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Model penurunan skala hibrida terbaru dikembangkan untuk memperoleh data presipitasi beresolusi tinggi dan berakurasi tinggi dalam skala harian.

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Beijing, China (Xinhua/Indonesia Window) – Sekelompok peneliti China bekerja sama dengan peneliti global dalam pengusulan sebuah model penurunan skala (downscaling) hibrida untuk memperoleh data presipitasi beresolusi tinggi dan berakurasi tinggi dalam skala harian, demikian menurut sebuah artikel penelitian yang baru-baru ini dipublikasikan dalam jurnal IEEE Transactions on Geoscience and Remote Sensing.

Produk presipitasi satelit yang ada saat ini umumnya dibatasi oleh resolusi spasial kasarnya, yang tidak mampu memenuhi permintaan data akurasi ruang dan waktu (spasio-temporal) tinggi dalam pemantauan banjir bandang, pengeluaran peringatan tanah longsor, dan pengelolaan sumber daya air yang berpresisi.

Para peneliti tersebut berasal dari berbagai institusi, seperti Institut Bahaya Pegunungan dan Lingkungan (Institute of Mountain Hazards and Environment) dari Akademi Ilmu Pengetahuan China (Chinese Academy of Sciences/CAS) di Chengdu, Provinsi Sichuan, China barat daya, dan Universitas Sun Yat-sen di Provinsi Guangdong, China selatan, serta para peneliti dari Spanyol dan Italia. Mereka mengembangkan model tersebut, yang didasarkan pada metode penurunan skala dan penggabungan presipitasi berbasis kelembapan tanah (soil moisture-based precipitation downscaling and merging/SMPD-MERG).

Metode ini dapat memberikan gambaran yang sangat rinci tentang variabilitas spasial presipitasi dalam berbagai kondisi musim, ungkap CAS.

Secara khusus, metode baru ini berhasil diterapkan pada pengukuran presipitasi global yang dilakukan di bagian tengah Semenanjung Iberia dari 2016 hingga 2018. Metode tersebut meningkatkan resolusi spasialnya dari 10 km menjadi 1 km.

Berbeda dari metode-metode penurunan skala empiris sebelumnya, metode yang diusulkan ini tidak hanya mempertimbangkan dinamika fisik dari proses presipitasi, tetapi juga mencapai integrasi yang baik dari keunggulan berbagai sumber data, papar artikel penelitian tersebut. Metode ini menunjukkan potensi yang baik untuk menghasilkan data presipitasi berkualitas tinggi dengan resolusi spasio-temporal yang tinggi, imbuh artikel penelitian.

Laporan: Redaksi","excerpt","Model penurunan skala hibrida terbaru dikembangkan untuk memperoleh data presipitasi beresolusi tinggi dan berakurasi tinggi dalam skala harian. Beijing, China...","seo_kw","model penurunan skala hibrida","seo_title","Model penurunan skala hibrida terbaru","seo_description","Model penurunan skala hibrida terbaru dikembangkan untuk memperoleh data presipitasi beresolusi tinggi dan berakurasi tinggi.","image","2025/08/abid-shah-gjypn0KD9Zo-unsplash-1-1-1-scaled.jpg","image_alt","image_caption","Ilustrasi. (Abid Shah on Unsplash)","image_url","https://indonesiawindow.sgp1.cdn.digitaloceanspaces.com/2025/08/abid-shah-gjypn0KD9Zo-unsplash-1-1-1-scaled.jpg","status","publish","tags",[61,77,84,91],{"_6":62,"_63":64,"_65":66,"_11":67,"_50":-5,"_68":-5,"_69":70,"_71":70,"_72":73},"019bb744-9e1e-71c6-913e-11b76cfdff09","old_id",15057,"name","cuaca ekstrem di Bumi","cuaca-ekstrem-di-bumi","description","created_at","2026-01-13T12:11:26.000000Z","updated_at","pivot",{"_74":18,"_75":62,"_69":76,"_71":76},"post_id","tag_id","2026-01-13T13:43:57.000000Z",{"_6":78,"_63":79,"_65":80,"_11":81,"_50":-5,"_68":-5,"_69":82,"_71":82,"_72":83},"019bb744-e132-7082-bc16-c5cf29d9277b",22725,"model meteorologi berbasis AI","model-meteorologi-berbasis-ai","2026-01-13T12:11:43.000000Z",{"_74":18,"_75":78,"_69":76,"_71":76},{"_6":85,"_63":86,"_65":87,"_11":88,"_50":-5,"_68":-5,"_69":89,"_71":89,"_72":90},"019bb745-04b5-705c-906a-07e138d11a71",26712,"basis data","basis-data","2026-01-13T12:11:52.000000Z",{"_74":18,"_75":85,"_69":76,"_71":76},{"_6":92,"_63":93,"_65":12,"_11":12,"_50":-5,"_68":-5,"_69":94,"_71":94,"_72":95},"019bb745-09ee-72e8-90da-631b0af8a8a6",27104,"2026-01-13T12:11:53.000000Z",{"_74":18,"_75":92,"_69":76,"_71":76},"comments",[],"2025-08-26","pagination",{"_101":102,"_103":102,"_104":105,"_106":102,"_107":102,"_108":109},"count",1,"total","perPage",12,"currentPage","totalPages","links",{},"pageSize","error","actionData","errors"]