384
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B.,
Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.
(2003). The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4, 1147–1167.
Arkin, P. A. and Meisner, B. N. (1987). The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84. Monthly Weather
Review, 115, 51–74.
Ba, M. and Gruber, A. (2001). GOES multispectral rainfall algorithm (GMSRA). Journal of
Applied Meteorology, 40, 1500–1514.
Bauer, P., Amayenc, P., Kummerow, C. D., and Smith, E. A. (2001). Over ocean rainfall
retrieval from multisensor data of the Tropical Rainfall Measuring Mission—Part
II: Algorithm implementation. Journal of Atmospheric and Oceanic Technology, 18,
1838–1855.
Berg, W. and Chase, R. (1992). Determination of mean rainfall from the special sensor microwave/imager (SSM/I) using a mixed lognormal distribution (SSM/I) using a mixed lognormal distribution. Journal of Atmospheric and Oceanic Technology, 9, 129–141.
Chang, A. T. C., Chiu, L. S., Kummerow, C., and Meng J. (1999). First results of the
TRMM microwave imager (TMI) monthly oceanic rain rate: Comparison with SSM/I.
Geophysics Research Letters, 26, 2379–2382.
Christian, K., Hirohiko, M., and Peter, B. (2007). A next-generation microwave rainfall
retrieval algorithm for use by TRMM and GPM—Chapter V: Measuring Precipitation
from Space: EURAINSAT and the Future. In Advances in Global Change Research,
Levizzani, V., Bauer, P., and Turk, F. J., eds., Springer, New York, 28, 235–252.
Dobrin, B. P., Viero, T., and Gabbouj, M. (1994). Fast watershed algorithms: Analysis and
extensions. In Nonlinear Image Processing, vol. 2180, 209–220, SPIE.
Ferraro, R. R. and Marks, G. F. (1995). The development of SSM/I rain rate retrieval algo, G. F. (1995). The development of SSM/I rain rate retrieval algoG. F. (1995). The development of SSM/I rain rate retrieval algorithms using ground- based radar measurements. Journal of Atmospheric and Oceanic
Technology, 12, 755–770.
Ferraro, R., Pellegrino, P., Turk, M., Chen, W., Qiu, S., Kuligowski, R., Kusselson, S.,
Irving, A., Kidder S. and Knaff, J. (2005). The tropical rainfall potential—Part 2:
Validation. Weather and Forecasting, 20, 465–475.
Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978).
Rain estimation from geosynchronous satellite imagery-visible and infrared studies.
Monthly Weather Review, 106, 1153–1171.
Grody, N. C. (1991). Classification of snow cover and precipitation using the Special Sensor
Microwave/Imager (SSM/I). Journal of Geophysical Research, 96, 7423–7435.
Gruber, A. and Levizzani, V. (2008). Assessment of global precipitation products. WCRP-128,
WMO Technical Document, 1430.
Hong, Y., Hsu, K., Sorooshian, S., and Gao, X. (2004). Precipitation estimation from remotely
sensed imagery using an artificial neural network cloud classification system. Journal of
Applied Meteorology, 43, 1834–1852.
Hong, Y., Hsu, K. L., Sorooshian, S., and Gao, X. (2005). Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation from satellite imagery at small scales. Water Resources Research, 41, W03008,
doi:10.1029/2004WR003142.
Hong, Y., Gochis, D., Chen, J. T., Hsu, K. L., and Sorooshian, S. (2007a). Evaluation of
Precipitation Estimation from Remote Sensed Information using Artificial Neural
Network-Cloud Classification System (PERSIANN-CCS) rainfall measurement using
the NAME Event Rain Gauge Network. Journal of Hydrometeorology, 8(3), 469–482.
Hong, Y., Adler, R. F., Hossain, F., and Huffman, G. J. (2007b). A first approach to global
runoff simulation using satellite rainfall estimation. Water Resources Research, 43,
W08502, doi:10.1029/2006WR005739.
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B.,
Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.
(2003). The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4, 1147–1167.
Arkin, P. A. and Meisner, B. N. (1987). The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84. Monthly Weather
Review, 115, 51–74.
Ba, M. and Gruber, A. (2001). GOES multispectral rainfall algorithm (GMSRA). Journal of
Applied Meteorology, 40, 1500–1514.
Bauer, P., Amayenc, P., Kummerow, C. D., and Smith, E. A. (2001). Over ocean rainfall
retrieval from multisensor data of the Tropical Rainfall Measuring Mission—Part
II: Algorithm implementation. Journal of Atmospheric and Oceanic Technology, 18,
1838–1855.
Berg, W. and Chase, R. (1992). Determination of mean rainfall from the special sensor microwave/imager (SSM/I) using a mixed lognormal distribution (SSM/I) using a mixed lognormal distribution. Journal of Atmospheric and Oceanic Technology, 9, 129–141.
Chang, A. T. C., Chiu, L. S., Kummerow, C., and Meng J. (1999). First results of the
TRMM microwave imager (TMI) monthly oceanic rain rate: Comparison with SSM/I.
Geophysics Research Letters, 26, 2379–2382.
Christian, K., Hirohiko, M., and Peter, B. (2007). A next-generation microwave rainfall
retrieval algorithm for use by TRMM and GPM—Chapter V: Measuring Precipitation
from Space: EURAINSAT and the Future. In Advances in Global Change Research,
Levizzani, V., Bauer, P., and Turk, F. J., eds., Springer, New York, 28, 235–252.
Dobrin, B. P., Viero, T., and Gabbouj, M. (1994). Fast watershed algorithms: Analysis and
extensions. In Nonlinear Image Processing, vol. 2180, 209–220, SPIE.
Ferraro, R. R. and Marks, G. F. (1995). The development of SSM/I rain rate retrieval algo, G. F. (1995). The development of SSM/I rain rate retrieval algoG. F. (1995). The development of SSM/I rain rate retrieval algorithms using ground- based radar measurements. Journal of Atmospheric and Oceanic
Technology, 12, 755–770.
Ferraro, R., Pellegrino, P., Turk, M., Chen, W., Qiu, S., Kuligowski, R., Kusselson, S.,
Irving, A., Kidder S. and Knaff, J. (2005). The tropical rainfall potential—Part 2:
Validation. Weather and Forecasting, 20, 465–475.
Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978).
Rain estimation from geosynchronous satellite imagery-visible and infrared studies.
Monthly Weather Review, 106, 1153–1171.
Grody, N. C. (1991). Classification of snow cover and precipitation using the Special Sensor
Microwave/Imager (SSM/I). Journal of Geophysical Research, 96, 7423–7435.
Gruber, A. and Levizzani, V. (2008). Assessment of global precipitation products. WCRP-128,
WMO Technical Document, 1430.
Hong, Y., Hsu, K., Sorooshian, S., and Gao, X. (2004). Precipitation estimation from remotely
sensed imagery using an artificial neural network cloud classification system. Journal of
Applied Meteorology, 43, 1834–1852.
Hong, Y., Hsu, K. L., Sorooshian, S., and Gao, X. (2005). Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation from satellite imagery at small scales. Water Resources Research, 41, W03008,
doi:10.1029/2004WR003142.
Hong, Y., Gochis, D., Chen, J. T., Hsu, K. L., and Sorooshian, S. (2007a). Evaluation of
Precipitation Estimation from Remote Sensed Information using Artificial Neural
Network-Cloud Classification System (PERSIANN-CCS) rainfall measurement using
the NAME Event Rain Gauge Network. Journal of Hydrometeorology, 8(3), 469–482.
Hong, Y., Adler, R. F., Hossain, F., and Huffman, G. J. (2007b). A first approach to global
runoff simulation using satellite rainfall estimation. Water Resources Research, 43,
W08502, doi:10.1029/2006WR005739.