Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Nghiên cứu so sánh giữa việc sử dụng giá trị mưa tại điểm đơn so với giá trị mưa trung bình khu vực từ một mô hình độ phân giải cao khi được xác nhận với những số liệu vệ tinh cho ba cơn bão tác động đến Philippines
Tóm tắt
Trong nghiên cứu này, chúng tôi đánh giá ảnh hưởng của việc sử dụng giá trị trung bình khu vực thay vì giá trị đầu ra tại điểm đơn từ một mô hình phân giải mây để xác minh các dự báo lượng mưa định lượng (QPF) của nó theo ma trận phân loại 2×2 so với các số liệu mưa vệ tinh cho ba cơn bão đã tấn công Philippines. Động lực của công việc này xuất phát từ thực tế rằng các phương pháp phân loại là các phương pháp điểm đến điểm, trong khi các ước lượng vệ tinh được coi là giá trị trung bình khu vực. Tổng thể, chúng tôi nhận thấy rằng việc sử dụng giá trị trung bình khu vực có tác động nhỏ nhưng tích cực đến các thống kê phân loại, chủ yếu là do sự đồng nhất tổng thể tốt hơn giữa lượng mưa thu được từ vệ tinh và đầu ra của mô hình sau khi trung bình khu vực với hiệu ứng làm mịn. Những tác động này cũng có ý nghĩa thống kê đáng kể với các ngưỡng cao khoảng ≥ 350 mm. Việc sử dụng giá trị trung bình khu vực từ mô hình khiến các chỉ số nguy cơ (TS) cải thiện ở các ngưỡng thấp khoảng 0.02–0.05, chủ yếu là do sự gia tăng xác suất phát hiện (POD) các sự kiện mưa quan sát được, do đó hiệu ứng làm mịn giúp chuyển đổi một số trường hợp bỏ lỡ thành những trường hợp chính xác. Trong những trường hợp với sai số tần suất thấp, các cải tiến tương tự trong TS cũng xảy ra ở các ngưỡng trung bình và thậm chí cao (lên đến 500–750 mm) khi cả POD và tỷ lệ thành công (SR) đều tăng, với SR thấp hơn cho thấy tỷ lệ báo động sai (FAR) giảm. Đối với các ngưỡng cực đoan, kết quả có phần phân tán hơn và mức độ tin cậy của sự quan trọng giảm, nhưng các TS tại đó đã ở mức thấp (≤ 0.08) so với dữ liệu vệ tinh bất kể phương pháp nào được áp dụng.
Từ khóa
#mưa định lượng #mô hình phân giải mây #số liệu vệ tinh #phương pháp đánh giá #cơn bão #phân tích thống kêTài liệu tham khảo
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