G-WADI PERSIANN-CCS GeoServer for extreme precipitation event monitoring

The Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (CHRS) has been collaborating with UNESCO’s International Hydrological Program (IHP) to build a facility for forecasting and mitigating hydrological disasters. This collaboration has resulted in the development of the Water and Development Information for Arid Lands--a Global Network (G-WADI) PERSIANN-CCS GeoServer, a near real-time global precipitation visualization and data service. This GeoServer provides to end-users the tools and precipitation data needed to support operational decision making, research and sound water management. This manuscript introduces and demonstrates the practicality of the G-WADI PERSIANN-CCS GeoServer for monitoring extreme precipitation events even over regions where ground measurements are sparse. Two extreme events are analyzed. The first event shows an extreme precipitation event causing widespread flooding in Beijing, China and surrounding districts on July 21, 2012. The second event shows tropical storm Nock-Ten that occurred in late July of 2011 causing widespread flooding in Thailand. Evaluation of PERSIANN-CCS precipitation over Thailand using a rain gauge network is also conducted and discussed.

[1]  Kuolin Hsu,et al.  Diurnal Variability of Tropical Rainfall Retrieved from Combined GOES and TRMM Satellite Information , 2002 .

[2]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[3]  S. Sorooshian,et al.  Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks , 1997 .

[4]  Moncef Gabbouj,et al.  Fast watershed algorithms: analysis and extensions , 1994, Electronic Imaging.

[5]  Kuolin Hsu,et al.  Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation , 1999 .

[6]  Kuolin Hsu,et al.  PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis , 2009 .

[7]  Kuolin Hsu,et al.  LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration , 2009 .

[8]  S. Sorooshian,et al.  Summertime evaluation of REFAME over the Unites States for near real-time high resolution precipitation estimation , 2012 .

[9]  Y. Hong,et al.  Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System , 2004 .

[10]  Kuolin Hsu,et al.  REFAME: Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates , 2010 .

[11]  S. Sorooshian,et al.  Daytime Precipitation Estimation Using Bispectral Cloud Classification System , 2010 .

[12]  Kuolin Hsu,et al.  Rainfall Estimation Using a Cloud Patch Classification Map , 2007 .

[13]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..