Meteorological Agency Research Articles

Extreme weather and contentious elections

The impact of extreme weather events on electoral processes is not well understood, yet as the climate changes, such events are predicted to become more common. This highlights the need for scholars to investigate how natural hazards affect election campaigns and electoral administration. Drawing on data from the Electoral Contention and Violence dataset, this article uses a difference-in-differences approach to assess the effect of tropical storms on electoral contention over seven elections held in the Philippines between 1992 and 2010. It finds that storms that occur in the year leading up to an election increase, and that this effect is likely accounted for by both economic grievances consequent upon the negative impact of storms on agricultural output and grievances generated by storm-induced disruptions to the electoral process. These findings suggest that as climate change intensifies, – and the violence that contention often entails – could become more common in a number of contexts. This has implications for electoral administration, and it implies the need for cooperation across electoral and meteorological agencies in places where weather extremes are likely to occur in the runup to electoral events.

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

Judgment and decision strategies used by weather scientists in southeast Asia to classify impact severity

Impact-based weather forecasting requires forecasters to predict what weather might do (impact information), rather than solely what weather might be (meteorological information). In a collaboration between the UK Met Office, UK psychologists, and weather scientists in Indonesia, Malaysia, the Philippines, and Vietnam, the present study employed Judgment Analysis and decision strategy comparisons to better understand weather scientists' impact severity judgments. In the Judgment Analysis Task, weather scientists (from Indonesia, Malaysia, the Philippines, and Vietnam) made numerical and categorical severity judgments for 70 hypothetical heavy rainfall events, each described via six impacts (e.g., number of deaths, number of people affected). The hypothetical impacts were generated from a multivariate distribution estimated from a distribution of real rainfall events. Subsequently, participants provided categorical severity classifications for a list of impact values for each type of impact (Threshold Identification Task) to aid the identification of decision strategies. In all four countries, weather scientists' severity judgments were best predicted by incorporating all six impacts via a compensatory judgment strategy. However, considerable individual differences were identified in the weights assigned to the different impacts and in the identified thresholds for each impact's categorical severity classification. To improve impact-based forecasting, meteorological agencies should seek to enhance consistency among forecasters.

A note on the seismicity of Sumatra, western Sunda Arc, Indonesia, in relation to the potential for back-arc thrusting

The existence of back-arc thrust faults along the eastern part of the Sunda Arc, ranging westwards from Flores to the western tip of Java, has been recognised for decades. In contrast, it is still unknown whether such back-arc thrust faults exist in Sumatra, which is located in the western part of the Sunda Arc. To investigate the possible existence of back-arc thrusts in Sumatra, we examine regional earthquake data reported by the Agency for Meteorology, Climatology and Geophysics of Indonesia, as well as global earthquake data reported by the International Seismological Centre and the United States Geological Survey. It appears that back-arc thrusts in the study area are not extensively developed, unlike in the eastern Sunda Arc, which may be caused by oblique subduction beneath the Sumatran forearc. The stress associated with the trench-parallel component of subduction is largely accommodated by the ~ 1650-km-long dextral strike-slip fault zone of the Great Sumatran Fault. The seismicity data from various sources do, however, show that there is a dipping seismogenic zone in several parts of the back-arc region of Sumatra, in the opposite direction to the NNE subduction of the Indo-Australian plate. This new observation may be related to the presence of spatially intermittent back-arc thrust faults in the study area, which may need to be taken into account when improving Indonesia's national earthquake hazard maps.

Performance evaluation of ten numerical methods for Weibull distribution parameter estimation applied to Nigerian wind speed data

Utilizing wind energy necessitates a thorough understanding of wind profiles as well as a precise forecast of wind speed at a study location. In this study, ten Numerical Methods (NEMs), which include the Empirical Method of Lysen (EML), Percentile Method (PCM), Maximum Likelihood Method (MLM), Modified Maximum Likelihood Method (MMLM), Empirical Method of Justus (EMJ), Alternative Moment Method (AMM), Median and Quartiles Method (MQM), Probability Weighted Moments Based on Power Density Method (PWMBPM), Method of Mabchour (MOMAB) and Energy Variance Method (EVM) were applied to estimate the two- parameter (k and c) Weibull (Wbl) distribution in five locations (Jos, Kano, Maiduguri, Abuja, and Akure) in Nigeria. The performance of these NEMs was assessed using five different metrics and the most effective NEM was determined for each studied location. Daily wind speed data spanning 11 years for the studied locations were sourced from the Meteorological Agency in Nigeria and used in this study. The k and c parameters range from 2.91 to 5.46 and 9.95 to 10.26 (Kano); 2.31 to 4.50 and 5.63 to 6.20 (Maiduguri); 3.19 to 7.61 and 12.16 to 12.99 (Jos); 2.18 to 6.77 and 4.99 to 5.50 (Abuja), and 1.84 to 3.18 and 3.83 to 3.90 (Akure). Findings revealed that the best methods for estimating Wbl parameters for the Kano, Maiduguri, Jos, Abuja, and Akure locations were MMLM, MMLM, MQM, MQM, and EMJ, EML, and AMM, respectively, as MOMAB remained the least performing NEM for all the studied locations. The results also showed that the Vms , Vmps , and V emax varied from 3.47 m/s to 11.63 m/s, 3.40 m/s to 11.90 m/s, and 4.58 m/s to 12.59 m/s, respectively, with the most recorded for Jos. The PWPD augmented from 36.45 W/m2 (Akure) to 1000.06 W/m2 Jos), at a hub height of 10 m.Based on these results Jos was the best location for installing wind turbines while Kano was an excellent place for integrating the grid. Additionally, the Maiduguri location was determined to be suitable for a stand-alone application while Abuja and Akure were considered to be unsuitable for wind energy applications.

Unveiling Water Allocation Dynamics: A Text Analysis of 25 Years of Stakeholder Meetings

Abstract Managing water resources in regions with high climate variability and frequent extreme weather events poses challenges for policymakers. To facilitate water allocation in these cases, participatory and collaborative decision-making approaches have become common. However, the evaluation of these approaches is hindered by the lack of structured methods and data to understand them. To address this knowledge gap, we propose a novel methodology that leverages text data to identify key topics, conflicts, and influential actors that shape water allocation dynamics. Our methodology is tested using records of 1020 water basin committee meetings held between 1997 and 2021 across twelve basin committees in Ceará, Brazil – a region known for its extensive history of droughts that have impacted water governance. To uncover key water management issues discussed during these meetings, we employed a three-step topic modeling framework: (1) sentence embedding, (2) dimensionality reduction, and (3) sentence clustering. Furthermore, we used entity recognition, dependency parsing, and network graphs to identify powerful actors influencing these meetings and, ultimately, the decisions taken. Our findings revealed stakeholders’ heightened concern for urban water supply over agricultural demand during droughts. We found that “reservoir operation” was the most recurring topic, especially in basins where the strategic reservoirs are located. Discussions related to “climate information” became significantly more important over time, which indicates that water allocation decisions are increasingly based on the seasonal forecast and data on oceanic indices provided by the meteorology agency. Despite the presence of local users in the committees, governmental representatives dominated the discussions and were central in all river basins. In conclusion, our proposed approach harnesses existing text data to uncover spatiotemporal patterns related to participatory water allocation. This study opens new avenues for investigating water governance using text-based analysis.

Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method

In Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.

A Preliminary Tomography Inversion Study on the Palu Koro Fault, Central Sulawesi Using BMKG Seismic Network

The Palu Koro Fault, which frequently causes solid and destructive earthquakes, is the most exciting aspect of Central Sulawesi’s tectonics. We used local earthquake data from the area in this preliminary study. Our preliminary investigation entails analyzing local seismic data and estimating the velocity structure and hypocenter location using a tomographic approach that employs P-wave arrival times from the Agency for Meteorology, Climatology, and Geophysics earthquake observation network from 2010 to 2019. We use a 1-D initial velocity structural model in this tomographic inversion method, and the results are evaluated using the checkerboard test, derivative weight sum, and ray hit count. To the west and east of the Palu Koro Fault area, preliminary tomographic inversion results show a relatively high seismic velocity. These areas are interpreted as West Sulawesi Mandala and East Sulawesi Mandala Poso Fault and Wekuli Fault. The low-velocity anomaly is found near the Palu Koro Fault. Because of the high-speed thrusting, the relocated hypocenters also clustered around the Palu Koro Fault area. In the following activity, we will determine the speed structure of the S wave, which is expected to provide better geological information in the interpretation.

Ensemble Prediction Model for Dust Collection Efficiency of Wet Electrostatic Precipitator

WESPs (Wet Electrostatic precipitators) are mainly installed in industries and factories where PM (particulate matter) is primarily generated. Such a wet type WESPs exhibits very excellent performance by showing a PM collection efficiency of 97 to 99%, but the PM collection efficiency may decrease rapidly due to a situation in which the dust collector and the discharge electrode is corroded by water. Thus, developing technology to predict efficient PM collection in the design and operation of WESPs is critical. Previous studies have mainly developed machine learning-based models to predict atmospheric PM concentrations using data measured by meteorological agencies. However, the analysis of models for predicting the dust collection efficiency of WESPs installed in factories and industrial facilities is insufficient. In this study, a WESPs was installed, and PM collection experiments were conducted. Nonlinear data such as operating conditions and PM measurements were collected, and ensemble PM collection efficiency prediction models were developed. According to the research results, the random forest model yielded excellent performance, with the best results achieved when the target was PM 7: R2, MAE, and MSE scores of 0.956, 0.747, and 1.748, respectively.

Reconstruction of shrimp catches in Brazil based on generalized linear models

Catch data comprises important information for assessing the status of several fisheries. However, it is not always available. A modeling approach using generalized linear models was performed to rebuild catch data supported by environmental variables. Catch information was provided by fisheries’ statistical bulletins about pink (Farfantepenaeus subtilis, F. brasiliensis, and F. paulensis), white (Litopenaeus schmitti), and seabob shrimp (Xiphopenaeus kroyeri). Sea surface temperature and rainfall information were collected from open-access databases by meteorological agencies. Due to low species discrimination over time, a general shrimp catch category was added to the models to help disaggregate quantities for each species. The general category was the most relevant variable, whereas temperature indices showed reduction patterns in catches over time, which may indicate the likely effects of temperature increase on shrimp fisheries. Beyond that, extreme peaks and falls testedthrough residual analysis indicate low reliability mainly in the 1970s and ’80s reports. Information gain varied according to the discrimination ability. States that took longer to discriminate the species presented predictions far from the reports, so the information gains were greater than 100%. Accordingly, reconstructions can be an alternative to restore outdated or missing information and help judge the reliability of official data.

The comparison of indoor and outdoor pyranometer calibration method in Jakarta

The increasing energy crisis motivated people to develop renewable energy, one of the promising renewable energies is solar energy. Considering that Indonesia has the potential for sunlight throughout the year, this country should be able to take full advantage of this solar radiation energy. To discover potential area in Indonesia, good and calibrated equipment are needed such as pyranometer, pyrheliometer etc. The objective of this study is to compare developed method of outdoor and indoor pyranometer calibration responsivity. There are 4 pyranometer calibration methods, one of them is called Continuous Sun and Shade Method (CoSSM). However, this method has never been carried out before, so it is going to be developed by the Agency for Meteorology, Climatology, and Geophysics (BMKG) Indonesia. The CoSSM method utilizes two standard components consist of direct and diffuse irradiance to obtain new responsivity values from calibrated global radiation sensor or so called Unit Under Test (UUT). This experiment used 4 UUT and carried out for 12 days. The new responsivity values then compared with the responsivity value that had been obtained from BMKG indoor calibration accredited by National Accreditation Committee (KAN). The results obtained from calibration process provided the highest percentage difference of 6.2% and the smallest 0.2%. This shows that outdoor calibration in cloudy and polluted conditions in Jakarta still give quite good result.

Operational risk assessment on power system based on weather regionalization considering power ramp of renewable energy generation

With the ever-increased installed capacity of renewable energy generation (REG) units, the power ramp of REG has become a common event, introducing an operational risk to the power system. In order to quantify the operational risk, this paper proposes a novel method for assessing the operational risk of power systems with a high proportion of renewable energy, taking into account the power ramp of REG. Firstly, a weather regionalization is proposed to divide the region of the power system into multiple areas, and then the weather data from the weather stations in these areas will be utilized to forecast the power of REG probabilistically. Moreover, an operational risk assessment model based on the cumulative prospect theory is built to reflect the irrational psychological factors of dispatchers, and two typical indices are introduced to show the operational risk of renewable energy curtailment and load curtailment caused by the power ramp of REG. Finally, numerical simulations are conducted in the actual CEPRI-RE power system in the northwest region of China, and the actual weather data from the meteorological agencies is used. The results show that the proposed method is able to assess the operational risk of the power system with a high proportion of renewable energy effectively, and achieve the early warning 30 min in advance. Besides, it can be found that the power system at the turning point between the power ramp and normal operation has a higher operational risk.

A Literature Review on Solar Energy Output Prediction using Various Machine Learning Techniques

This literature survey explores various machine learning and deep learning models used for solar energy forecasting. A total of 15 papers published between 2020-2023 were reviewed. The objective of the survey was to identify the advancements made in the field of solar energy forecasting using machine learning and deep learning techniques. The survey found that most studies focused on using artificial neural networks (ANN) and deep learning models such as long short-term memory (LSTM) and convolutional neural networks (CNN) for solar energy forecasting. Several studies used ensemble models, such as stacking and bagging, to improve the accuracy of solar energy forecasts. Other studies used feature selection techniques and autoencoders to reduce the dimensionality of data and improve the accuracy of predictions. The survey also found that the availability of data is crucial for accurate solar energy forecasting. Many studies used data from meteorological agencies, such as NASA, NOAA, and ECMWF, along with satellite images and sky cameras to generate accurate forecasts. The use of IoT devices and sensors was also explored in some studies to obtain real-time data for improved forecasting. Overall, the literature survey found that machine learning and deep learning techniques have shown great promise in improving the accuracy of solar energy forecasting. The use of ensemble models, feature selection techniques, and autoencoders has improved the accuracy of forecasts. However, the availability of data is crucial for accurate forecasting. The use of IoT devices and sensors can provide real-time data that can be used for accurate forecasting.

Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers

Clouds are a significant factor in regional climates and play a crucial role in regulating the Earth’s water cycle through the interaction of sunlight and wind. Meteorological agencies around the world must regularly observe and record cloud data. Unfortunately, the current methods for collecting cloud data mainly rely on manual observation. This paper presents a novel approach to identifying ground-based cloud images to aid in the collection of cloud data. However, there is currently no publicly available dataset that is suitable for this research. To solve this, we built a dataset of surface-shot images of clouds called the SSC, which was overseen by the Macao Meteorological Society. Compared to previous datasets, the SSC dataset offers a more balanced distribution of data samples across various cloud genera and provides a more precise classification of cloud genera. This paper presents a method for identifying cloud genera based on cloud texture, using convolutional neural networks. To extract cloud texture effectively, we apply Gamma Correction to the images. The experiments were conducted on the SSC dataset. The results show that the proposed model performs well in identifying 10 cloud genera, achieving an accuracy rate of 80% for the top three possibilities.

The Rainfall Variability at Gunung Kidul Regency, Indonesia and Farmer Knowledge on Climate Change

The survey was carried out to analyze rainfall variability, to gather information of farmer practise on cropping pattern and farmer knowledge of climate change. The survey was conducted from on 21 September to 1 October 2022 in Ngoro oro and Watu Gajah sub villages, at Gunung Kidul Regency, about 30 km Southern of Yogyakarta city, Indonesia. Field visit and the group discussion were conducted to explore farmer knowledge on climate change. Rainfall was correlated with previous monthly Southern Oscillation Index to enabale rainfall prediction. The main finding of the survey were the farmers had local knowledge that was good enough to anticipate of climate change such as observing the nature sign i.e. the growth of the gadung tree (Dioscorea hispida) and suweg plant (Amorphophallus sp), the use of Javanese calendar for agriculture and also checking the soil wetness were used to decide the first season of planting after dry season. However, the information from meteorology agency (BMKG) regarding the La Nina was not reaching the farmers. The analysis of rainfall revealed that rainfall at Gunung Kidul was closely related to El Nino and La Nina, and this affected the the time of first planting season after dry season. Also, SOI could be useful for prediction of fortcoming rainfall. The scientific aspect of climatic anomaly such as La Nina and El Nino was reasonably understood and it could be predicted by scientists, and this valuable information needed to be delivered by mediators to assist farmers in farming management. As the occurrence of El Nino and La Nina was known in advance, this information from BMKG needed to be delivered by mediators in simple words so that farmer would get the benefit of climate change to manage their farm.

Climate Change Adaptation and Mitigation Measures Initiated by Farmers in Aravalli Hill Zone of Rajasthan, India

Aims: To study about the adaptation and mitigation measures initiated by the farmers against climate change in local conditions.
 Study Design: Ex-post-facto research design was followed.
 Place and Duration of Study: The study was conducted at Department of Agricultural Extension and Communication, Bhagwant University, Ajmer during 2019-2022.
 Methodology: The study was conducted in Aravalli Hill Zone of Rajasthan to understand adaptation & mitigation measures employed by farmers to the adverse effects of climate change. In the investigation region, 240 farmers were randomly selected.
 Results: In the region 95.83 per cent of the farmers pursued that soil and water conservation was the most widely practiced strategy. Whereas, almost three fourths of the respondents were practicing adoption of drought tolerant and early maturing crop varieties were (75%), use of small-scale irrigation (73.75%), enhancing participatory forest management (73.33%) and afforestation/ reforestation (72.91%), while (70. 83%) practiced diversification of small ruminant animals and changing cropping calendar of agricultural activities. In the constants of the maize crop adaptation measures against excess rainfall was 70.83 per cent of delayed sowing dates; this change in sowing date was adopted by the farmers and adaptation measures in maize crop against deficit rainfall observed by the investigator that 69.58 per cent of the farmers found that delayed sowing dates had affected them.
 Conclusion: The local people employed different strategies to adapt the adverse effects of climate change, there were constraints that limit the farmer’s adaptation strategies i. e. lack of financial support, lack of climate information and lack of technical skills. Therefore, the local decision makers such as agricultural sector, microfinance sector, and meteorological agency should provide farmers credit access and climate information to reduce shortage of finance and lack of climate information. There is also need of providing training to the farmers on improved agricultural technology and market access to enhance their climate resilience conditions.

Ancaman Kebakaran Hutan di Taman Nasional Bantimurung Bulusaraung, Provinsi Sulawesi Selatan

The National Park of Bantimurung Bulusaraung located in Maros Regency, South Sulawesi Province, is a unique conservation area with a karst ecosystem. Forest fires have been an essential threat to the National Park. The study aims to analyze forest fire occurrences, identify causes and forest fire prevention efforts at the National Park of Bantimurung Bulusaraung, Maros Regency, South Sulawesi. Data used in this study consist of monthly forest fire data from the National Park of Bantimurung Bulusaraung and monthly rainfall data from the Agency of Meteorology, Climatology and Geophysics in the period of 2015-2020, and information on fire causes, community participation in forest fire prevention derived from respondents of villagers living surrounding the national park. Data analyses using JASP 0.14.1 software. The study revealed that forest fires occur every year at the National Park of Bantimurung Bulusaraung with fires frequency of 20 times and burned area of 163,455 ha from 2015 to 2020. The highest frequency occurred in 2015 of 8 times and burned area about 97,065 ha. The human factor has been the single fire causing factor, particularly negligence in fire use activities. There are several prevention measures conducted by the National Park management, including the socialization and training activities for community. The unique karst ecosystem seems to be a challenge in the fire suppression process. Therefore, forest fire prevention involving community living surrounding the national park is critical in forest fire control in the National Park of Bantimurung Bulusaraung.
 Keywords: community participation, fire causes, fire prevention, karst ecosystem

Indigenous weather forecasting among Gujii pastoralists in southern Ethiopia: Towards monitoring drought

Indigenous weather forecasting (IWF) is practised by various communities around the world. Access to meteorological weather forecasting is limited in eastern Africa’s pastoral regions. As a result, pastoralists frequently rely on indigenous weather forecasting indicators. This paper investigates the use of IWF in drought forecasting among Gujii pastoralists in southern Ethiopia. To collect data, household surveys, focus group discussions, and key informant interviews were used. Furthermore, meteorological data were used to determine the frequency of drought events in the area. A comparison was made between IWF and data from the Ethiopian National Metrological Service Agency to ensure consistency of the results of IWF and climatic data. For quantitative data analysis, descriptive statistics was used, while hermeneutic and narrative analyses were used for qualitative data analysis. Pastoralists have used a variety of weather forecasting indicators, such as reading livestock intestines, observing animal and insect behaviours, and interpreting the star and moon alignment. Both the indigenous drought event forecasting result and the drought history result from the meteorological agency indicate the presence of drought in the area. However, several internal and external factors are contributing to IWF’s gradual decline. Religious teachings, technology, and education were identified as external factors, while the growing generation gap was identified as an internal factor. Thus, to increase the accuracy of forecasting of weather events and improve pastoralist’s prediction capacity, the integration of this knowledge is highly appreciated.

Scientists for Ocean Literacy: An Ocean Decade Project to Empower Scientists as Ocean Champions

Abstract The Ocean Decade project “Scientists for Ocean Literacy” is helping marine scientists become more actively involved in public engagement. Scientists have the power, knowledge, and passion to explain the importance of the ocean for the planet. But scientists cannot do this alone. Scientists for Ocean Literacy aims at expanding the horizon of science communication towards a multi-stakeholder dialogue. The project is building on the best practices from the European community of oceanographers and meteorologists, bringing in other science disciplines, culture, and the arts, and promoting inclusiveness, diversity, and accessibility in the public engagement activities. This article explains the raison d'être for the Scientists for Ocean Literacy project, its objectives and actions, and gives recommendations on how to improve the ocean literacy offer in the publicly funded oceanographic and meteorological agencies and beyond.

Focal Mechanism Analysis of the Earthquakes Beneath the Sunda-Banda Arc Transition, Indonesia, Using the BMKG Data

Structural complexity in the Sunda-Banda arc transition is a topic of much debate amongst Earth scientists. We have processed focal mechanism study using moment tensor inversion for 20 events in the region using the Agency for Meteorology, Climatology, and Geophysics (BMKG) data from 2014-2016 for earthquakes of magnitude Mw ≥ 5.0. Our result shows different solutions that depend on the source region of the earthquakes that include subduction zones and collision zones, which host active faults and the back-arc thrusts. Earthquakes that occurred in the subduction zone appear to rupture on thrust faults for shallow and intermediate events, while the deep events have normal fault mechanisms. The shallow events in the collision zone occur on thrust faults with differing strike directions, but generally, there are largely parallel to the Timor trough. We also found normal fault mechanisms at play for deep events below the collision zone. The occurrence of deep earthquakes in this area is consistent with remnant slab activity that persists to the present day. Focal mechanism solutions for shallow events in the north of Sumbawa island indicate a thrust fault with a strike direction that is almost parallel to the back-arc thrust in this area. We also found evidence of strike-slip motion along local-scale active faults in the area.