Synergism of SeaWinds and AMSR

ABSTRACT A combination of spaceborn scatterometer and radiometer provides us with instantaneous observation of various physical parameters of air-sea interface, such as wind speed and direction, water vapor content, sea surface temperature (SST), and rain rate, which are essential to scientific studies of global climate changes. The combined observation also plays an important role in the operational applications, such as weather forecast and disaster prevention. The Japanese earth observation satellite, Advanced Earth Observing Satellite-II (ADEOS-II), which carried a microwave scatterometer, SeaWinds, and a microwave radiometer, Advanced Microwave Scanning Radiometer (AMSR), was launched by the National Space Development Agency of Japan (NASDA) in December 2002, and the simultaneous observation was started. However, ADEOS-II failed in October 2003. A follow-up mission is required to continue the data flow for the operational applications and scientific studies. The Japan Aerospace Exploration Agency (JAXA) is now proposing a new mission called Global Change Observation Mission-W (GCOM-W) that will carry a microwave scatterometer and radiometer. This mission will also offer unique opportunities to address key issues of the Earth Science Enterprise (ESE) at the National Aeronautics and Space Administration (NASA). The simultaneous operation of the active and passive microwave sensors will contribute to improve sensor algorithms to estimate physical parameters. The radiometer measurements will be used by the scatterometer for rain flagging and improving wind retrieval under rain. Scatterometer wind will be able to correct wind direction dependence of the radiometer measurements. The simultaneous observation of the water vapor, precipitation and marine surface vector winds will have a significant impact on studies of the water-energy cycle, which is one of the most important scientific topics and social issues. The mission will provide a unique data set to investigate the balance among horizontal convergence/divergence of the water vapor, evaporation from the sea surface, and precipitation. Monitoring these terms is critical to the characterization of the hydrological cycle and climate changes. The combined measurements of the vector wind, water vapor, SST, and rain will also help to monitor tropical climate including tropical convection systems and cloud clusters, the intertropical convergence zone (ITCZ), and the interannual climate anomalies of El Nino/Southern Oscillation (ENSO), which also influences the mid- and high-latitude climate variations. Scientific impacts of the mission will further extend to studies concerning various phenomena of the atmosphere and ocean, such as monsoon, tropical cyclones, ocean-atmosphere coupling mechanism, ocean’s response to wind and hydrologic forcing, and operational numerical weather prediction, and also to cryospheric studies and land applications.

[1]  Mary Jo Brodzik,et al.  Recent northern hemisphere snow extent: A comparison of data derived from visible and microwave satellite sensors , 2001 .

[2]  F. Gohin,et al.  Evolution of the passive and active microwave signatures of a large sea ice feature during its 2 , 1998 .

[3]  W. Timothy Liu,et al.  New evidence for enhanced ocean primary production triggered by tropical cyclone , 2003 .

[4]  Leung Tsang,et al.  A prototype AMSR-E global snow area and snow depth algorithm , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  Greg Holloway,et al.  Has Arctic Sea Ice Rapidly Thinned , 2002 .

[6]  Dara Entekhabi,et al.  Eurasian snow cover variability and northern hemisphere climate predictability , 1999 .

[7]  Paul R. Houser,et al.  A methodology for snow data assimilation in a land surface model , 2004 .

[8]  Mark A. Bourassa,et al.  Effects of Rain Rate and Wind Magnitude on SeaWinds Scatterometer Wind Speed Errors , 2002 .

[9]  M. Jeffries,et al.  Drifting Arctic sea ice archives changes in ocean surface conditions , 2004 .

[10]  D. Mocko,et al.  Impact of snow conditions in spring dynamical seasonal predictions , 2003 .

[11]  M. Clark,et al.  Relationships between Spring Snow Mass and Summer Precipitation in the Southwestern United States Associated with the North American Monsoon System , 2002 .

[12]  Ross D. Brown,et al.  Northern Hemisphere Snow Cover Variability and Change, 1915-97. , 2000 .

[13]  Matthias Drusch,et al.  ECMWF's Global Snow Analysis: Assessment and Revision Based on Satellite Observations , 2004 .

[14]  Michael H. Freilich,et al.  Wind Stress Curl and Wind Stress Divergence Biases from Rain Effects on QSCAT Surface Wind Retrievals , 2004 .

[15]  Alfred T. C. Chang,et al.  Quantifying the uncertainty in passive microwave snow water equivalent observations , 2005 .

[16]  Stephen E. L. Howell,et al.  An evaluation of SeaWinds/QuikSCAT data for the estimation of the decay status of first-year sea ice , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[17]  M. Anderson,et al.  Spatial and temporal variability in snowmelt onset over Arctic sea ice , 2001, Annals of Glaciology.

[18]  W. Timothy Liu,et al.  Determination of Monthly Mean Humidity in the Atmospheric Surface Layer over Oceans from Satellite Data , 1984 .

[19]  Yoji Furuhama.  GEOSS challenges and Asia-Pacific activities , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[20]  David G. Long,et al.  Evaluating the effect of rain on SeaWinds scatterometer measurements , 2004 .

[21]  D. Rothrock,et al.  Thinning of the Arctic sea‐ice cover , 1999 .

[22]  Chris Derksen,et al.  A comparison of 18 winter seasons of in situ and passive microwave-derived snow water equivalent estimates in Western Canada , 2003 .

[23]  Mark A. Bourassa,et al.  Calibrating the Quikscat/SeaWinds Radar for measuring rainrate over the oceans , 2003, IEEE Trans. Geosci. Remote. Sens..

[24]  A. S. Bamzai,et al.  Relation between Eurasian Snow Cover, Snow Depth, and the Indian Summer Monsoon: An Observational Study , 1999 .

[25]  R. Armstrong,et al.  Analysis of Gauge-Measured and Passive Microwave Derived Snow Depth Variations of Snowfields , 2004 .

[26]  Francis Gohin,et al.  Some active and passive microwave signatures of Antarctic sea ice from mid-winter to spring 1991 , 1995 .

[27]  D. Chelton,et al.  Satellite Measurements Reveal Persistent Small-Scale Features in Ocean Winds , 2004, Science.

[28]  W. Timothy Liu,et al.  Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas , 2002 .

[29]  David G. Long,et al.  Simultaneous wind and rain retrieval using SeaWinds data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[30]  W. Timothy Liu,et al.  Progress in Scatterometer Application , 2002 .

[31]  Neil Peacock,et al.  High interannual variability of sea ice thickness in the Arctic region , 2003, Nature.

[32]  Son V. Nghiem,et al.  Observations of Arctic environmental change , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[33]  Son V. Nghiem,et al.  Global snow cover monitoring with spaceborne Ku-band scatterometer , 2001, IEEE Trans. Geosci. Remote. Sens..

[34]  David G. Long,et al.  Assessing the quality of SeaWinds rain measurements , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Donald J. Cavalieri,et al.  30‐Year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability , 2003 .

[36]  Edward G. Josberger,et al.  Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm , 2004 .

[37]  Simon Yueh,et al.  Interplay between wind and rain observed in Hurricane Floyd , 2000 .

[38]  David G. Long,et al.  Validation of sea ice motion from QuikSCAT with those from SSM/I and buoy , 2001, IEEE Trans. Geosci. Remote. Sens..

[39]  Heiko Balzter,et al.  Retrieval of timber volume and snow water equivalent over a Finnish boreal forest from airborne polarimetric Synthetic Aperture Radar , 2002 .

[40]  Dorothy K. Hall,et al.  Nimbus-7 SMMR derived global snow cover parameters , 1987 .

[41]  W. Liu,et al.  Far-Reaching Effects of the Hawaiian Islands on the Pacific Ocean-Atmosphere System , 2001, Science.

[42]  Josefino C. Comiso,et al.  A rapidly declining perennial sea ice cover in the Arctic , 2002 .

[43]  Antony K. Liu,et al.  Arctic sea ice drift from wavelet analysis of NSCAT and special sensor microwave imager data , 1999 .

[44]  Stephen L. Durden,et al.  Corrections to the seawinds scatterometer wind vectors by removing the effects of rain from the NRCS , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[45]  Antony K. Liu,et al.  Sea‐ice motion derived from satellite agrees with buoy observations , 1998 .

[46]  Leung Tsang,et al.  Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow , 2000 .

[47]  F. Gohin,et al.  A first try at identification of sea ice using the three beam scatterometer of ERS-1 , 1994 .