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- Author or Editor: Yiping Wang x
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Abstract
Satellite rainfall estimates from a microwave emission-based algorithm by Wilheit et al. are verified using the noncontiguous rain gauge method incorporating monthly Pacific atoll rain gauge data. The results are compared with those obtained using an infrared-based satellite algorithm, the GOES precipitation index. Comparisons between satellite estimates with simple Spatial averages of point rain gauge data are shown to be ineffective at identifying statistically significant differences between the two algorithms due to substantial amounts of spatial sampling error in the rain gauge spatial averages. By effectively reducing this error, the noncontiguous rain gauge method reveals distinctive differences in the ability of each of the algorithms to accurately estimate monthly rainfall over the open ocean. The results indicate that the microwave algorithm, while slightly biased, is significantly less biased than the infrared, which tends to overestimate high rainfall values and underestimate low rainfall values. However, the random error associated with both algorithms is essentially the same.
Abstract
Satellite rainfall estimates from a microwave emission-based algorithm by Wilheit et al. are verified using the noncontiguous rain gauge method incorporating monthly Pacific atoll rain gauge data. The results are compared with those obtained using an infrared-based satellite algorithm, the GOES precipitation index. Comparisons between satellite estimates with simple Spatial averages of point rain gauge data are shown to be ineffective at identifying statistically significant differences between the two algorithms due to substantial amounts of spatial sampling error in the rain gauge spatial averages. By effectively reducing this error, the noncontiguous rain gauge method reveals distinctive differences in the ability of each of the algorithms to accurately estimate monthly rainfall over the open ocean. The results indicate that the microwave algorithm, while slightly biased, is significantly less biased than the infrared, which tends to overestimate high rainfall values and underestimate low rainfall values. However, the random error associated with both algorithms is essentially the same.
Abstract
An extreme weather and climate event does not only mean that an extreme occurs at an individual point (station), but more generally it has a certain impacted area and duration, which means that it is a regional extreme event (REE). How to identify a REE is the basis for studies in this area. An objective identification technique for REE (OITREE), which is based on the model of “the string of candied fruits,” is proposed in this study. This technique consists of five steps: to select a daily index for individual points (stations), to partition natural daily abnormality belts, to distinguish the event’s temporal continuity, to establish an index system for regional events, and to judge extremity for regional events. In the index system developed specially for regional events, there are five single indices, namely extreme intensity, accumulated intensity, accumulated area, maximum impacted area and duration, as well as an integrated index and the spatial location. In this study, the proposed method was first applied to examine four types of REEs in China: heavy precipitation, drought, high temperature, and low temperature. Results show that the technique is skillful in identifying REEs, demonstrating the usefulness of the proposed method in detecting and studying of REEs and operational application.
Abstract
An extreme weather and climate event does not only mean that an extreme occurs at an individual point (station), but more generally it has a certain impacted area and duration, which means that it is a regional extreme event (REE). How to identify a REE is the basis for studies in this area. An objective identification technique for REE (OITREE), which is based on the model of “the string of candied fruits,” is proposed in this study. This technique consists of five steps: to select a daily index for individual points (stations), to partition natural daily abnormality belts, to distinguish the event’s temporal continuity, to establish an index system for regional events, and to judge extremity for regional events. In the index system developed specially for regional events, there are five single indices, namely extreme intensity, accumulated intensity, accumulated area, maximum impacted area and duration, as well as an integrated index and the spatial location. In this study, the proposed method was first applied to examine four types of REEs in China: heavy precipitation, drought, high temperature, and low temperature. Results show that the technique is skillful in identifying REEs, demonstrating the usefulness of the proposed method in detecting and studying of REEs and operational application.
