Search Results
.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2 Kuligowski, R. J. , 2002 : A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor. , 3 , 112 – 130 . 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2 Kummerow, C. , and Giglio L. , 1995 : A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol. , 12 , 33 – 45 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Kummerow, C. , and Coauthors , 2001 : The evolution of the
.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2 Kuligowski, R. J. , 2002 : A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor. , 3 , 112 – 130 . 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2 Kummerow, C. , and Giglio L. , 1995 : A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol. , 12 , 33 – 45 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Kummerow, C. , and Coauthors , 2001 : The evolution of the
1. Introduction Hydrological forecasts for floods and landslides often require precipitation information at finer space and time scales than those available from spaceborne microwave observations. Statistical approaches have been used commonly to merge and downscale precipitation observations ( Huffman et al. 2007 ). There is an emerging interest in using data assimilation techniques to extract information from multiple data sources, combining with high-resolution modeling to downscale
1. Introduction Hydrological forecasts for floods and landslides often require precipitation information at finer space and time scales than those available from spaceborne microwave observations. Statistical approaches have been used commonly to merge and downscale precipitation observations ( Huffman et al. 2007 ). There is an emerging interest in using data assimilation techniques to extract information from multiple data sources, combining with high-resolution modeling to downscale
-based precipitation estimates contain considerable errors. This is primarily a result of the inherently indirect nature of precipitation remote sensing, which mostly derives precipitation rates from infrared (IR) or microwave signatures of cloud or ice particles, and to the limited spatial and temporal sampling of the space-borne sensors. Most of the current data products take advantage of the availability of multiple IR and microwave sensors to optimally intercalibrate and merge the retrievals from these sensors
-based precipitation estimates contain considerable errors. This is primarily a result of the inherently indirect nature of precipitation remote sensing, which mostly derives precipitation rates from infrared (IR) or microwave signatures of cloud or ice particles, and to the limited spatial and temporal sampling of the space-borne sensors. Most of the current data products take advantage of the availability of multiple IR and microwave sensors to optimally intercalibrate and merge the retrievals from these sensors
1. Introduction Past studies have shown that atmospheric rivers (ARs), which are regions of the lower atmosphere characterized by strong winds and large water vapor contents (usually associated with a surface cold front in the midlatitudes), are key features of the global water cycle (e.g., Zhu and Newell 1998 ), are detectable in satellite observations (see example in Fig. 1a ) ( Ralph et al. 2004 ; Neiman et al. 2008a ), and are associated with heavy rain and flooding on the U.S. West
1. Introduction Past studies have shown that atmospheric rivers (ARs), which are regions of the lower atmosphere characterized by strong winds and large water vapor contents (usually associated with a surface cold front in the midlatitudes), are key features of the global water cycle (e.g., Zhu and Newell 1998 ), are detectable in satellite observations (see example in Fig. 1a ) ( Ralph et al. 2004 ; Neiman et al. 2008a ), and are associated with heavy rain and flooding on the U.S. West
. 10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2 Kulmala, M. , and Coauthors , 2009 : Introduction: European Integrated Project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI)—Integrating aerosol research from nano to global scales. Atmos. Chem. Phys. , 9 , 2825 – 2841 . 10.5194/acp-9-2825-2009 Leijnse, H. , Uijlenhoet R. , and Stricker J. N. M. , 2008 : Microwave link rainfall estimation: Effects of link length and frequency, temporal sampling, power resolution, and
. 10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2 Kulmala, M. , and Coauthors , 2009 : Introduction: European Integrated Project on Aerosol Cloud Climate and Air Quality interactions (EUCAARI)—Integrating aerosol research from nano to global scales. Atmos. Chem. Phys. , 9 , 2825 – 2841 . 10.5194/acp-9-2825-2009 Leijnse, H. , Uijlenhoet R. , and Stricker J. N. M. , 2008 : Microwave link rainfall estimation: Effects of link length and frequency, temporal sampling, power resolution, and
sounding stations, profilers, radars, and rain gauges to help characterize convection and convective precipitation in this region. According to Gochis et al. (2007) , northwestern Mexican precipitation during the 2004 monsoon season was similar to the long-term climatological average, making this season an excellent choice for studying convection in this region. Results from radar and satellite observations during the project have helped establish that, during the monsoon season, shallow convection
sounding stations, profilers, radars, and rain gauges to help characterize convection and convective precipitation in this region. According to Gochis et al. (2007) , northwestern Mexican precipitation during the 2004 monsoon season was similar to the long-term climatological average, making this season an excellent choice for studying convection in this region. Results from radar and satellite observations during the project have helped establish that, during the monsoon season, shallow convection
recommendation of this report was to establish a Hydrometeorology Testbed (HMT) to accelerate research and improvements in QPFs—particularly extreme QPFs—through better physical understanding, observations, numerical modeling, and decision support systems—all of which are critical elements of the operational forecast process for QPFs and are important for future climate services related to extreme events (e.g., National Weather Service 1999 ; Antolik 2000 ; Morss and Ralph 2007 ). This paper follows a
recommendation of this report was to establish a Hydrometeorology Testbed (HMT) to accelerate research and improvements in QPFs—particularly extreme QPFs—through better physical understanding, observations, numerical modeling, and decision support systems—all of which are critical elements of the operational forecast process for QPFs and are important for future climate services related to extreme events (e.g., National Weather Service 1999 ; Antolik 2000 ; Morss and Ralph 2007 ). This paper follows a
