Ciencias de la Tierra Sólida
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Ciencias de la Atmósfera, Hidrosfera y Cambio Climático
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Ciencias de la Atmósfera, Hidrosfera y Cambio Climático
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Enlaces de Referencias
Superficial Urban Heat Island in the City of Santos, Brazil
(Technoscience Publications, 2025-12-01) Angeles Suazo, Julio; Angeles Vasquez, Roberto; Lavado Meza, Carmencita; Angeles Suazo, Nataly; de la Cruz Cerrón, Leonel; Meza Mitma, Pabel; Flores Rojas, José Luis; Abi Karam, Hugo
This contribution estimates the intensity of Urban Heat Island (UHI) during the period 2001 - 2020 for the city of Santos (CS), located in São Paulo, Brazil. The formation of the Surface Urban Heat Island (SUHI) was quantified from 2 methods: the first was Streutker’s method, which adjusts the surface soil temperature (LST) (urban and rural surface) to a Gaussian surface. The second, the quantile method proposed by Jose Flores, uses the difference between the 0.95 quantile of the LST of the urban area and the median of the LST of the rural area. Both methods use remote sensing data of LST at 0.05° resolution, obtained from the MODIS sensor on board the TERRA and AQUA satellites. In general, the quantile method can be used as a complementary analysis to the Streutker method for cities with high LST. The results of the CS analysis, during diurnal periods, indicate maximum values in May (5.09°C) and minimum values in August (3.87°C). During the night period, it presented maximum values in February (3.94°C) and minimum values in August (2.40°C) with the quantile method, and due to its proximity to the Small Ocean, the Streutker method presents interferences.
Study of local and non-local post-midnight equatorial spread-F generation based on long-term AMISR-14 observations
(Springer, 2025-11-27) Massoud, Alexander A.; Rodrigues, Fabiano S.; Sousasantos, Jonas; Kuyeng, Karim; Scipión, Danny; Padin, Carlos
We present results of a study of post-midnight equatorial spread F (ESF) events over the Jicamarca Radio Observatory (JRO) that examined unambiguous radar measurements of event origin in the American sector. Our analysis considers variations in post-midnight ESF generation due to changing seasonal, solar, and geomagnetic conditions. We ana lyzed 396 nights of observations made with the 14-panel version of the Advanced Modular Incoherent Scatter Radar (AMISR-14) between July 2021 and August 2023. We leveraged the 10-beam AMISR-14 mode, which effectively meas ures ~ 400 km zonally of the equatorial F-region ionosphere, to identify and classify post-midnight ESF as either local (i.e., generated within the instrument field of view) or non-local (i.e., generated outside the instrument field of view). Our results for the occurrence rates of post-midnight ESF exhibit a strong seasonal dependence, with maximum values in June solstice and minimum values for equinoxes. The results also show the post-midnight ESF occurrence rates are anticorrelated to the solar flux conditions. As for geomagnetic activity, the results indicate that occurrence rates decrease considerably under geomagnetically quiet conditions. The combination of these seasonal, solar flux, and geomagnetic activity influences suggests the weakened downward plasma drifts late at night during June solstice conditions can be reversed to upward drifts by contributions from disturbance drifts. In the case of upward drifts caused by geomagnetic disturbances, the reversed upward post-midnight drifts may then contribute to condi tions favoring ESF development provided that a prompt penetration or disturbance dynamo electric field with appro priate polarity, even from modest geomagnetic activity, is present. In support of this proposed post-midnight ESF generation mechanism, we also present and discuss simultaneous AMISR-14 and collocated incoherent scatter radar measurements of a June solstice 2023 event. Perhaps most importantly, our results show the occurrence rates of local and non-local post-midnight ESF as observed with AMISR-14 are nearly identical. That is, local events were observed effectively as often as non-local events, and vice versa, under all seasonal, solar, and geomagnetic conditions. There fore, data-driven forecasting approaches relying exclusively on local (i.e., “overhead”) measurements of ionospheric/ thermospheric conditions may not always be well-suited to reproducing the observed ESF phenomenology.
Automatic detection and classification of Spread-F in ionograms using convolutional neural network
(Elsevier, 2025-05-30) Yacoub, Moheb; Pacheco, Edgardo E.; Abdelwahab, Moataz; De la Jara, César; Mahrous, Ayman
Equatorial spread-F (ESF) is an irregularity caused by plasma instabilities on the night side that causes signal degradation and disruptions to the GNSS signals. Ionosondes could detect ESF as it appears as a diffused echo in the ionogram images. This study proposes a Convolutional Neural Network (CNN) model that can automatically detect ESF within the ionogram images and classify its type. The model has been trained using 2646 manually labeled ionograms from the Low Latitude Ionospheric Sensor Network (LISN) VIPIR Ionosondes in South America. The data used to train the model was measured from 2019 to 2024. The model was able to classify the testing images into six categories: Clear class, frequency spread-F (FSF), range spread-F (RSF), mixed spread-F (MSF), strong spread-F (SSF), and Unidentified class. It demonstrated high classification accuracy within the extracted test subset and a further random test, showcasing robustness and consistency in detection accuracy across all classes. Furthermore, the model performance has been evaluated and compared with other baseline models: VGG16, VGG19, ResNet18, and Inception-V3 in the same environment. Additionally, a comparison with published models is provided. Our model showed a higher consistency in classification accuracy across all classes compared to the mentioned models.
Informe Técnico Nº PpR/El Niño-IGP/2026-01
(Instituto Geofísico del Perú, 2026-02-24) Instituto Geofísico del Perú
En el corto plazo, la persistencia de ondas Kelvin cálidas frente a la costa peruana podría favorecer un incremento adicional de la TSM, particularmente en marzo, cuando estacionalmente se alcanzan los valores más altos del año. En la región norte, este calentamiento podría favorecer procesos de acoplamiento océano–atmósfera, que podrían incrementar la ATSM y mantener lluvias intensas en la zona norte del Perú, principalmente. Sin embargo, debido a la estacionalidad, dicho acoplamiento no debería extenderse más allá de abril o mayo. A una escala mayor, los modelos de NMME pronostican el desarrollo de un evento El Niño costero y luego, basado en el RONI, un evento El Niño en el Pacífico central a partir de junio. Sin embargo, la presencia de la barrera de predictibilidad limita la confiabilidad de los pronósticos más allá de abril.
Assessing environmental and anthropogenic drivers for the occurrence and extent of fires in high Andean Grasslands
(Taylor & Francis, 2025-12-18) Gutierrez Flores, Ivon; Mercado, Angela; Zubieta Barragán, Ricardo; Beltrán, Pablo; Oyague, Eduardo
The grasslands of the southern Andes are critical ecosystems for the rural population, but they have been significantly affected by fires. While fire ignitions are anthropogenic, their occurrence and spread are shaped by climatic, vegetational, and topographic factors. This study identified the main environmental and human drivers of fire occur rence and extent in high Andean grasslands. We developed generalized linear models with 14 and 22 variables for the fire occurrence and extent model, respectively. Various metrics (e.g. AIC, AUC, pseudo-R²) were applied to validate the best-performing model and assess its performance. Our findings suggest that elevation, maximum temperature, soil adjusted vegetation index, and topographic position index are the primary drivers of fire occurrence. For fire extent, grass cover, elevation, topographic position index, and rock cover were the most influential factors. The models explained 21% and 60% of the variability in fire occurrence and extent, respectively. This study identifies key environmental factors influencing fire occurrence and extent, providing valuable insights for improving fire management strategies, particularly in fire-prone ecosystems such as grasslands. Since the temperature was a contributing factor to fire occurrence, this highlights the importance of prevention and reduction strategies in the context of climate change.