This study proposes the Expanded Spatial Lag of Exogenous model, which captures the spatial autocorrelation of exogenous variables and extends to surrounding areas. The exogenous variables considered are air temperature, humidity, and surface pressure. Moran’s Index test confirms the appropriateness of applying these variables. Then, the model is abstracted as an RShiny-based web application, allowing users to interact. The model and web application are then implemented using climate data from Java Island, the region with the highest hydrometeorological risk in Indonesia. The data are sourced from NASA POWER, the large-scale climate database. Due to the large volume of data, the Data Analytics Life Cycle is utilized for data discovery, preparation, model planning, construction, evaluation, and deployment. The proposed model empirically achieves a Mean Absolute Percentage Error of 4.22% in-sample and 3.53% out-of-sample. This study provides a tool to predict the rainfall in some areas (spatial prediction), supporting hydrometeorological risk assessments.

This review offers a comprehensive analysis of convectively coupled equatorial waves (CCEWs) and their pivotal role in driving precipitation extremes across the Maritime Continent. It examines the current understanding of CCEWs, evaluates the performance of numerical models and forecasting techniques in predicting these phenomena, and pinpoints critical areas for improvement. The discussion centers on three key types of equatorial waves: equatorial Rossby waves, Kelvin waves, and mixed Rossby–gravity waves. By connecting scientific insights with practical forecasting applications, the review sheds light on the challenges of predicting these waves while identifying opportunities to advance both fundamental knowledge and forecasting accuracy. Designed as an educational resource, it targets operational forecasting centers, meteorologists, and researchers, aiming to enhance the prediction of extreme weather events in the region. Rainfall extremes in the Maritime Continent are among the most intense on Earth, posing major challenges for both society and weather forecasting. This review highlights the crucial role of convectively coupled equatorial waves—large-scale tropical weather systems—in triggering such events. Through both their direct influence and interactions with the diurnal rainfall cycle and other weather systems, equatorial waves amplify precipitation and create favorable conditions for extreme rainfall. Despite their important role in modulating precipitation extremes, they are still underrepresented in many numerical weather prediction models, limiting forecast accuracy. Improving understanding and representation of these waves offers a pathway toward better forecasts and greater resilience in one of the world’s most vulnerable and rain-prone regions.

The complexity of monsoonal systems in Indonesia, driven by large-scale ocean-atmosphere interactions and local climate variability, requires the practical use of models for accurate rainfall prediction. This study presents an enhanced forecasting model for the Indonesian Monsoon Index (IMI) that integrates the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) methodologies. We utilized ERA5, the European Centre for Medium-Range Weather Forecasts v.5 global reanalysis, zonal wind data at 850 and 200 hPa, and 30 years of precipitation data validated against in situ observations. This data was used to develop a modified IMI and assess its applicability in two selected regions: the North Coast of Java and East Kalimantan Province. Seasonal ARIMA modeling indicates robust short-term predictive capabilities (R² = 0.90) for five-month forecasts despite diminished performance in the presence of non-linear patterns or sudden climatic transitions. To address these limitations, a hybrid ANN-ARIMA model was adopted, which improved forecast accuracy by up to 8 months while increasing correlation (R² = 0.91) and reducing the risk of overfitting. This hybrid model effectively captures irregular seasonal variations, outperforming the conventional ARIMA, and provides more advanced tools for long-term forecasting under variable climatic conditions. In addition, the analysis revealed a notable connection between rainfall anomalies on the North Coast of Java and specific phases of the monsoon index, highlighting a significant influence of monsoons on local weather patterns. The improved predictive capabilities of the hybrid model is valuable for planning and decision-making in agriculture, water management, and disaster preparedness.

Mixed Rossby-gravity (MRG) waves are key equatorial disturbances that modulate convection and rainfall across the Maritime Continent, yet their representation in regional models remains underexplored. Accurate simulation of MRG waves induced vertical structure is critical for improving forecasts of tropical weather variability and associated hydrometeorological hazards. This study evaluates the Weather Research and Forecasting (WRF) model's ability to simulate vertical atmospheric profiles during an MRG wave event on the southern coast of West Java from 26 to 29 September 2022, using radiosonde observations at 06, 12, and 18 local time (LT). Eleven model configurations were evaluated, differing in domain schemes (two vs. three nesting steps), vertical resolution (33, 45, 60, 80, and 100 levels), and input data sources (Real-Time Global Forecast System [GFS] vs. Final Operational Global Analysis [FNL]). All model configurations used in the simulation have a fixed physics scheme parameterization. The simulations were compared with radiosonde observations and evaluated statistically using the correlation coefficient (R) and Normalized Mean Absolute Error (NMAE). The analysis demonstrates that the WRF model effectively captures MRG wave dynamics by simulating key atmospheric variables, including pressure (P), temperature (T), relative humidity (RH), zonal (U) and meridional (V) wind anomalies in strong agreement with observations. P is well represented, exhibiting the highest R (0.81), whereas RH is the lowest (0.21), likely reflecting the model's inability to capture fine‑scale observed moisture variations. Configurations that utilized a two-step nesting domain and the FNL input demonstrated the best performance, achieving higher R values and lower NMAE. Input data had a notable impact on model performance: the FNL analysis improved R by ~36% and reduced NMAE by ~12% compared to GFS, likely due to FNL's assimilation of observational data, which reduces uncertainty. Moreover, a domain scheme with a smaller outer domain and fewer nesting steps also improved R by ~36% and reduced NMAE by ~12%, suggesting that simpler domain configurations help limit error propagation. Additionally, increasing the vertical resolution from 33 to 100 levels enhanced the simulation of MRG wave structures, improving R by ~45% and reducing NMAE by ~22%. These findings enhance the understanding of MRG wave dynamics and offer valuable insights for improving regional weather forecasting.

The characteristics of atmospheric variables over the southern coast of West Java in the presence of Australian Monsoon (AUM) and Mixed Rossby-Gravity (MRG) waves were investigated by conducting a dedicated radiosonde observation campaign from 26 to 29 September 2022 at Pameungpeuk Station (107.7°E, 7.6°S). The vertical profiles of pressure, temperature, relative humidity, and wind in the presence of the AUM and MRG waves were obtained and analyzed. The Global Navigation Satellite System - Radio Occultation (GNSS-RO) and the latest ECMWF climate reanalysis (ERA5) data were used to identify the MRG waves features using Hovmöller, space-time spectral, and wavelet analyses. Furthermore, spatial and time-series analyses were performed to study the wave propagation. The results showed the characteristics of atmospheric variables over Pameungpeuk Station in the presence of the AUM and MRG waves. We found that the presence of AUM significantly reduced the relative humidity, particularly in the region from 2 to 10 km altitude, and enhanced the average wind speed during the campaign compared to the wet season. Moreover, we found the reduction of low-level pressure, the enhancement of temperature and relative humidity in the mid-troposphere, and the weakening of the easterly and southerly winds during the dry phase approaching the wet phase of the MRG waves over the SOUTHERN COAst of West Java following the wave structure and propagation.

Nusantara, the new capital city of Indonesia, and its surrounding areas experienced intense heavy rainfall on 15–16 March 2022, leading to devastating and widespread flooding. However, the factors triggering such intense heavy rainfall and the underlying physical mechanisms are still not fully understood. Using high-resolution GSMaP (Global Satellite Mapping of Precipitation) data, we show that a mesoscale convective system (MCS) was the primary cause of the heavy rainfall event. The rainfall peak occurred during the MCS’s mature stage at 1800 UTC 15 March 2022, and diminished as it entered the dissipation stage. To understand the large-scale environmental factors affecting the MCS event, we analyzed contributions from the MJO, equatorial waves, and low-frequency variability to column water vapor and moisture flux convergence. Results indicate a substantial influence of the MJO and equatorial waves on lower-level (boundary layer) meridional moisture flux convergence during the pre-MCS stage and initiation, with their contributions accounting for up to 80

The effect of the Cross Equatorial Northerly Surge (CENS) on the diurnal cycle of convection and moisture convergence over Jakarta and the surrounding area has been investigated. The data used in this study was the CENS indices, the Convective Available Potential Energy (CAPE), the Convective Inhibition Energy (CINH), the Vertically Integrated Moisture Fraction Convergence (VIMFC), and dew point temperature and rainfall from the fifth generation of the European Center for Medium-Range Weather Forecasting (ECMWF) Atmospheric Reanalysis (ERA5) during 2014. The data was depicted in a graph against the diurnal as well as the seasonal cycle, thus illustrating the multi-scale variability. The results showed a very strong influence of the CENS phenomena on the diurnal cycle of the CAPE, the CINH, the VIMFC, and rainfall in Jakarta and the surrounding area, over both the land and the sea areas. We found that in general, the CENS tended to suppress convection, but enhances moisture convergence. The important results of this study were that the increase of rainfall that occurred during active CENS was not actually caused by the increase of convection, but rather due to the increase in moisture convergence.

Hail is one of the atmospheric phenomena generally caused by weather anomalies due to convective storms with adequate updraft strength, sufficient availability of supercooled liquid water content, and conducive temperature and optimal time. This paper aims to analyze the atmospheric mechanism when hail occurred in Bandung on March 8, 2022. We used data analysis of the rain observation system by rain scanner to observe rain's distribution and temporal evolution. Also, we used analysis of satellite imagery data to observe the cloud type and cloud height during hail phenomenon in Bandung. Moreover, the atmospheric profile during the hail precipitation is analyzed using ERA5 data for several supporting parameters such as ice water content (ICW), liquid water content (LCW), moisture, vertical wind, and temperature profiles. The results indicate that heavy rain in the middle of Bandung area (study case: Antapani, Cicadas) was observed since 14:14 Local Time (LT) with rapid rain growth for approximately 1 h. The rain propagates and expands from south to north Bandung and converges with another cloud system developed in the northern area of Bandung. The rain is indicated to come from height clouds based on Himawari imagery. Persistent rainfall with a wide coverage lasts up to 15:30 LT and indicates cloud growth that reaches the height of the tropopause and stratosphere. This high cloud cluster is suspected to form ice precipitation falling to the study area at 14:40 LT. An indication of strong wind convergence since 14:00 LT is also one of the prominent features before the hail precipitation occurred over the study area.

The Raster-based Probability Flood Inundation Model (RProFIM) approach was proposed in this study as a new model for flood inundation modelling. Scenarios of changes in land use/land cover (LULC) and differences in return periods are used as the basis for flood modelling scenarios in the study area. The aims of this study are: (a) to estimate the discharge volume in the scenario of changing LULC between 1990 and 2050 and the difference in return period between 2 and 100 years; (b) to create and produce flood inundation maps using the RProFIM approach; (c) to analyse the flood-affected area based on the results given by overlaying the flood inundation maps with LULC data. In general, the results of the flood probability modelling from the RProFIM model can provide the same pattern and conditions as indicated by the reference data. The results of the study also found several potential flood-prone areas in the Citarum Watershed, West Java-Indonesia, based on the RProFIM approach in the Districts of Margaasih, Kutawaringin, Margahayu, Katapang, Dayeuhkolot, and Baleendah. Furthermore, the results of overlaying the flood probability model from the RProFIM model with LULC data are used to determine the flood-affected area. These results indicate that in general the greatest impact of flooding occurs on agricultural and built-up lands which continues to increase every year in the return period range of 2–100 years. This study is expected to be used as one of the considerations in managing environmental problems in dealing with flooding in the study area.

It has been well established that coastal flooding is caused by heavy rains, storm surges, and high tidal waves that potentially lead to tremendous damage. Using brightness temperature (TBB) data from the Himawari satellite and ocean surface wind data from ERA5, we have investigated meteorological factors influencing strong wind and increased seawater level over the northern coast of Semarang which caused the coastal flood on 23 May 2022. Our results indicated that there are two potential meteorological factors that contribute to the coastal flooding during that period. Firstly, the formation of MCS over the ocean on the northern coast of Semarang led to heavy rain over the coast and strong-surface wind speed that potentially enhances ocean tidal waves toward Semarang. Secondly, the unusually strong-surface easterly winds cause the increase in seawater level through the Ekman pumping mechanism. As for the latter, the strong easterly wind stress led to strong Ekman transport to the south of the flow (i.e., to the northern coast of Java), causing a large net transport of seawater toward this region as a result of a balance between Coriolis and turbulent (wind) drag forces. Our results provided a new perspective on the factors influencing the increased seawater level intruding into Semarang during the coastal flooding on 23 May 2022.

This study uses a hybrid machine learning method that combines Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Averages (ARIMA) to model Indonesian Monsoon Index (IMI) and predict rainfall anomalies in two regions of Indonesia. The North Coast of Java has a monsoonal rainfall pattern, while the Province of East Kalimantan has an equatorial rainfall pattern. The hybrid ANN-ARIMA model significantly improves predictions with a correlation value of 0.91 over an 8-month period. This study concludes that the IMI modeling with ANN-ARIMA hybrid approach can provide predictions with higher accuracy and a longer period than the conventional ARIMA method.

Rainfall is one of the climatological parameters that play a role in climate variability in Indonesia. Climate dynamics in Indonesia require a comprehensive analysis to determine the factors causing extreme events in Indonesia. The velocity potential of 200 hPa can describe the convective level of clouds in the troposphere. To analyze the dynamics of the Indonesian atmosphere, a temporal, spatial, and spectrum analysis was carried out at a potential velocity of 200 hPa. The results showed that the velocity potential of 200 hPa had oscillations for six months with peak oscillations in MAM and SON. In the case of extreme climates in Indonesia, especially in El Niño in 2015 and La Niña in 2020, the velocity potential of 200 hPa can capture changes in climate conditions both in Indonesia and in the Pacific Ocean as seen from the results of spatial analysis and the Hovmöller diagram shown. The velocity potential of 200 hPa has a negative correlation with rain. This explains that if the rainfall increases, the velocity potential of 200 hPa will decrease and vice versa. Based on the coefficient of determination test results, the potential velocity of 200 hPa contributes around 10–20%. In general, a potential velocity of 200 hPa can to rate the dynamics of the atmosphere that occurs in Indonesia, especially in the case of El Niño in 2015 and La Niña in 2020.

Unusually long duration and heavy rainfall from 5 to 6 February 2021 caused widespread and devastating floods in Semarang, Central Java, Indonesia. The heavy rainfall was produced by two mesoscale convective systems (MCSs). The first MCS developed at 13Z on 5 February 2021 over the southern coast of Sumatra and propagated towards Semarang. The second MCS developed over the north coast of Semarang at 18Z on 5 February 2021 and later led to the first peak of precipitation at 21Z on 5 February 2021. These two MCSs eventually merged into a single MCS, producing the second peak of precipitation at 00Z on 6 February 2021. Analysis of the moisture transport indicates that the strong and persistent north-westerly wind near the surface induced by CENS prior to and during the event created an intensive meridional (southward) tropospheric moisture transport from the South China Sea towards Semarang. In addition, the westerly flow induced by low-frequency variability associated with La Nina and the tropical depression over the North of Australia produced an intensive zonal (eastward) tropospheric moisture transport from the Indian Ocean towards Semarang. The combined effects of the zonal and meridional moisture transport provided favorable conditions for the development of MCSs, and hence extreme rainfall over Semarang. These results provide useful precursors for extreme weather-driven hazard prediction in Semarang and the surrounding regions in the future.

Nusantara, Indonesia’s new capital city, experienced a rare extreme rainfall event on 27–28 August 2021. This heavy rainfall occurred in August, the driest month of the year based on the monthly climatology data, and caused severe flooding and landslides. To better understand the underlying mechanisms for such extreme precipitation events, we investigated the moisture sources and transport processes using the Lagrangian model HYSPLIT. Our findings revealed that moisture was mostly transported to Nusantara along three major routes: from Borneo Island (BRN, 53.73%), the Banda Sea and its surroundings (BSS, 32.03%), and Sulawesi Island (SUL, 9.05%). Overall, BRN and SUL were the main sources of terrestrial moisture, whereas the BSS was the main oceanic moisture source, having a lower contribution than its terrestrial counterpart. The terrestrial moisture transport from BRN was mainly driven by the large-scale high vortex flow, whereas the moisture transport from the SUL was driven by the circulation induced by boreal summer intraseasonal oscillation (BSISO) and low-frequency variability associated with La Niña. The near-surface oceanic moisture transport from BSS is primarily associated with prevailing winds due to the Australian monsoon system. These insights into moisture sources and pathways can potentially improve the accuracy of predictions of summer precipitation extremes in Indonesia’s new capital city, Nusantara, and benefit natural resource managers in the region.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionRecord-Breaking Precipitation in Indonesia's Capital Jakarta in January 2020 Linked to the Northerly Surge, Equatorial Waves, and MJOAuthorsSandro W.LubisiDSamsonHagosEddyHermawaniDMuhamad ReyhanRespatiAinurRidhoFadhlil R.MuhammadJaka A. I.PaskiDian NurRatriSonnySetiawaniDDonaldi S.PermanaiDSee all authors Sandro W. LubisiDCorresponding Author• Submitting AuthorPacific Northwest National Laboratory (DOE)iDhttps://orcid.org/0000-0001-6615-9880view email addressThe email was not providedcopy email addressSamson HagosPacific Northwest National Laboratory (DOE)view email addressThe email was not providedcopy email addressEddy HermawaniDNational Research and Innovation AgencyiDhttps://orcid.org/0000-0002-2539-4240view email addressThe email was not providedcopy email addressMuhamad Reyhan RespatiSchool of Earth, Atmosphere and Environment, Monash Universityview email addressThe email was not providedcopy email addressAinur RidhoSearch Engine for Risk and Actions on Resilienceview email addressThe email was not providedcopy email addressFadhlil R. MuhammadSchool of Earth Sciences, University of Melbourneview email addressThe email was not providedcopy email addressJaka A. I. PaskiIndonesia Agency for Meteorology Climatology and Geophysicsview email addressThe email was not providedcopy email addressDian Nur RatriIndonesia Agency for Meteorology Climatology and Geophysicsview email addressThe email was not providedcopy email addressSonny SetiawaniDDepartment of Geophysics and Meteorology, IPB UniversityiDhttps://orcid.org/0000-0002-1638-2635view email addressThe email was not providedcopy email addressDonaldi S. PermanaiDIndonesian Agency for Meteorology Climatology and GeophysicsiDhttps://orcid.org/0000-0001-5674-9238view email addressThe email was not providedcopy email address

A record‐breaking extreme rainfall event, the highest amount recorded since 1866, hit Indonesia's capital, Jakarta, in early January 2020. This torrential rainfall was mainly caused by the convergence of moisture‐rich air due to an unusual blocking of cross‐equatorial northerly surge over Northwest Java by the southerly winds induced by a cyclonic flow over the Indian Ocean. This condition caused a local increase in the amount of water vapor over Jakarta that eventually led to the formation of clouds and heavy precipitation. In addition, the concurrent occurrences of convectively active phases of equatorial waves (Kelvin, TD‐type, and eastward propagating inertia‐gravity waves) and Madden‐Julian Oscillation during the event also partly contributed to the enhanced local moisture over the region by increasing low‐level moisture flux convergence. Together, these large‐scale dynamical drivers provided a convective environment that fostered the development of a massive rain‐producing mesoscale convective system and, consequently, extreme rainfall over the region.

In Indonesia, flooding is one of the natural hazards that often occurs during the rainy season. Surface runoff coefficient values are an essential indicator of the supply of regional water resources. The smaller the surface runoff value, the greater the water storage in the ground, and the smaller surface was running water. This study analyses the spatial and temporal distribution of the estimated surface runoff caused by land use/land cover changes in the upstream Citarum watershed. The study area is located in the upstream Citarum watershed, West Java, Indonesia. The site has a long history of flooding and various complex environmental problems. The geographic Information System method was used as a tool in analyzing the spatially and temporally. The research result shows that there has been a change in land cover in several periods of the year in the Citarum upstream watershed. The occurrence of the LULC phenomenon positively affects the surface runoff coefficient. The increasing area of Built land and plantation in the Citarum upstream watershed will further increase the surface runoff coefficient and, in the end, will potentially increase the surface runoff and contribute to flooding in the Bandung basin. This study results can be used to provide input in determining the direction and policies for watershed management, taking into account the varying characteristics of each subwatershed.

Abstract Study of interaction between MJO and Monsoon and their impact on the extreme rainfall over the Maritime Continent (MC) until now not yet fully understood due to the limitation of data observation. However, there many techniques to derive the extreme rainfall from the satellite data, especially for GSMap (Global Satellite Mapping of Precipitation) that already designed with good spatial-temporal resolution. In this study, we have investigated the interaction between MJO and Asian Monsoon when they are interactive simultaneously. By taking the Makasar city as a sample of big floods over South Sulawesi dated January 22, 2019, we found a good agreement between MJO, Monsoon, and their impact on the extreme rainfall in that time. We applied four techniques, namely; temporal, spatial, Hovmoller, and PSD (Power Spectral Density), respectively. Then, we found the MJO in phases 4 and 5 were responsible for the occurring of big rainfall over South Sulawesi. For this reason, we suspect the development of the MJO index model, especially for phases 4 and 5 when passing over South Sulawesi is very important for the next investigation.

Propagasi MJO memicu peningkatan aktifitas konveksi yang menyebabkan kenaikan probabilitas hujan. Intensitas hujan dan frekuensi hujan ekstrem saat MJO pada bulan DJF dan JJA di Indonesia dianalisis. Komposit data curah hujan harian tahun 2008-2018 (CHIRPS) dilakukan berdasar kategori tanggal kejadian MJO (kuat dan lemah) pada tiap fase (3,4,5) menggunakan data indeks Realtime Multivariate MJO. Hujan ekstrem dikategorikan berdasarkan intensitas curah hujan diatas presentil 95%. Hasil menunjukkan MJO kuat Fase 3, 4 dan 5 lebih sering terjadi saat DJF (frekuensi kejadian 50% lebih banyak dibanding saat MJO lemah). Saat JJA, frekuensi kejadian MJO kuat dan lemah tidak berbeda signifikan. Saat DJF, di Indonesia bagian barat terjadi peningkatan intensitas hujan saat MJO kuat Fase 3 dan 4. Di Indonesia bagian timur, peningkatan curah hujan mencapai hampir 100% di beberapa bagian Papua saat MJO kuat Fase 5 DJF. Di sebagian besar Sulawesi saat MJO kuat Fase 4 bulan JJA peningkatan curah hujan mencapai dua kali lipat. Wilayah dengan curah hujan lebih tinggi saat MJO lemah, diantaranya kawasan barat Indonesia (Sumatera dan Jawa) saat MJO Fase 3 di bulan JJA. Hujan ekstrem terjadi baik saat MJO kuat maupun MJO lemah. Frekuensi kejadian hujan ekstrem lebih tinggi saat MJO kuat di Sumatera bagian utara, Jawa bagian timur, Kalimantan bagian selatan, dan beberapa bagian di Pulau Papua saat Fase 3 di bulan DJF, dan pada wilayah Sulawesi dan Maluku saat Fase 4 di bulan JJA. Frekuensi curah hujan ekstrem lebih tinggi saat MJO lemah seperti pada wilayah Papua pada Fase 3 dan 4 bulan JJA.