Factors affecting severe weather threat index in urban areas of Turkey and Iran

Abstract

Background Distinguishing dynamic variations of the climate from the physical urban indicators is a challenge to assess the factors affecting weather severity. Hence, the time-series of the severe weather threat index (SWEAT) were considered in the four urban areas of Turkey and Iran to identify its affecting factors among the climatic variables and urban indicators in 2018. The SWEAT data were obtained from the upper-air sounding database of the University of Wyoming. The climatic variables were extracted from the Asia Pacific data research center (APDRC). The spatial statistics for urban expansion were collected from global human built-up and settlement extent (HBASE) data sets. A quantitative measuring of the Pearson correlation test was used to expose the relationships between dependent index (SWEAT) and independent variables (climatic and anthropogenic). Results Results revealed that the high and extreme severity classes of the weather condition in the Ankara, Istanbul, Mashhad, and Tehran are estimated as 7.7% (28 days), 15.3% (56 days), 1.1% (4 days), and 4.4% (16 days), respectively. The strongest values of the annual SWEAT index, exposing the unstable and severe weather conditions, were observed for Istanbul and Ankara urban regions. This result may be corresponding to the highest values of mean annual precipitation and relative humidity in addition to the largest values of urban expansion and sprawl index. The statistical correlation tests in annual scale confirmed the effective role of climatic elements of precipitation, relative humidity, and cloudiness (R from 0.94 to 0.99) and the urban expansion indicators (R from 0.86 to 0.91) in increasing annual severe weather index of SWEAT at above 85–95% of confidence level. Conclusions The correlations between the urban expansion indicators and outcome SWEAT index can be strengthened by some climatic elements (e.g., precipitation, humidity, and cloudiness), revealing the mediator and magnifier task. However, the mentioned correlations can be weakened by another climatic variable (i.e., air temperature), revealing a moderator and modifier task. Ultimately, investigation of the weather severity indices (e.g., SWEAT index) could be applied to identify the local and regional evidence of climate change in the urban areas.

Climate effects on the COVID-19 outbreak: a comparative analysis between the UAE and Switzerland

“…The independent climatic factors were assumed as the latest diurnal time series extracted from two global databases. On this basis, ten variables, namely surface pressure (Pascals), surface temperature (deg K), upward long-wave radiation flux (ULR) (W/m 2 ), upward solar radiation flux (USR) (W/m 2 ), relative humidity (RH) (%), specific humidity (SH) (Kg/Kg), precipitation rate (Kg/m 2 /s), evaporation rate (W/m 2 ), surface Omega (Pascal/s), and cloud coverage (%) were collected from the Asia Pacific Data Research Center (APDRC) data set, hosted by the National Oceanic and Atmospheric Administration (NOAA) via http://apdrc.soest.hawaii.edu/las/getUI.do , which has been defined by some scholars e.g., Rabbani et al ( 2020 ). All aforementioned variables, by diurnal scale, were extracted based on the geographical position and coordination of each country.…”

Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

“…For reliable forecasts, π (t) follows a uniform distribution. We use the α index (α) to summarize the reliability in each grid cell with the following equation to check the spatial patterns of forecast reliability (Renard et al, 2010):…”

“…As a variable measuring the evaporative demand of the atmosphere, reference crop evapotranspiration (ETo) has been widely used to estimate potential water loss from the land surface to the atmosphere (Hopson and Webster, 2009;Liu et al, 2019;Renard et al, 2010). Quantification of ETo has been increasingly performed to support efficient water use and water management (Mushtaq et al, 2019;Perera et al, 2016).…”

Comment on hess-2021-69

“…This investigation also provides valuable implications for the forecasting integrated variables, which need to be calculated with multiple NWP/GCM variables. Variables such as drought index (Zhang et al, 2017), bushfire danger index (Sharples et al, 2009), and severe weather index (Rabbani et al, 2020), are often derived from multiple NWP/GCM variables. Our investigation suggests that improving the input variables may help correct errors that could not be fixed when calibrating the integrated variables directly.…”