Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

Hong, Yang; Hsu, Kuolin; Sorooshian, Soroosh; Gao, Xiaogang

doi:10.1175/jam2173.1

Cited by 733 publications

(493 citation statements)

References 21 publications

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“…[29] uses geosynchronous satellite infrared cloud imagery, trained against satellite microwave data and ground-based gauges and radar, to achieve high spatial and temporal resolutions for precipitation estimation. The archived PERSIANN product was available for the Indian subcontinent at 0.04…”

Section: Gridded Precipitation Productsmentioning

confidence: 99%

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

et al. 2013

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Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0

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Section: Gridded Precipitation Productsmentioning

confidence: 99%

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

et al. 2013

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“…We used lagged correlation (or cross-correlation) as a simple indicator of dominant land surface interactions with local climate [29,30]. We computed the lagged correlation of the Enhanced Vegetation Index (EVI) to cumulative Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall [31] and land surface temperature (LST), between −60 to 60 degrees latitude at ~4.4-kilometer and 1-kilometer resolution, respectively. We used only one threshold, EVI > 0, in the computation of correlations.…”

Section: Introductionmentioning

confidence: 99%

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Clinton

et al. 2014

Remote Sensing

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Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008-2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes.

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“…For the past decade, there have been several multi-satellite based precipitation retrieval algorithms for operational and research purposes (Hong et al, 2004;Huffman et al, 2007;Joyce et al, 2004;Sorooshian et al, 2000). For this study, we used one of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products, 3B42 V6 given its 10+ year data availability.…”

Section: Nasa Tmpamentioning

confidence: 99%

Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data

Khan

Adhikari

Hong

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5-and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly Correspondence to: Y. Hong (yanghong@ou.edu) model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.

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Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

Cited by 733 publications

References 21 publications

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Hydroclimatology of Lake Victoria region using hydrologic model and satellite remote sensing data

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