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Điều Chỉnh Mô Hình Không Gian Hóa Lượng Mưa 10 Ngày Tại Trung Quốc Dựa Trên Dữ Liệu GPM IMERG Và Giải Thích Địa Hình Sử Dụng Thuật Toán BEMD
Tóm tắt
Dữ liệu lượng mưa 10 ngày có độ phân giải không gian cao liên tục là rất cần thiết cho các dịch vụ phát triển cây trồng và nghiên cứu sinh thái học. Trong nghiên cứu này, chúng tôi đầu tiên sử dụng thuật toán phân tích chế độ kinh nghiệm hai chiều (BEMD) để phân decomposit dữ liệu mô hình độ cao số (DEM) và thu được các địa hình tần số cao (OR3), tần số trung gian (OR5) và tần số thấp (OR8). Sau đó, chúng tôi đề xuất một mô hình không gian hóa lượng mưa tinh chỉnh, sử dụng dữ liệu quan sát khí tượng mặt đất, các sản phẩm vệ tinh đo lượng mưa toàn cầu tích hợp từ các vệ tinh (GPM IMERG), dữ liệu DEM, dữ liệu phân decomposit địa hình, dữ liệu hướng lượng mưa chủ yếu (PPD) và các dữ liệu đa nguồn khác, để xây dựng dữ liệu lượng mưa 10 ngày độ phân giải cao của Trung Quốc từ năm 2001 đến 2018. Kết quả phân decomposit cho thấy địa hình miền núi từ quy mô mịn đến thô; và các ảnh hưởng của độ cao, độ dốc và hướng đối với lượng mưa được thể hiện tốt hơn trong mô hình sau khi địa hình được phân decomposit. Hơn nữa, dữ liệu phân decomposit địa hình có thể được thêm vào mô phỏng mô hình để cải thiện chất lượng sản phẩm mô phỏng; chất lượng mô phỏng của mô hình vào mùa hè tốt hơn so với mùa xuân và mùa thu, và tương đối kém vào mùa đông; và OR5 cùng OR8 có thể được cải thiện trong mô phỏng, với OR5 và OR8 được chọn một cách động tốt hơn. Ngoài ra, việc tiền xử lý dữ liệu trước khi không gian hóa lượng mưa đặc biệt quan trọng. Ví dụ, thêm 0,01 vào giá trị 0 của lượng mưa, nhân các giá trị nhỏ của lượng mưa nhỏ hơn 1 với 10, và thực hiện biến đổi phân bố chuẩn (ví dụ: Yeo-Johnson) trên dữ liệu có thể cải thiện chất lượng mô phỏng.
Từ khóa
#lượng mưa #không gian hóa #dữ liệu GPM IMERG #phân decomposit địa hình #thuật toán BEMDTài liệu tham khảo
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