Aude Lemonsu ◽  
Robert Schoetter
Keyword(s):  
Urban Heat Island ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Temperature Fields ◽  
Specific Humidity ◽  
Heat Island ◽  
Short Term ◽  
Near Surface ◽  
Reconstructed Temperature ◽  
Urban Heat

AbstractRegional Climate Models (RCMs) are the primary climate information available to public stakeholders and city-planners to support local adaptation policies. However, with resolution in the order of ten kilometres, RCMs do not explicitly represent cities and their influence on local climate (e.g. Urban Heat Island; UHI). Downscaling methods are required to bridge the gap between RCMs and city scale. A statistical–dynamical downscaling methodology is developed to quantify the UHI of the city of Paris (France), based on a Local Weather Types (LWTs) classification combined with short-term high-resolution (1-km) urban climate simulations. The daily near-surface temperature amplitude, specific humidity, precipitation, wind speed and direction simulated by the RCMs are used for the LWTs attribution. The LWTs time series is associated to randomly selected days simulated with the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance to calculate the UHI corresponding to the successive LWTs. The downscaling methodology is applied to the EURO-CORDEX ensemble driven by the ERA-Interim reanalysis, and evaluated for the 2000–2008 period against station observations and a 2.5-km reanalysis. The short-term dynamical simulations slightly underestimate and overestimate near-surface minimum and maximum air temperature respectively, but capture the UHI intensity with biases in the order of a tenth of a degree. RCMs show significant differences in the variables used for the LWTs attribution, but the seasonal LWT frequencies are captured. Consequently, the reconstructed temperature fields maintain the small biases of the Meso-NH simulations and the statistical–dynamical downscaling greatly improves the UHI compared to the raw data of RCMs.

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