Spatio-temporal variations of climate variables and extreme indices over Iran during 1986-2015 

2021 ◽  
Author(s):  
SayedMorteza Malaekeh ◽  
Ammar Safaie ◽  
Layla Shiva
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Surface Albedo ◽  
Temporal Trends ◽  
County Level ◽  
Climate Variables ◽  
Temperature And Precipitation ◽  
Skin Reservoir ◽  
Spatio Temporal ◽  
Extreme Indices

<p>In order to better understand how climate changes have taken place in Iran, we carried out a comprehensive analysis of the spatio-temporal trends of various climate variables and extreme indices during 1986 to 2015 at the county-level across the country. Additionally, the interannual oscillation of the temperature and precipitation and their related extreme indices were examined throughout the research. In this study, ERA5-Land and AgrERA5 datasets with hourly, daily, and monthly temporal resolutions were aggregated to the county-level to calculate climate extreme indices. Subsequently, different approaches such as the original Mann-Kendall (MK) trend test, MK with block bootstrap modification, MK with variance correction modification, correlated seasonal MK (partial MK), original and seasonal Sen's Slope were implemented to detect the magnitude and the statistical significance of climatic trends for each county. Finally, the continuous wavelet transform was employed for whole country averages to investigate fluctuations and dominant periods of the variables and indices. The reanalysis model datasets offered us two advantages; firstly, it facilitates obtaining data in some regions with sparse weather stations and secondly, it allows us to inquire about some climate variables that were less studied in the literature, for instance, the wind speed, the surface air pressure, the solar radiation, the surface albedo, the runoff, the evaporation, and the skin reservoir content. The results showed a significant increasing trend in the temperature over all counties and a nonsignificant drying trend in the precipitation for almost the whole country. Other climate variables demonstrated more mixed spatio-temporal trends; however, generally, the wind speed and the solar radiation had an upward trend, the runoff, the skin reservoir content, and the surface albedo showed a downward trend, while the surface air pressure and the evaporation trends exhibited a great deal of variety. Furthermore, the hot climate extremes were increased throughout the country whereas the cold extremes and the extreme precipitations were quite in the opposite direction. It is noteworthy that the Continental and the Warm-Temperate climates were more vulnerable compare to the Arid and Semi-Arid Climates. At last, the wavelet power spectrum maps indicated the consistency between the temperature and precipitation and their related extremes and also showed a reduction in the fluctuation of the precipitation and a constant oscillation for temperature over the study period.</p>

scholarly journals Spatio-Temporal Patterns of the 2019-nCoV Epidemic at the County Level in Hubei Province, China

2020 ◽  
Vol 17 (7) ◽  
pp. 2563 ◽  
Author(s):  
Wentao Yang ◽  
Min Deng ◽  
Chaokui Li ◽  
Jincai Huang
Keyword(s):  
Temporal Dynamics ◽  
Spatial Interaction ◽  
Temporal Trends ◽  
Temporal Patterns ◽  
Temporal Analysis ◽  
Hubei Province ◽  
Incidence Rates ◽  
County Level ◽  
Spatial Clusters ◽  
Spatio Temporal

Understanding the spatio-temporal characteristics or patterns of the 2019 novel coronavirus (2019-nCoV) epidemic is critical in effectively preventing and controlling this epidemic. However, no research analyzed the spatial dependency and temporal dynamics of 2019-nCoV. Consequently, this research aims to detect the spatio-temporal patterns of the 2019-nCoV epidemic using spatio-temporal analysis methods at the county level in Hubei province. The Mann–Kendall and Pettitt methods were used to identify the temporal trends and abrupt changes in the time series of daily new confirmed cases, respectively. The local Moran’s I index was applied to uncover the spatial patterns of the incidence rate, including spatial clusters and outliers. On the basis of the data from January 26 to February 11, 2020, we found that there were 11 areas with different types of temporal patterns of daily new confirmed cases. The pattern characterized by an increasing trend and abrupt change is mainly attributed to the improvement in the ability to diagnose the disease. Spatial clusters with high incidence rates during the period were concentrated in Wuhan Metropolitan Area due to the high intensity of spatial interaction of the population. Therefore, enhancing the ability to diagnose the disease and controlling the movement of the population can be confirmed as effective measures to prevent and control the regional outbreak of the epidemic.

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

scholarly journals Determining the sensitivity of grassland area burned to climate variation in Xilingol, China, with an autoregressive distributed lag approach

2019 ◽  
Vol 28 (8) ◽  
pp. 628 ◽  
Author(s):  
Ali Hassan Shabbir ◽  
Jiquan Zhang ◽  
Xingpeng Liu ◽  
James A. Lutz ◽  
Carlos Valencia ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Strong Relationship ◽  
Maximum Temperature ◽  
Climate Variables ◽  
Area Burned ◽  
Distributed Lag ◽  
Temperature And Precipitation ◽  
Autoregressive Distributed Lag ◽  
The Relationship

We examined the relationship between climate variables and grassland area burned in Xilingol, China, from 2001 to 2014 using an autoregressive distributed lag (ARDL) model, and describe the application of this econometric method to studies of climate influences on wildland fire. We show that there is a stationary linear combination of non-stationary climate time series (cointegration) that can be used to reliably estimate the influence of different climate signals on area burned. Our model shows a strong relationship between maximum temperature and grassland area burned. Mean monthly wind speed and monthly hours of sunlight were also strongly associated with area burned, whereas minimum temperature and precipitation were not. Some climate variables like wind speed had significant immediate effects on area burned, the strength of which varied over the 2001–14 observation period (in econometrics terms, a ‘short-run’ effect). The relationship between temperature and area burned exhibited a steady-state or ‘long-run’ relationship. We analysed three different periods (2001–05, 2006–10 and 2011–14) to illustrate how the effects of climate on area burned vary over time. These results should be helpful in estimating the potential impact of changing climate on the eastern Eurasian Steppe.

scholarly journals Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models

Risks ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 117
Author(s):  
Zoe Gibbs ◽  
Chris Groendyke ◽  
Brian Hartman ◽  
Robert Richardson
Keyword(s):  
United States ◽  
Linear Models ◽  
Temporal Trends ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
County Level ◽  
Spatio Temporal ◽  
Highly Correlated ◽  
Mortality Improvement

The lifestyles and backgrounds of individuals across the United States differ widely. Some of these differences are easily measurable (ethnicity, age, income, etc.) while others are not (stress levels, empathy, diet, exercise, etc.). Though every person is unique, individuals living closer together likely have more similar lifestyles than individuals living hundreds of miles apart. Because lifestyle and environmental factors contribute to mortality, spatial correlation may be an important feature in mortality modeling. However, many of the current mortality models fail to account for spatial relationships. This paper introduces spatio-temporal trends into traditional mortality modeling using Bayesian hierarchical models with conditional auto-regressive (CAR) priors. We show that these priors, commonly used for areal data, are appropriate for modeling county-level spatial trends in mortality data covering the contiguous United States. We find that mortality rates of neighboring counties are highly correlated. Additionally, we find that mortality improvement or deterioration trends between neighboring counties are also highly correlated.

Spatio-temporal trends and climatology of atmospheric chlorinated hydrocarbons over Pakistan

2021 ◽  
Vol 14 (15) ◽  
Author(s):  
Ahmad Hasnain ◽  
Yong Zha ◽  
Muhammad Zaffar Hashmi ◽  
Fatima Rahim ◽  
Yufeng He ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Chlorinated Hydrocarbons ◽  
Spatio Temporal

scholarly journals Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

2011 ◽  
Vol 4 (10) ◽  
pp. 2273-2292 ◽  
Author(s):  
S. Schweitzer ◽  
G. Kirchengast ◽  
V. Proschek
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Thermal Radiation ◽  
Rayleigh Scattering ◽  
Infrared Laser ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Wind Speed Profile ◽  
Upper Troposphere ◽  
Trace Species

Abstract. LEO-LEO infrared-laser occultation (LIO) is a new occultation technique between Low Earth Orbit (LEO) satellites, which applies signals in the short wave infrared spectral range (SWIR) within 2 μm to 2.5 μm. It is part of the LEO-LEO microwave and infrared-laser occultation (LMIO) method that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and altitude levels from microwave signals and profiles of greenhouse gases and further variables such as line-of-sight wind speed from simultaneously measured LIO signals. Due to the novelty of the LMIO method, detailed knowledge of atmospheric influences on LIO signals and of their suitability for accurate trace species retrieval did not yet exist. Here we discuss these influences, assessing effects from refraction, trace species absorption, aerosol extinction and Rayleigh scattering in detail, and addressing clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation as well. We show that the influence of refractive defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle with a close frequency spacing of LIO absorption and reference signals within 0.5%. The influences of Rayleigh scattering and terrestrial thermal radiation are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions, but this influence can be made negligible by a close time spacing (within 5 ms) of interleaved laser-pulse and background signals. Cloud extinction loss generally blocks SWIR signals, except very thin or sub-visible cirrus clouds, which can be addressed by retrieving a cloud layering profile and exploiting it in the trace species retrieval. Wind can have a small influence on the trace species absorption, which can be made negligible by using a simultaneously retrieved or a moderately accurate background wind speed profile. We conclude that the set of SWIR channels proposed for implementing the LMIO method (Kirchengast and Schweitzer, 2011) provides adequate sensitivity to accurately retrieve eight trace species of key importance to climate and atmospheric chemistry (H2O, CO2, 13CO2, C18OO, CH4, N2O, O3, CO) in the upper troposphere/lower stratosphere region outside clouds under all atmospheric conditions. Two further species (HDO, H218O) can be retrieved in the upper troposphere.

scholarly journals Spatio-temporal impacts of aerial adulticide applications on populations of West Nile virus vector mosquitoes

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Karen M. Holcomb ◽  
Robert C. Reiner ◽  
Christopher M. Barker
Keyword(s):  
West Nile Virus ◽  
Temporal Trends ◽  
Larval Habitat ◽  
Post Treatment ◽  
Additive Models ◽  
Dispersal Ability ◽  
Stochastic Variation ◽  
West Nile ◽  
Spatial Coverage ◽  
Spatio Temporal

Abstract Background Aerial applications of insecticides that target adult mosquitoes are widely used to reduce transmission of West Nile virus to humans during periods of epidemic risk. However, estimates of the reduction in abundance following these treatments typically focus on single events, rely on pre-defined, untreated control sites and can vary widely due to stochastic variation in population dynamics and trapping success unrelated to the treatment. Methods To overcome these limitations, we developed generalized additive models fitted to mosquito surveillance data collected from CO2-baited traps in Sacramento and Yolo counties, California from 2006 to 2017. The models accounted for the expected spatial and temporal trends in the abundance of adult female Culex (Cx.) tarsalis and Cx. pipiens in the absence of aerial spraying. Estimates for the magnitude of deviation from baseline abundance following aerial spray events were obtained from the models. Results At 1-week post-treatment with full spatial coverage of the trapping area by pyrethroid or pyrethrin products, Cx. pipiens abundance was reduced by a mean of 52.4% (95% confidence intrval [CI] − 65.6, − 36.5%) while the use of at least one organophosphate pesticide resulted in a mean reduction of 76.2% (95% CI − 82.8, − 67.9%). For Cx. tarsalis, at 1-week post-treatment with full coverage there was a reduction in abundance of 30.7% (95% CI − 54.5, 2.5%). Pesticide class was not a significant factor contributing to the reduction. In comparison, repetition of spraying over three to four consecutive weeks resulted in similar estimates for Cx. pipiens and estimates of somewhat smaller magnitude for Cx. tarsalis. Conclusions Aerial adulticides are effective for achieving a rapid short-term reduction of the abundance of the primary West Nile virus vectors, Cx. tarsalis and Cx. pipiens. A larger magnitude of reduction was estimated in Cx. pipiens, possibly due to the species’ focal distribution. Effects of aerial sprays on Cx. tarsalis populations are likely modulated by the species’ large dispersal ability, population sizes and vast productive larval habitat present in the study area. Our modeling approach provides a new way to estimate effects of public health pesticides on vector populations using routinely collected observational data and accounting for spatio-temporal trends and contextual factors like weather and habitat. This approach does not require pre-selected control sites and expands upon past studies that have focused on the effects of individual aerial treatment events.

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