Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 5
Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting. However, obtaining high-resolution, spatially and temporally extensive observation dataset remains a challenge due to the limitations of in situ measurements. Therefore, this study addresses this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m3) using C-band dual-polarised doppler weather radar (DWR) data from IMD Jaipur station over 78 deep convective summer monsoon days in the years 2020-2022. A long-term climatological study (2003-2023) of total column cloud liquid water (TCCLW, kg/m2) from ERA5, liquid water cloud water content (LWCP, kg/m2) from MODIS and rainfall data from IMD, IMERG, and GPCP datasets is also performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of each equation is evaluated by comparing radar-derived VIL with satellite-derived parameters like MODIS cloud liquid water path (LWP, kg/m2) and TCCLW. The results show that VIL values increase with rainfall intensity and cloud vertical height, leading to higher estimation errors. Among the equations tested, the hybrid ZDR-based equation consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values which also captured more detailed spatial patterns of liquid water distribution and reduced bias, making it the most reliable estimator. Despite some limitations, such as beam blockage and slight spatial shifts due to interpolation, the study highlights the advantages of incorporating polarimetric radar products for VIL estimation. These findings provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics, with future work aimed at refining the methodology by addressing data gaps and enhancing cloud-type-specific estimators.
How to cite: Sabu, A., Syed, H. A., Das, S., Panda, S. K., Sharma, D., and Pal, J.: Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-888, https://doi.org/10.5194/egusphere-egu25-888, 2025.
In past few decades there has been a noticeable increase in the frequency and intensity of extreme rainfall events (EREs) globally, including India. The Clausius-Clapeyron relationship explains how the warmer air can significantly hold more moisture. Hence, in present climate change scenario increasing temperature along with other factors can lead to further increase in EREs. Effective management strategeis in various sectors like disaster preparedness, smart-city planning, water quality, public health, agriculture planning, etc. can get improved, through proper understanding on the distribution and frequency of EREs. Keeping in mind the socio-economic impacts of EREs; this study aimed to identify the hotspot regions for EREs in India.
India is a country with vast spatio-temporal variability in rainfall pattern. Hence, this study implemented objective criteria to identify the spatio-temporal rainfall variability of EREs over four rainfall homogeneous regions for pre-monsoon, monsoon and post-monsoon seasons. Based on frequency distribution of daily accumulated rainfall, suitable rainfall threshold values for defining EREs are identified for each homogeneous region and each season. These threshold values vary region-wise as well as season-wise. Distribution of EREs show interannual as well as seasonal variability.
Clustering algorithms, popular unsupervised Machine Learning (ML) techniques, are handy tools to identify hotspots of extreme rainfall regions with similar spatial variability. To understand the ERE distribution and to identify rainfall hotspots based on long term daily gridded rainfall data, this study implemented K-means clustering and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithms. Comparative area distribution study between K-means and DBSCAN clustering help to identify the EREs hotspots in India. Overall, the K-means method shows more scattered hotspots compared to DBSCAN method, which are further validated using Davies-Boulding Index (DBI), Silhouette score, Calinski-Harabasz (CH) score and Dunn's Index. These score analysis methods serve as potential tools to support the clustering validation method. In addition to the area distribution, this study has addressed the temporal variability of the EREs hotspots. ST-OPTICS ( Spatio-Temporal Ordering Points to Identify the Clustering Structure) algorithm results clustering of hotspots based on their spatial and temporal similarity. This study shows that ML algorithms prove to be promising techniques for detecting and analyzing spatial as well as temporal variability of EREs hotspots which is effective for future management practice in various sectors.
Keywords: Extreme Rainfall Events; DBSCAN Clustering; K-Means Clustering; ST-OPTICS.
How to cite: Putatunda, I. and Vasudevan, R.: Extreme rainfall hotspots in India based on spatio-temporal variability of rainfall using unsupervised clustering techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7852, https://doi.org/10.5194/egusphere-egu25-7852, 2025.
Quantifying uncertainties is a key aspect of data assimilation systems since it has a large impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms, such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additive Gaussian noises and use both inflation and localization to avoid filter degeneracy and compensate for misspecifications. This introduces different stochastic parameters which need to be carefully estimated in order to get a reliable estimate of the latent state of the system. A classical approach to estimate unknown parameters in data assimilation consists in using state-augmentation, where the unknown parameters are included in the latent space and are updated at each iteration of the EnKF. However, it is well-known that this approach is not efficient to estimate stochastic parameters because of the complex (non-Gaussian and non-linear) relationship between the observations and the stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm fails to estimate high-dimensional systems due to the curse of dimensionality. The strengths of these two methods are gathered in the proposed algorithm, where the PF first generates the particles that estimate the stochastic parameters, then using the mean particle the EnKF generates the members that estimate the geophysical variables. This generic method is first detailed for the estimation of parameters related to the model or observation error and then for the joint estimation of inflation and localization parameters. Numerical experiments are performed using the Lorenz-96 model to compare our approach with state-of-the-art methods. The results show the ability of the new method to retrieve the geophysical state and to estimate online time-dependent stochastic parameters. The algorithm can be easily built from an existing EnKF with low additional cost and without further running the dynamical model.
How to cite: Guillot, J.: State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-125, https://doi.org/10.5194/egusphere-egu25-125, 2025.
The Multi-sensor Advection Diffusion Weather Research and Forecast (MAD-WRF) model is a state-of-the-art addition to the WRF model that includes a fast cloud-initialization procedure, making it more suitable for hydrometeors analysis and clouds forecasts. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis. During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode (Jiménez et al., 10.1016/j.solener.2022.04.055). As such, MAD-WRF has been extensively used for solar energy predictions.
Here we have investigated the feasibility of using MAD-WRF to improve the accuracy of intense precipitation forecasts. An extreme precipitation event over Israel that led to urban floods and two casualties in Tel-Aviv during January 4th, 2020, has been chosen as a case study. The extreme accumulated precipitation responsible for noon and early afternoon floods was triggered by a persistent cloud train that developed over the area several hours before. MAD-WRF model has been configured with 3-nested domains with 9, 3 and 1 km grid-sizes. We have run MAD-WRF in active mode incorporating satellite-retrieved cloud-top heights provided by the European Space Agency EUMETSAT in all three domains. EUMETSAT data are available in near real-time making it suitable for operational forecasts.
Independent precipitation data measured by the Israel Meteorological Service radar at Bet-Dagan (about 10 km south-east of Tel-Aviv) has been used for forecasts verification. Comparison between radar data and MAD-WRF forecasts with and without incorporation of EUMETSAT cloud-tops retrievals reveal the advantage MAD-WRF cloud initialization. The significant improvement in the forecast of the location and rate of the precipitation is observed up to 12 hours ahead in time.
On-going work focuses on the evaluation of the precipitation distributions and improvement of the forecast of dry areas.
How to cite: Gelman, A., Morin, E., Jiménez, P., Sheu, R.-S., and Rostkier-Edelstein, D.: Improving forecasts of extreme precipitation with MAD-WRF mesoscale model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17568, https://doi.org/10.5194/egusphere-egu25-17568, 2025.
A novel deep learning (DL) transformer model, named the 3D-Geoformer, has been developed for ENSO-related modeling studies in the tropical Pacific. Multivariate input predictors and output predictands are selected to adequately represent ocean-atmosphere interactions; so, this purely data-driven model is configured in such a way that key fields for the coupled ocean-atmosphere system are collectively and simultaneously utilized, including three-dimensional (3D) upper-ocean temperature and surface wind stress fields, which represents the coupled ocean-atmosphere interactions known as the Bjerknes feedback in the region. The 3D-Geoformer achieves high correlation skills for ENSO prediction at lead times of up to one and a half years. The reasons for the successful prediction with interpretability are explored comprehensively by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions using the 3D-Geoformer. This is achieved by investigating how the thermal precursors contribute to ENSO prediction skills, with the dependence of the precursor representations on preconditioning multi-month input predictors elucidated. Results reveal the existence of ENSO‐related upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific in the DL framework. The research demonstrates that 3D thermal fields and their basinwide evolution during multi-month time intervals act to enhance long‐lead prediction skills of ENSO. It is demonstrated that the 3D-Geoformer can not only have its ability to effectively improve prediction skills of sea surface temperature variability in the eastern equatorial Pacific, but also explain how and why it is so, thus enhancing model explainability.
How to cite: Zhou, L. and Zhang, R.: Deep learning-based ENSO modeling and its prediction and predictability study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4905, https://doi.org/10.5194/egusphere-egu25-4905, 2025.
Persistent Dry Spells (PDS) during winter in the eastern Mediterranean are crucial to understanding the regional challenges of water resources and mitigating agricultural and economic impacts. Winter dry spells significantly affect ecosystem stability, public health, and socioeconomic conditions in a region susceptible to climate variability. Therefore, extending the forecast horizon of these extreme weather events to subseasonal time scales is a key challenge. With this aim, we examine the covariability of the sea surface temperature of the Indian Ocean and Persistent Dry Spells during winter over the eastern Mediterranean. The positive Indian Ocean Dipole (IOD) phase alters global circulation patterns, notably increasing the geopotential height at 500 hPa and the sea-level pressure over western Russia, eastern Europe, and the eastern Mediterranean during PDS events. Concurrently, the positive IOD phase enhances moisture fluxes and decreases sea level pressure and geopotential height at 500 hPa in the Western Mediterranean, suggesting increased cyclonic activity in that region. This type of activity probably influences the formation of PDS in the eastern Mediterranean through latent heating and the formation of ridges downstream of the cyclones. The baroclinic, subtropical, and polar regimes are large-scale synoptic regimes alternately prevailing during PDS events. Changes due to the DMI phase are not identical under these regimes and sometimes have opposite trends. The baroclinic regime is the most frequent regime during PDS events. Consequently, the average changes in pressure intensity during PDS events strongly resemble those during baroclinic days. Positive DMI case studies exemplify the effect of these large-scale regimes. We provide evidence for a link between the positive phase of IOD in December and the frequency of longer (> 15 days) PDS events. The normalized frequencies of persistent 15-20-day events under the positive dipole mode index (DMI) are ~ 2% higher than the frequency of negative DMI. The frequencies of 6-7 day events are ~20% lower. Finally, we emphasize the sensitivity of persistent dry spells during winter to event definition, the chosen precipitation data source, and threshold definitions for climate indices. These considerations are essential for improving the accuracy of regional weather and climate predictions, further enhancing our understanding of the climatic impacts of IOD and other teleconnection patterns in the eastern Mediterranean and worldwide.
How to cite: Berkovic, S. and Hochman, A.: Links between the Indian Ocean Dipole and Persistent Dry Spells in the Eastern Mediterranean Winter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-56, https://doi.org/10.5194/egusphere-egu25-56, 2025.
Dengue fever outbreaks impose a severe healthcare burden in Vietnam, therefore the development of an early Dengue warning system is key to improve public health planning and mitigate the future burden produced by this disease. This study assessed the ECMWF ensemble re-forecast skill for relative humidity, temperature and precipitation, which are key factors for vector-borne disease transmission in Vietnam between 1-4 weeks in advance. We focused the analysis on the rainy season (May-October) using ERA5 reanalysis as a reference dataset. Re-forecast data was pre-processed using a quantile mapping technique to reduce the bias between re-forecast and observations. Results showed that corrected re-forecasts of weekly mean temperature, relative humidity and accumulated precipitation are skilful up to 2-3 weeks in advance and rank histograms verified the forecast reliability. Nonetheless the model is less skillful for the region of South Vietnam and seems to struggle at predicting extremely high/low values of temperature, relative humidity and precipitation. Results from this study demonstrate that ECMWF ensemble forecasts are suitable to use as inputs for a dengue early warning system up to 14-21 days in advance
How to cite: Perez, I., Sparrow, S., Weisheimer, A., Wright, M., and Main, L.: Verification of weather variables linked to Dengue incidence inthe sub‐seasonal scale in Vietnam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19687, https://doi.org/10.5194/egusphere-egu25-19687, 2025.
This study investigated convective initiations (CIs) in the western Jiangnan region of China using radar data spanning April to September from 2018 to 2021. An integrated approach combining objective identification and subjective validation was applied to identify, track and validate CIs, resulting in a more accurate CIs dataset. Based on this dataset, this study delved into the spatiotemporal variations and key environmental conditions associated with CIs. The results indicated distinct seasonal and diurnal patterns in CIs events. Seasonally, the spatial variations of CIs were demarcated by the Nanling Mountains, exhibiting higher frequency to the south and lower to the north. Generally, the seasonal distribution of CIs followed a unimodal pattern, peaking during June to August and reaching minima in April and September. Notably, CIs exhibited a pronounced convection feature in the afternoon, particularly during June to August, when the majority of CIs occurred between 11:00 and 19:00. Furthermore, the spatial variations influenced by terrain were prominent. With the Nanling Mountains as the dividing line, CIs in the northern region were located near relatively higher mountains, while in the southern region, they were concentrated in smaller mountains and coastal areas. Utilizing the K-means clustering method, CIs that could develop into Mesoscale Convective Systems are classified into four circulation types: the Western Pacific Subtropical High (WPSH) Control type (Type I), the WPSH Edge type (Type II), the Southwest Airflow type (Type III), and the Low Trough Shear type (Type IV). CIs under Type I and II were primarily attributed to afternoon thermal convection occurring under conditions of strong moisture and thermal instability. The distribution of CIs triggers for these types tended to cluster in the vicinity of high-elevation terrain. In contrast, CIs belonging to Type III and IV were primarily driven by the synergy of abundant moisture conditions and systematic dynamic factors such as low-level jets, upper-level troughs, and shear lines. These exhibited a north-low and south-high frequency distribution, with high-frequency CIs trigger zones observed particularly in regions of strong moisture flux convergence and near complex terrain.
How to cite: Wu, Z., Han, Y., Song, N., Ye, C., and Xiang, G.: Exploring the spatiotemporal variations and key environmental conditions of convective initiations in the Western Jiangnan Region of China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3431, https://doi.org/10.5194/egusphere-egu25-3431, 2025.
Based on the traditional satellite-based convective initiation (CI) detection method, an improved algorithm for the identification and tracking of CIs using satellite data has been proposed. This algorithm then undergoes spatio-temporal matching with ground-based observation data such as radar and precipitation data. Incorporating experts domain knowledge, the algorithm utilizes a subjective-objective interactive approach to complete the verification and calibration of the satellite-drived CI identification results. This process results in a high-resolution annotation dataset of convective initiation that can be used for detection and forecasting of CI and artificial intelligence models.
Firstly, within a spatial-temporal window of 30 minutes before and after the satellite CIs trigger time and a radius of 20km, the satellite-derived CIs are matched with radar-identified CIs. Additionally, within a spatial-temporal window of 60 minutes after the satellite CI trigger and extending 2km outside the CI cloud clusters movement zone, the satellite-derived CIs are also matched with precipitation data. The two matching results are combined to form a comprehensive identification of CIs. Furthermore, using a calibration system and a back-to-back verification method by forecasters, the CI annotation results are revised, resulting in a high-resolution and reliable CI annotation dataset.
Using this methodology, a high spatio-temporal resolution CI dataset was established for the years 2018-2023, which allowed for the statistical analysis of CI distributions across different precipitation levels in each month. The highest proportion of CI events occurred in August, followed by July. Among these, CI events with moderate precipitation accounted for 46.2%, weak precipitation accounted for 34.4%, and strong precipitation accounted for 19.3%.
It can be seen that there is a noticeable northward shift in the occurrence of CI events, especially those associated with heavy precipitation, from April to August. In April, these events are mainly concentrated in a few provinces in the central and southern parts of the country. Subsequently, they gradually expand from south to north, covering the entire central and eastern research area by August. In September, they retreat back to the central and southern regions. This spatial evolution pattern of CI events once again verifies that the occurrence of severe convection events is closely related to the position changes of the Intertropical Convergence Zone (ITCZ) and the monsoon.The frequency of CI occurrences has also been proven to peak between 11 a.m. and 3 p.m., regardless of precipitation intensity.
How to cite: Peng, L., Ye, C., and Ou, X.: Convection Initiation Identification and The Construction of A High-value Dataset Using the Fengyun-4A Satellite, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6767, https://doi.org/10.5194/egusphere-egu25-6767, 2025.
In order to solve the problem of quantity traceability of precipitation phenomenon instrument, a precipitation phenomenon checking device was developed. By simulating the precipitation particles of 4.3 mm and 9.5 mm, corresponding to the velocities of 2m/s, 7M/s and 12M/s respectively, the on-site verification of the precipitation phenomenometer and the test program of the upper computer software are carried out, the relevant particle channels are recorded and displayed in the map, and the performance of the precipitation phenomenometer is judged automatically. It has many advantages, such as complete function, reasonable design, easy to carry, friendly software interface, one-button detection, automatically judge whether the equipment is qualified, and according to the template to generate a verification report. The practical application proves that the device provides a strong support for the meteorological department's equipment support personnel to carry out the verification work of the precipitation phenomenometer, improves the working efficiency, and plays a role in supervising and inspecting the quality of the precipitation phenomenometer's equipment, it has a good application prospect in the field verification of precipitation phenomenometer.
How to cite: Han, Y.: Development and application of the calibration device of precipitation phenomenometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17395, https://doi.org/10.5194/egusphere-egu25-17395, 2025.
Based on the minute by minute precipitation observation data from 46 national weather stations in the Yangtze River Delta region of China and hourly ERA5 reanalysis data from June to August 2018 to 2021, the temporal and spatial characteristics and environmental parameters of short-term heavy precipitation were analyzed. The short-term heavy rainfall was classified and compared according to the 19 environmental parameters representing water vapor, dynamic and thermal conditions. The results showed that:(1) There were more short-term heavy rainfall in the Yangtze River Delta in summer, and 58.7% of the weather stations appeared more than 5 times a year on average; most of short-term heavy rainfall appeared in August, accounting for 40.7%; From 14:00 PM to 17:00 PM was the high incidence period of short-term heavy rainfall; The duration of short-term heavy rainfall was mostly within 60 minutes, accounting for 85.9%, and the longest process lasted 282 minutes.(2) At the beginning of short-term heavy rainfall, water vapor was sufficient, PWAT generally exceeded 63mm, and the relative humidity at 850 hPa and 700 hPa exceeded 80%; The energy condition was good, and the average value of cape was 1516.9 J/kg; The vertical wind shear of 0-6 km was mainly distributed in the range of 8.1~16.7 m/s, belonging to medium weak or weak intensity; The thickness of warm clouds was large, most of which were more than 4395.2 m, which was conducive to higher precipitation efficiency.(3) The environmental parameters of the three types of short-term heavy rainfall were quite different. The water vapor of the first type was mainly concentrated in the lower layer, with high cloud base height and large Cape value, 75% of which was more than 1700 J/kg. The thermal conditions were prominent, and the dynamic effect was weak. The water vapor of the whole layer of the second type was sufficient, and the Cape value was high, with an average value of 1401.1 J/kg, the uplift condition of the middle and low layers was the best of the three types. The water vapor, thermal and dynamic effects were relatively balanced; The third type was rich in water vapor, with prominent water vapor conditions, large vertical wind shear in the lower layer and weak thermal effect.
How to cite: Zhang, C. and Peng, L.: Characteristics of environmental parameters of short-term heavy rainfall in the Yangtze River Delta region in summer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3935, https://doi.org/10.5194/egusphere-egu25-3935, 2025.
In this talk, I will highlight our recent advances and findings in changes in climatic extremes over east Asia monsoon region. I will focus specifically on monsoon duration, intensity, rainfall extremes changes, and mechanism, with dynamic and thermodynamic factors controlling rainfall extremes over East Asia in late summer. Moreover, I will present our latest research on climatic extremes such as heatwaves based on dry conditions and stationary waves. Despite increasing future rainfall, rainfall extremes and rainfall variability in many areas, our recent studies suggest also an increase in drought risk over eastern Asia as a result of changes in evapotranspiration. However, the underlying mechanisms of heat waves and potential atmospheric and land feedbacks are still not fully understood. Through feedback attribution analysis, we found that there are dry and hot heat waves with very different underlying physical processes and feedbacks. The increasing global warming is expected to exacerbate atmospheric water demand, worsening future conditions of extreme droughts and heatwaves. Compound drought and heatwaves (DHW) events have much attention due to their notable impacts on socio-ecological systems. However, studies on the mechanisms of DHW related to land-atmosphere interaction are not still fully understood in regional aspects. Here, we investigate drastic increases in DHW from 1980 to 2019 over northern East Asia, one of the strong land-atmosphere interaction regions. Heatwaves occurring in severely dry conditions have increased after the late 1990s, suggesting that the heatwaves in northern East Asia are highly likely to be compound heatwaves closely related to drought. Moreover, the soil moisture–temperature coupling strength increased in regions with strong increases in DHW through phase transitions of both temperature and heat anomalies that determine the coupling strength. As the soil moisture decreases, the probability density of low evapotranspiration increases through evaporative heat absorption. This leads to increase evaporative stress and eventually amplify DHW since the late 1990s. Focusing on changes in stomatal conductance due to CO2 changes, our research results reveal an increase in surface resistance with CO2 elevation. Particularly under drought conditions, potential evapotranspiration tends to overestimate drought severity in the East Asian region by approximately 17% when scenarios considering vegetation are not taken into account. Additionally, intensified land-atmosphere interactions due to soil moisture deficiency lead to more frequent and amplified occurrences of compound heatwaves and droughts over northern East Asia. Understanding the relationship between soil moisture and vegetation can contribute to comprehending future severe droughts and heatwaves under diverse surface conditions with warming and moistening.
How to cite: Ha, K.-J., Yeo, J.-H., and Seo, Y.-W.: Dynamics and Characteristics of Climatic Extremes over East Asia Monsoon region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3896, https://doi.org/10.5194/egusphere-egu25-3896, 2025.
The tropical middle atmosphere is characterized by long-period oscillations such as the Quasi Biennial Oscillation and the Semiannual Oscillation which are primarily driven by the interaction of a broad spectrum of atmospheric waves with the background flow. Using reanalysis datasets and independent rocket soundings from a low latitude location, we identified a hitherto unreported variability in the tropical middle atmosphere that appears at a variable interval of 2-5 years in the late 20th century and 7-9 years in the early 21st century. The newly identified variability, Quasi-Periodic Easterly Bursts (QPEBs) as we call them, manifests as enhanced easterlies during the easterly phase of the Stratopause Semiannual Oscillation around May-July. QPEBs are found to have remote influences on the Southern Hemispheric polar vortex as well as residual circulation in the lower mesosphere. A momentum budget analysis reveals that QPEBs are found to be primarily caused by enhanced cross-equatorial advection as well as gravity wave drag. Even though a close association with the Quasi Biennial Oscillation winds is observed, the cause of the observed periodicity remains elusive.
How to cite: Koushik, N. and Kumar, K. K.: An Enigmatic Variability in the Tropical Middle Atmosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7147, https://doi.org/10.5194/egusphere-egu25-7147, 2025.
Fog significantly reduces visibility, impacting transportation and safety, particularly in regions like the United Arab Emirates (UAE) where it is a regular
occurrence, in particular in the winter months. This study develops a machine learning-based approach for automated fog detection and masking from near real-time observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation spacecraft to enhance fog detection and forecast. We evaluated six basic machine learning (ML) models trained with four different methods: (1) supervised training using SEVIRI pixel data and fog observations over airport stations; (2) as approach (1) but incorporating infrared channel data; (3) training with labeled fog and no-fog regions identified in SEVIRI night microphysics Red-Green-Blue (RGB) images through k-means clustering; and, (4) a fusion approach combining station-labeled data (approach 1) and k-means clustered-labeled data (approach 3). Among the models, the eXtreme Gradient Boosting (XGBoost) demonstrated slightly higher performance. Models trained on station data (approach 1) achieved a Probability of Detection (POD) of 0.73 and a False Alarm Ratio (FAR) of 0.11. For spatial fog masking, models trained on a combination of station-labeled and k-means cluster-labeled data (approach 4) performed best. Overall, the XGBoost method and the fusion approach (4) are recommended for fog detection and masking in the hyper-arid UAE. These findings demonstrate the potential for trained ML models to deliver accurate, near real-time fog detection and masking, enhancing monitoring over broad areas.
How to cite: Nelli, N. R., Francis, D., Cherif, C., Fonseca, R., and Ghedira, H.: Automated Nighttime Fog Detection and Masking Using Machine Learning from Near Real-Time Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14973, https://doi.org/10.5194/egusphere-egu25-14973, 2025.
The Indo-Gangetic plains (IGP) in India are frequently affected by fog during the winter months of December, January, and February, which manifests in severe consequences for air and road traffic, thereby leading to health as well as economic losses. This region, which includes highly populated cities like the National Capital Territory of Delhi, also experiences a high concentration of aerosols during this period. While studies have indicated the importance of the role of aerosols in fog processes in the region, the role of different aspects of aerosol-radiation interaction (ARI) has not been studied in detail for the formation of fog in the region. Current numerical weather prediction models (NWP) still struggle to predict fog accurately because of the uncertainties in the representation of processes leading to fog formation, sustenance, and dissipation. The present study aims to understand the influence of aerosols and ARI on the fog over IGP with a focus on dense fog conditions using the Delhi Model with Chemistry and aerosol framework (DM-Chem1.0), which is a high-resolution (330 m) model used for operational forecasting of wintertime visibility and air quality at the National Centre for Medium-Range Weather Forecasting (NCMRWF), India. Four experiments (along with a Control experiment) were designed to analyze how both the scattering and absorbing nature of ARI influence the evolution of dense fog from temporal and spatial perspectives. Two experiments isolated the absorbing and scattering effect of aerosols, while the third excluded both these effects. The fourth experiment analyzed pristine conditions with minimal aerosol presence. The study indicated that turning off absorption had the greatest impact, significantly increasing dense fog-impacted areas and fog-associated parameters like cloud liquid water mixing ratio and cloud droplet number concentration (CDNC). Satellite data for the absorbing aerosol index also corroborated the greater contribution of absorbing aerosols in the model domain. Further, the study also indicates the importance of a realistic representation of aerosol for better model performance during daytime. The study highlights the importance of correctly representing radiative interactions in the numerical models for fog prediction. The policy measures need to focus on regulating high aerosol concentrations over IGP to mitigate the adverse effects of fog.
How to cite: Bhati, S., Anurose, T. J., Jayakumar, A., Mohandas, S., and Prasad, V. S.: Aerosol-Radiation Interaction During Dense Fog in the Indo-Gangetic Plains Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3008, https://doi.org/10.5194/egusphere-egu25-3008, 2025.
Air quality monitoring is an essential procedure to ensure that pollutant levels remain within safe limits and do not pose a threat to public health, particularly for vulnerable populations. The deployment and maintenance of stationary air quality monitoring stations can be expensive, especially when a large number is required to create a comprehensive network. As a result, there has been growing interest in utilizing small, low-cost sensors that are easier to deploy and provide a more flexible and cost-effective alternative. In addition to these sensors, satellite systems have become valuable tools for air quality monitoring, offering high temporal resolution data that facilitates the assessment of air pollution over larger areas. This study looks into data fusion techniques to combine data from both stationary and mobile low-cost sensors with satellite data to analyze the air quality at the University of the Philippines, Diliman campus. Seven small sensors were deployed across the university, a mixed-use area with both vegetation and buildings, to measure pollutant concentrations, such as particulate matter. Satellite data from MODIS, Sentinel-5P, and ERA5 reanalysis were used to monitor aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and meteorological conditions. The time-series analysis focused on a three-day period during which mobile air quality data from an e-trike were collected around the university. The data from these mobile sensors, along with the stationary sensor measurements, were used to estimate PM2.5 concentrations across the campus. Kriging interpolation, a geostatistical method that estimates unknown values based on the spatial correlation of known data points, was employed to generate smooth surfaces of PM2.5 concentration across the university. Kriging interpolation was used on the stationary sensor dataset to predict the PM2.5 levels at the location of the mobile sensors at a given timeframe. Moreover, cokriging was also applied by incorporating multiple correlated variables, improving predictions by utilizing relationships between the primary variable (PM2.5) and secondary variables, such as aerosol optical depth or SO2 and NO2 concentrations. The results obtained from both Kriging and Cokriging methods were compared with data collected from mobile sensors to assess the air quality at the University of the Philippines, Diliman. The interpolated PM2.5 values were compared with the data from the mobile sensors (SEN55 and PMS7003) as ground truth, and a mean absolute percentage error (MAPE) of 43.00% to 57.23% was obtained. Initial results of cokriging with NO2 showed MAPE of 36.67% to 52.55%. Further work is expanding the dataset and refining the interpolation models to enhance the accuracy and reliability of air quality assessments across the university. By integrating more data and conducting additional tests, this approach can provide more comprehensive air quality monitoring at reduced costs and address data gaps.
How to cite: Hizon, J. R., Torres, R. A., Togonon, A. C. E., Recto, B. A., Apostol, F. A., Magpantay, P., Eslit, J. J., Ganhinhin, J., Rosales, M., Austria, I., de Guzman, J., de Leon, M. T., Cajote, R., Co, P. J., and Ramos, R.: Air Quality Assessment In The University Of The Philippines Diliman Campus Through The Integration Of Small Sensors, Satellite Data, And Kriging Interpolation Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14960, https://doi.org/10.5194/egusphere-egu25-14960, 2025.
The forecast of monthly rainfall is a significant topic for water resource management and hydrological disaster prevention. A critical need for precise hydrological forecasts in water resource management is addressed in this study by analyzing machine learning (ML) models for precipitation forecasting in the Boudh district of Odisha, India. Although machine learning (ML) models have demonstrated significant promise in rainfall forecasting due to their high performance, often surpassing that of certain physical models, the intricate physical processes involved in rainfall creation mean that a single ML model is typically insufficient to provide reliable rainfall projections. A thorough set of meteorological parameters, including precipitation wind speed, temperature, and humidity, are utilized to create four distinct models: Support Vector Regression (SVR), long and short memory neural networks (LSTM), Bi-LSTM and Convolutional neural network with LSTM (CNN-LSTM). The performance of these models is thoroughly assessed utilizing a range of evaluation metrics. In this work, the correlations between precipitation and climate factors are assessed using the cross-correlation function (XCF). With maxima consistently reported during months across all four sites, the XCF analysis shows a number of significant trends, including a strong correlation amid precipitation and maximum temperature. Moreover, precipitation is significantly correlated with wind speed and relative humidity. The results demonstrate the effectiveness of hybridized ML techniques in raising the precision of precipitation forecasts. The CNN-LSTM models, which have R2 values between 0.93 and 0.97, generally perform better. Their remarkable accuracy highlights their efficacy in precipitation forecasting, outperforming rival models during both training and testing. These findings have important ramifications for hydrological processes, particularly in Odisha's Boudh region, where sustainable water resources management depends on precise precipitation forecasting.
How to cite: Samantaray, S., Sahoo, A., and Satapathy, D. P.: Rainfall Prediction using Hybrid CNN-LSTM approach: A case study in the Boudh district, Odisha, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1542, https://doi.org/10.5194/egusphere-egu25-1542, 2025.
Severe weather events often develop rapidly and cause extensive damage, resulting in billions of dollars in losses annually. This paper explores Large Language Models (LLMs) to effectively reason about the adversity of weather hazards. To tackle this issue, we gathered National Weather Service (NWS) flood reports covering the period from June 2005 to September 2024. Two pre-trained LLMs including Bidirectional and Auto-Regressive Transformer (BART) models (large) and Bidirectional Encoder Representations from Transformers (BERT) were employed to classify flood reports according to predefined labels. These models encompass a range of sizes with parameter counts of 406 million, and 110 million parameters, respectively. We employed the Low-Rank Adaptation (LoRA) fine-tuning technique to enhance performance and memory efficiency. The fine-tuning and few-shot learning capabilities of these models were evaluated to adapt pre-trained language models for specific tasks or domains. The methodology was applied in Charleston County, South Carolina, USA— a vulnerable region to compound flooding. Extreme events reported during the training periods were unevenly distributed across training period, resulting in imbalanced datasets. The “Cyclonic” category represents significantly fewer instances in the report text data, while the “Flood” and “Thunderstorm” categories appeared more frequent. The findings revealed that while few-shot learning significantly reduced computational costs, fine-tuned models resulted in more stable and reliable performance. Among multiple LLMs applied in this research, the BART model achieved higher F1 scores in the “Flood,” “Thunderstorm,” and “Cyclonic” categories—requiring fewer training epochs to reach optimized performance levels. Furthermore, the BERT model demonstrated a shorter overall training time (12 hours 17 minutes) compared to other LLMs, demonstrating efficient cost of computing. This comprehensive evaluation of LLMs across diverse NWS flood reports enhanced our understanding of their capabilities in text classification and offered valuable insights into leveraging these advanced techniques for weather disaster assessment.
How to cite: Neupane, A., Zafarmomen, N., and Samadi, V.: Leveraging Large Language Models for Enhancing and Reasoning Adverse Weather Hazard Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13882, https://doi.org/10.5194/egusphere-egu25-13882, 2025.
Agricultural yield prediction plays a crucial role in food security and economic planning, yet existing models often struggle with the complexity and high dimensionality of agricultural data. This study presents a framework that combines explainable artificial intelligence (XAI) with feature reduction methodology to enhance the accuracy and efficiency of rice yield prediction. Our approach addresses the dual challenges of model interpretability and computational efficiency while maintaining high prediction accuracy.
The framework begins with a systematic development of prediction models utilizing advanced machine learning (ML) and deep learning (DL) techniques. We implemented comprehensive pre-processing steps, including data normalization, feature engineering, and missing value handling, to ensure data quality. Our evaluation encompassed various models, including Random Forest, Gradient Boosting Machines, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks with attention mechanisms. To optimize model performance, we employed hyperparameter tuning through grid search, effectively mitigating issues of overfitting and underfitting.
A notable innovation of our framework is the incorporation of SHapley Additive exPlanations (SHAP), enabling transparent insights into the model's decision-making process. Leveraging this XAI approach, we introduced a novel feature reduction methodology that systematically identifies and removes negatively contributing features while maintaining model accuracy. Our analysis of a multivariate dataset which is a public dataset from rice fields in the an Giang province of the Mekong Delta, Vietnam, required the integration of diverse satellite datasets, including optical data from Landsat and radar data from Sentinel-1. This revealed distinct patterns of feature influence on yield prediction, facilitating the optimization of the feature set for maximum effectiveness. Key radar polarization bands, VV (Vertical-Vertical) and VH (Vertical-Horizontal), provided crucial surface backscatter data, capturing information on crop structure, growth stages, and post-harvest soil conditions. Notably, the feature min_vh consistently emerged as the most significant predictor.
The implementation of our feature reduction strategy resulted in significant improvements in both model performance and computational efficiency. By removing 15-20 number of identified negatively contributing features, we achieved approximately 3-5% improvement in prediction accuracy while substantially reducing the computational overhead and model training time. This enhancement in efficiency did not compromise the model's interpretability, demonstrating the robust nature of our framework.
Our methodology represents a significant advancement in agricultural modeling by successfully addressing the challenges of high-dimensional data while maintaining model interpretability. The framework's ability to identify and eliminate non-contributing features while improving prediction accuracy demonstrates its potential for wide-scale application in agricultural yield prediction. Furthermore, the reduced computational requirements make it a practical solution for real-world applications where computational resources may be limited.
These results validate the effectiveness of our integrated approach in handling complex agricultural data while providing actionable insights for yield prediction. The framework offers a scalable, interpretable, and computationally efficient solution that can be adapted for various agricultural prediction tasks, potentially transforming how we approach agricultural yield forecasting.
How to cite: Dey, A., Saha, A., Sarkar, S., Mondal, A., and Mitra, P.: An Explainable AI-Driven Feature Reduction Framework for Enhanced Agricultural Yield Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7965, https://doi.org/10.5194/egusphere-egu25-7965, 2025.
Since the inception of physics-informed neural networks (PINNs) by Raissi et al. in 2019, it has been seen as a promising approach to outperform conventional algorithms in terms of computational efficiency, reduced costs, and improved prediction accuracy, especially in small data regimes.PINNs incorporate known physical governing equations in the form of partial differential equations (PDEs) or ordinary differential equations (ODEs) into neural networks, and occasionally the governing equations are derived from observational or simulated data, allowing PINNs to address specific atmospheric systems.Moreover, depending on the problem being solved, most work adds the physical constraints directly into the loss or cost function, while others enhance performance using modified architectures or preprocessing techniques.In the realm of atmospheric sciences, challenges remain, including a heavy reliance on simulated data and limited use of observational datasets, which does not show the real-world applicability of PINNs. A detailed review of available results shows critical gaps in scalability, hybrid data integration, and standardization in atmospheric science.We identified a hybrid methodology by combining simulated and observational data, which includes optimizing hybrid loss functions to balance physics-based and observational accuracy, applying adaptive training techniques, and standardizing preprocessing schemes to handle multi-scale atmospheric phenomena.Results demonstrate the ability of PINNs to deliver faster computation, enhanced prediction accuracy, and robustness in sparse data environments. This highlights the transformative advantages of PINNs over traditional methods and suggests future directions for leveraging their capabilities in atmospheric science applications.
How to cite: Ekue, J. A., Hammond, D., and Agyei-Yeboah, E.: Envisioning the Role of Physics-Informed Neural Networks in Atmospheric Science: Advancements, Challenges, and Future Prospects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-243, https://doi.org/10.5194/egusphere-egu25-243, 2025.
An on-going campaign monitors the greenhouse gases emissions of biogas plants in the Grand Est region, in France, using airborne in situ CO2 and CH4 concentrations and wind measurements from Uncrewed Aerial System, associated with a mass balance method. During 16 days in 2024, we quantified the instantaneous emissions of 19 agricultural biogas plants, with installed methane productions ranging from 128 to 312 Nm3.h-1,producing biogas injected into the network mainly from manure, energy crops and agricultural wastes.
Observations obtained to date were used to quantify emissions either representative of the globality of a biogas plant or of specific targeted sources inside a site (inputs, effluents, digesters or biogas purification). Global plant methane emissions among all sites range from 1.5 to 26 kg.h-1, with average emissions of 10 kg.h-1. Repeated measurements of emissions on the same site at different dates depict a significant temporal variability, however overwhelmed by the variability of emissions among all sites. We estimated instantaneous methane losses ranging from 1.7 to 10 %, comparing monitored emissions with the installed productions. Emissions of targeted sources among sites suggest that inputs and effluents might be the predominant methane sources on the sites, while biogenic CO2 emissions might be mostly attributed to the biogas purification process.
This campaign highlighted several limits intrinsically linked with the mass balance method. One of them is the sensitivity to contamination by parasite sources, which has to be anticipated during the field campaign preparation. Another difficulty is the risk of measuring truncated plumes, as the mass balance method requires the monitoring of an entire plume cross-section to provide quantifications representative of the complete source emissions. These limitations could be overturned in the future by alternative quantification methods, such as inversion methods based on Large Eddy Simulation of the atmospheric transport, considering the highly variable nature of the turbulent plume. These new developments, associated with evolutions of the monitoring protocol, may improve the reliability and precision of the results.
How to cite: Bonne, J.-L., Dumelie, N., Lauvaux, T., Abdallah, C., Burgalat, J., Albora, G., Vincent, J., Cousin, J., Parent, F., Moncourtois, V., and Joly, L.: Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5789, https://doi.org/10.5194/egusphere-egu25-5789, 2025.
Agricultural burning in Tucumán, Argentina, has been a major contributor to air pollution, particularly during the dry season (April to September). This environmental issue is mainly due to the limited availability of modern machinery for sustainable harvesting, leading to heavy reliance on traditional biomass burning for crop residue management. The combustion process generates large amounts of fine particulate matter (PM2.5), which severely affects air quality and public health. To address this challenge, an inter-institutional collaboration under the Networking Initiative Breathe2Change.org, supported by the Alexander von Humboldt Foundation, facilitated the creation of the first air quality monitoring network in Tucumán. This initiative aimed to raise awareness and provide actionable data to local communities and scientists.
A custom sensor module was designed, integrating an OPC Plantower PMS5003 sensor for real-time PM2.5 detection, CO2 sensors using NDIR technology, as well as humidity and temperature sensors. A forced ventilation system was also incorporated to ensure representative air circulation inside the module without affecting airflow into the OPC sensor. The network, consisting of 25 sensor modules deployed throughout the 22,500 square kilometers of Tucumán, provided continuous data collection for 12 months in 2023. The data were shared on a publicly accessible data platform, developed as part of the Breathe2Change Initiative, which facilitated both citizen consultation and analysis by the scientists involved in the project.
During an initial 3-week intercomparison phase, 10 sensor modules were assessed for consistency, yielding a high correlation (R² > 0.9), confirming the reliability of the modules. Afterward, 23 of the 25 sensors were deployed across urban, suburban, and rural areas, including regions directly affected by agricultural fires. High- and low-flow reference samplers were used to collect daily PM2.5 concentrations from August to December, coinciding with the peak biomass burning period. During this period, two of the sensor modules were co-located with the reference samplers to allow for direct comparison. This phase was essential for deriving a local correction factor for the sensors.
Results showed considerably high PM2.5 concentrations, with monthly averages exceeding 60 µg/m³ in fire-impacted areas, well above the daily limits set by the World Health Organization (WHO). Even urban areas recorded average levels of 30 µg/m³, surpassing WHO guidelines. The region’s mountainous terrain and climate further exacerbated the pollution, triggering thermal inversion phenomena that trapped pollutants near ground level. Using the corrected sensor network, spatial distribution maps of PM2.5 were generated through Kriging interpolation, revealing a strong correlation between elevated pollutant levels and fire activity. Higher PM2.5 concentrations were observed in the central-eastern part of the province, likely linked to sugarcane production areas, and possibly influenced by rural traffic and biomass burning. Kriging analysis confirmed this spatial trend, with a marked reduction in localized concentrations after September, likely due to rainfall events.
This study underscores the degradation of air quality during biomass burning events and the need for regulatory measures and sustainable agricultural practices to mitigate environmental and health impacts. It also highlights the potential of low-cost sensors as effective tools for monitoring air pollution in resource-limited regions.
How to cite: Gibilisco, R. G., Aguilera Sammaritano, M., Reynoso Posse, F., Huber, K., Elizondo, J., Torkar, S., Saez, M. M., Scaglioti, A., Tames, F., Puliafito, E., Castellano, M. J., Diaz, M., Parellada, N., Ciancaglini, G., Schillman, B., Kurtenbach, R., Wiesen, P., Caggiano, A., Ben Altabef, A., and Teruel, M.: Spatio-Temporal Distribution of PM 2.5 and its Association with Agricultural Fires in Northern Argentina., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11635, https://doi.org/10.5194/egusphere-egu25-11635, 2025.
Background: The increasing availability and usage of low-cost air quality sensors (LCS) presents both opportunities and challenges in terms of data accuracy, reliability, precision and interpretation. Various low cost sensors types differ in the degree of accuracy reliability and precision They can also be influenced by environmental conditions like temperatures and humidity. This study assesses three LCS, E-Samplers, ModulairTM and AirQO, deployed alongside a reference-grade Beta Attenuation Monitor (BAM-1022) in Nairobi, Kenya, to upraise their performance under varying conditions and explore the strategies for calibration and integration into the monitoring networks.
Methods: The study used BAM-1022 data to validate and calibrate the LCS installed at the University of Nairobi’s Parklands Campus (27 February 2024 to 26 December 2024). We analyzed sensor accuracy, precision and response to pollution across wet and dry seasons and varying temperature and humidity levels. We aligned the LCS data with BAM-1022 measurements using tailored correction factors and multiple linear regression (MLR) models. We used the coefficient of determination, represented by R-squared (R2), a statistical measure of how close the data from the LCS are from the data from the BAM and the Pearson correlation, r to show the strength of the linear relationship between the sensor measurements and reference measurements. Additionally, we conducted paired t-tests to determine whether statistically significant differences existed between the BAM-1022 and each LCS, and one-sample t-tests to find out if there was a statistically significant difference in the values recorded by low-cost sensors themselves. The study also explored the potential of LCS to improve spatial coverage and resolution while addressing challenges like sensor drift and environmental interference.
Results: The ModulairTM sensor showed closer measurements in reference to BAM-1022 measurements (R2= 0.82, r =0.9458) followed by AirQO (R2=0.54, r =0.8933) and E-Sampler (R2=0.36, r =0.7166). During wet season, ModulairTM maintained the closer measurements (R2=0.73, r =0.9123) with AirQO (R2=0.36, r =0.7219) and E-Sampler (R2=0.21, r =0.7812) showing lower alignment. Similar trend was observed in dry season with ModulairTM (R2=0.8, r=0.8124) followed by AirQO (R2=0.51, r=0.7001) and E-Sampler (R2=0.28, r=0.6996). During high PM2.5 concentration periods (July to December), ModulairTM reported higher values than the BAM on certain days. AirQO generally recorded lower values except during these high concentration periods while the E-Samplers fluctuated between higher or lower values across the collocation period. Consequently, correction factors of -12.5, 31.55 and 29.65 were derived for ModulairTM,AirQO and E-Samplers respectively. Statistical analysis revealed a significant difference between the BAM measurements and LCS (p-value < 0.001). However, no significant differences were observed between the measurements of each of the low-cost sensors.
Conclusion: The LCS can enhance air quality monitoring networks when collocated appropriately and, consistently and carefully calibrated. The readings should be corrected against reference sensor for accurate and reliable data. Collocation with reference monitors or among the LCS units for regions with limited access to high-end monitoring infrastructure such as Nairobi is key before deployment. Air quality modeling can create a comprehensive monitoring networks hence improved spatial resolution and public health insights.
How to cite: Nyamondo, J., Oguge, N., Anyango, S., Afulloh, A., Adera, N., and Okoth, B.: Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor Deployment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20267, https://doi.org/10.5194/egusphere-egu25-20267, 2025.
Air quality monitoring is crucial for assessing environmental health and supporting mitigation strategies. This research focuses on the co-location of various low-cost particulate matter (PM) sensors—uRADMonitor, AirGradient, PurpleAir, Clarity, and sensors from community initiatives—alongside a mobile laboratory equipped with a reference-grade GRIM EDM 180 analyzer. The primary goal is to identify and quantify bias among these low-cost sensors for PM2.5 and PM10 measurements at the same location.
By systematically analyzing the measurement discrepancies, a generalized correction formula is derived, enabling the harmonization of readings across different sensor types. The corrected data will form the basis of a hybrid air quality monitoring network, which standardizes PM2.5 and PM10 concentrations regardless of the sensor manufacturer. This approach leverages the affordability and scalability of low-cost sensors while ensuring data quality comparable to reference instruments.
The results aim to address limitations in the current low-cost sensor ecosystem, enhance interoperability, and provide communities and policymakers with reliable, high-resolution air quality data. Ultimately, this study supports the development of inclusive and sustainable monitoring frameworks that empower both urban and rural regions with actionable environmental insights, using all kinds of sensors.
How to cite: Luchiian, A.: Harmonizing Low-Cost Air Quality Sensors for a Hybrid Monitoring Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10631, https://doi.org/10.5194/egusphere-egu25-10631, 2025.
The rapid urbanization of modern cities presents significant challenges, with air pollution emerging as a critical concern for public health and environmental sustainability. In Greece, while the government collects extensive air quality data as mandated by the EU Directive 2881/2024 (recast of 2008/50, 2004/107), limited efforts are made to communicate this data to the public. The existing network of large monitoring stations is often inaccessible to the pubic and primarily serving scientists and policymakers.
Addressing this gap, the FAIRCITY (ATTP4-0360457) project—a collaboration between the National Observatory of Athens, the National and Kapodistrian University of Athens and the Greek Innovation Company Energy4Smart—introduces the “Smart Stations” an innovative solution incorporating public benches powered by photovoltaics, equipped with free charging sockets for people with electrical wheelchairs as well as other smart city sevices, with embedded low cost air quality sensors, designed to make air quality data accessible, timely, and engaging. This initiative not only aligns with global sustainability goals but also serves as a model for other cities seeking to improve urban liveability. The low-cost sensors embedded within the bench at a height of approximately 3 meteres above ground, were selected based on size, technology and price criteria to continuously monitor eight key pollutants: three fractions of Particulate Matters (PM1, PM2.5, PM10), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), ozone (O3) and sulfur dioxide (SO2).
The Smart Stations are deployed in open, public spaces (e.g., commercial areas, residential zones, parks), at a distance from major pollutant sources and in collaboration with interested municipalities of the Attica Region. Their aim is to record the local air quality and pollutant diurnal variations in order to highlight the sources responsible (i.e., Korydallos high NO2, NO, PM concentrations from traffic) and estimate the population exposure. Citizens can walk up to these stations, sit down and instantly access critical information about their local air quality from digital displays that provide in near real-time the simplified Air Quality Index (AQI) along with health protection and other environmental infomation.
Preliminary results indicate that the diurnal variations of the monitored pollutants follow closely the local anthropogenic activities (traffic by passing the area, central heating, cooking). The pollutant levels are similar across the different municipalities, presenting peaks at different times depending on the type of area. The hourly AQI is mainly affected by larger scale events such as an extensive air pollution episode or dust intrusion event.
How to cite: Assimakopoulos, V., Fameli, K.-M., Kladakis, A., Efthymiou, C., Charalampidou, C., Sotiropoulou, M., Antoniou, I. –. M., Kytrilaki, A., Massas, A., and Assimakopoulos, M.-N.: Bridging the Gap: Smart Benches for Accessible Urban Air Quality Monitoring and Public Engagement in the Region of Attica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10268, https://doi.org/10.5194/egusphere-egu25-10268, 2025.
Air quality monitoring remains a significant challenge in urban areas, particularly where high-cost infrastructure is unavailable or difficult to maintain. Traditional monitoring systems are often limited in scope due to expense and logistical constraints, leading to data gaps, especially in resource-constrained environments. Low-cost air quality sensors have the potential to transform environmental monitoring by providing accessible, affordable tools for collecting air quality data, especially in urban settings. As part of the SCORE project, a low-cost sensor system was developed to support real-time air quality monitoring across European cities. These sensors provide a more granular understanding of air pollution trends, making air quality data collection both scalable and accessible to a wider range of stakeholders, including local communities. This presentation will highlight the deployment of these sensors in Dublin, Ireland, where they have been successfully integrated into citizen science initiatives, enabling communities to actively participate in environmental data collection and contribute to air quality management.
Ensuring data accuracy and reliability is a key challenge in the use of low-cost sensors. We will examine the technical challenges of deploying low-cost sensors, such as calibration, accuracy, and long-term reliability in small-scale urban environments. The presentation will also discuss strategies for integrating sensor data into authoritative air quality monitoring networks to enhance overall data quality and spatial coverage.
In Dublin, the citizen science air quality initiative has built strong connections between local communities, researchers and policymakers. This collaboration exemplifies how co-created initiatives, backed by accessible technology, can empower citizens and bridge the gap between public engagement and formal policy processes. The outcomes of the Dublin case study suggest broader applicability for the SCORE model in other cities facing similar air quality challenges. By offering a replicable and scalable solution, low-cost sensors provide an affordable alternative to high-end monitoring stations, enabling resource-limited municipalities to expand their air quality infrastructure. The project demonstrates how engaging local communities in the data collection process can foster long-term, sustainable environmental stewardship. These insights underscore the importance of equitable partnerships between citizens, researchers, and governments in tackling air pollution, particularly in cities where financial or technical constraints have traditionally limited comprehensive air quality monitoring.
How to cite: Daruari, H., Crowley, S., Cocco, C., and Barrón, J. P. G.: Building Equitable Air Quality Networks: Low-Cost Sensors and Community-Led Monitoring in Dublin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1073, https://doi.org/10.5194/egusphere-egu25-1073, 2025.
Black carbon (BC) is a short-lived atmospheric aerosol having light absorbing properties with climate-changing potential. In addition, BC aerosols are also responsible for several adverse health effects including cardiovascular and respiratory problems. Here, we examine the long-term changes in BC, using MERRA-2 (Modern-Era Retro spective analysis for Research and Applications) and Emissions Database for Global Atmospheric Research (EDGAR) data for the period 2000–2019, and the associated health burden in rural India. This study finds a decreasing trend in BC in the rural IGP (Indo-Gangetic Plain) and NWI (North West India) during 2007–2019, at about -0.004 and –0.005 μg/m3/yr, respectively. A significant reduction in BC (from 0.03 to 0.01 μg/m3/yr after 2006) is observed in the rural Peninsular India (PI), where the reduced wind speed limits the transport of BC aerosols from other regions and thus, limits the BC concentration there. Our assessment finds that government policies such as BS (Bharat Stage) emission norms, electrification of rail routes, use of electric and compressed natural gas-based vehicles, the transformation of brick kilns to zig-zag technology, mechanised farming for on- site handling of crop residues and recent changes in atmospheric drivers (e.g. winds in IGP) contributed to this reduction in BC. However, the health burden associated with BC causes the highest all-cause mortality to be around 5,17,651 and 34,082 inhabitants in winter (December-February) and post-monsoon (October-November) seasons, respectively, in the rural IGP in the latest year 2019. In brief, the reduction of BC in rural India indicates that it complements the government policies. However, an improvement in the policy implementation might prove to be conducive to reduce the BC-driven mortality and regional climate warming.
How to cite: Pathak, M., Kuttippurath, J., and Kumar, R.: Long-term changes in black carbon aerosols and their health effects in rural India during the past two decades (2000–2019), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9946, https://doi.org/10.5194/egusphere-egu25-9946, 2025.
