GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Kloog, Itai  (2)
  • Geography  (2)
Material
Publisher
Language
Years
Subjects(RVK)
  • Geography  (2)
RVK
  • 1
    Online Resource
    Online Resource
    Wiley ; 2020
    In:  International Journal of Climatology Vol. 40, No. 6 ( 2020-05), p. 3099-3117
    In: International Journal of Climatology, Wiley, Vol. 40, No. 6 ( 2020-05), p. 3099-3117
    Abstract: Mapping spatial and temporal variability of urban microclimate is pivotal for an accurate estimation of the ever‐increasing exposure of urbanized humanity to global warming. This particularly concerns cities in arid/semi‐arid regions which cover two fifths of the global land area and are home to more than one third of the world's population. Focusing on the desert city of Be'er Sheva Israel, we investigate the spatial and temporal patterns of urban–rural and intra‐urban temperature variability by means of satellite observation, vehicular traverse measurement, and computer simulation. Our study reveals a well‐developed nocturnal canopy layer urban heat island in Be'er Sheva, particularly in the winter, but a weak diurnal cool island in the mid‐morning. Near surface air temperature exhibits weak urban–rural and intra‐urban differences during the daytime ( 〈 1°C), despite pronounced urban surface cool islands observed in satellite images. This phenomenon, also recorded in some other desert cities, is explained by the rapid increase in surface skin temperature of exposed desert soils (in the absence of vegetation or moisture) after sunrise, while urban surfaces are heated more slowly. The study highlights differences among the three methods used for describing urban temperature variability, each of which may have different applications in fields such as urban planning, climate change mitigation, and epidemiological research.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1491204-1
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Online Resource
    Online Resource
    Wiley ; 2020
    In:  International Journal of Climatology Vol. 40, No. 14 ( 2020-11-30), p. 6106-6121
    In: International Journal of Climatology, Wiley, Vol. 40, No. 14 ( 2020-11-30), p. 6106-6121
    Abstract: Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near‐surface air temperature ( T a ). Neither ground‐based nor satellite‐borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two‐stage machine learning‐based hybrid model to estimate 1 × 1 km 2 gridded intra‐daily T a from surface skin temperature ( T s ) across the complex terrain of Israel during 2004–2016. We first applied a random forest (RF) regression model to impute missing T s from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating T s from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium‐Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed T s are in turn fed into the Stage 2 RF‐based model to estimate T a at the satellite overpass hours of each day. We evaluated the model's performance applying out‐of‐sample fivefold cross validation. Both stages of the hybrid model perform very well with out‐of‐sample fivefold cross validated R 2 of 0.99 and 0.96, MAE of 0.42°C and 1.12°C, and RMSE of 0.65°C and 1.58°C (Stage 1: imputation of T s , and Stage 2: estimation of T a from T s , respectively). The newly proposed model provides excellent computationally efficient estimation of near‐surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1491204-1
    SSG: 14
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...