Judul: Comparison of Bias Correction Methods on ECMWF Precipitation Data as the Basis for Meteorological Drought Analysis
Abstrak: Kekeringan meteorologis disebabkan oleh curah hujan yang rendah dalam kurun waktu tertentu. Riau merupakan salah satu provinsi di Indonesia yang rawan mengalami kekeringan. Analisis kekeringan dapat dilakukan dengan menghitung indeks kekeringan salah satunya menggunakan metode Standardized Precipitation Index (SPI). Menghitung SPI memerlukan data curah hujan sebagai input yang dapat diperoleh melalui data observasi ataupun data model satelit jika ketersediaan data observasi terbatas. Namun, luaran data model masih memiliki bias terhadap data observasi sehingga perlu dikoreksi sebelum digunakan. Penelitian ini bertujuan membandingkan dua metode koreksi bias pada data curah hujan model European Center for Medium Range Weather Forecast (ECMWF) di Provinsi Riau tahun 2007-2016 dan mengidentifikasi metode yang menghasilkan luaran data terbaik, yaitu memiliki korelasi tertinggi dan nilai error (RMSE) terendah terhadap curah hujan observasi. Metode koreksi bias yang digunakan yaitu Quantile Mapping dan Rasio Rata-rata. Curah hujan hasil koreksi terbaik kemudian digunakan untuk menghitung SPI. Hasil analisis menunjukkan bahwa curah hujan ECMWF yang dikoreksi menggunakan metode Rasio rata-rata memberikan hasil terbaik dibandingkan metode Quantile Mapping karena memiliki korelasi tertinggi dan RMSE terendah. Berdasarkan analisis temporal SPI di Provinsi Riau (rata-rata seluruh grid) tahun 2007-2016 menggunakan curah hujan ECMWF terkoreksi metode Rasio Rata-rata, telah terjadi kekeringan sebanyak 17 kali dengan rata-rata durasi kejadian selama 2 bulan dan durasi maksimum hingga 4 bulan. Pada bulan Oktober tahun 2015 Provinsi Riau (bagian selatan) mengalami kekeringan yang diikuti oleh kejadian IOD positif (menandakan kekeringan di Indonesia) dengan nilai SPI mencapai -1,77 dan masuk ke dalam kategori sangat kering., Meteorological drought caused by low precipitation within a certain period of time. Riau is one of the provinces in Indonesia which is prone to drought events. Drought analysis can be done by calculating the drought index named Standardized Precipitation Index (SPI). The drought index (SPI) requires precipitation data as an input that can be obtained through observation data or satellite data model if the observation data are limited. However, satellite data model has a bias against observation data and needs to be corrected before being used as an input. This study aims to compare two different bias data correction methods on European Center for Medium Range Weather Forecast (ECMWF) precipitation data in Riau Province on 2007-2016 which shows the best corrected data output (highest correlation and lowest RMSE). The bias correction method that used in this study are Quantile Mapping and Average Ratio. The best corrected output of ECMWF precipitation data then used to calculate SPI. The results of this analysis show that the Average Ratio method has the better results than Quantile Mapping method as it has a lower error value (RMSE) and higher correlation (r). Based on temporal SPI analysis in Riau Province (all grids average) on 2007-2016 using corrected ECMWF precipitation by Average Ratio method shows that there were 17 times of drought with 2 months average duration and a maximum duration is 4 months. In October 2015, Riau Province (southern area) suffered drought which was followed by positive IOD (indicating drought in Indonesia) with -1,77 SPI value and classified as the very dry category.
Keyword: average ratio, meteorological drought, quantile mapping, SPI

Judul: Meteorological  Drought  Index Based on ECMWF Model Corrected for Characterization of Potential Forest  and Land  Fires
Abstrak: The occurrence of drought in Indonesia is almost an event that occurs every year. This drought affects many sectors and tends to occur more frequently. Indonesia is in 4th position (behind India, Japan and China) with potential losses of up to US 50 billion from the top 10 countries in the Asia Pacific region that have the potential to lose due to natural disasters and drought. Drought tends not to have a universal definition. The impact of drought is generally non-structural in nature so that the area of impact spreads wider than the damage caused by other natural hazards. The potential for drought can also increase if regional scale climate phenomena such as El Nino and positive Indian Ocean Dipole Mode appear. Drought conditions that are getting worse have resulted in a large number of forest and land fires occurring. The incidence of forest and land fires has had a very significant impact. The total losses due to forest and land fires in 2019 borne by Indonesia reached US5.2 billion, equivalent to Rp 72.95 trillion. At least 900,000 residents have had their respiratory health affected, and have had operational problems at 12 national airports. It is estimated that 620,201 hectares of forest and land were burned with the impact of losses of up to US157 million in damage to assets and US5 billion due to lost potential from economic activity. Forest fires can be identified by hotspots which can be obtained by utilizing MODIS (Moderate Resolution Imaging Spectro-radiometer) remote sensing measurements on the Terra-Aqua satellite and / or by utilizing Himawari satellite imagery. Observing the sequence of events as described above, apart from anthropogenic factors, from a climatic perspective it is very important to be able to know the conditions of climate dynamics that exist in Indonesia (and the conditions of the dynamics of the global climate surrounding Indonesian territory which affect the dynamics of the atmosphere in Indonesia) as one trigger point in the process of starting a drought which could have an impact on the potential for forest and land fires. By utilizing the numerical weather prediction model output from the ECMWF (European Center for Medium-Range Weather Forecasts) it can be used to identify potential drought. To reduce the potential bias from the model output, validation and correction are necessary. The output of the model was validated and corrected using the Piani method and the Brier Skill Score to the climate observation data in the field from 774 selected rain observation posts. This step produces corrected ECMWF rainfall data which has a high correlation of 0.7844 at the significant level α = 0.05 with an RMSE value of 87.6 on the observed rainfall data. This validation and correction results in an increase in the output performance of the corrected model. This is indicated by the monthly variability of rainfall, which is initially not detected to be detected, and the bias correction value is smaller. The output of the corrected ECMWF model is used to calculate the drought index using the SPI (The Standardized Precipitation Index) method to describe meteorological drought
Keyword: hotspots, ECMWF, drought index, peatland, Sumatra

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.
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