Previous studies have shown that the climatological variability of TC frequency could be regulated by the ITCZ location in idealized models, and the Coriolis parameter is an important determinant. As ITCZ moves poleward, the increased Coriolis parameter and cyclonic absolute vorticity will favor more TC genesis. Using observations, the relationship between the ITCZ position and the TC frequency, as well as the ENSO influence, have been examined in this study on interannual timescale over different ocean basins. The result shows the expected linkage between the ITCZ position and TC frequency. The correlation coefficient is significant over North Atlantic (NA), Western North Pacific (WNP) and Northern Hemisphere (NH) both before and after removing the ENSO influences. The TC genesis location follows the ITCZ migration in almost all basins except that in NA, but further analysis shows the relationship in the tropical NA (south of 20˚N) agrees with that in other basins. Both ITCZ and TC activities could be affected by ENSO on interannual timescale since they are all related to tropical SST (sea surface temperature) spatial distributions. There is a better relationship between ITCZ position and TC genesis location than TC frequency over basins in South Hemisphere.

Emission of particulate matter and its size and distribution from Municipal Solid Waste (MSW) burning leads to degradation of air quality, the impact on human health, particularly during winter months when Delhi faces extreme pollution events every year. Controlled conditions laboratory measurement was conducted to determine particle size distribution emitted from MSW burning collected across National Capital Territory. The present study aims to determine the emission of number and mass concentration of particles ranging from 0.34 µm to 9.05 µm from MSW burning using an optical particle sizer (TSI 3330). MSW samples collected from different sanitary landfilling sites in the NCT of Delhi have been burnt in controlled conditions in the laboratory. We have determined the emission of particles of different size during different stages of burning (ignition, flaming smoldering, smoldering and pyrolysis) of the samples. Our results correspond to the assumption that MSW burning emits the maximum number of particles (90%) in the range of 1.0 µm, or fine mode, of aerosol.

At present, 4.2 million deaths occur every year due to ambient air pollution, according to the World Health Organization. To reduce such figure, local administrations are enacting Air Quality Plans, for which accurate air quality monitoring is essential. Expanding monitoring networks to support local air quality planning is a complex task. On the one hand, the high costs of reference monitors make their large-scale adoption prohibitive, while the easily scalable low-cost sensors often feature significantly lower data quality and lack of calibration. Near-reference monitors have been voiced as a promising solution, as they exhibit limited costs, although specific studies assessing their performance against reference monitors are still missing. This article provides an in-depth assessment of three near-reference sensors’ performance, through their collocation with reference monitors from December 2021 onwards. Two sensors were positioned at high-traffic locations, while the third recorded background pollution levels in Milan, Italy. Sensors’ performance was quantified not only via the coefficient of determination (R²) and the regression model, but also with the Root Mean Squared Error, the Mean Normalized Bias (MNB), and the Coefficient of Variation (CV), which are often overlooked in the literature. Finally, a non-parametric Wilcoxon Signed-Rank test was performed to determine the statistical significance of observed differences. After a first measurement period, sensors were re-calibrated to also appraise their behavioral change, showing a general performance increase. Preliminary results show high correlation for all hourly-recorded pollutants, with peaks for Nitrogen Dioxide (NO₂) (R² = 0.92) and Black Carbon (R² = 0.91). Very low MNB was also recorded for NO₂ (MNB = 0.005), and for Carbon Monoxide (CO) (MNB = 0.0001). Similarly, promising CVs were consistently found for NO₂ (CV = 0.26), and for CO (CV = 0.31). Although preliminary, such results show the high potential of near-reference sensors to support urban air quality planning.

Precipitation is a major component of the global water cycle, hence its accurate measurement, especially over complex topography, requires a dense gauge network, which is often limited for many parts of the world. In recent decades, Gridded Precipitation Datasets (GPDs) that merge information from satellites, numerical weather prediction models, and available ground data could be a potential alternative source for many hydro-climatic studies. However, their validation is a prerequisite task before utilizing them for different applications. This study aims to evaluate the spatio-temporal consistency of CHIRPSv2.0 and MERRA-2 datasets over different elevation ranges in Turkey based on five hydrological years (2015–2019) under the Kling-Gupta Efficiency (KGE) metric for daily and monthly time steps. Moreover, three categorical indicators, including Threat Score (TS), Peirce Skill Score (PSS), and Gilbert Skill Score (GSS), are employed to address GPD detectability strength for various precipitation intensities. In general, GPDs show high performance for monthly (median KGE of; 0.62–0.76) time step than daily (median KGE of; 0.19–0.28), and MERRA-2 outperforms CHIRPSv2.0 considering daily precipitation, while CHIRPSv2.0 shows higher performance for monthly precipitation, comparatively.

In recent years, the stochastic model has been growing due to the high complexity and dynamics of the atmosphere, especially the rainfall process. Various concepts have been applied to rainfall modeling, ranging from simplistic approaches to more complex models. It is important to understand different stochastic rainfall modeling approaches as well as their advantages and limitations. This paper determines the development of the latest stochastic rainfall models in the Asia region, where different concepts of stochastic rainfall models were highlighted. It reviews different methodologies used, including rainfall forecasting, spatio-temporal analysis, and extreme event. We selected 30 articles from 1,571 literature published between 2013-2022 from the Scopus database. The results show that the stochastic models often used in the literature consist of Markov Chain, Weather Generator, Probability Distribution, ARIMA, and Bayesian Model. In the recent development in Asia, stochastic models in rainfall modeling research are widely used to generate the occurrence and amount of rainfall data, statistical downscaling, future rainfall trends, and estimation of extreme values. The difference in Spatio-temporal, climate conditions, and the parameters model cause the performance of each model can be different.

The water-soluble ionic species (NH₄⁺, SO₄^2-, NO₃^- and Cl^-) of PM_2.5 and trace gases (NH₃, NO, NO₂, SO₂, HNO₃) were estimated to study the relationship of ambient NH₃ in the formation of secondary inorganic aerosols in Delhi, India from January 2013 ̶ December 2018. During the study period, the average concentrations of NH₃, NO, NO₂, SO_2, and HNO₃ were 19.1±3.8 ppb, 20.8±4.3 ppb, 17.9±4.2 ppb, 2.45±0.47 ppb, 1.11±0.35 ppb, respectively. The concentrations of trace gases were higher during the post-monsoon season whereas the concentrations of ionic components in PM_2.5 were estimated higher in winter. The correlation matrix of trace gases reveals that the ambient NH₃ neutralizes the acid gases (NO, NO_2, and SO₂) at the study site. The study reveals that the abundance of particulate NH₄⁺ at the study site neutralized the SO₄^2-, NO₃^-, and Cl^- particles during all the seasons.

Precipitation is widely considered an important parameter and a key indicator of the evolving climate change. The intensity as well as the frequency of precipitation can be largely affected by disturbances of the hydrological cycle as a result of the increasing temperature of the atmosphere and the oceans. Through a variety of statistical methods it is possible to assess changes in precipitation over the recent years both regionally and globally. In this work, precipitation data from seven WMO stations in the Greek region are studied over the period 1990-2020. By analyzing a set of extreme precipitation indices and applying the Sen and Mann-Kendall statistical methods, the trends and statistical significance of precipitation in the area of study are investigated. The results reveal an increase in the yearly number of days with extreme precipitation events as well as in the total amount of precipitation.

Different regions of the world are expected to experience an increase in the frequency, duration and intensity of drought phenomena as a result of the ongoing climate change. Enhanced evaporation and water deficiency over large time periods under warmer conditions will have devastating consequences in the natural ecosystems, food production as well as in the global economy. In order to monitor the evolution of climate conditions that may lead to increasing drought phenomena at local, regional or global level, a series of indicators and statistical methods have been developed. In our work, the values of various drought indices from observational data in Greece were studied over the period 1990-2020. By applying the Sen and Mann-Kendall methods, the trends and statistical significance were examined. The results showed that serious drought events take place during summer especially in the southern regions. However, the number of consecutive dry days tends to decrease.

The study of human bioclimate conditions is becoming popular in climate perception for the improvement of public health system. The objective of the present study is to analyze the past and future thermal bioclimate conditions over 15 stations in West Bengal (WB), India. The bioclimate conditions are measured by daily Physiologically Equivalent Temperature (PET) based on climate data extracted from Coordinated Regional Downscaling Experiment (CORDEX)-South Asia. The initial purpose of this study is to present the interannual distribution of PET classes over the considered stations of WB for the past period (1986-2005) and three future time periods namely (i) near future (2016-2035), (ii) mid future (2046-2065), (iii) far future (2080-2099). The results from the monthly distribution of PET reveal heat stress conditions from April to June and acceptable thermal conditions from November that persists till March for all the stations except Darjeeling. To focus on future PET changes over WB in context to reference period (1986-2005), warm and hot PET classes show prominent rise in all the future epochs under the RCP4.5 and RCP8.5 emission scenarios. Highest percentage in warm PET class (35.7-43.8 °C) during far future time slice under RCP8.5 conditions appears in stations close to Bay of Bengal such as Digha, Diamond Harbour, Canning, Baruipur. Simultaneously, hot PET class (˃43.8 °C) records up to 10% for Kolkata, Dum Dum, Kharagpur, Siliguri and more than 10% in Sriniketan, Malda, Asansol and Birbhum. Darjeeling will have the largest decrease in very cool PET class (˂3.3°C) in far future period. The explicit amount of change in temperature is seemingly connected to the increasing levels of heat stress over WB are evident from the relative mean monthly changes in PET.

Despite advances in dynamic weather and climate modelling, simulations still present systematic errors in several regions of the globe. One way to improve models is the regionalized analysis of long-term simulations. In this context, this study aimed to analyze the seasonal and interannual variability of simulated rainfall over two contrasting regions of Tropical South America. Unlike several previous studies, our analyses were focused on areas with different rainfall regimes within two major regions: the Amazon Basin (AMZ) and Northeast Brazil (NEB). For this purpose, we used the RegCM4.6 climate model and performed two continuous 30-year simulations (1981-2010) with a 50 km grid spacing. In the EXP_EM simulation we used the convection parameterization of Emanuel (1991) and in the EXP_GR experiment we used Grell’s parameterization (1993). Simulations were compared with gridded precipitation data originated from different official sources in Brazil. The average monthly precipitation and its interannual variability were analyzed. Differences between simulations and observations were assessed using the Student’s t-test, with a p-value>0.01. The mean bias and Willmott’s coefficient of agreement were calculated. Considering these metrics, the EXP_EM simulation presented an overall advantage over the EXP_GR simulation. The experiments were more accurate in reproducing seasonality and interannual variability in the NEB. However, the model failed to accurately reproduce rainfall in areas closer to the geographic equator in the NEB, which may be associated with the double Intertropical Convergence Zone systematically formed by the model. We conclude that the model presents a clear dry bias over the equatorial AMZ and NEB, although it seems to be suitable to simulate climate variability in most of the studied regions. This must be taken into consideration when using the RegCM model for long term analysis or applications in seasonal forecast over the AMZ and the NEB.