Meteorological Studies Research Articles

Air Quality Investigation Pre-COVID-19

In the populated and developing countries, governments consider the regulation and protection of environment as a major task and should take into consideration the concept of Smart Environment Monitoring. The main motive of these systems is to enhance the environment with various technology including sensors, processors, data sets and other devices connected across the globe through a network. This system can basically help in monitoring air quality which is necessary in the field of meteorological studies and movement factors. Also, these factors contribute a lot in air pollution. The values of major pollutants like SO2, PM2.5, CO, PM10, NO2, and O3. In recent years, machine learning in most emerging technology for predicting on historical data with 99.99% of accuracy. To prognosticating air quality index of NCR, India in different aspects like stubble farming, motor vehicle emission, and open construction practices which result in polluting the air quality of NCR. The manuscript is helping to frame a structured view of air quality prediction methods in reader's mind.

Atacama Large Aperture Submillimeter Telescope (AtLAST) science: Planetary and cometary atmospheres.

The study of planets and small bodies within our Solar System is fundamental for understanding the formation and evolution of the Earth and other planets. Compositional and meteorological studies of the giant planets provide a foundation for understanding the nature of the most commonly observed exoplanets, while spectroscopic observations of the atmospheres of terrestrial planets, moons, and comets provide insights into the past and present-day habitability of planetary environments, and the availability of the chemical ingredients for life. While prior and existing (sub)millimeter observations have led to major advances in these areas, progress is hindered by limitations in the dynamic range, spatial and temporal coverage, as well as sensitivity of existing telescopes and interferometers. Here, we summarize some of the key planetary science use cases that factor into the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST), a proposed 50-m class single dish facility: (1) to more fully characterize planetary wind fields and atmospheric thermal structures, (2) to measure the compositions of icy moon atmospheres and plumes, (3) to obtain detections of new, astrobiologically relevant gases and perform isotopic surveys of comets, and (4) to perform synergistic, temporally-resolved measurements in support of dedicated interplanetary space missions. The improved spatial coverage (several arcminutes), resolution (~ 1.2'' - 12''), bandwidth (several tens of GHz), dynamic range (~ 10 5) and sensitivity (~ 1 mK km s -1) required by these science cases would enable new insights into the chemistry and physics of planetary environments, the origins of prebiotic molecules and the habitability of planetary systems in general.

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Unsupervised Classification of Landsat-8 Satellite Imagery-Based on ISO Clustering

Remote sensing, specifically satellite imagery, is gaining prominence in computer science nowadays, in the era of artificial intelligence, in an attempt to deliver more precise information. The satellite images of Earth are gathered, evaluated, and processed for use in civil and military applications with a military aim. Satellite images do have a wide range of services. The areas of study of agriculture, fishery, oceanography, and meteorology include geology, biodiversity, cartography, land use planning, and armed conflict. Transformation is the goal of the categorization of satellite images. Transformation of satellite images into information that can be used rather than having an image of a location. This paper classified a scene of the Landsat-8 satellites with specifications (Path=168 and Row=38). This scene was classified into four categories (Water, Vegetation, bare land, and Build-up) based on the unsupervised classification method (ISO Clustering). The ISO Clustering method is found in the Arc Map program. The results regarding classification accuracy are a good percentage compared to unsupervised Classification.

Reassessing and Extending the Composite Rainfall Record of Manchester, Northwest England: 1786–Present

A monthly composite rainfall record for the period 1786–present representative of Manchester, northwest England is presented. The 235-year record ranks as the second-longest instrumental rainfall record available for northern England, and the fifth-longest for the UK, and contributes to a growing network of long homogenous rainfall series. A composite record is constructed, extended, and homogenised, and the record is analysed in terms of annual and seasonal variability, with a focus on extreme wet/dry events. Three primary meteorological stations in Manchester, located within 2 km of one another, form the basis of the reconstruction, with other records identified for infilling and extension based on their longevity, continuity, and proximity to the primary stations. A linear regression analysis is applied to produce a continuous record, and adjustment factors are applied to ensure homogeneity. Record homogeneity is assessed via cross-comparison with long-term records from the region (Carlisle, Chatsworth House and HadNWEP), and the methods are applied to assess relative homogeneity include the double-mass curve and Standard Normal Homogeneity tests. The Manchester record is deemed to be homogenous overall but includes two periods of increased uncertainty: 1786–1819, comprising the earliest observations and greatest number of different stations, and 1883–1911, which encompasses multi-year and multi-decadal drought events of (1883–1885 and 1890–1910) as identified by other long-term meteorological studies. The analysis of the entire record reflects long-term rainfall variability with an increasing, although not significant, trend in annual rainfall observed. Seasonally, a significant increase in winter rainfall is exhibited, in keeping with patterns observed in other regional studies. Seasonal rainfall totals are found to be highly variable at the decadal timescale. Several well-documented extreme wet (e.g., autumn 2000) and dry (e.g., summer 1976) seasons are identified, including historic events (e.g., the floods of summer 1872 and drought of summer 1887) from the less-well documented eighteenth and nineteenth centuries.

Ground thermometric studies at the MSU meteorological observatory site

The paper presents the first results of the drilling and construction of a thermometric well in an open area without natural cover at the site of the MSU meteorological observatory taking core samples from the well. Information is given on the moisture, thermal conductivity, and heat capacity of the ground, as well as on the freezing temperature. The results of meteorological studies and studies of the factors that significantly influence snow cover are also presented.

An incremental feature selection approach for dynamic feature variation

In numerous domains, there is ample evidence indicating that a significant portion of real-world data exhibits temporal variations, such as medical research and meteorological studies. Particularly in the era of big data, not only does the size of data change dynamically but also its dimensions at an unprecedented pace. Consequently, employing traditional methods to handle such dynamic data becomes highly impractical. To address this limitation, this paper proposes an incremental feature selection algorithm tailored for dynamic feature variations. For scenarios involving an increase in features, the novel algorithm efficiently identifies a target subset with effective features. In order to showcase the efficacy of the proposed algorithm, this paper conduct experiments using four commonly used classifiers and five UCI data sets. The experimental results further validate both the feasibility and efficiency of the new approach.

Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand.

Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature

AbstractFine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.

Clustering and assessment of precipitation climates using satellite precipitation products and improved machine learning precipitation models

AbstractA deep understanding of the different features of various rainfall regimes is essential for water resources management and meteorological studies. Due to the extensive spatial coverage, satellite precipitation products have afforded researchers an excellent opportunity to investigate various precipitation climates. This study employs cutting‐edge satellite precipitation data and improved modelling techniques to innovatively investigate Iran's diverse precipitation climates. By harnessing the power of machine learning (ML), precipitation models were developed that significantly improved the accuracy of satellite‐based precipitation estimates. The complexities of Iran's precipitation climates were investigated by combining satellite data and ML models with the K‐means++ algorithm. Furthermore, the Mann–Kendall test was employed to scrutinize potential trends in precipitation frequency across varying months and climates to enrich the current body of knowledge further. In addition, a range of probability distribution functions was fitted to the ML precipitation estimates, evaluating the outcomes with the Akaike information criterion (AIC). The results show that both artificial neural network (ANN) and random forest (RF) models improved satellite product correlation results, increasing them from 0.84 to an outstanding 0.93. Moreover, the Mann–Kendall test reveals a positive trend in the frequency of precipitation associated with convective rainfalls during May, as indicated by satellite data, ML models and synoptic precipitation observations. Ultimately, this research holds significant value for water resource management and the ongoing pursuit of refining satellite rainfall products in the context of diverse rainfall regimes.

Do satellite-based products suffice for rainfall observations over data-sparse complex terrains? Evidence from the North-Western Himalayas

Remotely sensed observations are crucial for conducting meteorological studies, particularly over complex mountainous terrains such as, the North-Western Himalayas (NWH), which are characterized by a very sparse and uneven distribution of ground observatories. These complex terrains are subject to huge inter-basin climatic and topographic heterogeneity, which often induces large inconsistencies in the form of varying rainfall estimations and observations among the satellite-based gridded precipitation products (GPPs). This underpins the need for a detailed sub-regional validation of GPPs and attribution of the observed discrepancies over such complex terrains. This study provides a comprehensive validation analysis of four major high-resolution GPPs (TMPA-3B42V7, CHIRPS-V2.0, CMORPH-CDR, and PERSIANN-CDR) against the in-situ gridded observations provided by IMD, across the six sub-basins of NWH. Utilizing various descriptive, statistical, and categorical metrics, we evaluated the daily, seasonal, and annual rainfall climatology for each dataset and also extracted their longitudinal and latitudinal error profiles and detection capabilities as a function of elevation and local relief. High inconsistencies were observed among the datasets, with each dataset performing differently over different sub-basins. All the datasets underperformed IMD over higher elevations, while TMPA-3B42V7 performed better even over the Upper Indus sub-basin. CHIRPS-V2.0 performed well over Jhelum, and Chenab sub-basins, whereas the CDR version of CMORPH, performed well over the flatter terrains of Ravi, Beas, and Satluj sub-basins, particularly during the summer monsoons. The IR-based PERSIANN-CDR, on the other hand, failed to capture the daily rainfall estimates and underperformed IMD throughout NWH. The findings further highlight low detection capability of datasets at higher rainfall thresholds, indicating underestimation of extreme rainfall. With respect to the relationship between GPPs performance and topographic components, the results showed that the occurrence frequency of rain detection in GPPs decreases, while the occurrence frequency of over-detection increases with increase in elevation and local relief. Although, the performance of GPPs co-varies with both topographic components, local relief governed by the multi-step topographic gradients comprising valleys, orographic fronts, and orographic interiors was observed to have a more profound effect on error profiles. Lastly, the study identifies and discusses the probable regional (e.g., rainfall structure, orographic influence, biophysical, data scarcity, spatial resolution), and product-specific (e.g., source of generation, remote sensing instrumentation, satellite algorithms, data assimilation techniques) factors attributing to the inconsistencies observed among the datasets. The findings from the present research offer insights to the algorithm developers for improving product accuracy and hydro-meteorologist for utilizing appropriate GPP over respective sub-basin.

Long-Term Observational Characteristics of Different Severe Convective Wind Types around Australia

Abstract Regional understanding of severe surface winds produced by convective processes [severe convective winds (SCWs)] is important for decision-making in several areas of society, including weather forecasting and engineering design. Meteorological studies have demonstrated that SCWs can occur due to a number of different mesoscale and microscale processes, in a range of large-scale atmospheric environments. However, long-term observational studies of SCW characteristics often have not considered this diversity in physical processes, particularly in Australia. Here, a statistical clustering method is used to separate a large dataset of SCW events, measured by automatic weather stations around Australia, into three types, associated with strong background wind, steep lapse rate, and high moisture environments. These different types of SCWs are shown to have different seasonal and spatial variations in their occurrence, as well as different measured wind gust, lightning, and parent-storm characteristics. In addition, various convective diagnostics are tested in their ability to discriminate between measured SCW events and nonsevere events, with significant variations in skill between event types. Differences in environmental conditions and wind gust characteristics between event types suggests potentially different physical processes for SCW production. These findings are intended to improve regional understanding of severe wind characteristics, as well as environmental prediction of SCWs in weather and climate applications, by considering different event types. Significance Statement The purpose of this study is to improve regional understanding of different types of severe wind events in Australia, specifically those associated with atmospheric convection. We did this by constructing a dataset of 413 severe convective wind events, using weather station and radar data within 20 regions around Australia. We then split those events into three different types, based on the environmental conditions that they occur within. We found that each event type tends to occur at different times of the year and in different regions, while also having different wind gust and lightning characteristics. In addition, the atmospheric conditions that are helpful for prediction of severe wind events differs between each type. These results are intended to be useful for prediction of severe wind events associated with convection and assessing their variability, characteristics, and impacts, in both weather forecasting and climate analysis.

Comparison of ZTD derived from CARRA, ERA5 and ERA5-Land over the Greenland based on GNSS

High-accuracy reanalysis products are beneficial to the retrieval and modeling of zenith tropospheric delay (ZTD) in GNSS positioning and the study of GNSS Meteorology. The performance of meteorological parameters and ZTD derived from Copernicus Arctic Regional ReAnalysis (CARRA), ERA5 and ERA5-Land over the Greenland covering the year of 2020 are comprehensively compared and analyzed in this study. Firstly, the accuracies of the atmospheric pressure, temperature and water vapor pressure retrieved from three reanalysis data are compared with the observations obtained from 6 radiosondes and 30 automatic weather stations. The results show that the accuracy of temperature data from ERA5 pressure-level in the height range of 23–30 km is higher than that of CARRA pressure-level. The atmospheric pressure, temperature and water vapor pressure from CARRA single-level are superior to those of ERA5 single-level and ERA5-Land. Secondly, the ZTD derived from CARRA, ERA5 and ERA5-Land are compared with GNSS final ZTD data from 37 UNAVCO stations. The Root Mean Square error (RMS) values of ERA5 pressure-level ZTD is 6.7 mm, showing slightly better accuracy than CARRA pressure-level ZTD of 10.7 mm. The RMS values of ZTD derived from CARRA single-level, ERA5 single-level and ERA5-Land based on the Saastamoinen model are 11.9 mm, 13.4 mm and 22.7 mm, respectively, which indicates that CARRA single-level ZTD shows best consistency with GNSS ZTD among three single-level reanalysis data. Moreover, the reanalysis-derived ZTD show obviously poorer accuracy in warm-season than that in cold-season.

The early meteorological network of the Societas Meteorologica Palatina (1781–1792): foundation, organization, and reception

Abstract. The Societas Meteorologica Palatina arranged the first international meteorological network in a modern sense, being in operation between 1781 and 1792 during the last period of enlightenment. A total of 39 stations contributed observations. The original aim was to investigate influences of the moon and planets on the atmosphere. Instruments were provided free of charge; a physically very advanced instruction guaranteed reliable observational results, and the data collected at 3 different hours per day were printed at high cost in the Ephemerides Societatis Meteorologicae Palatinae (denoted Ephemerides hereafter) of Mannheim. This wealth of data has become a famous treasure trove for scientists and has been used later very often for climatic studies, for climatic comparisons of different locations in textbooks, for overcoming wrong but generally accepted or even outdated (e.g. scholastic) views; for finding new explanations for meteorological phenomena, and for studying extremes of meteorological parameters. Even in modern times, the data were evaluated and used to reconstruct historical weather maps. Although, meanwhile, some problems of the historical instruments have been recognized, most of the conclusions are still basically correct. The data were also used for verifying geomagnetic models or proxy data from tree-ring analysis. This network stimulated many scholars for special meteorological studies, and it was attractive for new stations to join the network. The early death of the meteorological secretary Johann Jakob Hemmer and the Napoleonic Wars brought about the end of the project. Nevertheless, many of the stations continued the observations using the available instruments.

Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset

Abstract. The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ∼ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).

Dalton’s Long Journey from Meteorology to the Chemical Atomic Theory

The purpose of this paper is to review Dalton’s contributions to science in various fields of research in relation to the first intimation of the chemical atomic theory. Early “germs” of his physical ideas may be found in the initial meteorological studies where water vapour is viewed as an “elastic fluid sui generis” diffused in the atmosphere and not as a species chemically combined with the other atmospheric gases. The next object of Dalton’s attention was atmosphere itself. He discarded affinity between atmospheric gases as a possible cause of homogeneity and, making recourse to Newtonian Principles, considered the repulsive forces among particles. Experiments on the “nitrous air test” and on the diffusion and solubility of gases were instrumental to arrive at the chemical atomic theory. The slow, laborious, and persevering work of Dalton to get the first table of atomic weights is a fascinating piece of science which may be fully appreciated by referring to his laboratory notebooks.

Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review

Tropical cyclones (TCs) seriously endanger human life and the safety of property. Real-time monitoring of TCs has been one of the focal points in meteorological studies. With the development of space technology and sensor technology, satellite remote sensing has become the main means of monitoring TCs. Furthermore, with its superior data mining capability, deep learning has shown advantages over traditional physical or statistical-based algorithms in the geosciences. As a result, more deep-learning algorithms are being developed and applied to extract TC information. This paper systematically reviews the deep-learning frameworks used for TC information extraction and then gives two typical applications of deep-learning models for TC intensity and wind radius estimation. In addition, the authors present an outlook on the future perspectives of deep learning in TC information extraction.摘要热带气旋 (TC) 严重危害人类生命和财产安全, TC的实时监测一直是研究热点, 随着空间和传感器技术的发展, 卫星遥感已成为监测TC的主要手段. 此外, 深度学习具有卓越的数据挖掘能力, 在地球科学中的表现优于基于物理或统计的算法, 越来越多的深度学习算法被开发和应用于TC信息的提取, 本文系统地回顾了深度学习在TC信息提取中的应用, 并给出了深度学习模型在TC强度和风圈半径提取中的应用. 此外, 本文还展望了深度学习在TC信息提取中的应用前景.

Language approaches to the study of meteorology

Meteorology is an important science which has constantly evolved in the last centuries, as people became more and more aware of the importance of understanding meteorological conditions and predicting the weather, as well. An analysis of existing studies in meteorology reveals that this field received little or no attention from language or communication perspectives, although we believe it is essential for both specialists and different types of audience to send and understand messages easily and take action accordingly. The aim of this paper is to present a state-of-the-art analysis of different language and communication approaches to meteorology, and to come up with a series of recommendations on possible directions of research in the field.

Groundhog Gets It Wrong by Jess Townes

Reviewed by: Groundhog Gets It Wrong by Jess Townes Kara Forde Townes, Jess Groundhog Gets It Wrong; illus. by Nicole Miles. Dial, 2023 [40p] Trade ed. ISBN 9780593326152 $18.99 E-book ed. ISBN 9780593326169 $10.99 Reviewed from digital galleys R 3-7yrs Coming from a long line of lauded meteorologists, Groundhog is unconcerned about taking over the family profession and making his debut prediction about the end of the winter season this February. Encouraged by sage advice and well-wishes from those who came before him, Groundhog wakes up early on the big day and can’t find his shadow anywhere, and so makes the formal proclamation to the gleeful crowd of townspeople that winter is officially over. With plans for a spring carnival in place, there’s no chance he could be mistaken, right? Townes provides a witty take on the bizarre tradition of Groundhog Day, enhanced by a well-paced plot that humorously relies on the tension caused by Groundhog’s unearned authority. The saturated colors and multi-paneled page layouts of Miles’ digital illustrations highlight the action and humorous moments, and auxiliary sound effects and speech bubbles will support a spirited storytime rendition. Following a funny spread depicting his chaotic vocational crisis, Groundhog’s eventual dedication to the careful study of meteorology adds a layer of depth to the story, as it reinforces the value of science and diligent inquiry over blind faith. Closing papers include a brief background of the history and tradition of Groundhog Day, plus information about Punxsutawney Phil’s (low) rate of accuracy and lack of meteorology training. [End Page 165] Copyright © 2022 The Board of Trustees of the University of Illinois

The pioneering scientific endeavor and contributions of José María González Benito (1843-1903), the first Colombian modern astronomer

Astronomical interest within the current Colombian territory has its roots in the Botanical Expedition of the New Kingdom of Granada (1783-1813), which stimulated the creation of an astronomical observatory in 1803, the first one established in the New World to pursue systematic observations and meteorological studies. After the death in 1816 of its first director, Francisco José de Caldas (1768-1816), during the convulsive independence period, no major astronomical observations were made for decades, with few exceptions. In this work, we delve into the contributions of the astronomer José María González Benito (1843-1903), the main reactivator of the Observatorio Astronómico Nacional de Colombia in the second half of the 19th century focusing on his pioneering efforts that draw worldwide attention to the national observatory and to his own private observatory.González Benito stands out as one of the figures most committed to the development of astronomyin the country and the most renowned Colombian in the international astronomical research sceneof his time.