Sensitivity of Satellite-Derived Wind Retrieval Over Cloudy Scenes to Target Selection in Tracking and Pixel Selection in Height Assignment

Satellite-derived atmospheric motion vectors (AMVs) are useful in weather analyses such as for identifying tropical lows, wind shears, and jet locations. AMVs are assimilated into numerical weather prediction models, particularly for ocean areas where wind observations are sparse. An AMV's accuracy is closely related to the processes of target tracking and height assignment (HA). The objective of this paper is to investigate the sensitivity of satellite-derived wind retrieval in cloudy scenes to the main components in these processes. AMVs are retrieved by identifying and tracking targets using advanced pattern-matching techniques based on cross-correlation statistics. In tracking targets, the main components of the AMV algorithm are the target selection methods such as the target box size, the grid size, the time interval between satellite images, and the method for determining the locations of targets. This study reveals that the optimal sizes of the target and grid could be determined differently according to the channel used for wind observation. The time interval between satellite images has a significant impact on the number of vectors with high quality and high accuracy. The HA method is also an important factor in determining the AMVs' accuracy. The heights of most vectors are assigned to cloud-top pressures using the representative radiances, and the current algorithm uses the coldest pixels to set these representative radiances. The template image used for feature tracking may contain various clouds with different movements and different heights. Therefore, without any information on feature tracking, the current approach may lead to HA errors. To mitigate these HA errors, a new approach using the individual-pixel contribution rate is tested. It tends to correct the heights of the AMVs using the water vapor channel and reduces the wind speed bias and root-mean-square vector difference.

[1]  J. Schmetz,et al.  Operational Cloud-Motion Winds from Meteosat Infrared Images , 1993 .

[2]  William L. Smith,et al.  Improved Cloud Motion Wind Vector and Altitude Assignment Using VAS. , 1983 .

[3]  Régis Borde,et al.  A Direct Link between Feature Tracking and Height Assignment of Operational EUMETSAT Atmospheric Motion Vectors , 2014 .

[4]  Kenneth Holmlund,et al.  The Utilization of Statistical Properties of Satellite-Derived Atmospheric Motion Vectors to Derive Quality Indicators , 1998 .

[5]  Gary J. Jedlovec,et al.  A Satellite-Derived Upper-Tropospheric Water Vapor Transport Index for Climate Studies , 2000 .

[6]  F. Bretherton,et al.  Cloud cover from high-resolution scanner data - Detecting and allowing for partially filled fields of view , 1982 .

[7]  W. Paul Menzel,et al.  A Report on the Recent Demonstration of NOAA's Upgraded Capability to Derive Cloud Motion Satellite Winds , 1991 .

[8]  Mats Hamrud,et al.  586 The RTTOV-9 upgrade for clear-sky radiance assimilation in the IFS , 2009 .

[9]  Steven J. Nieman,et al.  Fully automated cloud-drift winds in NESDIS operations , 1997 .

[10]  Donald W. Hillger,et al.  Estimating Noise Levels of Remotely Sensed Measurements from Satellites Using Spatial Structure Analysis , 1988 .

[11]  L. F. Hubert,et al.  WIND ESTIMATION FROM GEOSTATIONARY-SATELLITE PICTURES , 1971 .

[12]  W. Paul Menzel,et al.  Monthly Mean Large-Scale Analyses of Upper-Tropospheric Humidity and Wind Field Divergence Derived from Three Geostationary Satellites , 1995 .

[13]  Christopher S. Velden,et al.  The Impact of Satellite Winds on Experimental GFDL Hurricane Model Forecasts , 2001 .

[14]  Régis Borde,et al.  A DIRECT LINK BETWEEN FEATURE TRACKING AND HEIGHT ASSIGNMENT OF OPERATIONAL ATMOSPHERIC MOTION VECTORS , 2008 .

[15]  H. Fischer,et al.  Water Vapor Structure Displacements from Cloud-Free Meteosat Scenes and Their Interpretation for the Wind Field , 2006 .

[16]  J. Mecikalski,et al.  Application of Satellite-Derived Atmospheric Motion Vectors for Estimating Mesoscale Flows , 2005 .

[17]  C. Velden,et al.  GLOBAL ATMOSPHERIC MOTION VECTOR INTER-COMPARISON STUDY , 2008 .

[18]  G. Mills,et al.  An Operational System for Generating Cloud Drift Winds in the Australian Region and Their Impact on Numerical Weather Prediction , 1994 .

[19]  W. Paul Menzel,et al.  The Impact of Satellite-derived Winds on Numerical Hurricane Track Forecasting , 1992 .

[20]  Steven J. Nieman,et al.  Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations , 1997 .

[21]  Steven J. Nieman,et al.  A Comparison of Several Techniques to Assign Heights to Cloud Tracers , 1993 .

[22]  Jaime Daniels,et al.  A NEW NESTED TRACKING APPROACH FOR REDUCING THE SLOW SPEED BIAS ASSOCIATED WITH ATMOSPHERIC MOTION VECTORS (AMVS) , 2008 .

[23]  Xiaolei Zou,et al.  Impact of GMS-5 and GOES-9 Satellite-Derived Winds on the Prediction of a NORPEX Extratropical Cyclone , 2002 .

[24]  L. Hodges,et al.  SYNOPTIC USE OF RADIATION MEASUREMENTS FROM SATELLITE TIROS I 1 , 1961 .

[25]  R. Borde,et al.  The impact of window size on AMV , 2008 .

[26]  H. Laurent Wind Extraction from Meteosat Water Vapor Channel Image Data , 1993 .

[27]  Masami Tokuno,et al.  OPERATIONAL SYSTEM FOR EXTRACTING CLOUD MOTION AND WATER VAPOR MOTION WINDS FROM GMS-5 IMAGE DATA , 2010 .

[28]  B. Soden Tracking upper tropospheric water vapor radiances: A satellite perspective , 1998 .

[29]  Christopher S. Velden,et al.  Toward Improved Use of GOES Satellite-Derived Winds at the National Centers for Environmental Prediction ( NCEP) , 2003 .

[30]  W. Shenk,et al.  Suggestions for improving the derivation of winds from geosynchronous satellites , 1991 .

[31]  Christopher S. Velden,et al.  The Impact of Multispectral GOES-8 Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995. Part II: NOGAPS Forecasts , 1998 .