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All Journal Agromet Indonesian Journal of Geography Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sumberdaya Lahan Journal of Mathematical and Fundamental Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sumberdaya Lahan Jurnal Tanah dan Iklim Jurnal Meteorologi dan Geofisika The International Journal of Remote Sensing and Earth Sciences (IJReSES)

Akhmad Faqih

Agrometeorology Division, Department Of Geophysics And Meteorology, Faculty Of Mathematics And Natural Sciences, IPB University, Campus IPB Dramaga, Indonesia

Author-ID : 223454

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Energy Environmental Science Mathematics Physics

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Articles

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Dynamical Downscaling Luaran Global Climate Model (GCM) Menggunakan Model REGCM3 untuk Proyeksi Curah Hujan di Kabupaten Indramayu Syamsu Dwi Jadmiko; Akhmad Faqih
Agromet Vol. 28 No. 1 (2014)
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

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.

Prediksi Awal Musim Hujan di Jawa Menggunakan Data Luaran Regional Climate Model Version 3.1 (RegCM3) Fithriya Yulisiasih Rohmawati; Rizaldi Boer; Akhmad Faqih
Agromet Vol. 28 No. 1 (2014)
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

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).

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs Laode Nurdiansyah; Akhmad Faqih
Agromet Vol. 32 No. 1 (2018): JUNE 2018
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

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.

Frost Predictions in Dieng using the Outputs of Subseasonal to Seasonal (S2S) Model Erna Nur Aini; Akhmad Faqih
Agromet Vol. 35 No. 1 (2021): JUNE 2021
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.35.1.30-38

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.

Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia Qurrata A'yun Kartika; Akhmad Faqih; I Putu Santikayasa; Amsari Mudzakir Setiawan
Agromet Vol. 37 No. 1 (2023): JUNE 2023
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.37.1.54-65

Western Java is densely populated with high socio-economic activity. Climate-related disasters can be mitigated with the support of an understanding of systems that produce reliable climate predictions. One of the climate variables included in hydrometeorological disasters is rainfall. The characteristics of rainfall in Western Java cannot be separated from the sea surface temperature (SST) around the area. This study compares the relationship between SST and rainfall with singular value decomposition (SVD) and compares it with Pearson's correlation. SVD Model performance was evaluated using square covariance fraction (SCF) and Pearson correlation. The results showed that rainfall has a higher correlation with SST Anomaly (SSTA) by using SVD, with a correlation of about 0.63 in 6 to 9 months without lag time. Rainfall in western Java was closely related to the positive SSTA anomaly in southern Indonesia, especially the waters south of Java Island, and negative anomalies in other areas. Furthermore, atmospheric dynamic analysis showed that the positive coefficient expansion is followed by warmer SST, lower surface air pressure, higher water vapor, and higher rainfall, all were respective to their normal conditions around western Java. This study concludes that warmer SSTA around Western Java causes increased rainfall in western Java than normal and potentially impacts the hydrological disaster in West Java.

Co-Authors Adyaksa Budi Raharja Agita Vivi Wijayanti Agus Buono Alexander Knohl Alif Akbar Syafrianno Alif Kurniawan Alimatul Rahim Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Ardhasena Sopaheluwakan Ashehad Ashween Ali Bony Septian Pandjaitan Christian Stiegler Daniel Murdiyarso Dodo Gunawan Dodo Gunawan Eddy Hermawan Ela Hasri Windari Elza Surmaini Erna Nur Aini Fildza Novadiwanti Fithriya Yulisiasih Rohmawati Furqon Alfahmi Furqon Alfahmi Gita Adhani Jadmiko, Syamsu Dwi Kartika, Qurrata A'yun Laode Nurdiansyah M. Mukhlis Mamenun Mamenun Misnawati, Misnawati Mulyono R. Prabowo Mulyono R. Prabowo Naziah Madani P. Perdinan Rahmat Hidayat Rini Hidayati Rizaldi Boer Santikayasa, I Putu Sanusi Sanusi Sitanggang, Imas S. Supari Supari Suria Tarigan Syamsu Dwi Jadmiko Tania June Ummu Ma'rufah Yonny Koesmaryono

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