Judul: Perbandingan Metode Koreksi Bias Deep Learning dan Empirical Quantile Mapping pada Prediksi Sub-seasonal to Seasonal di Pulau Jawa
Abstrak: Climate information and predictions on the sub-seasonal to seasonal (S2S) scale are important and useful for planning and decision-making in various fields such as disaster management, health, energy, hydrology and agriculture. However, until now, the prediction accuracy on the S2S scale is still relatively low compared to weather and seasonal predictions. The existence of bias and uncertainty from the global climate model (GCM) output is a factor causing the low accuracy of S2S predictions. Problems related to the bias of the GCM output can be overcome through bias correction. This study compares the performance of conventional bias correction methods, empirical quantile mapping (EQM) and DL based on convolutional neural networks (CNN) on S2S rainfall prediction. Daily rainfall observation data from 477 stations and rain posts spread across the island of Java were selected based on the level of data availability of more than or equal to 80% for a period of 20 years (1999-2019). The rain post data is then used to build grid-based daily rainfall data with a resolution of 0.05x0.05° through multiple linear regression methods and residual kriging with CHIRPS satellite data predictors, latitude, longitude and elevation. The resulting grid data is then used to perform bias corrections on the output of the Sub-seasonal Experiments (SubX) model. The output of the SubX model consists of 7 (seven) individual models, including CCSM4, CFSv2, FIMr1p1, GEFS, GEPS6, GEOS_v2p1, and NESM. Each model has a member number that varies from 1 to 21. The empirical quantile mapping (EQM), convolutional autoencoder (CAE) and UNET method is then applied to bias correct the SubX model output. The evaluation was carried out on aggregate weekly rainfall during the training period, namely 1999 – 2016, through kfold cross-validation with k=3, and during the testing or unseen data period, namely 2018 – 2019. The performance indicators used to evaluate the bias correction method are the correlation coefficient, root mean square error (RMSE) and continuous ranked probability skill scores (CRPSS). The DL-based bias correction method for the individual SubX model outputs generally performs better than the EQM method. It is indicated by the average value of the correlation coefficient and CRPSS in the output of the individual SubX- corrected DL model, which is generally higher than the results of the EQM correction. The performance advantage of this DL-based bias correction method consistently occurs, both in the training period and the testing period. In the individual SubX models, the DL-based bias correction method increased the correlation coefficient by up to 16%, decreased the average RMSE by 26.5%, and increased the average CRPSS by up to 95.1% in the training period. However, the EQM-based bias correction decreases the average correlation coefficient value in most models, reduces the average RMSE value to 18.5% and increases the CRPSS value to 68.3%. During the testing period, the DL-based bias correction method succeeded in increasing the average correlation coefficient value by up to 15%, reducing the average RMSE value by up to 6.9% and increasing the CRPSS value by up to 59%. However, in the EQM bias correction, the average correlation coefficient only increases to
Keyword: bias  correction, deep learning, multi-model ensemble, precipitation, sub-seasonal to seasonal

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: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
Keyword: