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Integrasi Prediksi Musim dengan Model Simulasi Tanaman untuk Penentuan Waktu Tanam Padi Elza Surmaini; Tri Wahyu Hadi; Kasdi Subagyono; M. Ridho Syahputra
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 42, No 2 (2018)
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v42n2.2018.99-110

Abstrak. Penyesuaian waktu tanam merupakan upaya dengan biaya yang paling efisien untuk meningkatkan produktivitas, menstabilkan, bahkan meningkatkan ketahanan pangan. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat digunakan untuk memberikan rekomendasi waktu tanam padi dengan hasil yang optimal. Dua tahap analog digunakan untuk memprediksi curah hujan harian untuk satu musim tanam. Analog tahap pertama untuk memprediksi curah hujan harian untuk 120 hari. Tahap kedua mencari satu analog terbaik prediksi sekuens curah hujan 120 hari. Basis data hasil tanaman padi periode 1982-2009 dengan interval harian dibangun menggunakan model simulasi tanaman. Rekomendasi waktu tanam ditentukan berdasarkan perubahan hasil dibandingkan dengan waktu tanam awal. Hasil penelitian menunjukkan bahwa prediksi curah hujan musim dengan lead time 6-9 bulan menggunakan metode downscaling dengan dua tahap analog dapat memperpanjang lag prediksi 2 bulan sebelum tanam sehingga dapat digunakan untuk peringatan dini. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat memberikan informasi selang waktu tanam yang berpotensi untuk mendapatkan hasil yang lebih tinggi. Prediksi waktu tanam dalam bentuk selang waktu diperlukan petani , karena berbagai faktor non teknis yang menyebabkan penanaman tidak dapat dilakukan pada rekomendasi waktu tertentu. Informasi tersebut dapat digunakan oleh pengambil kebijakan dan penyuluh untuk rekomendasi kepada petani tentang waktu tanam dengan hasil padi yang lebih tinggi.Abstract. Adapting planting time is a very cost-efficient way to increase crop productivity and stabilise or even increase food security. Linking seasonal rainfall prediction with crop simulation model is used to evaluate planting date with optimal rice yield. We used a two step analogue method. The first step is to predict 30 daily rainfall analogues for the next 120 days. The second step is to look for best analogue of 120 day rainfall prediction. Daily planting dates were simulated within 1982-2009 using crop simulation model. The second step is to determine the best analoque for the 120 day sequence. Planting time recommendation is adjusted using the difference between the earliest and later planting dates.The result concluded that 6-9 lead time seasonal rainfall prediction using two step analogue could increase lead time 2 months prior to planting time, therefore can be use for early warning. Linking season rainfall prediction with crop simulation model to adjust interval of planting time that provide higher rice yield. Farmers need that interval, due to non-technical factors are caused crop could not planted timely as recommended. In addition, the recommendation of planting time should be used by decision makers and extension workers to recommend appropriate planting time with higher yield to the farmers.

PREDIKSI HUJAN BULANAN MENGGUNAKAN ADAPTIVE STATISTICAL DOWNSCALING Agus Safril; Tri Wahyu Hadi; Safwan Hadi; Bayong Tjasyono H. Kasih
Jurnal Meteorologi dan Geofisika Vol 14, No 1 (2013)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v14i1.143

Permasalahan pada prediksi hujan bulanan menggunakan Global Circulation Model (GCM) adalah resolusi yang rendah sehingga tidak dapat memberikan informasi yang rinci sampai tingkat regional. Permasalahan lain adalah akurasi prediksi yang rendah yang disebabkan pola curah hujan yang non linier dan non stasioner. Prediksi hujan dengan adaptive statistical downscaling diaplikasikan untuk memecahkan permasalahan tersebut. Variabel prediktor prediktor dipilih dari korelasi tertinggi antara prediktor dan curah hujan menggunakan Singular Value Decomposition (SVD). Hasil prediksi hujan bulanan dengan metoda adaptif menggunakan ANFIS (Adaptif Neuro Fuzzy Inference System) menunjukkan nilai korelasi antara prediksi dan observasi lebih tinggi dari pada hasil prediksi curah hujan keluaran model sirkulasi global (GCM). Nilai RMSE (Root Mean Square Error) pada prediksi statistical downscaling juga menunjukkan nilai yang lebih kecil dibandingkan prediksi hasil keluaran model sirkulasi global. Hasil prediksi hujan menunjukkan bahwa nilai korelasi (r) antara prediksi dan hujan observasi di daerah dengan siklus hujan tahunan > 0,66, di daerah dengan siklus hujan semi tahunan adalah sedang (0,33 ≤ r ≤ 0,66) kecuali di Meulaboh, Sibolga, dan Lhokseumawe dengan kategori r < 0,33. The problem arised in monthly prediction of precipitation using GCM (Global Circulation Mode) was on the coarse resolution that did not provide detailed information for regional scale. Another problem arised was on the low accuracy of prediction that was caused by non-linier and non- stationary rainfall patterns. Adaptive statistical downscaling method was applied to solve those problems. Predictor variables were selected from the highest correlation between predictor and precipitation using Singular Value Decomposition (SVD). The result of adaptive monthly prediction using ANFIS showed that the correlation between prediction and observation was higher than dinamical prediction. RMSE (Root Mean Square Error) in statistical downscaling prediction was smaller then the output of GCM. The result of precipitation prediction showed thet correlations between prediction and precipitation in the annual region) (r) > 0,66), in the semi annual cycle was moderate (0,33 ≤ r ≤ 0,66), except in Meulaboh, Sibolga, and Lhokseumawe stations (r < 0,33).

VERIFIKASI PREDIKSI CURAH HUJAN ENSEMBLE MENGGUNAKAN METODE ROC Elza Surmaini; Tri Wahyu Hadi
Jurnal Meteorologi dan Geofisika Vol 21, No 1 (2020)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v21i1.618

Prediksi musim dibutuhkan untuk merencanakan waktu tanam adalah 1-2 musim ke depan. Informasi jumlah curah hujan dan deret hari kering merupakan parameter yang diperlukan dalam perencanaan pertanian. Penelitian bertujuan untuk menguji kemampuan model prediksi curah hujan musim ensemble, menentukan peluang optimal pengambilan keputusan, dan menentukan akurasi prediksi berdasarkan peluang optimal. Verifikasi model dilakukan untuk musim kemarau (MK) I (Februari-Mei) dan MK 2 (Mei-Agustus) pada daerah dengan pola hujan monsunal (Kabupaten Indramayu) dan MK 1 (Mei-Agustus) untuk pola hujan lokal (Kabupaten Bone). Keluaran prediksi musim dari Climate Forecast System (CFS) v2 digunakan untuk men-downscale jumlah curah hujan (CH) dan deret hari kering ≥15 hari (DHK15) di wilayah penelitian. Downscaling menggunakan metode Constructed Analogue dengan prediktor angin pada paras 850 hPa pada lima wilayah monsun. Metode yang digunakan untuk mengevaluasi keandalan prediksi probabilistik adalah Relative Operating Characteristics. Peluang optimal berdasarkan cut point ditentukan menggunakan Youden Indeks, dan akurasi prediksi pada peluang optimal ditentukan dengan metode Proportion of Correct. Hasil penelitian menunjukkan bahwa pengambilan keputusan menggunakan peluang optimal berdasarkan cut point untuk pengambilan keputusan dapat meningkatkan keandalan prediksi jumlah curah hujan sebesar 5-17% pada MK1 dan 3-24% pada MK2, dan frekuensi DHK15 sebesar 2-10%. The seasonal predictions are needed to adjust planting time for the following 1-2 seasons. Information on the amount of rainfall and dry spell is an appropriate parameter in agricultural planning. The research aimed to examine the skill of ensemble seasonal rainfall prediction models, to determine an optimal probability for making decisions, and to determines the skill of seasonal prediction based on optimal probability. Model verifications were assessed in Dry Season Planting (DSP)1 (February-May) and DSP2 (May-August in Monsoonal (Indramayu District) dan DSP1 (Mei-August) in Local (Bone District) Rainfall Pattern. We used Relative Operating Characteristics to evaluate the skill of probabilistic predictions. The optimal cut-point was assessed using the Youden Index, and the skill of prediction at an optimal cut point was determined using the Proportion of Correct method. In conclusion, the results show that the use of the optimal probability at the cut point in decision-making increase the skill of rainfall prediction 5-17% in DSP1 and 3-24% in DSP2. As for the frequency of DHK15, the skill increases by 2-10%.

AKURASI PREDIKSI CURAH HUJAN HARIAN OPERASIONAL DI JABODETABEK : PERBANDINGAN DENGAN MODEL WRF Indra Gustari; Tri Wahyu Hadi; Safwan Hadi; Findy Renggono
Jurnal Meteorologi dan Geofisika Vol 13, No 2 (2012)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v13i2.126

Akurasi prakiraan curah hujan harian operasional yang dibuat oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG) dikaji dengan cara diverifikasi berdasarkan kategori hujan dikotomi, lebat dan sangat lebat terhadap data dari 25 titik pengamatan di Jakarta. Prosedur yang sama juga diterapkan pada prakiraan curah hujan model Weather Research and Forecasting (WRF) dengan teknik multi-nesting yang di-downscale dari keluaran Global Forecast System (GFS). Hasilnya memperlihatkan bahwa kedua metode prediksi tersebut memiliki akurasi yang baik untuk prediksi dikotomi tetapi hampir gagal dalam memprediksi curah hujan lebat dan sangat lebat. Khususnya, kegagalan prediksi operasional dalam mendeteksi tiga kejadian hujan sangat lebat dalam periode kajian. Dalam hal ini, model WRF yang cenderung menghasilkan false alarm memperlihatkan prospek yang bagus untuk pengembangan sistem prediksi cuaca skala lokal/regional yang lebih akurat di Indonesia. The accuracy of daily rainfall forecasts produced operationally by the Meteorological, Climatological, and Geophysical Agency (BMKG) has been assessed by verifying the prediction of dichotomous, heavy, and very heavy rain events against observed data at 25 stations in Jakarta. Similar procedure was applied to raw hindcasts performed using the Weather Research and Forecasting (WRF) model with multi-nesting technique up to 3 km resolution downscaled from NOAA global forecast system (GFS) outputs. The results show that both forecasts have quite favorable accuracy for dichotomous rain events but almost no meaningful score for the predictions of heavy and very heavy rain events was obtained. Particularly, none of as many as three observed very heavy rain events was predicted by the operational forecast. In this case, WRF tend produce false alarms indicating a better prospect for future development of more accurate local/regional weather forecasting system in Indonesia.

The Study of Wind Field ERA-20C in Monsoon Domains for Rainfall Predictor in Indonesia (Java, Sumatra, and Borneo) Trinah Wati; Tri Wahyu Hadi; Ardhasena Sopaheluwakan; Lambok M Hutasoit
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.37.1.34-43

In recent years, various research institutions have developed diverse global data reanalysis projects. This provides an opportunity to gain long-term of meteorological data for local scale. This study aims to select the potential predictor of wind fields u and v of the ERA-20C dataset, a reanalysis dataset, at 850 mb from seven domains or windows of Asian, Maritime Continent, Australian, and Western North Pacific monsoon related physically to rainfall anomaly patterns in Indonesia. The vector wind velocity scalar was obtained by using a Helmholtz decomposition to separate the total circulation v = (u,v) into the divergent component/velocity potential (Ï‡) or Phi and rotational component/stream function (Ïˆ) or Psi for obtaining the scalar variable of vector wind velocity. The method applied Singular value decomposition (SVD) to identify pairs of spatial patterns (expansion coefficients) between the predictors of Phi and Psi in seven domains, with rainfall data from 48 stations in Java, Sumatra, and Borneo Islands from 1981 to 2010. The results revealed that spatial patterns correlations of Java Islands were the highest in the Maritime Continent monsoon domain (80oâˆ’150o E and 15oSâˆ’15o N), while Sumatra and Borneo Island were in the Western North Pacific monsoon domain (100oâ€“130o E and 5oâ€“15o N) with predictor Psi. The lowest correlation for Java, Sumatra, and Borneo was the Australian monsoon domain (110o Eâ€“130o E and 5o Sâ€“15o S) with predictor Phi. In general, spatial pattern correl-ations of Java Island were higher than others, agreeing with monsoonal rainfall type dominantly in the region.

VERIFIKASI PREDIKSI CURAH HUJAN ENSEMBLE MENGGUNAKAN METODE ROC Elza Surmaini; Tri Wahyu Hadi
Jurnal Meteorologi dan Geofisika Vol. 21 No. 1 (2020)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v21i1.618

Prediksi musim dibutuhkan untuk merencanakan waktu tanam adalah 1-2 musim ke depan. Informasi jumlah curah hujan dan deret hari kering merupakan parameter yang diperlukan dalam perencanaan pertanian. Penelitian bertujuan untuk menguji kemampuan model prediksi curah hujan musim ensemble, menentukan peluang optimal pengambilan keputusan, dan menentukan akurasi prediksi berdasarkan peluang optimal. Verifikasi model dilakukan untuk musim kemarau (MK) I (Februari-Mei) dan MK 2 (Mei-Agustus) pada daerah dengan pola hujan monsunal (Kabupaten Indramayu) dan MK 1 (Mei-Agustus) untuk pola hujan lokal (Kabupaten Bone). Keluaran prediksi musim dari Climate Forecast System (CFS) v2 digunakan untuk men-downscale jumlah curah hujan (CH) dan deret hari kering ≥15 hari (DHK15) di wilayah penelitian. Downscaling menggunakan metode Constructed Analogue dengan prediktor angin pada paras 850 hPa pada lima wilayah monsun. Metode yang digunakan untuk mengevaluasi keandalan prediksi probabilistik adalah Relative Operating Characteristics. Peluang optimal berdasarkan cut point ditentukan menggunakan Youden Indeks, dan akurasi prediksi pada peluang optimal ditentukan dengan metode Proportion of Correct. Hasil penelitian menunjukkan bahwa pengambilan keputusan menggunakan peluang optimal berdasarkan cut point untuk pengambilan keputusan dapat meningkatkan keandalan prediksi jumlah curah hujan sebesar 5-17% pada MK1 dan 3-24% pada MK2, dan frekuensi DHK15 sebesar 2-10%. The seasonal predictions are needed to adjust planting time for the following 1-2 seasons. Information on the amount of rainfall and dry spell is an appropriate parameter in agricultural planning. The research aimed to examine the skill of ensemble seasonal rainfall prediction models, to determine an optimal probability for making decisions, and to determines the skill of seasonal prediction based on optimal probability. Model verifications were assessed in Dry Season Planting (DSP)1 (February-May) and DSP2 (May-August in Monsoonal (Indramayu District) dan DSP1 (Mei-August) in Local (Bone District) Rainfall Pattern. We used Relative Operating Characteristics to evaluate the skill of probabilistic predictions. The optimal cut-point was assessed using the Youden Index, and the skill of prediction at an optimal cut point was determined using the Proportion of Correct method. In conclusion, the results show that the use of the optimal probability at the cut point in decision-making increase the skill of rainfall prediction 5-17% in DSP1 and 3-24% in DSP2. As for the frequency of DHK15, the skill increases by 2-10%.

Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island Muharsyah, Robi; Hadi, Tri Wahyu; Indratno, Sapto Wahyu
Agromet Vol. 34 No. 1 (2020): JUNE 2020
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from an ensemble prediction in the form of predictive Probability Density Function (PDF). BMA is commonly used to calibrate Ensemble Prediction System (EPS) in a shorter-range forecast. Here, we applied the BMA for a longer forecast at a seasonal interval. This study aimed to develop the implementation of the BMA method to calibrate the seasonal forecast (long range) of monthly rainfall from the RAW output of the EPS European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated with observational data from 26 stations over Java Island in 1981-2018. BMA predictive PDF was generated with a gamma distribution, which was obtained based on two training schemes, namely sequential (BMA-JTS) and conditional (BMA-JTC) training windows. Generally, both of BMA-JTS and BMA-JTC were able to produce better distribution characteristics of ensemble prediction than that of RAW model ECS4. Both BMA methods showed a good performance as indicated by a high accuracy, small bias, and small uncertainty to the observed rainfall. Our findings revealed that BMA-JTC was able to improve the quality of probabilistic forecasts of below and above normal events. The improvement was shown in most stations over Java Island, in which the model was a good skill forecast based on Brier Skill Score (BSS).

EVALUASI METODE KOREKSI BIAS UNTUK PREDIKSI CURAH HUJAN BULANAN ECMWF SEAS5 DI INDONESIA Hutauruk, Rheinhart C H; Rahmanto, Edi; Al Habib, Abdul Hamid; Yoku, Priskila Wilhelmina; Giriharta, I Wayan Gita; Trilaksono, Nurjanna Joko; Hadi, Tri Wahyu
Jurnal Meteorologi dan Geofisika Vol. 25 No. 2 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i2.1124

The seasonal rainfall forecast from ECMWF SEAS5 often suffers from biases that reduce its accuracy, limiting its use in applications like water resource management and agricultural planning. This study evaluates the effectiveness of bias correction methods in enhancing the skill of ECMWF SEAS5 seasonal precipitation forecasts in Indonesia. Observational data from 148 BMKG rain gauges and SEAS5 raw output from 2011 to 2020 are used. Three bias correction methods—linear scaling (LS), empirical distribution quantile mapping (EQM), and gamma distribution quantile mapping (GQM)—are applied to the raw model. Model performance is assessed using scatter plots, root mean square error (RMSE), correlation, and Taylor diagrams. The results show LS consistently outperforms EQM and GQM, reducing RMSE from 128 to 102 and improving correlation from 0.57 to 0.65. Additionally, Brier Score (BS) and Relative Operating Characteristic (ROC) analysis highlight significant improvements in probabilistic predictions, especially in areas with high rainfall variability. These findings indicate LS as a particularly effective approach for bias correction, enhancing accuracy and reliability. This study underscores the potential of applying bias correction methods like LS to improve ECMWF SEAS5 forecasts, supporting better decision-making for climate change adaptation and mitigation in Indonesia.

Determining Monsoon Onset Dates in Makassar Using Rainfall Anomalies and Moisture Source Trajectory Analysis (1991–2020) Hutauruk, Rheinhart; Hadi, Tri Wahyu; Muharsyah, Robi; Yolanda, Selvy
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1162

Makassar exhibits a typical monsoonal rainfall regime, characterized by a strong annual cycle with peak rainfall occurring in January–February. Understanding the onset of the rainy season in this region is crucial for water resource management and disaster preparedness, yet previous studies have generally relied only on rainfall-based criteria with coarse temporal resolution. This study aims to determine the onset date of the rainy season in Makassar by combining local rainfall anomalies with regional-scale moisture-source trajectories. Daily rainfall data for 1991–2020 were analyzed using harmonic reconstruction to identify the climatological peak of the monsoon season, which then guided the moisture trajectory analysis. The results show that most rainy-season onsets occur in November–December, with high interannual variability influenced by large-scale climate drivers such as ENSO. Moisture transport during the peak rainy months is predominantly derived from the Northern Maritime (58.8%) and Tropical Maritime (40.5%) sources, highlighting the essential role of cross-equatorial water-vapor advection. In addition, changes in zonal wind direction at 850 hPa consistently coincide with the onset, providing an independent dynamical indicator of the transition from dry to wet phase. By explicitly linking rainfall anomalies with the timing of dynamical shifts and dominant moisture pathways, this approach reduces ambiguities commonly found in rainfall-only methods and produces onset estimates that align more closely with regional atmospheric dynamics. Compared to previous rainfall-only approaches, this combined local–regional method provides a more representative onset estimate at daily resolution, offering new insight into the mechanisms of monsoon rainfall in coastal areas of eastern Indonesia.

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