Judul: Evaluasi Kemampuan Luaran Model Subseasonal to Seasonal (S2S) dalam Menentukan Awal Musim Hujan Di Pulau Jawa
Abstrak: Penentuan awal musim hujan sangat penting untuk pengambilan keputusan terutama pada sektor pertanian. Penelitian ini bertujuan untuk menguji kemampuan model ensambel subseasonal to seasonal (S2S) dalam menentukan awal musim hujan di Pulau Jawa periode tahun 2017-2021. Data luaran model S2S yang digunakan yaitu data lead time 44 hari dari Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP), dan United Kingdom Meteorological Office (UKMO). Kriteria awal musim hujan ditentukan berdasarkan definisi agronomi. Metode quantile mapping digunakan untuk mengoreksi bias model S2S. Hasil menunjukkan model S2S terkoreksi mampu mereproduksi pola sebaran tanggal awal musim hujan data observasi, dengan nilai korelasi Pearson rata-rata 0,805. Kemampuan model S2S dalam menentukan awal musim hujan bervariasi untuk tiap titik kajian, hal ini dipengaruhi oleh pemilihan ensambel data, lead time, dan wilayah. Evaluasi kemampuan model S2S lead time 44 hari terkoreksi menunjukkan skill score rata-rata sebesar -0,015. Model S2S NCEP controlled forecast memiliki kemampuan yang lebih baik dari model UKMO dan KMA dengan nilai skill score tertinggi sebesar 0,207. Studi ini menemukan bahwa koreksi bias yang diterapkan masih belum dapat meningkatkan kemampuan model S2S dalam memprediksi awal musim hujan pada Pulau Jawa., Determining onset of rainy season is very important for decision making especially the agricultural sector. This study aims to test the ability of the sub seasonal to seasonal (S2S) ensemble model in determining onset of rainy season in Java for the 2017-2021 period. The output data of the S2S model used is a 44-day lead time from the Korea Meteorological Administration (KMA), the National Centers for Environmental Prediction (NCEP), and the United Kingdom Meteorological Office (UKMO). The criteria for rainy season onset are determined based on the agronomic definition. The quantile mapping method is used to correct the bias of the S2S model. The results show that the corrected S2S model can reproduce the distribution pattern of the rainy season onset date of observation data, with an average Pearson correlation value of 0,805. The ability of the S2S model to determine onset of rainy season varies for each study point, this is influenced by the choice of data ensemble, lead time, and region. Evaluation of the ability of the S2S model with a corrected 44-day lead time shows an average skill score of -0,015. The S2S NCEP controlled forecast model has better abilities than the UKMO and KMA models, with the highest skill score value of 0,207. This study found that the bias correction applied still could not improve the ability of the S2S model to predict the onset of rainy season on Java Island.
Keyword: ensemble, bias correction, predictive reliability, skill score

Judul: Model prediksi awal musim hujan di Pulau Jawa dengan menggunakan informasi suhu muka laut di kawasan Pasifik dan India
Abstrak: The rainfall variability is one of an important role to play onset of wet season. This research aims to analyze that SST (Sea Surface Temperature) exerting an influence on rainfall onset over Java. The significant scale (15°S – 15°N; 80°E- 100°W) of SST patterns are observed to derive a potential predictor rainfall onset over Java. The onset wet season is define as the first 10-day that receive at least 50 mm followed by 2 consecutive 10-days. In Java generally it starts from 1 September. In this study, The regression Multivariate method was offered to develop a rainfall onset model prediction based on SST circulation patern. The principal component and regression technique are improved to achieve a better models and cross validation technique were applied to validate models. Domain predictor of models were derived using spatial correlation analysis result between Rainfall onset data each ZOM over Java and SST anomaly from Indian Ocean to Pacific Ocean during 1978-2007 . There are 28 from 30 ZOM which have potential predictors that be trained to models. The high spatial coherence of the rainfall onset is SST pattern at Juni-Juli-August that represent SST on eastern equatorial Pacific as characteristic of El Nino Southern Oscillation (ENSO), SST on Western Sumatera Island as characteristic of Indian Ocean Dipole Mode (IOD) and SST on around Java region as Characteristic of local condition. Three representative correlation patterns of Java Rainfall onset with SST Indian-Pacific area are investigated that clarify of ENSO, IOD and local condition. Verification result for those models showed promising skill prediction during test in year of 2008 with standart deviation 10 days. In Java generally, skill prediction when rainfall onset advanced exhibited better result than skill prediction when it delayed. The Indonesian ocean area that has local characteristic have influenced better rainfall onset over Java than others. It indicated that models which developed using predictor of Indonesian SST have better skill prediction than SST of Indian or pacific ocean area. However, predictor which has combination domain area of Indonesian SST and Pacific have main influenced rainfall onset over Java. It can be explained that 18 ZOM’s model have been using predictor from those domain.
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Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
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