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How the Impact of ENSO Events on the Indonesian Throughflow and Interannual Variability of Circulation in the Halmahera Sea
(2026-09-01)
Lubis, Muhammad Zainuddin
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Dwinovantyo, Angga
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Batara, B.
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Kausarian, Husnul
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Hidayat, Syarief
This study investigates the influence of El Niño–Southern Oscillation (ENSO) on the Indonesian Throughflow (ITF) in the Halmahera Sea (HS) during 2013–2023. Although ITF variability has been widely studied, the response of the HS to different ENSO phases remains insufficiently understood. Using a validated CROCO model, we quantified changes in HS throughflow and associated oceanographic conditions under El Niño and La Niña events. EOF analysis of sea surface height (SSH), meridional velocity, and salinity revealed dominant modes explaining more than 50% of the total variance and exhibiting clear ENSO-related variability. During El Niño events, ITF transport weakened substantially, with volume transport becoming less negative from − 4.0 ± 2.0 Sv to − 1.5 ± 1.5 Sv in the upper layer (0–100 m) and from − 5.0 ± 2.5 Sv to − 2.0 ± 2.0 Sv in the intermediate layer (0–400 m). These conditions were associated with a shallower thermocline, lower sea surface temperatures (25.5–26.0°C), and reduced surface salinity (34.74 PSU). In contrast, La Niña events strengthened the ITF, with transport reaching −7.0 ± 1.5 Sv in the upper layer and −7.0 ± 2.0 Sv in the intermediate layer, accompanied by a deeper thermocline and higher SSTs (29.8–30.42°C). The results suggest that ENSO-related variability in the HS is associated with coupled changes in thermocline structure, SSH gradients, and salinity, highlighting the importance of the HS in regulating ITF transport variability.
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Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake
(2026-08-01)
Tahreem, Azka
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Shah, Munawar
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Jamjareegulgarn, Punyawi
Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.
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Climate thresholds and yield elasticity of durian, mangosteen, and coffee under hydroclimatic variability in Thailand
(2026-08-01)
Ansari, Kutubuddin
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Tanır Kayıkçı, Emine
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Jamjareegulgarn, Punyawi
This study examines the empirical relationships between hydroclimatic variability and the yields of durian, mangosteen, and coffee across Thailand using meteorological observations from 56 stations and provincial level yield statistics for 2008 to 2024. An integrated statistical framework was applied, including exploratory correlation analysis, nonlinear quadratic additive modelling, lagged climate response analysis, elasticity estimation, model comparison, mangosteen regional sensitivity analysis, and a Climate Risk Index (CRI). The results show clear spatial gradients in temperature, humidity, and precipitation across the six agroclimatic regions of Thailand, broadly corresponding to regional crop productivity patterns. Annual anomaly analysis indicates that warm and humid years are generally associated with higher durian and mangosteen yields, whereas excessive rainfall and warming are associated with reduced coffee productivity. Pairwise linear correlations between annual climate variables and crop yields are generally weak, suggesting that simple linear models may not fully capture crop climate relationships. Nonlinear modelling indicates approximate empirical temperature turning points near 28.0°C for durian, 27.3°C for mangosteen, and 26.6°C for coffee, with humidity related turning points around 76–78%. Lagged climate response models suggest that multi-year hydroclimatic conditions may influence perennial crop productivity, particularly humidity for durian, precipitation for coffee, and temperature and precipitation for mangosteen. The mangosteen sensitivity analysis shows that humidity and precipitation thresholds are affected by regional composition, especially when marginal production regions are included. The CRI formulation under the equal weight, mangosteen shows the highest rainfall related climate risk signal, while coffee is more sensitive to temperature related risk and durian shows moderate vulnerability to prolonged humid and wet conditions.
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Ionospheric gradients in multi-constellation global navigation satellite system signals onboard UAV using GIM and Klobuchar model over Thailand region
(2026-07-01)
Ansari, Kutubuddin
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Panda, Sampad Kumar
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Venkatesh, Kavutarapu
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Jamjareegulgarn, Punyawi
The effects of the ionosphere on Global Navigation Satellite System (GNSS) signals have been a focal point of research nowadays. During adverse ionospheric conditions, ionospheric gradients become more pronounced and disruptive compared to quiet days, potentially leading to increased positioning errors or loss of satellite signal lock. We introduce an ionospheric spatial gradient estimation method to detect the anomalous gradients from multi-constellation GNSS signals (i.e., GPS, GLONASS, and Galileo) signals recorded by the onboard sensor of flying real-time kinematic unmanned aerial vehicle (RTK UAV) over the Thailand region. We employ the Klobuchar model and global ionospheric maps (GIMs) for estimating the slant total electron contents (STECs) and the corresponding ionospheric spatial gradients between base station and rover (RTK UAV) receivers among the studied multi-constellation systems. The results show that the STEC values estimated from IGS-GIM are larger than those computed by Klobuchar model. Such kind of gradient variation cannot show a perfect correlation due to limited accuracy of Klobuchar model parameters. As for our analysis, the ionospheric spatial gradients estimated from GPS satellites are higher than those calculated from GLONASS and Galileo satellites due to the smallest differences between the two successive positions of flying rover estimated from GPS satellites. The outcomes from this study complement the multi-GNSS cooperative strategy for monitoring ionospheric gradients, thereby mitigating the adverse effects in dynamic positioning and navigation solutions over low-latitude regions.
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An operational machine learning framework for calibrating COSMIC radio occultation TEC to ground-based GNSS-derived TEC
(2026-03-15)
Okoh, Daniel
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Habarulema, John Bosco
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Nava, Bruno
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Cesaroni, Claudio
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Baki, Paul
The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) provides global Radio Occultation (RO) measurements of ionospheric total electron content (TEC), but these values are systematically underestimated relative to ground-based Global Navigation Satellite System (GNSS)-derived TEC due to the exclusion of the plasmaspheric contribution. This study presents a machine learning calibration framework that transforms COSMIC TEC into GNSS-equivalent values. Using co-located COSMIC and GNSS observations from 2006 to 2025, we developed neural network models (ROTEC-A and ROTEC-B) trained on (19 and 22) input features respectively, including COSMIC profile parameters, spatiotemporal descriptors, and optionally, solar and geomagnetic activity indices. Results show that the calibration effectively mitigates systematic underestimation, reducing mean bias from 6.97 TECU (uncalibrated COSMIC) to near zero (0.02–0.03 TECU). The calibrated products also substantially reduce skewness in residuals, yielding nearly symmetric error distributions suitable for data assimilation. Across various latitudinal, local time, and seasonal sectors, mean absolute errors were reduced by 50–75%, with the best performance at mid-latitudes and slightly elevated errors in high-latitude and equatorial regions. Although, the inclusion of solar and geomagnetic indices yielded marginal improvements, statistical tests confirmed no significant advantage over the baseline model. The operationally oriented framework outputs calibrated GNSS-equivalent TEC in near real-time, providing enhanced ionospheric monitoring capability, especially over GNSS-sparse regions such as oceans and deserts. These results demonstrate the potential of COSMIC RO data, once calibrated, to serve as a reliable complement to GNSS observations for ionospheric research, space weather monitoring, and operational applications.
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Drought vulnerability assessment using morphometric features and extreme precipitation indicators to prioritize sub-basins: AI-based Fuzzy Logic approach
(2026-03-01)
Nigam, Utkarsh
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Patel, Vinodkumar M.
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Patel, Dhruvesh P.
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Jodhani, Keval H.
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Gupta, Nitesh
The identification of watersheds and extraction of drainage networks are essential for effective hydrological and geomorphological modelling. This study investigates the influence of morphometric factors and extreme precipitation events on the hydrological responses of the Sabarmati River Basin (SRB), India, to identify the drought-vulnerable sub-basins. Watershed prioritization was carried out using satellite remote sensing, GIS, and secondary data, including topographic sheets and ASTER DEM with a spatial resolution of 90 m. The SRB was divided into nine sub-watersheds, and 28 morphometric parameters were evaluated, comprising 07 linear, 15 areal, and 06 relief parameters. A compound factor (CF) was derived using multi-criteria decision-making techniques such as Weighted Sum Analysis (WSA), Principal Component Analysis (PCA), Analytic Hierarchy Process (AHP), Fuzzy-AHP (FAHP), and TOPSIS. Sub-watersheds were ranked based on CF value, where a lower CF indicated higher priority for runoff management strategies. Additionally, 42 years of precipitation data were analysed using the Standardized Precipitation Index (SPI) at timescales ranging from 3 to 24 months to assess trends in drought and extreme precipitation events. The analysis indicate decline in runoff potential in several sub-basins, however others (e.g., SB2, SB3, SB4, SB5) exhibit positive precipitation trends, making them suitable for runoff enhancement. This integrated methodology offers a comprehensive framework for managing sub-basins, optimizing runoff potential, and supporting sustainable water conservation. The results provide actionable insights for policymakers and planners to better utilize the SRB water resources based on its geomorphological and climatic characteristics.
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How the interannual and seasonal variability in connectivity to ITF and its relation to ENSO events in the Halmahera Sea: A modeling approach and observation
(2026-02-01)
Lubis, Muhammad Zainuddin
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Hu, Song
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Simanjuntak, Andrean V.H.
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Dwinovantyo, Angga
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Arnold, Ekoué Ewane Blaise
Understanding circulation dynamics in the Halmahera Sea (HS) is essential for clarifying the interactions between El Niño and La Niña events and ITF variability, which significantly impacts the global climate. Despite studies on air-sea interactions in the HS, gaps remain regarding interannual and seasonal variability, leading to uncertainties in understanding ocean circulation and throughflow dynamics. Specifically, there is a lack of understanding of how seasonal temperature and salinity shifts influence the strength and direction of ocean currents in the HS region. This study investigated how temperature and salinity variations during interannual and different seasons affect ocean current patterns in the HS. We used the CROCO model from 2019 to 2023 and observed it in September 2021. Our findings indicate distinct interannual patterns in SST, with the northwest monsoon SST ranging from approximately 28.3–29.8°C and the southeast monsoon SST ranging from approximately 26.8–29.0°C. The SSS varied between approximately 34.1 and 34.6 PSU, with the maximum mean water mass transport in the subsurface recorded at −0.5479 ± 0.6583 Sv at 0.5°S, indicating transport toward the Banda Sea. The SSH ranged from ∼0.69 to ∼0.73 m in the northwest and ∼0.66 to ∼0.69 m in the southeast monsoon. The densities at the surface, subsurface, and intermediate layers were 22.5, 25.5, and 26.5 σ<inf>ѳ</inf>. This research advances our understanding of the factors affecting HS circulation dynamics and their responses to El Niño and La Niña events, paving the way for Indo-Pacific water exchange studies.
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Understanding green house gases emission dynamics from forest fires in Thailand using predictive models
(2026-02-01)
Shahzad, Fahad
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Mehmood, Kaleem
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Anees, Shoaib Ahmad
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Adnan, Muhammad
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Hussain, Khadim
Forest fires are a major driver of carbon emissions, particularly in tropical regions where climate variability and land use practices intensify their frequency and impact. This study investigates the spatiotemporal trends and emission dynamics of forest fires across Thailand's three dominant vegetation types- Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Grassland over three climatic seasons (Dry, Hot, and Wet) in the period 2001–2023. Using the Mann-Kendall trend test and Sen's Slope estimator, we observed significant declines in burnt area during the Dry season in EBF and Grasslands, with no consistent trend in DBF. Fire–vegetation interactions revealed seasonally specific effects: positive correlations between fire count and Net Primary Productivity (NPP) were detected in the Wet and the hot seasons in the case of DBF and Grasslands, respectively. Emission analysis showed that CO₂ was the dominant greenhouse gas released, with the Dry season contributing to most emissions, although Hot season emissions have increased over time. Machine learning models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) explained over 78 % of the variance in CO₂ emissions on test data (R<sup>2</sup> = 0.79 for RF, 0.78 for XGBoost), despite higher Root Mean Square Error (RMSE) values (∼550) on unseen data. The Shapley Additive Explanations (SHAP) analysis identified wind components and solar radiation as key predictive variables. Central, Northeastern, and Northern Thailand emerged as emission hotspots. These findings improve our understanding of emission dynamics from tropical fires and underscore the need for region-specific mitigation strategies to inform carbon inventories and climate policy.
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Multi-temporal scale of urban rainfall in the Eastern Northeast based on observed data and gridded products: physiographic factors and changes in land use and occupation
(2026-02-01)
da Silva, Luis Felipe Francisco Ferreira
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de Oliveira Romão, William Max
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Lyra, Gustavo Bastos
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da Silva, Elania Barros
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da Silva Costa, Micejane
Rainfall is one of the most important meteorological variables in the daily lives of urban populations. The city of Maceió, the capital of Alagoas, located in the eastern part of the Northeast of Brazil (ENEB), has 50 neighborhoods and a population of approximately one million people, with few studies on the subject. The objectives were: (i) to validate the CHIRPS product; (ii) to identify the preferential rainfall periods in Maceió via GIS; (iii) to map areas for the installation of in situ stations in the city with the aim of supporting the prevention of hydrometeorological disasters; and (iv) creation of a theoretical-conceptual rainfall model. The statistical indicators (R², ρ, BIAS, MAPE and RMSE) were used to validate the gridded precipitation product CHIRPS from 11 CEMADEN rain gauge stations. Monthly rain occurrence maps via Spline tension were developed by QGIS (Quantum GIS) software. The HAND model was applied at neighborhood level for the assessment of urban floods. Waterborne disease data were obtained from SINAN, Natural Disaster data via S2iD from the period 2000 to 2023, and the NDVI and EVI indices in the years 2015 and 2022 were evaluated in the study. All stations were monotonically positive (ρ > 0.65) and significant (p-value < 0.001), indicating that CHIRPS is able to capture rainfall variability despite the influences of the coast, Lagoa Mundaú, and topography. Most stations showed underestimation (negative BIAS) and lower errors (MAE and RMSE). Spatially, the increase in rainfall on the coastal plateau is due to the interaction of the wind regime with the relief, driven by the circulation of breezes and the influence of trade winds. The preferential rainfall period occurs between 04:00 am and 07:00 am. The HAND model identified very high and high susceptibility, mainly on the coast, in areas adjacent to Lagoa Mundaú, and in neighborhoods crossed by rivers and urban canals, and low susceptibility in densely populated neighborhoods. Waterborne diseases together with transformations via NDVI and EVI indicated that rainfall amplifies risk scenarios for the most vulnerable and densely populated populations. In light of this, it is perceived that the rainfall patterns in Maceió are due to the interaction of physiographic and/or anthropogenic factors and meteorological systems – theoretical-conceptual model – which requires improvements in infrastructure and an active monitoring system.
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Development of a Global Climate Model for Atmospheric Temperature Using Machine Learning
(2026-01-01)
Okoh, Daniel
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Awuor, Adero
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Ochieng, George
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Baki, Paul
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Habarulema, John Bosco
This article presents a novel three-dimensional global model of atmospheric temperature developed using Artificial Neural Networks (ANNs) trained on radio occultation (RO) data from the COSMIC I and COSMIC II satellite missions. Over 14.7 million quality-controlled profiles were used, providing approximately 9.5 billion data points that capture temperature variability across latitude, longitude, altitude (0-60 km), and time (2006-2025). The global domain was divided into 1296 spatial grid cells (10° × 5°) to enable localized ANN training and ensure efficient handling of regional atmospheric dynamics. Model performance was evaluated through cross-validation and independent testing against radiosonde measurements from 684 stations worldwide. Results show mean absolute errors of 1.5 °C-4.5 °C and root-mean-square errors of 2.5 °C-6.5 °C, with best performance in the tropical troposphere and increasing errors toward high latitudes. The model successfully reproduces key climatological structures (including the tropospheric lapse rate, stratospheric inversion, and seasonal hemispheric asymmetries), and accurately captures diurnal and annual thermal cycles. Long-term simulations (2006-2025) reveal a distinct tropospheric warming trend (∼+0.07 °C per year at 11 km) and a corresponding stratospheric cooling (∼-0.03 °C per year near 32 km), consistent with established satellite and reanalysis records. These results demonstrate that ANN-based frameworks can effectively model global atmospheric thermal structure and evolution, providing a scalable approach for future climate monitoring and forecasting applications.

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