Hasil untuk "Meteorology. Climatology"

A. Sparks

nasapower is an R (R Core Team, 2018) package providing functionality to interface with the NASA POWER API (Stackhouse et al., 2018) for reproducible data retrieval using R. Three functions, get_power(), create_met() and create_icasa() are provided. The get_power() function provides complete access to all functionality that the POWER API provides, which includes three user communities, AG (agroclimatology), SSE (Surface meteorology and Solar Energy) and SB (Sustainable Buildings); three temporal averages, Daily, Interannual and Climatology; three geographic options, single point, regional and global for the appropriate parameters offered. nasapower uses lubridate (Grolemund & Wickham, 2011) internally to format and parse dates which are passed along to the the query constructed using crul (Chamberlain, 2018) to interface with the POWER API. The query returns a json response, which is parsed by jsonlite (Ooms, 2014) to obtain the url of the .csv file that has been requested. The .csv file is downloaded to local disk using curl (Ooms, 2018) and read into R using readr (Wickham, Hester, & Francois, 2017). Data are returned in a tidy data frame (Wickham, 2014) as a tibble (Müller & Wickham, 2018) with a custom header, which provides POWER metadata. Two other functions provide functionality to generate weather input files for agricultural crop modelling. The create_met() function is a wrapper for the get_power() function coupled with the prepareMet() and writeMet() functions from APSIM (Fainges, 2017) to simplify the process of querying the data and creating text files in the .met format for use in Agricultural Production Systems sIMulator (APSIM). While the create_icasa() function wraps the get_power() into a function that generates and locally saves a text file in the International Consortium for Agricultural Systems Applications (ICASA) format for use in the Decision Support System for Agrotechnology Transfer (DSSAT) framework (G. Hoogenboom et al., 2017; J. W. Jones et al., 2003). Extended documentation is provided with examples of converting it to spatial objects using raster (Hijmans, 2017).

Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Accurate assessment of atmospheric nitrogen dioxide (NO$_2$) and sulfur dioxide (SO$_2$) is essential for understanding climate-air quality interactions, supporting environmental policy, and protecting public health. Traditional monitoring approaches face limitations: satellite observations provide broad spatial coverage but suffer from data gaps, while ground-based sensors offer high temporal resolution but limited spatial extent. To address these challenges, we propose PollutionNet, a Vision Transformer-based framework that integrates Sentinel-5P TROPOMI vertical column density (VCD) data with ground-level observations. By leveraging self-attention mechanisms, PollutionNet captures complex spatiotemporal dependencies that are often missed by conventional CNN and RNN models. Applied to Ireland (2020-2021), our case study demonstrates that PollutionNet achieves state-of-the-art performance (RMSE: 6.89 $μ$g/m$^3$ for NO$_2$, 4.49 $μ$g/m$^3$ for SO$_2$), reducing prediction errors by up to 14% compared to baseline models. Beyond accuracy gains, PollutionNet provides a scalable and data-efficient tool for applied climatology, enabling robust pollution assessments in regions with sparse monitoring networks. These results highlight the potential of advanced machine learning approaches to enhance climate-related air quality research, inform environmental management, and support sustainable policy decisions.

Guowen Li, Xintong Liu, Yang Liu et al.

Accurate Subseasonal-to-Seasonal (S2S) forecasting is vital for decision-making in agriculture, energy production, and emergency management. However, it remains a challenging and underexplored problem due to the chaotic nature of the weather system. Recent data-driven studies have shown promising results, but their performance is limited by the inadequate incorporation of climate states and a model tendency to degrade, progressively losing fine-scale details and yielding over-smoothed forecasts. To overcome these limitations, we propose TianQuan-S2S, a global S2S forecasting model that integrates initial weather states with climatological means via incorporating climatology into patch embedding and enhancing variability capture through an uncertainty-augmented Transformer. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate that our model yields a significant improvement in both deterministic and ensemble forecasting over the climatology mean, traditional numerical methods, and data-driven models. Ablation studies empirically show the effectiveness of our model designs. Remarkably, our model outperforms skillful numerical ECMWF-S2S and advanced data-driven Fuxi-S2S in key meteorological variables. The code implementation can be found in https://github.com/zhangminglang42/TianQuan.

Lisa Dellmuth, Maria-Therese Gustafsson, Suanne Mistel Segovia-Tzompa

Abstract There is a lively debate about the legitimacy of the international climate regime, as represented by the United Nations Framework Convention on Climate Change, and the quality of non-state actor participation in the regime. This commentary examines perceptions of involved non-state actors from 2021–2022 regarding their participation and regime legitimacy. The findings reveal no legitimacy crisis for the adaptation and mitigation regimes, but the surveyed NSAs are divided in their legitimacy beliefs. NSAs also express significant disappointment about their opportunities for participation.

Song Yang

The Southeast Asian monsoon is characterized by many features that are distinct from those of the East Asian monsoon, including monsoon intensity and evolution. They are also influenced differently by external factors and affect global climate in diverse ways. Studies that consider these factors should yield a better understanding of both monsoon components.

Melissa Blum, Donato DeIngeniis, Daniela K. Shill et al.

Introduction: Rising ambient temperatures threaten vulnerable populations such as pregnant women, with urban populations bearing a greater risk due to the urban heat island effect. Here, we assessed the independent effects of trimester-specific warm season exposure during pregnancy and neighborhood heat vulnerability on maternal outcomes, including gestational diabetes, hypertensive disorders of pregnancy, genitourinary infections, and operative delivery. Methods: This retrospective study analyzed 819 participants from the Stress in Pregnancy Study (2009–2014), a longitudinal birth cohort study in New York City. Generalized linear models examined associations between trimester-specific warm season exposure, New York City Heat Vulnerability Index (ranging 1-5), and adverse maternal outcomes, adjusting for demographics, parity, and substance use. Results: First trimester warm season exposure was associated with increased odds of gestational hypertension (adjusted odds ratio [AOR] 4.50, 95%CI 1.17-17.27), preeclampsia (AOR 4.38, 95%CI 1.51-12.75), and genitourinary infection (AOR 2.27, 95%CI 1.14-4.51). Each unit increase in heat vulnerability index was associated with increased odds of preeclampsia (AOR 1.38, 95%CI 1.05-1.81) and genitourinary infection (AOR 1.32, 95%CI 1.11-1.57). Conclusions: Both early pregnancy warm weather exposure and neighborhood vulnerability independently increased the risk of adverse maternal complications. Our findings provide evidence in support of targeted heat mitigation strategies to limit heat exposure in at-risk communities as climate change progresses.

Generich H. Capuli

Because of the rudimentary reporting methods and general lack of documentation, the creation of a severe weather database within the Philippines has been difficult yet relevant target for climatology purposes and historical interest. Previous online severe weather documentation i.e. of tornadoes, waterspouts, and hail events, has also often been few, inconsistent, inactive, or is now completely decommissioned. Several countries or continents support severe weather information through either government-sponsored or independent organizations. For this work, Project SWAP stands as a collaborative exercise, with clear data attribution and open avenues for augmentation, and the creation of a common data model to store the phenomenon's information will assist in maintaining and updating the aforementioned online archive in the Philippines. This paper presents the methods necessary for creating the SWAP database, provide broader climatological analysis of spatio-temporal patterns in severe weather occurrence within the Philippine context, and outline potential use cases for the data. We also highlight the project's current limitations as is to any other existing and far larger database, and emphasize the need for understanding these events' and their mesoscale environments, inline to the current severe weather climatologies across the globe.

Shane Gardner, Edward White, Brent Langhals et al.

The Lightning Launch Commit Criteria (LLCC) are a set of complex rules to avoid natural and rocket-triggered lightning strikes to in-flight space launch vehicles. The LLCC are the leading source of scrubs and delays to space launches from Cape Canaveral Air Force Station (CCAFS) and NASA Kennedy Space Center (KSC). An LLCC climatology would be useful for designing launch concept of operations, mission planning, long-range forecasting, training, and setting LLCC improvement priorities. Unfortunately, an LLCC climatology has not been available for CCAFS/KSC. Attempts have been made to develop such a climatology, but they have not been entirely successful. The main shortfall has been the lack of a long continuous record of LLCC evaluations. Even though CCAFS/KSC is the world's busiest spaceport, the record of LLCC evaluations is not detailed enough to create the climatology. As a potential solution, the research in this study developed a proxy climatology of LLCC violations by using the long continuous record of surface electric field mills at CCAFS/KSC.

Fengfan Bian, Carson K. Leung, Piers Grenier et al.

Recognizing the challenges with current tornado warning systems, we investigate alternative approaches. In particular, we present a database engi-neered system that integrates information from heterogeneous rich data sources, including climatology data for tornadoes and data just before a tornado warning. The system aids in predicting tornado occurrences by identifying the data points that form the basis of a tornado warning. Evaluation on US data highlights the advantages of using a classification forecasting recurrent neural network (RNN) model. The results highlight the effectiveness of our database engineered system for big data analytics on tornado climatology-especially, in accurately predict-ing tornado lead-time, magnitude, and location, contributing to the development of sustainable cities.

Nazario Tartaglione, Fabien Desbiolles, Anna delMoral‐Méndez et al.

Abstract Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect.

Eduardo Traversi de Cai Conrado, Rosmeri Porfírio da Rocha, Michelle Simões Reboita et al.

Since the beginning of the satellite era, only three tropical cyclones have been recorded over the South Atlantic Ocean. To investigate the potential occurrence of such systems since the 1900s, ERA20C, a centennial reanalysis, was utilised. This study first evaluates the performance of ERA20C in reproducing the climatology of all cyclone types over the southwestern South Atlantic Ocean by comparing it with a modern reanalysis (ERA5) for the period 1979–2010. Despite its simpler construction, ERA20C is able to reproduce key climatological features, such as frequency, location, seasonality, intensity, and thermal structure of cyclones similar to ERA5. Then, the Cyclone Phase Space (CPS) methodology was applied to determine the thermal structure at each time step for every cyclone between 1900 and 2010 in ERA20C. The cyclones were then categorised into different types (extratropical, subtropical, and tropical), and systems exhibiting a warm core at their initial time step were classified as tropical cyclogenesis. Between 1900 and 2010, 96 cases of tropical cyclogenesis were identified over the South Atlantic. Additionally, throughout the lifetime of all cyclones, a total of 1838 time steps exhibited a tropical structure, indicating that cyclones can acquire a warm core at different stages of their lifecycle. The coasts of southeastern and southern sectors of northeast Brazil emerged as the most favourable for cyclones with tropical structures during their lifecycle. The findings of this study highlight the occurrence of tropical cyclones in the South Atlantic prior to the satellite era, providing a foundation for future research into the physical mechanisms that enabled these events.

Alexandre Calzavara Yoshida, Patricia Cristina Venturini, Fábio Juliano da Silva Lopes et al.

The Atmospheric Dynamics Mission ADM-Aeolus was successfully launched in August 2018 by the European Space Agency (ESA). The Aeolus mission carried a single instrument, the first-ever Doppler wind lidar (DWL) in space, called Atmospheric LAser Doppler INstrument (ALADIN). Aeolus circled the Earth, providing vertical profiles of horizontal line-of-sight (HLOS) winds on a global scale. The Aeolus satellite’s measurements filled critical gaps in existing wind observations, particularly in remote regions such as the Brazilian Amazon. This area, characterized by dense rainforests and rich biodiversity, is essential for global climate dynamics. The weather patterns of the Amazon are influenced by atmospheric circulation driven by Hadley cells and the Intertropical Convergence Zone (ITCZ), which are crucial for the distribution of moisture and heat from the equator to the subtropics. The data provided by Aeolus can significantly enhance our understanding of these complex atmospheric processes. In this long-term validation study, we used radiosonde data collected from three stations in the Brazilian Amazon (Cruzeiro do Sul, Porto Velho, and Rio Branco) as a reference to assess the accuracy of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy wind products. Statistical validation was conducted by comparing Aeolus L2B wind products and radiosonde data covering the period from October 2018 to March 2023 for Cruzeiro do Sul and Porto Velho, and from October 2018 to December 2022 for Rio Branco. Considering all available collocated winds, including all stations, a Pearson’s coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mi>r</mi><mo>)</mo></mrow></semantics></math></inline-formula> of 0.73 was observed in Rayleigh-clear and 0.85 in Mie-cloudy wind products, revealing a strong correlation between Aeolus and radiosonde winds, suggesting that Aeolus wind products are reliable for capturing wind profiles in the studied region. The observed biases were −0.14 m/s for Rayleigh-clear and −0.40 m/s for Mie-cloudy, fulfilling the mission requirement of having absolute biases below 0.7 m/s. However, when analyzed annually, in 2022, the bias for Rayleigh-clear was −0.95 m/s, which did not meet the mission requirements.

Jordan P. Brook, Joshua S. Soderholm, Alain Protat et al.

In Australia, hailstorms present considerable public safety and economic risks, where they are considered the most damaging natural hazard in terms of annual insured losses. Despite these impacts, the current climatological distribution of hailfall across the continent is still comparatively poorly understood. This study aims to supplement previous national hail climatologies, such as those based on environmental proxies or satellite radiometer data, with more direct radar-based hail observations. The heterogeneous and incomplete nature of the Australian radar network complicates this task and prompts the introduction of some novel methodological elements. We introduce an empirical correction technique to account for hail reflectivity biases at C-band, derived by comparing overlapping C- and S-band observations. Furthermore, we demonstrate how object-based hail swath analysis may be used to produce resolution-invariant hail frequencies, and describe an interpolation method used to create a spatially continuous hail climatology. The Maximum Estimated Size of Hail (MESH) parameter is then applied to a mixture of over fifty operational radars in the Australian radar archive, resulting in the first nationwide, radar-based hail climatology. The spatiotemporal distribution of hailstorms is examined, including their physical characteristics, seasonal and diurnal frequency, and regional variations of such properties across the continent.

Laurens P. Stoop, Karin van der Wiel, William Zappa et al.

We propose an index to quantify and analyse the impact of climatological variability on the energy system at different timescales. We define the Climatological Renewable Energy Deviation Index (CREDI) as the cumulative anomaly of a renewable resource with respect to its climate over a specific time period of interest. For this we introduce the smooth, yet physical, hourly rolling window climatology that captures the expected hourly to yearly behaviour of renewable resources. We analyse the presented index at decadal, annual and (sub-)seasonal timescales for a sample region and discuss scientific and practical implications. CREDI is meant as an analytical tool for researchers and stakeholders to help them quantify, understand, and explain, the impact of energy-meteorological variability on future energy system. Improved understanding translates to better assessments of how renewable resources, and the associated risks for energy security, may fare in current and future climatological settings. The practical use of the index is in resource planning. For example transmission system operators may be able to adjust short-term planning to reduce adequacy issues before they occur or combine the index with storyline event selection for improved assessments of climate change related risks.

Chung-Chieh Wang, Wei-Kuo Soong, Chih-Wei Chien et al.

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.

Guojin He, Shengyun Ji, Rongjun Wu et al.

In the field of electronic communication warfare, accurately predicting the range and intensity of shortwave interference signals presents a significant challenge due to the complex interplay between the ionospheric parameters and the electromagnetic environment. To address this challenge, we designed a novel tool to assess the interference impact area of shortwave interference signals in a dynamically changing ionospheric environment. Considering sophisticated ionospheric radio wave propagation models and innovative spatial grid methods, this tool finishes the comprehensive spatial distribution of the interference impact area and delivers grid-based insights into the interference intensity. Furthermore, the test verification of the tool demonstrated a mean error of 8.42 dB between the measured and simulated results, underscoring the efficacy and reliability of this tool. This pioneering work is poised to make substantial contributions to the field of communication electronic warfare and holds significant promise for guiding the development of interference countermeasures.

P. T. Brown, D. Farnham, K. Caldeira

Wind and solar electricity generation is projected to expand substantially over the next several decades due both to rapid cost declines as well as regulation designed to achieve climate targets. With increasing reliance on wind and solar generation, future energy systems may be vulnerable to previously underappreciated synoptic-scale variations characterized by low wind and/or surface solar radiation. Here we use western North America as a case study region to investigate the historical meteorology of weekly-scale “droughts” in potential wind power, potential solar power and their compound occurrence. We also investigate the covariability between wind and solar droughts with potential stresses on energy demand due to temperature deviations away human comfort levels. We find that wind power drought weeks tend to occur in late summer and are characterized by a mid-level atmospheric ridge centered over British Columbia and high sea level pressure on the lee side of the Rockies. Solar power drought weeks tend to occur near winter solstice when the seasonal minimum in incoming solar radiation co-occurs with the tendency for mid-level troughs and low pressure systems over the U.S. southwest. Compound wind and solar power drought weeks consist of the aforementioned synoptic pattern associated with wind droughts occurring near winter solstice when the solar resource is at its seasonal minimum. We find that wind drought weeks are associated with high solar power (and vice versa) both seasonally and in terms of synoptic meteorology, which supports the notion that wind and solar power generation can play complementary roles in a diversified energy portfolio at synoptic spatiotemporal scales over western North America.

Robert B. Lund, Xueheng Shi

Changepoint methods have multiple uses in climatology, including stationary checks and record homogenization. There are still many open problems in the area, especially in the multiple changepoint setting, and statisticians are needed to help develop the methods and analyze the data.

Jianping Guo, Xinyan Chen, T. Su et al.

The variability of the lower tropospheric temperature inversion (TI) across China remains poorly understood. Using seven years’ worth of high-resolution radiosonde measurements at 120 sites, we compile the climatology of lower tropospheric TI in terms of frequency, intensity, and depth during the period from 2011 to 2017. The TI generally exhibits strong seasonal and geographic dependencies. Particularly, the TI frequency is found to be high in winter and low in summer, likely due to the strong aerosol radiative effect in winter. The frequency of the surface-based inversion (SBI) exhibits a “west low, east high” pattern at 0800 Beijing time (BJT), which then switches to “west high, east low” at 2000 BJT. Both the summertime SBI and elevated inversion (EI) reach a peak at 0800 BJT and a trough at 1400 BJT. Interestingly, the maximum wintertime EI frequency occurs over Southeast China (SEC) rather than over the North China Plain (NCP), likely attributable to the combination of the heating effect of black carbon (BC) originating from the NCP, along with the strong subsidence and trade inversion in SEC. Correlation analyses between local meteorology and TI indicate that larger lower tropospheric stability (LTS) favors more frequent and stronger TIs, whereas the stronger EI under smaller LTS conditions (unstable atmosphere) is more associated with subsidence rather than BC. Overall, the spatial pattern of the lower tropospheric TI and its variability in China are mainly controlled by three factors: local meteorology, large-scale subsidence, and BC-induced heating. These findings help shed some light on the magnitude, spatial distribution, and underlying mechanisms of the lower tropospheric TI variation in China.

Claude Bardos, Francois Golse, Olivier Pironneau

Using theoretical and numerical arguments we discuss some of the commonly accepted approximations for the radiative transfer equations in climatology.