US Geological Survey, Center for Earth Resources Observation and Science, 47914 252nd St., Sioux Falls, South Dakota 57198, USA.
UC Santa Barbara Climate Hazards Group, Santa Barbara, California 93106, USA.
Sci Data. 2015 Dec 8;2:150066. doi: 10.1038/sdata.2015.66.
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
气候灾害组织红外降水与台站(CHIRPS)数据集建立在以前的“智能”插值技术和基于红外冷云持续时间(CCD)观测的高分辨率、长记录降水估计的基础上。该算法 i)建立在 0.05°气候学基础上,该气候学包含卫星信息以代表稀疏测量的位置,ii)包含每日、每五天和每月 1981 年至今的 0.05° CCD 基降水估计,iii)混合台站数据以生成具有约 2 天延迟的初步信息产品和具有平均延迟约 3 周的最终产品,iv)使用一种新的混合过程,该过程结合了 CCD 估计的空间相关结构来分配插值权重。我们介绍了 CHIRPS 算法、全球和区域验证结果,并展示了如何使用 CHIRPS 来量化减少降水和上升气温对非洲之角的水文影响。我们使用可变入渗能力模型,展示了 CHIRPS 如何支持埃塞俄比亚东南部的有效水文预报和趋势分析。