High-resolution (space, time) anthropogenic heat emissions: London 1970-2025

2011 ◽  
Vol 32 (11) ◽  
pp. 1754-1767 ◽  
Author(s):  
Mario Iamarino ◽  
Sean Beevers ◽  
C. S. B. Grimmond
Keyword(s):  
High Resolution ◽  
Space Time ◽  
Anthropogenic Heat

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

scholarly journals A Partition Modeling for Anthropogenic Heat Flux Mapping in China

2019 ◽  
Vol 11 (9) ◽  
pp. 1132 ◽  
Author(s):  
Shasha Wang ◽  
Deyong Hu ◽  
Shanshan Chen ◽  
Chen Yu
Keyword(s):  
Climate Change ◽  
Heat Flux ◽  
Spatial Distribution ◽  
High Resolution ◽  
Urban Areas ◽  
Thermal Environment ◽  
Modeling Method ◽  
Anthropogenic Heat ◽  
Regional Characteristics ◽  
Anthropogenic Heat Flux

Anthropogenic heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating anthropogenic heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded anthropogenic heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.

High‐resolution DOA estimation for cooperation networks using space‐time hopping

2016 ◽  
Vol 52 (11) ◽  
pp. 978-980 ◽  
Author(s):  
Xiangyu Li ◽  
Suxia Guo ◽  
Liang Jin ◽  
Kaizhi Huang ◽  
Lu Xia
Keyword(s):  
High Resolution ◽  
Doa Estimation ◽  
Space Time ◽  
Time Hopping

scholarly journals Integrated high-resolution dataset of high intensity Euro-Mediterranean flash floods

2018 ◽  
Author(s):  
William Amponsah ◽  
Pierre-Alain Ayral ◽  
Brice Boudevillain ◽  
Christophe Bouvier ◽  
Isabelle Braud ◽  
...  
Keyword(s):  
High Resolution ◽  
High Intensity ◽  
Flash Flood ◽  
Flash Floods ◽  
Space Time ◽  
Radar Rainfall ◽  
Catchment Areas ◽  
Flood Response ◽  
The Mediterranean ◽  
Resolution Dataset

Abstract. This paper describes an integrated, high-resolution dataset of hydro-meteorological variables (rainfall and discharge) concerning a number of high-intensity flash floods that occurred in Europe and in the Mediterranean region from 1991 to 2015. This type of dataset is rare in the scientific literature because flash floods are typically poorly observed hydrological extremes. Valuable features of the dataset (hereinafter referred to as EuroMedeFF database) include i) its coverage of varied hydro-climatic regions, ranging from Continental Europe through the Mediterranean to Arid climates, ii) the high space-time resolution radar-rainfall estimates, and iii) the dense spatial sampling of the flood response, by observed hydrographs and/or flood peak estimates from post-flood surveys. Flash floods included in the database are selected based on the limited upstream catchment areas (up to 3000 km2), the limited storm durations (up to 2 days), and the unit peak flood magnitude. The EuroMedeFF database comprises 49 events that occurred in France, Israel, Italy, Romania, Germany, and Slovenia, and constitutes a sample of rainfall and flood discharge extremes in different climates. The dataset may be of help to hydrologists as well as other scientific communities because it offers benchmark data for the identification and analysis of the hydro-meteorological causative processes, evaluation of flash flood hydrological models and for hydro-meteorological forecast systems. The dataset also provides a template for the analysis of the space-time variability of flash flood-triggered rainfall fields and of the effects of their estimation on the flood response modelling. The dataset is made available to the public as a "public dataset" with the following DOI: (https://doi.org/10.6096/mistrals-hymex.1493).

scholarly journals An Investigation of the Short-Term Predictability of Precipitation Using High-Resolution Composite Radar Observations

2012 ◽  
Vol 51 (5) ◽  
pp. 912-925 ◽  
Author(s):  
Evan Ruzanski ◽  
V. Chandrasekar
Keyword(s):  
High Resolution ◽  
Space Time ◽  
Radar Reflectivity ◽  
Short Term ◽  
Spatial And Temporal Scales ◽  
Precipitation Patterns ◽  
Upper Limit ◽  
Space Time Structure ◽  
Meteorological Radar ◽  
Reflectivity Data

AbstractThe short-term predictability of precipitation patterns observed by meteorological radar is an important concept as it establishes a means to characterize precipitation and provides an upper limit on the extent of useful nowcasting. Predictability also varies on the basis of spatial and temporal scales of the observed meteorological phenomena. This paper describes an investigation of the short-term predictability of precipitation patterns containing microalpha (0.2–2 km) to mesobeta (20–200 km) scales using high-resolution (0.5 km–1 min–1 dBZ) composite radar reflectivity data, extending the analysis presented in previous work to smaller space and time scales. An experimental approach is used in which continuous and categorical lifetimes of radar reflectivity fields in Eulerian and Lagrangian space are used to quantify short-term predictability. The space–time scale dependency of short-term predictability is analyzed, and a practical upper limit on the extent of Lagrangian persistence-based nowcasting is estimated. Connections to the predictability of larger scales are made within the context of previous work. The results show that short-term predictability estimates in terms of lifetime are approximately 14–15 and 20–21 min in Eulerian and Lagrangian space, respectively, and suggest that a linear relationship exists between predictability and space–time structure from microalpha to macrobeta (2000–10 000 km) scales.

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