Atmósfera (Journal)
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Changes in surface air temperature for Mediterranean climate in Turkey
Full text linkLocal climate influences of inland water bodies, complex topography, and surrounding seas cause temperate, arid, and continental climate properties to prevail with local variations in different parts of Turkey. The intra-regional variability of environmental factors creates uncertainties and challenges in climate modeling. Multi-model ensemble analysis is suggested to be used to characterize the uncertainties and minimize the generalization error in projections. This study is part of a research on climate change impacts in Turkey, focusing on the impacts on surface air temperature through a multi-model ensemble analysis of high-resolution climate models. The ensemble set comprises 12 EURO-CORDEX RCMs and two models from the Japan Meteorological Research Institute (MRI). Firstly, historical model data are validated with temperature records from 59 meteorological stations. Furthermore, changes in temperature climatology in the future in short- (2020-2030), medium- (2031-2050), and long-term (2051-2100) horizons are analyzed and compared with the precipitation changes. In the ensemble, two MRI models (MRI-AGCM, NHRCM) and two CORDEX RCMs nested in the HadGEM2-ES (RCA4 and CCLM4-8-17) perform best to replicate the spatial variability of climatology. The 14-member ensemble projects a gradual increase in the temperature up to 4.5 and 6.6 ºC under RCP4.5 and RCP8.5 scenarios, respectively. The projections agree on an inverse relationship between temperature and precipitation changes. More substantial impacts are projected in inland compared to coastal regions
Satellite-based analysis of climate oscillations: Implications for precipitation in an arid watershed in Mexico
Full text linkClimate oscillations are known to have an important influence on weather patterns across the world. While the impact of El Niño Southern Oscillation (ENSO) has been well documented, there is a scarcity of studies examining the effects of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). This study uses satellite data to confirm that ENSO significantly influences precipitation in the Nazas-Aguanaval watershed from October to March, as evidenced by Spearman correlation coefficients. In contrast, the PDO influence is registered during specific months (January, March, November and December), while AMO impacts precipitations during April-June, November, and December. These results were corroborated using ANOVA, reinforcing the influence of ENSO and indicating a limited impact of PDO and AMO on this watershed. Finally, a linear model was developed to estimate monthly precipitation anomalies based on the phase of these three indices for the different sub-basins. Notably, monthly precipitation anomalies ranged between 140% and –78% in dry months. Our results demonstrate the influence of climate oscillations in precipitation in the Nazas-Aguanaval watershed and the usefulness of satellite data for conducting these analyses. Likewise, we set a starting point for investigating the implications of climate oscillation phases for water management and drought disaster prevention
The influence of rainfall on the extinction coefficient and the meteorological optical range
Full text linkThe meteorological optical range (MOR) is an objective parameter for assessing atmospheric visibility. Data collected using optical disdrometers (PWS100) were used to analyze MOR estimates when extreme rainfalls occurred at two locations in Mexico: Chamela, on the Pacific coast, and Juriquilla, a continental sampling site. The performance of the disdrometer for rainfall estimation was found to be consistent and satisfactory when compared to rain gauges. Analyses based on rainfall rate (R) outcomes from tipping bucket rain gauge data showed that MOR measurements registered the most significant decreases during periods of highest R. The assessed coefficients for the extinction coefficient (σ) and R power-adjusted relationships are comparable to those obtained in previous studies, and the statistical performance of the fitted equations in modeling σ values is excellent. The equation coefficients for these mathematical expressions indicate that precipitation at the sampling sites is initiated from mixed (Bergeron-type) clouds, and it can be inferred that mixed-phase thunderstorms were predominant at the sampling sites during the chosen periods. Our findings indicate that MOR and σ are closely related to the large raindrop proliferation and the broadening of the raindrop size distribution (inferred from the increase of the median volume diameter). Despite the underestimation of small raindrops by the optical disdrometer, this study demonstrates the reliability of MOR estimates obtained with the PWS100 during rainfall events
Performance evaluation of random forest and boosted tree in rainfall-runoff process modeling for sub-basins of Lake Urmia
Full text linkThis study aimed to develop rainfall-runoff (P-Q) modeling using machine learning models in the sub-basins of Lake Urmia, Iran. In this research, chronological records of hydrological parameters and meteorological inputs at a regional scale were analyzed using Random Forest (RF) and Boosted Tree (BT) heuristic methods. This study compared the performance of these two models for the Urmia Basin over the period from 1976 to 2019. The results showed that the RF model provided better estimates in Akhula, Daryan, and Ghermez Gol stations in the eastern sub-basin and Miandoab, Pole Ozbak, Abajalu Sofla, Nezam Abad, and Pole Bahramlu stations in the western sub-basin. In contrast, the BT model performed better at Pole Senikh, Shishvan, Gheshlagh Amir, Shirin Kandi, and Khormazard stations in the eastern sub-basin and Babarud, Keshtiban, and Yalghoz Aghaj stations in the western sub-basin. Additionally, the time series analysis showed changes in yearly rainfall frequency and a decreasing trend in flow discharge in most years. These findings highlight a significant reduction in inflow to Lake Urmia over the past 43 years, with a particularly sharp decline in recent years
New experimental device for measuring electrical charge of precipitation particles
Full text linkA new approach is presented in this work to measure the electrical charge carried by precipitation particles and their corresponding fall velocity. The instrument represents an improved version of our previous device, with the primary goal of increasing the sampling rate of charged droplets to improve and make the statistical analysis of charged raindrops more robust. Additionally, the instrument incorporates a computational program for detecting individual raindrop passages, enabling automatic calculation of its electrical charge and fall velocity. To test the new device’s performance, it was simultaneously used with our previous instrument during a storm in Córdoba on November 21, 2023. It was observed that the latest instrument increased the sampling rate nearly fivefold compared to the old one. The results demonstrate a high degree of consistency across different devices, validating the reliability and reproducibility of the new device
Physical processes of fog in the Brazilian Northeast: Forecast by PAFOG and FogVIS models
Full text linkA specific model for low-visibility forecasting in the Brazilian Northeast (BNE) has not been developed; therefore, the German Parameterized Fog (PAFOG) model was adapted for the region. Additionally, Fog Visibility (FogVIS), a simple equation-based tool, was developed and requires further testing. From 2008 to 2020, Meteorological Aerodrome Report and Terminal Aerodrome Forecast surface data were collected via the Meteorology Network of the Brazilian Air Force Application Programming Interface, identifying 218 fog events across three airports: Maceió (32 events), Recife (1 event), and Campina Grande (185 events). GOES satellite images were accessed from the Center for Weather Forecasting and Climate Studies database, and synoptic and thermodynamic analyses were performed using ERA5 reanalysis data. Humidity from nearby water sources (lagoon for Maceió, dam for Campina Grande) was a primary factor in fog formation. PAFOG demonstrated strong predictive performance for Maceió and Recife’s single brief events, especially in 12-h forecasts, particularly when fog events were preceded or followed by mist or light rain. In contrast, FogVIS often aligned closely with the observed visibility range and provided complementary results 18 hours in advance for Campina Grande’s events, which were more intense but less associated with rain or mist, and also showed higher Fog Stability Index results. Both models demonstrated efficiency, with PAFOG excelling in Maceió and FogVIS in Campina Grande, highlighting the applicability and accuracy of both models in predicting fog for the BNE
Quantifying of surface urban heat island intensity in Isfahan metropolis using MODIS\Terra\LST data
Full text linkHeat island characteristics depend on the background climate of the site where the city is located. Therefore, an index was defined for the Isfahan metropolitan area to quantify the surface urban heat island intensity. This new index is based on the representative pixels of urban and non-urban areas. For this purpose, MODIS land cover type product (MCD12Q1) data were used to distinguish between urban and non-urban areas. Also, data from the MODIS/Terra land surface temperature product (MOD11A1) from 2000 to 2018 were utilized for daytime and nighttime to study the surface heat island intensity. Then, the representative pixels of urban and non-urban areas were identified using the spatial correlation method, and the heat island index was calculated for the metropolitan area of Isfahan. The study showed that the frequency distribution of the nighttime heat island index follows a normal distribution and is often 3.5 to 4º K above the temperature of the surrounding areas of the city. The 365-day floating mean of the surface urban heat island reveals that this index has increased in recent years. The research of temporal behavior showed that the intensity of the surface urban heat island reaches its maximum in January and becomes weaker in summer, while the survey of spatial behavior showed that the core of the surface urban heat island extends towards downtown areas, where the oldest part of the city is located
Prediction of hydrological drought by the Standardized Precipitation Evapotranspiration Index in Chihuahua, Mexico, using machine learning algorithms
Full text linkDespite being very common in the territory of Chihuahua, Chihuahua, Mexico, to experience drought, its consequences continue to severely impact the population without prior warning. Machine learning has proven to have a significant capacity for predicting time series, and the Standardized Precipitation Evapotranspiration Index (SPEI) is emerging as the most accurate drought indicator. In this study, predictive models were developed using Artificial Neural Networks (ANN), Long-Short Term Memory (LSTM), and Support Vector Regression (SVR) for estimating SPEI. Temporal scales of 12 months (SPEI 12) and 24 months (SPEI 24) for the period 1901-2020 in the mentioned territory were considered. This was done in order to simulate the behavior of drought cycles and enhance the ability to anticipate consequences. The accuracy indices used to evaluate the models were the mean squared error (MSE), mean absolute error (MAE), mean bias error (MBE), coefficient of determination (R2), and Kendall coefficient. In total, 956 experiments were conducted using the three methods, varying parameters such as the number of neurons, kernel, and polynomial degree. The two best models for each method were selected, and the average results revealed MSE = 0.0051, MAE = 0.0537, MBE = 0.0218, R2 = 0.8495, and Kendall coefficient = 0.7592 for SPEI 12; and MSE = 0.0024, MAE = 0.0375, MBE = 0.0162, R2 = 0.9218, and Kendall coefficient = 0.8558 for SPEI 24
Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East
Full text linkDried lake beds are one of the largest sources of dust in the world, causing environmental problems in the surrounding areas. In this study, the desiccated Urmia Lake was the primary source of dust for all nearby synoptic stations during the April 24-25, 2017 dust episode. Synoptic analysis revealed that the heavy dust storm was triggered by a strong Black Sea cyclone and a low-pressure system over central Iraq in conjunction with a vast high-pressure system. HYSPLIT-based trajectory analysis showed that high PM10 recorded over the Urmia Lake region on April 23-26, 2017, influenced western Azerbaijan, the south of the Caspian Sea, southwestern Kazakhstan, northwestern Uzbekistan, and western Turkmenistan. The dustiest air masses (PM10 > 400 µg m–3) affected the south of the Caspian Sea and western Azerbaijan. Furthermore, the WRF-Chem model was run to evaluate the spatial distribution of dust particles in the study region. The vertical profile revealed that the simulated dust concentration ascended to 5 km from the lake. The WRF-Chem dust schemes accurately simulated dust propagation and the vertical dust profile over Urmia Lake; however, the AFWA and GOCART dust schemes showed that PM10 fluctuating changes were earlier than the measured surface PM10 at five stations around Urmia Lake on April 23-26, 2017. Furthermore, the maximum amount anticipated by the model simulation was 12 h earlier than the maximum surface mass concentration of measured PM10 at the stations throughout the period
Chemical composition and trajectories of atmospheric particles at the Machu Picchu Peruvian Antarctic scientific station (62.09º S, 58.47º W)
Full text linkAntarctica is a remote and relatively pristine region, but the regional transport of aerosols may be a source of pollution, especially in the Antarctic Peninsula. Few studies have characterized atmospheric aerosols and evaluated the contribution of their emission sources. The Peruvian Antarctic research station Machu Pichu (ECAMP, by its Spanish acronym) is located on King George Island in the Antarctic Peninsula. During February 2020, atmospheric particulate mass (PM10 and PM2.5) was sampled and analyzed to characterize its elemental composition and was supplemented by measurements of equivalent black carbon and aerosol size distributions. Chemical elements were analyzed by inductively coupled plasma mass spectrometry (ICP-MS), multivariate techniques, and enrichment factors. The most abundant elements in PM10 and PM2.5 were Na, Fe, Mg, and Si, with the most important local sources being marine (Na, Mg, Mn, Ca) and crustal (Fe, Al, P). Sources of weathering (Ba and Si) from glacial thawing and sources of combustion linked to the use of oil (V) and emission of black carbon were recorded. Air mass back-trajectory analysis using the HYSPLIT model helped identify external sources of particulate matter in the air masses reaching the ECAMP site. Overall, this study supports the growing evidence of the anthropogenic impact of distant and local sources on the white continent