Japan Meteorology Agency (JMA) developed SATAID (Satellite Animation and Interactive Diagnosis) application to display and to retrieve some meteorology parameter values in satellite image data. This paper studies the use of the application in analyzing the Jakarta flood February 1st, 2008 and the Yogyakarta Tropical cyclone February 18th, 2007. The procedure described in this paper can be applied in another issues as a reference material in analyzing SATAID image data.

Natural events following the activity of the Tropical Cyclone Seroja in April 2021 are investigated. During its active phase, Tropical Cyclone Seroja generated extreme rainfall events in some sub-provinces of East Nusa Tenggara (NTT): Ngada, Alor, Belu, Rote Ndao on 4 April, 2021, Kupang on 4 to 5 April, 2021, East Sumba on 4 to 6 April, 2021. Moreover, these extreme rainfall events triggered flood in Alor, East Flores, Lembata, The City of Kupang, Kupang, East Sumba, Malaka, Belu, and North Central Timor. The maximum sea wave height of the Indian Ocean at the Southern part of NTT was also increasing, from 4 meters on 1 to 2 April, 2021 up to 6 meters on 3 April, 2021, and rose to higher than 7 meters on 4 to 6 April, 2021. On 7 to 9 April, 2021, the sea wave height declined as the Tropical Cyclone Seroja moved to the Southwest of NTT.

Abstract. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) was productedby blending Satellite-only Climate Hazards Group InfraRed Precipitation (CHIRP) with Stasion observations data. The blending process was aimed to reduce bias of CHIRP. However, Biases of CHIRPS on statistical moment and quantil values were high during wet season over Java Island. This paper presented a bias correction scheme to adjust statistical moment of CHIRP using observation precipitation data. The scheme combined Genetic Algorithm and Nonlinear Power Transformation, the results was evaluated based on different season and different elevation level. The experiment results revealed that the scheme robustly reduced bias on variance around 100% reduction and leaded to reduction of first, and second quantile biases. However, bias on third quantile only reduced during dry months. Based on different level of elevation, the performance of bias correction process is only significantly different on skewness indicators.

<p>To explore the characteristics of Northerly Cold Surge during Years of the Maritime Continent Campaign 2021, intensive observation was used to detect the modification processes of the air mass at the head of cold surge, convection development, and severe weather including torrential rainfall using several methods such as the intensive upper-air observation at Jakarta and Pangkal Pinang, vapor variation observation with GNSS network, and precipitation radar network. During this campaign, 7 CENS (Cross-Equatorial Northerly Surge) events were observed according to Hattori’s criteria. The results of the intensive observation show that all of 7 CENS events occurred in association with the negative SST anomaly over the Java Sea with CENS6 (18 – 21 February 2021) induced extreme rainfall (over 150 mm/day) in the southern part of Jakarta. The significant negative SST anomaly was continued over the inland & marginal seas of Indonesia under the strong northerly surge condition during this campaign.</p>

Aquacrop is free-licensed Food and Agricultural Organization’s crop modelling that requires minimum inputs of climate variables namely rainfall, maximum temperature, minimum temperature variables and geographic information of the area to be simulated (longitude, latitude, altitude). This study aims to measure the difference in irrigated and rainfed rice productivity from the Aquacrop crop modelling simulation to the influence of climate pattern variations in Java Island, Indonesia. The k-means clustering method applied to the rainfall, maximum, and minimum temperature variables from the bias-corrected MERRA2 data resulted in two climate regions. The principal component analysis result showed that the maximum and minimum temperature variables are the variables that most contribute to the determination of the clustering area using the k-means method compared to the rainfall variable. This study has calculated the probability of the irrigated and rainfed rice productivity resulting from the Aquacrop simulation in those climate regions during La Nina [El Nino] years that will be higher [smaller] than the mean value of rice productivity during neutral years. However, the validation between the actual irrigated and rainfed rice productivity with the Aquacrop simulation results from 2001-2014 showed low correlation values that vary between negative and positive values in all climate regions. Meanwhile, the validation on the El Nino composite years generally showed positive correlation values. In addition, the neutral and La Nina composite years resulted in varying correlation values between negative and positive correlation.

Siklon tropis memiliki pengaruh yang sangat luas terhadap beragam parameter meteorologi. Analisis terhadap kejadian siklon tropis dapat dilakukan pada berbagai macam parameter dengan berbagai teknik yang berbeda. Pada penelitian ini dibahas mengenai identifikasi pola data curah hujan di Jawa dan Madura bertepatan dengan terjadinya siklon tropis Cempaka pada akhir bulan November 2017 di perairan selatan Jawa. Analisis data dilakukan menggunakan Self-Organizing Map (SOM) dan Hierarchical Clustering. Hasil yang diperoleh menunjukkan terdapat 4 kelompok pola curah hujan dominan berkaitan dengan siklon tropis Cempaka. Analisis temporal dan spasial terhadap keempat kelompok tersebut menunjukkan bahwa siklon tropis Cempaka berpengaruh kuat terhadap sebaran curah hujan di Jawa dan Madura.

Nonlinear (NL) method is the most effective bias correction method for correcting statistical bias when observation precipitation data can not be approximated using gamma distribution. Since NL method only adjusts mean and variance, it does not perform well in handling bias on quantile values. This paperpresents a scheme of NL method with additional condition aiming to mitigate bias on quantile values. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to estimate parameter of NL method. Furthermore, to investigate suitability of application of NSGA-II, we performed Single Objective Genetic Algorithm (SOGA) as a comparison. The experiment results revealed NSGA-II was suitable when solution of SOGA produced low fitness. Application of NSGA-II could minimize impact of daily bias correction on monthly precipitation. The proposed scheme successfully reduced biases on mean, variance, first and second quantile However, biases on third and fourth moment could not be handled robustly while biases on third quantile only reduced during dry months.

Fog is one of the atmospheric phenomena that affect airport operations. It can reduce visibility which impacts flight operations (taxiing, take-off, landing). Therefore, fog prediction is needed to support flight safety. The biggest challenge in making weather predictions is the chaotic and complicated process of the atmosphere. This research tries to use artificial intelligence (AI) to predict fog events at Wamena Airport. Design of model prediction using hourly synoptic data set from January 2015 till May 2018. Variables input such as dry ball temperature, wet ball temperature, dew point, relative humidity, cloud cover, wind direction, wind speed, visibility, and present weather for the past six hours ago are used to predict fog or no fog events. We performed a grid search parameter tuning on five algorithms such as Distributed Random Forest (DFR), Deep Learning (DL), Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), and Extreme Randomized Tree (XRT). The best model is obtained from the ensemble model Stacked Ensemble (SE) with an accuracy of above 90% for the fog forecast from one to three hours later.