Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction
Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction
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DOI：
10.1029/2003jd003986
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发表时间：
2004-04-08
影响因子：
4.4
通讯作者：
Anagnostou, EN
中科院分区：
文献类型：
--
作者：
Hossain, F;Anagnostou, EN
[1]研究了当前基于被动微波(PM)和红外(IR)的卫星雨量反演和采样方法在中等尺度流域洪水预报中的适用性。根据热带雨量测量任务(TRMM)的降水雷达雨量测量结果，推导了PM和IR传感器的降雨反演误差参数。PM雨的反演是从TRMM微波成像仪(TMI)降雨估计算法的陆地部分推断的，而IR的反演是从每小时PM校准的IR雨场获得的，这是NASA/GSFC产生的可变降雨产品(VAR)阵列的一部分。在模拟研究中，基于反演误差参数，建立了卫星测雨的概率误差模型。PM的雨探测能力明显高于IR，而无雨探测的成功率分别为93%和88%。研究发现，红外线反演给出的假警报雨率大约是PM的两倍PM传感器星座包括两个特殊的传感器微波成像器(SSM/I)(F14和F15)、TMI和高级微波传感辐射计(AMSR-E)。研究发现，目前的PM采样与洪水预报的不确定性有关，比全球降水测量(GPM)任务计划的典型3小时采样高出约50%-100%。由于IR反演在捕捉正确的时空降雨结构方面存在较大的局限性，因此在与PM反演进行合并时，增加了预测峰值径流时间的误差。结果表明，减少的标准误差(<；100%)与较高的降雨探测概率(>；0.90)相结合，可以有效地减少峰值径流预报的不确定性。为了在到达峰值的时间上减少误差，可能需要进一步的改进，例如降低IR检索的虚警率，再加上更高的POD。在总径流量方面，POD的适度改进和现有IR算法的误差方差相结合，足以减少预测的不确定性。
[1] The adequacy of current passive-microwave-(PM)- and infrared-(IR)-based satellite rainfall retrieval and sampling for flood prediction of a medium-sized watershed is investigated. On the basis of Tropical Rainfall Measuring Mission ( TRMM) Precipitation Radar rainfall measurements, rain retrieval error parameters for PM and IR sensors are derived. PM rain retrievals are inferred from the overland component of the TRMM Microwave Imager (TMI) rain estimation algorithm, while IR retrievals are obtained from hourly PM-calibrated IR rain fields, which are part of a variable rainfall product (VAR) array produced at NASA/GSFC. A probabilistic error model is developed for satellite-based precipitation measurements on the basis of retrieval error parameters in this simulation study. The PM rain detection ability was found to be significantly more sensitive than that of IR while the successful no-rain detection probabilities were found to be 93% and 88%, respectively. The IR retrieval was found to give false alarm rain rates about twice as large as that of PM. The PM sensor constellation comprised two Special Sensor Microwave Imagers (SSM/I) (F14 and F15), the TMI, and the Advanced Microwave Sensing Radiometer (AMSR-E). It was found that current PM sampling is associated with flood prediction uncertainty approximately 50 - 100% higher than that of a canonical 3-hourly sampling planned for the Global Precipitation Measurement (GPM) mission. The comparatively greater limitation in capturing the correct space-time rain structure by IR retrievals had the effect of increasing the error in predicting the time of peak runoff when merging was performed with PM retrievals. It was found that a reduced standard error (< 100%) in IR retrieval combined with a higher probability of rain detection ( POD) (> 0.90) can make IR retrievals useful in reducing uncertainty in the prediction of peak runoff. To reduce the error in time to peak, further improvement, such as reduction in the IR retrieval's false alarm rates coupled with an even higher POD, may be necessary. In terms of overall runoff volume, combined moderate improvements in POD and error variance of current IR retrieval algorithms are sufficient for the reduction of prediction uncertainty.