Judul: Perbandingan Metode Koreksi Data Radar Cuaca Dengan Penakar Hujan Wilayah Bengkulu
Abstrak: Weather radar data can provide spatio temporal rainfall forecast at high- resolution. Radar covers a large area and captures the spatial variability of rainfall that increases a short-term predictability of rain. However, the rainfall derived from radar is inherently an estimated value that may contain bias. Also, the estimated value is derived indirectly from the radar reflectivity equation. Therefore, adjusting the estimated rainfall value based on the local observed rain gauge remains a research challenge. This study employed Bengkulu weather radar data and combined with the rainfall from automatic weather station at the Bengkulu Climatology Station. Observed rainfall datasets from 34 rain gauges over Bengkulu for 2015 to 2020 were used for the analysis, we selected December-February as representative of wet season, whereas June-August was for the dry season. Correction of radar data with rain gauge is calculated using the Mean Field Bias (MFB), Local Bias Correction (LBC), and Quantile Mapping (QM) approaches. MFB is a method for estimating the overall bias (average) for radar in a large domain. The LBC correction is a correction by considering the distance factor of the rain gauge to the radar location. Correction of bias QM corrects the data distribution using the probability approach. The Conversion of reflectivity to intensity using three different Z-R relationship, namely Marshall Palmer, Rosenfeld, and the new Z-R equation refers to previous research on the island of Sumatra. The results of analyses in the present study strongly indicated an improvement in radar data quality after the correction method was applied. The Local Bias Correction (LBC) method gives the best results compared to the MFB and QM methods for the Bengkulu region, with the highest correlation of 0.51. The best Z-R relationship is obtained from the Marshall Palmer equation. Bias correction performance in the wet season (DJF) has better results than in the dry season (JJA).
Keyword: Reflectivity, radar, correction, rain, method

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: 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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