National Meteorological Service

<title>Abstract</title> Low visibility conditions due to the presence of fog can lead to delays or cancellations of arriving and departing flights to avoid possible incidents or accidents at airports. Therefore, accurate visibility forecasts are required to keep airport capacity as high as possible. Recently, some studies explored the use of machine learning algorithms and routine surface meteorological observations to produce fog forecasts. This study explores the potential of machine learning (ML) models trained solely on surface observations for short-term fog nowcasting at Ezeiza International Airport in Argentina, within 6 hours from initialization time. Three simple ML methods—a logistic regression, a decision tree, and a random forest—were evaluated using 20 years of data. Results show that nonlinear methods (decision tree and random forest) required fewer, simpler predictors to match or exceed the forecasting skill of the logistic regression, which relied more heavily on complex variable transformations. All methods significantly outperformed climatological and persistence-conditional reference models. In all three methods, the most important predictor was the fog condition at initialization time. A seasonal and daily data-splitting strategy improved performance at almost all forecasting time horizons. The models trained in this study represent a promising operational tool for aviation forecasting and could also complement operational numerical models, whose ability to resolve fog-scale processes remains limited.

Abstract The Bolivian High (BH) is an upper-tropospheric anticyclonic circulation associated with the South American Monsoon System, exerting a significant influence on the seasonal hydroclimate of the Andes. However, its short-term (daily and monthly) relationship with Andean hydrology remains understudied due to challenges in long-term detection of BH. An automatic method for tracking the BH applied to the ERA5 reanalysis was used to examine its short-term position (latitude, longitude, and altitude) in relation to precipitation and streamflow in the Southern Highlands of Peru (SHP), with emphasis on droughts during ENSO phases. Climatologically, the BH centre lies at 16.5°S, 65.2°W, and 12448.6 mgp at 200 hPa during December–February (1981–2020). Daily positions vary more in longitude than in latitude. A seasonal migration pattern is observed between November-April, with the BH shifting from northeast Bolivia to the south-southwest and returning northeast. A south-westward (north-eastward) BH shift usually brings wetter (drier) SPH conditions, especially along the western Andes. These shifts enable or inhibit the moisture transport from the Amazon basin to the Andes. Droughts in the SHP are linked to El Niño's northeastward shift since January. Conversely, during La Niña, the BH shifts southward, favouring above-average precipitation and streamflow, particularly on the western slope. This study improves understanding of the short-term relationship between the BH and hydroclimatic conditions in the SHP. It provides a tool for BH monitoring in the context of weather and climate prediction and climate change scenarios.

La variabilidad de la irradiancia solar introduce limitaciones a la producción de energía solar fotovoltaica(PV) ya que dificulta su despacho. Por lo tanto, su cuantificación es importante para el desarrollo en gran escala desistemas PV y su contribución relativa a la matriz eléctrica. Argentina posee un gran potencial para la generación solar,con un extenso territorio que recibe una irradiación promedio anual de 5 kWh/m2/día. Además, cuenta con el respaldode políticas públicas y regulaciones que fomentan la transición hacia fuentes de energía renovable. En este trabajo secuantificó la variabilidad de corto plazo (minutal, 10-minutal y horaria) mediante la desviación estándar de los cambiosen el índice de cielo claro (kc). Se analizaron las series temporales de los años 2019 y 2020 en 6 estaciones de la redArgentina de radiación solar Saver-Net. Se encontró un promedio de variabilidad nominal en los sitios de 0,09, 0,13 y0,15 para 1 minuto, 10 minutos y 1 hora, respectivamente. Estos valores sugieren una variabilidad intermedia del recursosolar a lo largo del territorio.Palabras clave: Fluctuaciones, GHI, Energía solar

ABSTRACTThis study characterises fog events across 13 major airports in Argentina from 2000 to 2019, providing insights into their occurrence and impact on aviation. Using the complete surface synoptic observations (SYNOP) database from the National Meteorological Service of Argentina, this research improves upon previous studies that relied on incomplete meteorological aerodrome report (METAR) datasets. The analysis evaluated various criteria to identify fog events, accounting for their duration, intermittency, and aviation impact. Key findings reveal significant regional variability in fog characteristics, influenced by diverse latitudes, topographies, and environmental conditions. Metrics such as monthly occurrence, onset and dissipation times, duration, and visibility median and minimum were analysed. An extreme event identification criterion highlighted aviation‐disruptive fog events, comprising 7%–20% of cases. As a critical issue for flight operations, airports prone to persistent fog events over consecutive days were also identified, showing undiscovered fog's behaviour that wasn't addressed in previous analysis. All results were associated with theoretical behaviours linked to physical mechanisms influencing fog formation and dissipation. These findings provide valuable tools for improving fog forecasting in Argentina and underscore the need for further research into the physical processes governing fog's life cycle to enhance aviation safety and operational efficiency.

Extreme weather events associated with deep moist convection pose significant social risks, requiring advanced technologies for anticipatory measures. Numerical forecasting, particularly at convection-resolving scales, relies heavily on high-quality initial conditions obtained through the assimilation of complex remote-sensing-based observations. Integrating these observations into assimilation systems presents challenges due to the nonlinear relationships between observed quantities and model variables. This research explores an iterative implementation of the Local Ensemble Transform Kalman Filter based on the tempering of the observation likelihood (tempered LETKF), which can partially handle these non-linearities.In this work, we use an N-variable Lorenz model for its simplicity and low computational cost to evaluate the performance of the method against the traditional implementation of the LETKF. We conducted comparisons under various levels of uncertainty concerning both the model and the observations. Additionally, we tested the behavior of the system for different ensemble sizes and for varying degrees of tempering. The initial findings show notable enhancements in the estimation of initial conditions and the stability of the data assimilation cycle, indicating potential benefits in more realistic model applications. The encouraging results motivate further research on tempering methods in mesoscale modeling systems, especially for predicting severe weather events linked to deep moist convection.

Abstract. The lack of aerosol optical depth (AOD) data at night can be partially addressed through moon photometer measurements or fully covered with star photometer observations. However, the limited availability and complexity of star photometers has motivated this study to use all-sky cameras to extract starlight signals and derive AOD at night using star photometry. For this purpose, eight all-sky cameras were configured and deployed in nine different locations to capture raw images with varying exposure times every 2 minutes during the night. This work proposes a novel methodology to extract the starlight signal from the raw data of all-sky cameras and convert it into AOD values. This process consists of the following steps: removing the background image, selecting the pixels and extracting the signal for each star from a predefined list of 56 stars, performing in-situ Langley calibration of the instruments and retrieving the total optical depth (TOD), calculating the effective wavelength for each camera channel, deriving the AOD by subtracting the gas contribution to TOD, and averaging, cloud-screening, and quality-assuring the AOD time series. The AOD time series obtained through this methodology are compared with independent AOD measurements from collocated moon photometers in the nine locations. The obtained results show that the AOD values derived with the proposed method generally correlate with reference values, often achieving correlation coefficients (r) above 0.90. The AOD values retrieved using the cameras tend to overestimate the reference values by approximately 0.02, and exhibit a precision of around 0.03–0.04. The agreement between both datasets varies with wavelength and decreases at high-latitude locations, likely due to the poorer performance of Langley calibration in these regions. AOD values align well with day-to-night transitions obtained by solar photometers, demonstrating their reliability. Despite the slight overestimation, the AOD values derived by this new method approximate the real values and provide coverage throughout the entire night, without requiring the presence of the Moon. Therefore, they serve for studying and monitoring the nocturnal evolution of AOD.

Abstract Explosive volcanic eruptions inject hot mixtures of solid particles (tephra) and gasses into the atmosphere. Entraining ambient air, these mixtures can form plumes rising tens of kilometers until they spread laterally, forming umbrella clouds. While the largest clasts tend to settle in proximity to the volcano, the smallest fragments, commonly referred to as ash (≤2 mm in diameter), can be transported over long distances, forming volcanic clouds. Tephra plumes and clouds pose significant hazards to human society, affecting infrastructure, and human health through deposition on the ground or airborne suspension at low altitudes. Additionally, volcanic clouds are a threat to aviation, during both high‐risk actions such as take‐off and landing and at standard cruising altitudes. The ability to monitor and forecast tephra plumes and clouds is fundamental to mitigate the hazard associated with explosive eruptions. To that end, various monitoring techniques, ranging from ground‐based instruments to sensors on‐board satellites, and forecasting strategies, based on running numerical models to track the position of volcanic clouds, are efficiently employed. However, some limitations still exist, mainly due to the high unpredictability and variability of explosive eruptions, as well as the multiphase and complex nature of volcanic plumes. In the next decades, advances in monitoring and computational capabilities are expected to address these limitations and significantly improve the mitigation of the risk associated with tephra plumes and clouds.

Abstract Passive‐microwave hail detection methods rely on the scattering signal produced by large ice hydrometeors. Recently, passive‐microwave hail retrieval methods have been trained by pairing Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Global Precipitation Measurement (GPM) Microwave Imager (GMI) data with United States surface hail reports to present a global hail climatology. In particular, Southeastern South America (SESA) stands out as a hotspot for hail occurrence, more specifically northeastern Argentina. However, the analysis performed with the available ground hail reports in Argentina shows some differences from the available ground hail reports in Argentina, as it locates the most frequent hail storms in the southwestern (Mendoza and Córdoba) and northeastern (Misiones) regions of SESA. To assess the retrieval from a regional perspective, we take a case study approach and focus on polarization‐corrected temperature features (PCTFs) with a detected . We mainly rely on polarimetric weather radars and a hydrometeor identification algorithm (HID) as a proxy for ground truth. Results indicate that the hail retrieval responds mainly to low 37‐GHz polarized corrected (brightness) temperatures (PCTs) and large PCTF area, while HID outputs show that high probabilities of hail also correspond to abundantly suspended graupel. The observed passive‐microwave hail detection hotspot could be influenced by mesoscale convective systems with deep columns of graupel‐sized frozen hydrometeors, common in northeastern Argentina.