March 9, 1999Comet Class: SatMet DERIVED MOTION FIELDS from the GOES SATELLITES Jaime Daniels NOAA/NESDIS Office of Research and Applications Forecast Products Development Team and Donald G. Gray NOAA/NESDIS GOES Products Manager Office of Systems Development

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: TOPICS F Philosophy F Review of GOES visible, IR, WV channels F Basic methodology F GOES-Next optimized data processing strategies F GOES wind products - What’s new ? F Verification

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: TOPICS cont’d F Current and new/planned applications F Summary F Product availability and recommended reading F Discussion/questions

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: PHILOSOPHY F Clouds are “passive” tracers of winds at a single level –use infrared and visible radiances F Water vapor features (ie., moisture gradients are “passive” tracers of winds) –both in clear air and cloudy conditions –use water vapor infrared radiances F We can properly assign height of tracer

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: GOES Visible, IR, WV Channels F Imager –Water vapor channel (6.7um) Band 3 –Longwave IR window chan. (10.7um) Band 4 –Visible Channel (0.65um) Band 1 F Sounder –Water vapor channel (7.3um) Band 10 –Water vapor channel (7.0um) Band 11

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: BASIC METHODOLOGY F Image acquisition F Automated registration of imagery F Target selection process F Height assignment of targets F Target tracking F Quality control (Autoeditor)

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Image Acquisition F Select 3 consecutive images in time F Which channels are selected is a function of which wind product (cloud-drift, water vapor, visible) is to be generated F Extended Northern Hemisphere F Southern Hemisphere Coverage Diagrams

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Auto-registration of Imagery F Registration is a measure of consistency of navigation between successive images F Landmark features (ie., coastlines) must remain stationary from image to image F Satellite-derived winds are much more sensitive to changes in registration than to errors in navigation F Navigation of the 3-axis stabilized GOES satellites much more difficult

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Auto-registration (Cont’d) F Manual registration corrections applied operationally to imagery 5% of the time F New automated registration QC : –hundreds of landmarks used –each landmark is sought in all images –middle image in loop is assumed to have “perfect” navigation –mean line and element correction is computed and possibly applied for the 1st and 3rd image

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: TARGET SELECTION PROCESS F Consider small sub-areas (target area) of an image in succession F Perform a spatial coherence analysis of all targets. Filter out targets where: –scene is too “coherent” –multi-deck cloud signatures are evident

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: TARGET SELECTION PROCESS (Cont’d) F Locate maxima in brightness F Select target/feature associated with strongest gradient F Target density is controlled by size of target selector area

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Height Assignment of Targets F Infrared window technique –oldest method of assigning heights to cloud- motion winds –not suitable for assigning heights of semi- transparent cloud (ie., thin cirrus) –still provides a suitable fallback to other methods

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Target Height Assignment (Cont’d) F CO 2 Slicing Technique –most accurate means of assigning heights to semi-transparent tracers –utilizes IR window and CO 2 (13um) absorption channels viewing the same FOV –However, CO 2 absorption band absent on current GOES imagers

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Target Height Assignment (Cont’d) F H 2 O Intercept Method –Utilizes WV channel (6.7um) Band 3 and longwave IR window chan. (10.7um) Band 4 –Algorithm: these two sets of radiances from a single-level cloud deck vary linearly with cloud amount –Adequate replacement of CO 2 slicing method

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: TARGET TRACKING ALGORITHM F Define tracking area centered over each target F Search area in second image which best matches radiances in tracking area F Confine search to “search” area centered around guess (AVN Forecast) displacement of target F Two vectors per target: 1 for image 1&2; 1 for image 2&3

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Quality Control (Autoeditor) F Functions –Target height reassignment –Wind quality estimation flag F Method (4 Steps) – 1)3-dimensional objective analysis of model forecast wind field on 1st pass – 2)Incorporate satwinds into analysis on 2nd pass. Remove those differing significantly from analysis

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Quality Control (Cont’d) F Method (Cont’d) –3)Target heights readjusted by minimizing a penalty function which seeks the optimum “fit” of the vector to the analysis –4)Perform another 3-dimensional objective analysis (at reassigned pressures) and assign quality flag

March 9, 1999Comet Class: SatMet Height Assignment Related to Satellite Wind Type (Approximations) Imager Cloud Drift Winds Imager Water Vapor Winds Imager Visible Winds Sounder Water Vapor Winds 100mb - 250mb - 400mb - 600mb - 250mb 400mb 600mb 1000mb 35% 30% 20% 15% 55% 40% <5% <5% N/A N/A N/A 30% % <5% 55% 40% <5%

March 9, 1999Comet Class: SatMet GOES High Density Water Vapor Winds 100mb - 250mb 250mb - 400mb 400mb - 700mb

March 9, 1999Comet Class: SatMet GOES High Density Cloud Drift Winds 100mb - 400mb 400mb - 700mb Below 700mb

March 9, 1999Comet Class: SatMet GOES High Density Winds (Cloud Drift, Imager H2O, Sounder H2O)

March 9, 1999Comet Class: SatMet GOES High Density Visible Winds

March 9, 1999Comet Class: SatMet GOES High Density Visible Winds Tropical System Circulations Hurricane Earl 800mb-1000mb > 34 Knots (Tropical Storm Strength)

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Optimal Data Processing Strategies F Take advantage of new sensor technology –silicon photodiode detectors (improved signal-to-noise) –higher spatial resolution and bit depth –improved spectral sampling & sampling rates F Take advantage of automation techniques and processing power –eliminate manual labor-intensive tasks –increase data volume

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Optimal Data Processing Strategies F Take advantage of improved viewing capability –temporal sampling (including rapid scans) –independent imager and sounder F Optimize processing strategy –high data volume/density (x,y,z,t) coverage –multi-spectral data integration (H 2 O winds) –multi-satellite (data fusion)

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Optimal Data Processing Strategies F Focus processing strategy towards the meteorology –circulations and environmental features F Adapt the data quality control F Take advantage of improved communications –timely data dissemination

March 9, 1999Comet Class: SatMet GOES-10 Visible Winds Impact of Higher Sampling Rates

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: GOES Wind Products:What’s New ?

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Current and New/Planned Applications F Mid-latitude Oceanic Analyses –NWS offices have access to high density wind products via internet; AWIPS access to follow via internet; AWIPS access to follow F Numerical Weather Prediction (NWP) and Data Assimilation –What’s happening at NCEP/EMC ? –ECMWF is utilizing GOES high density wind products F Tropical Cyclone Analysis and Forecasting –Tropical Prediction Center (TPC) has access to the GOES multi-spectral wind data sets –GFDL & NRL are performing model impact studies using the GOES multi-spectral winds to improve tropical storm track forecasts –CIMSS routinely generating water vapor and visible winds from GMS-5

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: NWP and Data Assimilation F EMC Status/Plans Operational use of high density Cloud Drift winds in Global and Regional forecast models began in December Evaluation of high density Water Vapor (imager and sounder) and Visible winds planned for focus on assimilation of layer wind estimates.

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: NWP and Data Assimilation F NESDIS Status/Plans –Routine production of GOES sounder WV and VIS winds began in late Work with EMC to support evaluation in EMC operational database in –NESDIS/CIMSS and FSL will coordinate on model impact study involving the generation of multi-spectral (vis,ir,wv) windsand their assimilation into the MAPS/RUC models –NESDIS/CIMSS and FSL will coordinate on model impact study involving the generation of multi-spectral (vis,ir,wv) winds and their assimilation into the MAPS/RUC models.

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: V erification F Sources of errors in satellite-derived winds F Satellite winds vs. rawinsondes vs. model colocation statistics F Model impact studies F Satellite minus forecast wind field F Mean tropical storm track forecast errors

March 9, 1999Comet Class: SatMet Comparison of Model Forecast and Satellite Derived Wind Fields AVN Forecast AVN Forecast + Sat Winds

March 9, 1999Comet Class: SatMet Impact of GOES Winds - Hurricane Edouard 1996

March 9, 1999Comet Class: SatMet Impact of GOES Winds - Hurricane Fran 1996

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Sources of Errors F Assumption that clouds and water vapor features are passive tracers of the wind field F Image registration errors F Target identification and tracking errors F Inaccurate height assignment of target

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Summary F Higher resolution data, improved science, and full automation - resulted in satwinds which are superior in both quality and quantity to any done previously at NOAA/NESDIS F Improved automated QC is the most significant change in the winds processing system over the past 5 years F Improved target selection avoids mix-level scenes and concentrates on providing greater targeting density for features of interest. Water vapor intercept method. F Numerous applications

March 9, 1999Comet Class: SatMet Satellite Derived Motion Fields: Product Availability & References F F Web Sites – – F Reference Material –Nieman et al., 1997: Fully automated cloud-drift winds in NESDIS operations. Bull. Amer. Meteor. Soc., 78, –Veldon et. al., 1997: Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc., 78,