Maritime Meteorology Research Articles

Comparing Monthly Rainfall Prediction in West Sumatra Using SARIMA, ETS, LSTM, and XGBoosting Methods

<p><em>T</em><em>he West Sumatra Province, serving as the trading center on the island of Sumatra, and boasting various attractive tourist destinations, is not immune to incidents of high precipitation leading to hydro-meteorological disasters such as floods and landslides. Therefore, the accurate prediction of monthly rainfall is crucial to minimize the impacts of high precipitation. This research aims to determine the best method for predicting monthly rainfall using data from 1992 to 2022, which can adequately represent its climatological conditions.</em><em> The results indicate that the Extreme Gradient Boosting method outperforms the Seasonal Autoregressive Integrated Moving Average (SARIMA), Exponential Smoothing (ETS), and Long Short-Term Memory (LSTM) methods in West Sumatra Province, represented by three weather observation points from the BMKG (Climatology Station of West Sumatra, Maritime Meteorology Station of Teluk Bayur, and Minangkabau Meteorology Station). This method exhibits the lowest error values and the strongest correlation between predicted and actual data. This is evident from the Nash-Sutcliffe Efficiency (NSE) values, which are 0.188214535, 0.613823746, and 0.545734162 (unsatisfactory-satisfactory), as well as the obtained correlation values of 0.472103386, 0.795586268, and 0.743002591 (moderate-strong). However, this method is unable to perfectly capture outlier values. These outliers arise as a result of unusual conditions, such as natural disasters or climate changes, and atmospheric phenomena like El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), leading to exceptionally high or low precipitation</em><em>.</em></p>

IMPLEMENTATION OF BACKPROPAGATION AND HYBRID ARIMA-NN METHODS IN PREDICTING ACCURACY LEVELS OF RAINFALL IN MAKASSAR CITY

Hybrid ARIMA-NN is a combined approach of the ARIMA model used to capture linear patterns in time series data and Artificial Neural Networks (ANN) to handle non-linear and stochastic patterns. Using a gradient descent algorithm, backpropagation adjusts synaptic weights based on the error between the network's prediction and actual training data values. In this study, a comparison was made between the Backpropagation method and Hybrid ARIMA-NN in forecasting rainfall in Makassar City. Rainfall data in Makassar City uses data from the rainfall measuring station at the Paotere Maritime Meteorological Station in Makassar. The activation functions used are ReLU and Leaky ReLU with epoch parameters set at 350, and learning rates of 0.01, 0.001, 0.0001, and 0.00001. The two best methods selected for further evaluation are Backpropagation with architecture 12-32-16-8-1 and Hybrid ARIMA-NN (ARIMA [4,0,1]-NN 12-256-128-64-1). The ARIMA model (4,0,1) with AIC values of 1303.4 and RMSE 162,369 is the best compared to other models, which aligns with the advantages of backpropagation architecture. The results showed that the Backpropagation method excelled with an RMSE value of 137.320 or 0.1149, indicating high accuracy in forecasting changes in seasonal trends and patterns. Hybrid ARIMA-NN gives good results with RMSE 145.834, as residues contain better nonlinearity compared to ARIMA models (4,0,1), although it shows a slightly higher error rate compared to Backpropagation.

Wave Height Characteristics Due to The Influence of Land and Sea Breezes on Fishermen’s Activities in The Coastal Area of Makassar City Using Doppler Weather Radar

Makassar City, located on the southwest coast of Sulawesi Island, exhibits dominant land and sea breeze circulation, with sea breezes being stronger during the day and land breezes at night. The use of weather radar is effective in identifying these wind characteristics, which are crucial for fishing activities and renewable energy. This study investigates the characteristics of land and sea breezes and their relation to wave heights in the Spermonde Waters of Makassar during the east and west seasons, providing important information for maritime activities in Makassar City. The research area is located between 4°59’20”S 119°27’46”E and 5°11’26”S 119°22’54”E. This study uses weather observation data, including weather radar data, weather observations, and wave heights from January 2020 to December 2020, divided into three seasonal periods (rainy season, transition period, and dry season). The weather observation data were obtained from Hasanuddin Makassar Meteorological Station and Paotere Makassar Maritime Meteorological Station. Additionally, questionnaires were distributed to fishermen at three locations: PPI Paotere Makassar, PPI Untia Makassar, and Kuri Caddi-Maros Beach Fishermen Village, with 15 respondents at each location. The study shows that land breezes in Makassar are dominant from 18:05 to 19:00 LT with a duration of 9.1 to 11 hours, while sea breezes dominate from 08:00 to 09:00 LT with a duration of 5.1 to 7 hours. This phenomenon is most frequently detected during the dry season (JJA). Land and sea breezes can intrude up to 20.1 to 30 km from the coastline. There is a highly significant positive correlation between wind and wave height in January and August, indicating a strong influence of winds on waves in the Spermonde Waters of Makassar. About 80% of fishermen utilize these wind conditions for fishing activities. Overall, the land and sea breeze patterns significantly impact wave heights and the activities of fishermen in the coastal areas of Makassar.

REDESIGN SHORE PROTECTION KAWASAN INDUSTRI KENDAL (KIK)

Abrasion incidents on the coastline in the KIK area continue to occur and cause the area boundaries to decline. so that appropriate coastal protection structures are needed. The aim of this research is to carry out re-planning in the Kendal Industrial Area (KIK) in the future area boundary plan. In carrying out this research, daily wind data for the period 2017 - 2023 is needed from a nearby location, namely the Tanjung Emas Maritime Meteorological Station, Semarang. The analysis carried out includes fetch, windrose, wave calculations, building design and force calculations in shore protection construction. From the results of the windrose analysis, the dominant wave direction is northwest. Analysis of waves in the deep sea obtained a height value of 2.45 m and a wave period of 8.32 sec. Based on the calculation results, breaking waves will occur at a depth of 2.02 m. Because the building location is at a depth of 2m, where breaking waves occur, the breaking wave height is used in building planning. The coastal protection building chosen is shore protection construction or a tetrapod sea wall. Peak elevation of 4.76 meters, peak width of 3.55 meters, building base length of 22.45 meters, back slope of 1:1, arm slope of 1:2, the first type of protective layer material uses tetrapods, second and third layer using split stone pairs with 2 size combinations.

Analysis of the Comfort Level of Climate in the Padang Coastal Tourism Area Using the Holiday Climate Index (HCI) Method

Padang city is one of the regions of Indonesia that has potential in the tourism sector, one of which is beach tourism. The tourism sector is one of the largest sectors and the main mainstay for the economy in various countries, including Indonesia. Countries that rely on the tourism sector must pay attention to several factors that attract tourists, one of which is climate comfort. Climate information is a major factor in choosing a tourist trip. This study aims to analyze the level of climate comfort in the coastal tourism area of Padang City using the Holiday Climate Index (HCI) method. HCI uses climate parameters such as air temperature, humidity, wind speed, rainfall, and cloud cover. HCI calculation data in the form of observation data obtained from the Meteorology Climatology Geophysics Agency (BMKG) station at Minangkabau International Airport and the Teluk Bayur Maritime Meteorology Station from 2011-2020, direct measurement data from August to September, and questionnaires. The results of the HCI analysis show that the city of Padang is comfortable throughout the year, with the HCI value in the range of 58.6-71.6 which is categorized as acceptable to very good. The results obtained through direct measurements also obtained comfortable results throughout the day with HCI values in the range of 70-80. Perceptions of tourists interviewed also stated that overall tourists felt comfortable with the percentage of respondents as much as 74.1%. HCI can be used as a source of information to determine the value of climate comfort in tourist areas.

PRELIMINARY OBSERVATIONS OF THE DEVELOPMENT OF GONAD PIGMENTATION AS A REPRODUCTIVE PATTERNS OF FAVIIDAE IN SPERMONDE ARCHIPHELAGO, INDONESIA

Coral spawning occurs throughout the year in Indonesia due to tropical climate conditions that influence coral development and spawning. As representative data on coral gonad development in the Spermonde Archipelago, observations on the development of the Faviidae gonads were conducted on Barrang Lompo, Kodingareng Keke, Samalona, and Bonetambung Island. Environmental elements are measured in the form of surface water temperature and rainfall. Based on the abundance of this type of gigantic coral at the study site, coral tissue sampling was concentrated on the Faviidae. The pigmentation of the gonads of Faviidae has been studied visually in nature. The samples were carved on the coral before being photographed. To make it easier to detect the level of pigmentation in each species, coral gonadal tissue was imaged. If there is an orange, yellow, or blue tint, it indicates that the coral has colored gonads and is mature gonads, indicating that coral spawning will occur soon. A water lever logger was used to measure the temperature of the water. The results of water temperature measurements vary and can be linked to the development of coral pigmentation as well as observations from November to March when the third phase (pigmented) process in Faviidae was considered to have happened in large numbers. It is possible that Faviidae can breed during this sensitive period. Rainfall data was gathered from Makassar City's Paotere Maritime Meteorological Station.

WEATHER FORECAST FROM TIME SERIES DATA USING LSTM ALGORITHM

Accurate weather forecasts play an important role in today's world as various sectors such as marine, navigation, agriculture and industry are basically dependent on weather conditions. Weather forecasts are also used to predict the occurrence of natural disasters. Weather forecasting determines the exact value of weather parameters and then predicts future weather conditions. In this study the parameters used are. Different weather parameters were collected from the Serang Maritime Meteorological Station and then analyzed using a neural network-based algorithm, namely Long-short term memory (LSTM). In predicting future weather conditions using LSTM neural networks are trained using a combination of different weather parameters, the weather parameters used are temperature, humidity, rainfall, and wind speed. After training the LSTM model using these parameters, future weather predictions are performed. The prediction results are then evaluated using RMSE. Prediction results show that the model is more accurate when predicting temperature data with RMSE 0.37, then RMSE wind speed 0.72, RMSE sunlight 2.79, and RMSE humidity 5.05. This means that the model is very good at studying weather data, inversely proportional to humidity data.

PENGARUH BESAR PENGETAHUAN METEOROLOGI MARITIM DALAM KEMENANGAN PERTEMPURAN NORMANDIA 1944

The Battle and Landing of Normandy 1944 was four years in the making. General Dwight D. Eisenhower as the Allied Commander wanted the invasion of Normandy, D-Day to be carried out immediately, but the operation plan almost failed due to bad weather. The Battle of Normandy 1944 began with the German conquest of France and the Allies wanted to free France from German occupation. The writing methodology uses a descriptive method by analyzing the facts that occurred at the Battle of Normandy 1944 supported by literature studies, literature, and scientific journals. Meanwhile, Maritime Meteorological information at the time of the Normandy 1944 landing was obtained from the analysis of weather reports and literature studies. From this incident, it can be concluded that the TNI, especially the Navy, must take maritime weather into account in supporting operations at sea and needs to collaborate with BMKG to obtain Maritime Meteorological information. BMKG provides impact-based maritime weather that issues weather impact map information to sailing ships. The Impact-Based Weather Map provided will provide a color that indicates the risk status of maritime weather based on the type of ship, which can help TNI-AL's equipment in navigation at sea. Maritime Meteorological information and data are obtained using BMKG's ocean prediction model - Ocean Forecasting System (BMKG - OFS) and BMKG's InaWIS (Indonesia Weather Information for Shipping). The InaWIS system is integrated with the Automatic Identification System (AIS) shipping navigation data that will be able to identify ship risks to maritime weather such as wind, currents, and waves. InaWIS is also equipped with real-time weather observation data that is able to model sea predictions for the next 10 (ten) days which makes it easier for TNI AL's equipment to navigate at sea to avoid areas that have the potential for extreme weather and also to support landing operations.

Changes in Rainfall Intensity, Rising Air Temperature, Wind Speed, and Its Relationship with Land Use in Makassar City

Makassar City is one of the big cities that is growing very rapidly in Indonesia which has a large coastal area. This city is very vulnerable to the impact of changes in climate variables such as rainfall, temperature and wind speed, especially when there are indications of massive land use changes. The aim of the research is to identify changes in the trend of high-intensity rainfall, changes in air temperature, wind speed, and their relationship to changes in land use in Makassar City which have an impact on climate change. Observation data at Maritime Meteorological Station of Paotere, Makassar for 30 years is used to detect changes in climate variables by using slope calculations on linear equations, line graphs, and boxplots. The results showed that the air temperature in Makassar has a lower increasing (0.06oC/year) than Indonesian region which is around 0.3 oC. The slope values at 07.00 WITA, 13.00 WITA, and 18.00 WITA representing temperatures in the morning, afternoon, and evening are 0.0387, 0.0476, and 0.0417. While the average slope of air temperature is 0.042. However, Rising of air temperature is followed by a decrease in the accumulation of annual rainfall to below 3000 mm/year. In addition, heavy rains that cause flooding, increasing the maximum wind speed also need to be observed because wind speed is one of the causes of hydrometeorological disasters that often occur.

Prediksi Curah Hujan Menggunakan Long Short Term Memory

The importance of predicting rainfall in fields that require rainfall information such as in agriculture, transportation and industry. Prediction of rainfall with statistics is done to solve the problems of this paper, thus this paper proposes prediction of rainfall using Long Short Term Memory in the case study: Surabaya City. The data used is rainfall data at two Surabaya stations, namely the Perak Meteorological Station I and the Tanjung Perak Maritime Meteorology Station from 2015 to 2020. The prediction test was carried out using the Long Short Term Memory algorithm with accuracy measurement results MSE 0.489, MAE 0.537 and R2 0.497. from these results prove that the Long Short Term Memory algorithm is better than previous studies.

LONG-SHORT TERM MEMORY (LSTM) FOR PREDICTING VELOCITY AND DIRECTION SEA SURFACE CURRENT ON BALI STRAIT

The strategic role of the Bali Strait as a connection between the islands of Java and Bali is growing in line with the increase in the economy and tourism of the two islands. Therefore, it is necessary to have a further understanding of the condition of the waters in the Bali strait, one of which is ocean currents. This study aims to predict future ocean currents based on 30-minute data in the Bali Strait in the range of 16 May 2021 to 9 June 2021 obtained from the Perak II Surabaya Maritime Meteorological Station. In this study, the Long Short Term Memory method was used. The parameters used are hidden layer, batch size, and learn rate drop. Based on the parameters used, the results showed that the smallest MAPE value was 18.64% for U ocean current velocity data and 5.29% for V ocean current velocity data.

Rainfall Prediction Using Backpropagation with Parameter Tuning

Rainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed to handle the impacts caused by high rainfall. The data was obtained from the website dataonline.bmkg.go.id with observations made by the Tanjung Perak Surabaya Maritime Meteorological Station. The prediction method uses an artificial neural network with Backpropagation. Autocorrelation function is used to determine the number of input neurons with the best features in the Artificial Neural Network. Rainfall data is divided into two parts,: January 2008 to December 2019 used for training data and January to August 2020 for testing data. The validation technique used is 10-Fold Cross Validation. The experiment uses parameter tuning of iteration and learning rate. The training process obtained the best learning rate was 0.2 and 1000 iterations with a MSE validation score of 0.02591.Finally, the testing process has a Mean Square Error value of 0.02769 and a percentage of true rain character of 62.5%.

Thornthwaite Models for Estimating Potential evapotranspiration in Medan City

Evapotranspiration plays a big role in the hydrology process. Potential Evapotranspiration (PET) always keeps soil moisture available, although an amount of water evaporates through evaporation and transpiration. The Thornthwaite equation uses air temperature and latitude from meteorological observations for estimating PET. Medan City is one of the biggest cities in Indonesia that have a problem with land-use change that affected water balance. This study is to estimate the PET and to learn the water balance in Medan City. The monthly temperature data for the period 2011-2020 is collected from three meteorological stations for estimating PET using the Thornthwaite equation. The highest monthly temperature is in Belawan Maritime Meteorological Station yet the lowest rainfall. The trends of PET depend on the month. The highest PET in Jan.-Apr. and Sep.-Dec. are in Belawan Maritime Meteorological Station, while the highest PET in May-Aug. is in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan. The P-PET has shown negative and positive values. The lowest P-PET is found in Belawan Maritime Meteorological Station in March and the highest P-PET is found in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan in October.

Correlation of Ambient Temperature with Increasing of Covid19 Cases in a Tropical City

Introduction: On March 26, 2020, the government confirmed the first positive case of Covid19 in Padang City. This condition continues increasing, although several policies were created in preventing the spread of Covid19 . The geographical location of Padang City, which is close to the equator, causes the ambient temperature to be very optimum. It needs to be examined from the ambient temperature indicator, which is considered correlated with the spread of the coronavirus. Methods: This study used an ecological design. Spearman rank correlation test was carried out between daily temperature and daily cases due to Covid19 in one year to determine their correlation and test for time lag 0, 7, 14, and 21 according to coronavirus incubation. The cases of Covid19 data were obtained from the Padang City Health Office daily report, and the ambient temperature data were obtained from the Meteorology, Climatology and Geophysics Agency, Teluk Bayur Maritime Meteorology Station, Padang City. Results and Discussion: This study proves a significant negative correlation between minimum temperature (20.7°C - 26.5°C) with daily cases in all-time lags and average temperature (24.4°C - 29.3°C) with a daily case in time lag 7, 14, and 21. This range becomes a suitable temperature to reduce the transmission of Covid19 in Padang city. Conclusion: The optimum ambient temperature in the equatorial region is a factor in slowing the incubation of the coronavirus. Nevertheless, strict implementation of health protocols and efforts to trace contacts with positive Covid19 patients are still needed to prevent coronavirus transmission between humans.

RMetS Meteorological Masterclass Series: Anticipating floods, droughts and heatwaves

<scp>RMetS M</scp>eteorological <scp>M</scp>asterclass <scp>S</scp>eries: Anticipating floods, droughts and heatwaves

Extreme Weather Impacts on Inland Waterways Transport of Yangtze River

The impact of extreme weather events on the navigation environment in the inland waterways of the Yangtze River is an interdisciplinary hotspot in subjects of maritime traffic safety and maritime meteorology, and it is also a difficult point for the implementation of decision-making and management by maritime and meteorological departments in China. The objective of this study is to review the variation trends and distribution patterns in the periods of adverse and extreme weather events that are expected to impact on inland waterways transport (IWT) on the Yangtze River. The frequency of severe weather events, together with the changes in their spatial extension and intensity, is analyzed based on the ERA-Interim datasets (1979–2017) and the GHCNDEX dataset (1979–2017), as well as the research progresses and important events (2004–2016) affecting the navigation environment. The impacts of extreme weather events on IWT accidents and phenomena of extreme weather (e.g., thunderstorms, lightning, hail, and tornadoes) that affect the navigation environment are also analyzed and discussed. The results show that: (1) the sections located in the plain climate zone is affected by extreme weather in every season, especially strong winds and heat waves; (2) the sections located in the hilly mountain climate zone is affected particularly by spring extreme phenomena, especially heat waves; (3) the sections located in the Sichuan Basin climate zone is dominated by the extreme weather phenomena in autumn, except cold waves; (4) the occurrence frequency of potential flood risk events is relatively high under rainstorm conditions and wind gusts almost affect the navigation environment of the Jiangsu and Shanghai sections in every year; (5) the heat wave indices (TXx, TR, and WSDI) tend to increase and the temperature of the coldest day of the year gradually increases; (6) the high occurrences of IWT accidents need to be emphasized by relevant departments, caused by extreme weather during the dry season; and (7) the trends and the degree of attention of extreme weather events affecting IWT are ranked as: heat wave &gt; heavy rainfall &gt; wind gust &gt; cold spell &gt; storm. Understanding the seasonal and annual frequency of occurrence of extreme weather events has reference significance for regional management of the Yangtze River.

A historical Southern Ocean climate dataset from whaling ships’ logbooks

AbstractHistorical ship logbooks provide vital historic meteorological observations in the Southern Ocean, one of the largest climate‐data deficient regions on the Earth. Christian Salvesen Whaling Company logbooks from whaling ships operating in the Southern Ocean, starting from the 1930s through the 1950s, are examined. Meteorological information contained in these logbooks has been extracted to produce a historical climate dataset. We discuss various instructions recommended by the British Admiralty to observe and record weather conditions on‐board whaling ships. Statistical tests were used to flag erroneous values and corrections were made using neighbouring values. Meteorological parameters such as air pressure, air and sea temperature and wind force on the Beaufort scale were standardized, converting imperial to metric units. The data were structured according to the internationally accepted International Maritime Meteorological Archive format, which includes the most commonly reported meteorological variables, including the time, location and ship‐related meta‐data. Hence, a readily accessible, error‐corrected and standardized historical climate dataset of the Weddell Sea sector of the Southern Ocean is presented.Open Practices This article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.17863/CAM.31530. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

The 'genie of the storm': cyclonic reasoning and the spaces of weather observation in the southern Indian Ocean, 1851-1925.

This article engages with debates about the status and geographies of colonial science by arguing for the significance of meteorological knowledge making in late nineteenth- and early twentieth-century Mauritius. The article focuses on how tropical storms were imagined, theorized and anticipated by an isolated - but by no means peripheral - cast of meteorologists who positioned Mauritius as an important centre of calculation in an expanding infrastructure of maritime meteorology. Charles Meldrum in particular earned renown in the mid-nineteenth century for theoretical insights into cyclone behaviour and for achieving an unprecedented spatial reach in synoptic meteorology. But as the influx of weather data dried up towards the end of the century, attention turned to developing practices of 'single-station forecasting', by which cyclones might be foreseen and predicted not through extended observational networks, but by careful study of the behaviour of one set of instruments in one place. These practices created new moral economies of risk and responsibility, as well as a 'poetry', as one meteorologist described it, in the instrumental, sensory and imaginative engagement with a violent atmospheric environment. Colonial Indian Ocean 'cyclonology' offers an opportunity to reflect on how the physical, economic and cultural geographies of an island colony combined to produce spaces of weather observation defined by both connection and disconnection, the latter to be overcome not only by infrastructure, but also by the imagination.

ICOADS Release 3.0: a major update to the historical marine climate record

ABSTRACTWe highlight improvements to the International Comprehensive Ocean‐Atmosphere Data Set (ICOADS) in the latest Release 3.0 (R3.0; covering 1662–2014). ICOADS is the most widely used freely available collection of surface marine observations, providing data for the construction of gridded analyses of sea surface temperature, estimates of air–sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3.0 therefore includes changes designed to enable effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists and the public. These user‐driven innovations include the assignment of a unique identifier (UID) to each marine report – to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive common data format incorporate new near‐surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Coupled with enhanced ‘preliminary’ monthly data and product extensions past 2014, R3.0 provides improved support of climate assessment and monitoring, reanalyses and near‐real‐time applications.

COMPARISON AND CORRELATION AMONG MEASUREMENT RESULTS OF OBSERVATORY, HELLMAN, AND TIPPING BUCKET SENSORS

Rainfall data analysis from three types of sensors installed on Maritime Meteorology Station owned by Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG’s) in Pontianak has been carried out. The three types of sensors were Observatory (OBS), Hellman, and Tipping Bucket (TB). This research used analysis of variance (ANOVA) and correlation tests because although calibrations to the rainfall sensors are performed every year, their measurement results are still different.Comparison results showed that there were no significant differences among them, while correlation tests revealed that there were strong correlations between the measurement results of OBS and Hellman (0.9457), OBS and TB (0.9869), and Hellman and TB (0.9651).