Articles
<![CDATA[Perbandingan Metodologi Koreksi Bias Data Curah Hujan CHIRPS ]]>
<![CDATA[Misnawati, Misnawati]]>;
<![CDATA[Boer, Rizaldi]]>;
<![CDATA[June, Tania]]>;
<![CDATA[Faqih, Akhmad]]>
<![CDATA[ LIMNOTEK - Perairan Darat Tropis di Indonesia Vol 25, No 1 (2018) ]]>
Publisher : <![CDATA[Research Center for Limnology ]]>
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<![CDATA[Penggunaan data global makin meningkat dalam mengatasi permasalahan ketersedian data curah hujan observasi. Salah satu data global yang sering digunakan yaitu data Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). Namun demikian, data CHIRPS tidak bebas dari permasalahan bias, sehingga perlu divalidasi dan dikoreksi dengan menggunakan data observasi hasil pengamatan di lapangan. Penelitian ini bertujuan untuk mengidentifikasi metode koreksi bias yang memberikan performa paling baik dalam memperbaiki inkonsistensi data curah hujan CHIRPS terhadap curah hujan observasi. Metode-metode yang digunakan dalam penelitian ini adalah metode interpolasi error, metode Piani, metode Lenderink dan metode regresi power. Evaluasi performa masing-masing metode tersebut dilakukan berdasarkan nilai R2 dan MSE. Hasil penelitian menunjukkan bahwa metode koreksi bias intepolasi error memberikan hasil yang terbaik dengan nilai R2 dan MSE paling kecil. Pola curah hujan harian dan bulanan CHIRPS terkoreksi metode interpolasi error juga menunjukkan konsistensi yang paling baik terhadap curah hujan observasi.]]>
<![CDATA[KOREKSI BIAS LUARAN MODEL IKLIM REGIONAL UNTUK ANALISIS KEKERINGAN ]]>
<![CDATA[Jadmiko, Syamsu Dwi]]>;
<![CDATA[Murdiyarso, Daniel]]>;
<![CDATA[Faqih, Akhmad]]>
<![CDATA[ Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 41, No 1 (2017) ]]>
Publisher : <![CDATA[Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian ]]>
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DOI: 10.21082/jti.v41n1.2017.25-35
<![CDATA[Abstrak: Luaran simulasi model iklim regional perlu dikoreksi karena memiliki bias sistematis secara spasial dan temporal. Kajian ini membahas simulasi koreksi bias menggunakan metode statistik. Data yang dikoreksi adalah data curah hujan luaran simulasi RegCM4.4 pada periode 1981-2005. Dari simulasi koreksi bias tersebut kami mendapati bahwa koreksi bias menggunakan regeresi linear tidak mampu memperbaiki distribusi spasial maupun pola hujan. Namun, dengan menggunakan regresi polinomial, koreksi bias menunjukkan luaran yang lebih baik terutama dengan regresi polinomial orde 3. Lebih dari itu, regresi polinomial orde 3 yang dikombinasikan dengan intersep yang dikembalikan pada nilai nol memberikan luaran koreksi bias yang terbaik dan dapat digunakan untuk melakukan analisis kekeringan lahan. Kami mendapati bahwa analisis kekeringan dengan metode Standardized Precipitation Index (SPI) yang diuji menggunakan skala waktu 1, 3, 6 dan 12 bulan memberikan hasil terbaik jika menggunakan skala waktu lebih dari 1 bulan. Hal ini dapat dilihat dari hubungannya dengan nilai anomali curah hujan dan jejak kekeringan yang terjadi pada tahun El-Nino seperti tahun 1982/1983, 1986/1987 dan 1997/1998.Abstract. The outputs of regional climate model simulations need to be corrected because of their systematic spatial and temporal biases. This study simulates bias correction using the statistical methods on rainfall data outputs generated by RegCM4.4 during the period of 1981-2005. We found that linier regression did not improve the spatial distribution and pattern of rainfall data. However, by using polynomial regression better results were performed especially third order polynomial. Moreover, when the third order of polynomial regression was combined with the zero intercept, it gave the best bias correction and therefore, can be further used for drought analysis. Standardized Precipitation Index (SPI) method was used to analyze drought index with different time scale of 1, 3, 6 and 12-months. We found that SPI performed well when implemented for time scale more than 1-month. This was demonstrated by the relationship with the rainfall anomaly and drought history during El-Nino years of 1982/1983, 1986/1987 and 1997/1998.]]>
<![CDATA[PREDIKSI AWAL MUSIM HUJAN BERDASARKAN INDEKS VARIABILITAS IKLIM DI PULAU JAWA ]]>
<![CDATA[Rohmawati, Fithriya Yulisiasih]]>;
<![CDATA[Boer, Rizaldi]]>;
<![CDATA[Faqih, Akhmad]]>
<![CDATA[ Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 38, No 1 (2014) ]]>
Publisher : <![CDATA[Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian ]]>
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DOI: 10.21082/jti.v38n1.2014.35-42
<![CDATA[Abstrak. Informasi terkait awal musim hujan (AMH) memiliki peranan penting dalam penyusunan strategi tanam guna meningkatkan hasil pertanian yang optimum. Penelitian ini bertujuan menyusun model prediksi AMH di Jawa sebagai daerah sentra pangan di Indonesia menggunakan indeks variabilitas iklim seperti El Nino Southern Oscilation (ENSO), El Nino Modoki, Indian Ocean Dipole (IOD) dan Sea Surface Temperature (SST) serta Madden Julian Oscillation (MJO). Model persamaan AMH disusun menggunakan model regresi linier dan skill model prediksi dievaluasi menggunakan Relative Operating Characteristics (ROC). Hasil penelitian menunjukkan bahwa ENSO (indeks anomali SST Nino 3.4) menjelaskan sebagian besar variabilitas AMH di Jawa. Oleh karena itu, ENSO bulan Juli dan Agustus digunakan sebagai prediktor AMH. Model persamaan yang disusun berdasarkan indeks tersebut mempunyai skill baik. Rata-rata skill model prediksi mencapai 84% (ENSO bulan Juli) dan 76% (ENSO bulan Agustus) untuk AMH maju dari normal dan 83% (ENSO bulan Juli) dan 86% (ENSO bulan Agustus) untuk AMH mundur dari normal. Dengan hasil tersebut, maka model persamaan dalam penelitian ini cukup dapat memberikan solusi terhadap masalah keakuratan informasi AMH terutama untuk AMH mundur dari normal yang berdampak pada kegagalan panen. Abstract. Monsoon onset information plays an important role in setting up planting strategy for achieving optimum yield. This study aimed to develop forecasting model for the monsoon onset in main rice growing areas of Java, Indonesia using climate variability indices, namely the El Niño Southern Oscillation (ENSO), El Nino Modoki, Indian Ocean Dipole (IOD), and Sea Surface Temperature (SST) and Madden Julian Oscillation (MJO). The forecasting models of the monsoon onset were developed using a linear regression model and that skill of the prediction models were evaluated using Relative Operating Characteristics (ROC). It was found that ENSO (anomaly SST Nino 3.4) explained most of the variability of monsoon onset across Java. Therefore, the SST Nino 3.4 index (in July and August) can be used as one of predictors for predicting the onset. The models developed using this index have a better skill. The average skill of the models for forecasting advanced monsoon onset reached 84% (July?s ENSO) and 76% (August?s ENSO), then for the delayed monsoon onset reached 83% (July?s ENSO) and 86% (August?s ENSO). According to this result, the equation?s model can provide a sufficient solution for the accuracy of monsoon onset information particularly if there is a delay in monsoon onset that can lead to the crop failure.]]>
<![CDATA[Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu ]]>
<![CDATA[Syamsu Dwi Jadmiko]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Agromet Vol. 28 No. 1 (2014) ]]>
Publisher : <![CDATA[PERHIMPI (Indonesian Association of Agricultural Meteorology) ]]>
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DOI: 10.29244/j.agromet.28.1.9-16
<![CDATA[Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875o × 1.875o). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.]]>
<![CDATA[Prediksi Awal Musim Hujan di Jawa Menggunakan Data Luaran Regional Climate Model Version 3.1 (RegCM3) ]]>
<![CDATA[Fithriya Yulisiasih Rohmawati]]>;
<![CDATA[Rizaldi Boer]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Agromet Vol. 28 No. 1 (2014) ]]>
Publisher : <![CDATA[PERHIMPI (Indonesian Association of Agricultural Meteorology) ]]>
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DOI: 10.29244/j.agromet.28.1.17-22
<![CDATA[Monsoon onset information plays an important role in setting up planting strategy for achieving optimum yield. This study aimed to develop forecasting model for the monsoon onset in main rice growing area of Java used Regional Climate Model Version 3.1 (RegCM3). The forecasting models of the monsoon onset and September-Oktober-November (SON) rainfall data were developed using regression model that have the highest coefficient determination and the models were tested using likelihood ratio test. It was found that the forecasting models of the monsoon onset and September-Oktober-November rainfall data were polynomial orde 2 or cuadratic that have coefficient determination 69%, 74%, 80% and 86%. Likelihood ratio test found that RegCM3 rainfall data was not significantly different with observation rainfall data (α = 0.05). Onset in Java between 25th until 34th of 10-days period (early September until early December).]]>
<![CDATA[Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs ]]>
<![CDATA[Laode Nurdiansyah]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Agromet Vol. 32 No. 1 (2018): JUNE 2018 ]]>
Publisher : <![CDATA[PERHIMPI (Indonesian Association of Agricultural Meteorology) ]]>
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DOI: 10.29244/j.agromet.32.1.1-10
<![CDATA[Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.]]>
<![CDATA[Frost Predictions in Dieng using the Outputs of Subseasonal to Seasonal (S2S) Model ]]>
<![CDATA[Erna Nur Aini]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Agromet Vol. 35 No. 1 (2021): JUNE 2021 ]]>
Publisher : <![CDATA[PERHIMPI (Indonesian Association of Agricultural Meteorology) ]]>
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DOI: 10.29244/j.agromet.35.1.30-38
<![CDATA[Dieng volcanic highland, where located in Wonosobo and Banjarnegara regencies, has a unique frost phenomenon that usually occurs in the dry season (July, August, and September). This phenomenon may attract tourism, but it has caused losses to farmers due to crop damage. Information regarding frost prediction is needed in order to minimize the negative impact of this extreme event. This study evaluates the potential use of the Subseasonal to Seasonal (S2S) forecast dataset for frost prediction, with a focus on two areas where frost usually occurs, i.e. the Arjuna Temple and Sikunir Hill. Daily minimum air temperature data used to predict frost events was from the outputs of the ECMWF model, which is one of the models contributed in the Subseasonal to Seasonal prediction project (S2S). The minimum air temperature observation data from the Banjarnegara station was used in conjunction with the Digital Elevation Model Nasional (DEMNAS) data to generate spatial data based on the lapse rate function. This spatial data was used as a reference to downscale the ECMWF S2S data using the bias correction approach. The results of this study indicated that the bias-corrected data of the ECMWF S2S forecast was able to show the spatial pattern of minimum air temperature from observations, especially during frost events. The S2S prediction represented by the bias-corrected ECMWF model has the potential for providing early warning of frost events in Dieng, with a lead time of more than one month before the event.]]>
<![CDATA[PEMODELAN JARINGAN SYARAF TIRUAN UNTUK PREDIKSI PANJANG MUSIM HUJAN BERDASAR SEA SURFACE TEMPERATURE ]]>
<![CDATA[Agus Buono]]>;
<![CDATA[M. Mukhlis]]>;
<![CDATA[Akhmad Faqih]]>;
<![CDATA[Rizaldi Boer]]>
<![CDATA[ Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2012 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia ]]>
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<![CDATA[Penelitian ini difokuskan pada pemodelan Jaringan Syaraf Tiruan untuk prediksi Panjang Musim Hujan, dengan mengambil studi kasus stasiun Sumur Watu di Indramayu. Peubah yang dipergunakan sebagai prediktor adalah Suhu Permukaan Laut pada bulan Juni, Juli dan Agustus yang berupa data grid dan dipilih berdasar nilai korelasi pada taraf nyata 5% dan 10%. Sedangkan peubah respon adalah panjang musim hujan satu periode ke depan yang diukur dalam dasarian (10 harian). Dari 17 tahun periode data, selanjutnya dilakukan pemodelan JST dengan 4 variasi jumlah hidden neuron (5, 10, 20 dan 40) dan 3 laju pembejaran (0.3, 0.1 dan 0.001) pada 6 data set kombinasi dari 3 jenis bulan dan 2 jenis taraf nyata, dan dilakukan dengan 4-fold cross validation untuk melihat skil dari model dalam melakukan prediksi . Selain itu juga dilakukan pemodelan jaringan syaraf tiruan dengan menggunakan grid yang secara konsisten nyata berpengaruh pada panjang musim hujan baik untuk suhu muka laut pada bulan Juni, Juli, ataupun Agustus. Hasil percobaan menunjukkan bhawa suhu muka laut pada bulan agustus memberikan skil tertinggi dengan akurasi 81% dan 84%. Sedangkan untuk bulan Juni dan Juli berkisar sekitar 50%. Prediksi dengan SST pada grid yang konsisten memberikan akurasi sebesar 65%.]]>
<![CDATA[PERAMALAN AWAL MUSIM HUJAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION LEVENBERG-MARQUARDT ]]>
<![CDATA[Agus Buono]]>;
<![CDATA[Alif Kurniawan]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2012 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia ]]>
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<![CDATA[Penelitian ini difokuskan pada pemodelan Jaringan Syaraf Tiruan propagasi balik Levenberg-Marquardt untuk prediksi Awal Musim Hujan (AMH), dengan mengambil studi kasus Kabupaten Indramayu. Peubah yang dipergunakan sebagai prediktor adalah Southern Oscillation Index (SOI). Pemilihan bulan untuk data SOI berdasar nilai korelasi pada taraf nyata 5%. Sedangkan peubah respon adalah awal musim hujan satu periode ke depan yang diukur dalam dasarian (10 harian). Dari 30 tahun periode data (1978-2007), selanjutnya dilakukan pemodelan JST dengan 4 variasi jumlah hidden neuron (5, 10, 15 dan20) dan divalidasi dengan metode Leave One Out (LOO) cross validation untuk melihat skil dari model dalam melakukan prediksi. Hasil percobaan menunjukkan bahwa SOI bulan Juni, Juli dan Agustus mempunyai korelasi yang kuat dengan awal musim hujan, dengan korelasi masing-masing sebesar -0.46, -0.368, dan -0.364. Berdasar SOI pada 3 bulan tersebut dibangun model JST dengan output AMH. Skil model JST diukur menggunakan korelasi antara observasi dengan hasil prediksi. Korelasi tertinggi diperoleh dengan menggunakan hidden neuron 20, yaitu sebesar 0.99. Sedangkan untuk hidden neuron 5, 10 dan 15 masing-masing menghasilkan prediksi dengan korelasi sebesar 0.58, 0.7 dan 0.8.]]>
<![CDATA[Kejadian Iklim Ekstrem dan Dampaknya Terhadap Pertanian Tanaman Pangan di Indonesia ]]>
<![CDATA[Elza Surmaini]]>;
<![CDATA[Akhmad Faqih]]>
<![CDATA[ Jurnal Sumberdaya Lahan Vol 10, No 2 (2016) ]]>
Publisher : <![CDATA[Indonesian Center for Agriculture Land Resource Development ]]>
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DOI: 10.21082/jsdl.v10n2.2016.%p
<![CDATA[Abstrak. Perubahan iklim telah menganggu sistem iklim global dan menyebabkan meningkatnya frekuensi dan intensitas kejadian iklim ekstrem. Tulisan ini merupakan tinjauan mengenai proyeksi skenario iklim, faktor pengendali kejadian iklim ekstrem, serta dampaknya terhadap sektor pertanian di Indonesia. Dampak kejadian iklim ekstrim yang dominan pada sektor pertanian adalah kerusakan tanaman akibat kekeringan dan banjir. Akibat perubahan iklim, kekeringan dan banjir diproyeksikan akan meningkat frekuensi dan intensitasnya di masa akan datang. Informasi prediksi musim dapat digunakan untuk mengetahui intensitas dan wilayah yang terdampak dalam 1-2 musim ke depan. Sedangkan dampak jangka panjang 2-3 dekade ke depan dapat diketahui berdasarkan skenario proyeksi iklim. Prediksi musim telah banyak di manfaatkan untuk menyusun strategi dan kebijakan operasional seperti menyesuaikan waktu tanam, pemilihan komoditas, dan distribusi peralatan pertanian. Namun, kajian proyeksi iklim dan dampaknya terhadap produksi pangan masih sangat terbatas. Informasi tersebut diperlukan dalam perencanaan arah dan pembangunan pertanian ke depan. Oleh karena itu, kajian proyeksi iklim dan dampaknya terhadap produksi pangan perlu menjadi prioritas penelitian pertanian di Indonesia.Abstract. Climate change has disrupted the global climate system and lead to increase frequency and intensity of extreme climate events. This paper is an overview of future climate scenarios, driving force of extreme climate events, and its impacts on the agricultural sector in Indonesia. The common impacts of extreme climate events in Indonesia’s agriculture are crop damaged due to drought and flood. Due to climate change, drought and flood events is projected to intensify in the future. Seasonal prediction have been widely used to formulate operational strategies and policies such as planting time, commodity choice, and distribution of agricultural equipment. While, the climate projections are required for the forthcoming decades. However, the study of climate projections and their impact on food production for the next decades is still very limited. The information are required for planning and direction of future agricultural development. Therefore, the study of climate projections and their impact on food crop should be a priority of agricultural research in Indonesia.]]>
