Severe storms research is characterized by temporal scales ranging from minutes (for thunderstorms and tornadoes) to hours (for hurricanes and extra-tropical cyclones). Spatial scales range from tens to hundreds of kilometers. Sources of observational data include a variety of ground based and satellite systems. Requirements for registration and intercomparison of data from these various sources are examined and the potential for operational forecasting application of techniques resulting from the research is discussed. The sensor characteristics and processing procedures relating to the overlay and integrated analysis of satellite and surface observations for severe storms research are reviewed

Dalton, J. T.

English

NASA Technical Reports Server

4.5 Data Regj-tration and Integration Requirements 
for 
Severe Storms Ftesearch 
John T. Dalton 
Interactive Systems Developnent Branch 
Goddard Space Flight Center 
Inf oma t ion Extract ion Divi sion 
November 1981 
Presented at the W. Workshop or. 
Registration and Rectification 
96 
https://ntrs.nasa.gov/search.jsp?R=19820020835 2020-03-21T08:00:47+00:00Z
1. Introduction 
Severe storms research is characterized by temporal scales ranging fran 
minutes (for thunderstorms and tornadoes) to hours (for hurricanes and 
extra-tropical cyclones). Spatial scales similarly range fran tens to 
hundreds oi kilometers, depending on the phenomenon being observed. 
Sources of observational data include a variety of grornd based and 
satellite systems. As one would expect, requirements for registration apd 
intercomparison of data fran these various sources are a function of the 
research being performed and the potential for operational forecasting 
application of techniques resulting from the research. 
'ihis p p r r  presents an overview of the sensor characteristics and 
processing procedures relating to the overlay and integrated analysis of 
sateilite and surface observations for severe storms research. It is based 
on a review of the literature, discussions with meteorolqist researchers 
in the Troposphere Branch of Goddard's Laboratory for Atmospheric Sciences, 
and on experience in the developnent of the Atmospheric and Oceanographic 
Information Processing System (ADIS). 
2. Severe Stom Data Sources 
Data sources include geostationary satellites, polar orbiting satellites, 
radar, aircraft, balloons, and meteorological models. Satellite, aircraft, 
and radar data are frequently in image form, uhile the remaining sources 
and information derived fran satellite imagery are either in the forn of 
gridded fields or station observations. 
data sources, identifying the informatim provided and the spatial and 
temporal characteristics. 
mis section surveys the major 
2.1 
The GOES V i s i b l e  and Infrared Spin Scanning Radiometer 18 
provides visible imagery showing cloud structure and patterns, and infrared 
imagery s h o w i n 3  surface and cloud top temperature. 
generally scanned every 30 minutes, however, limited area north-south scans 
may be m a n d e d  at intervals as frequent as 3 minutes. 
sequences of these images provides meansurements of cloud motion winds and 
storm growth rates. Spatial resolution is naninally 1 km visible and 8 lun 
IR at the sbsatellite point, but degrades away from that point due to 
curvature of the earth's surface. 
km, for exmrple, at 40' latitude. 
derived fran VISSR are essentially randomly spaced corresponding to 
locations where cloud features can be identified. 
An improved Sensor based on the V I S S R  - the VISSR Atmospheric Sounder 
(VAS) is currently bein<; operated in a research/evaluation mode. 
of 12 IR channels provi6.e measurements of temperature and hunidity 
integrated over different layers of the atmosphere. When operated in Dwell 
Sounding mode, all 12 channels are scanned for a select4 north-south 
extent. 
of the radiances, an area the size of the U.S. requires approximtely 3 
hours to image. 
Satellite sources - Geostationary Orbit 
(VISSR) 
A full earth image is 
Analysis of 
m e  IR resolution is approximately 10 
Wind and cloud height observations 
3 
A total 
Because of nultiple spin averaging to improve the signal to noise 
Focr IR channels may be selected by ground comnand to scan 
97 
at VISSR rates (30  minutes for full earth). 
mode provides time seqienced imagery of temperature and water vapor 
patterns and mtion. 
hunidity profiles derived from VAS are generally randomly spaced 
observations . 
This multispectrn? imqi;q 
Like wind fields derived f r m  VISSR, temperaturl nd 
Eartii location infornation for the VISSR a& VAS data is derived from 
interactive identification of lanhark locations in visible imagery, 
The initial method used was to fit Keplsr orbital elements to direct 
observations of satellite psition and to fit the parameters for 
spacecraft attitude and Sensor qeometry to landmark observations. 
Ir. 19'758 techniques were developed 6 8 7  to derive toth orbit and 
attitude state from image landmarks. Chebyshev polpmials are now 
to model spacecraft position and imqe-sun aqle as a function of time. 
This allows predictive parameters to be generated and thus Dennits earth 
location of image features inmediately after acq,iisition. The Chebyshev 
coefficients have been transmitted by N(3AA in the image iine documentation 
since 1977. 
4 8  5 
?he VAS navigation procedure correlates prestored 16 x 16 image chips to 
locate lancfnarks. 
iterative eight& least squares technique. 
Orbit, attitude and camera biases arc estimateci using an 
Ccmpari.son of image locations derived using this d e l  with NOAA 
landmark observations yields the following statistics for residuals: 
U - mean -
pixel -0.06355 1.335 
line 9.0132 1-58 
L)sinq 20 landmark cbservations pzr day for 6 3 da.y period allows nasigation 
prameters to be extraplated f ~ r  48 hotirs with 3 pixel accuracy. Vie mean 
pixel a:d line error vs. predictioa interval is shim in Figure 1. 
2.2 Satellite sources - Polar Orbit 
Scn.%rs on-board polar orbiters typically repeat coverage a t  12 :lout 
periods. Because this is mtich laxer than time scales of severe storm 
dynamics, this source of data is not as heavily utilized as the GOES 
sensors. The primry uses have been model initializaticn and comparison, 
extraction of pcirameters not adailable frm! qeostationary sensors, and 
comparison with geostationary observations. 
smxized here for illustration md miparison. 
'NQ sensor systems will be 
The Electrically Scanninq Microwave Radiometer (ESVR) o r  idimbus-6 provides 
radiometric measurements in two kinds f ~ r  LWC? polariiations. "tie 
instrunent scans in a 70" area ahead o t  the spacecraft motion with a 
constant SQ incidence q l e  with the earth's surface. Nominal resolution 
is 29 km cross-track and 4 5  km along-track. 
classification techniques are use3 Lc identify areas of r3inf:bll.9 
0 
Maltispctr31 
5.1 
4.1 
v) g 3.1 
z 
a 
a 
- 
0 
U 
w 
2 5 2.t 
s 
1 .[ 
ORlGlNAl PAGE IS 
OF POOR QUAUn 
DATA BASE USED: 
MAY 5, 1979 
THROUGH 
MAY 8.1979 
I I I I 
0 1 2 3 4 5 
LENGTH OF PREDICT (DAYS) 
Figure 1. L ine  and p i x e l  n a v i g a t i o n  p red ic t i on  
e r r o r  f o r  GOES VAS. 
99 
Four instrunents on the TIROS-N and NCNA-6 satellites provide 10 
visible and infrared imagery along with sounding radiances. 
The Advanced Very High Resolution Radiometer (AVHRR) Scans a 1650 km swath 
in 4 visible and infrared bands. 
factor of 1.362 to produce 2048 samples per line at approximately 800 meter 
separation. NQAA generates latitude/longitude values every 40 pixels (51 
points per line). 
The 1.i km IFOV is oversampled by a 
The High Resolution Infrared Radiation Sounder (HIRS/2) scans a 2240 km 
swath in 20 infrared bands. 
nadir, degrading to 58.5 km (cross-track) by 29.9 km (along-track) at 
maximun scan angle. 
position. 
The 56 step scan produces IFov's of 17.4 km at 
NOAA generates latitude/lorqitude values for each scan 
The Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) 
scan in 8 and 11 steps respectively, resulting in IFQV's exceeding 120 km. 
2.3 Aircraft and Ground Based Observations 
Aircraft flights ate used to measure smaller scale cloud properties and to 
test new sensors. ?he scanning resolution depends on the instrunent and 
aircraft altitude. 
cross track resolution and a 2E8 meter along-t ck resolution. Aircraft 
navigation data is used to define image georeferencirq parameters. 
The Cloud 'lbp Scanner, for -xample, has a 100 meter 
Ground based digital radar provides measurements of rainfall intensity and, 
for doppler radars, velocity. 
with varying range, range resolution, and azimuth angle resolution. "be 
Norman, Ok. doppler radar for example has a range of 456.2 km, range gate 
resolution of 0.6 km, and azimuth angle resolution of approximately 0.8 . 
Elevation angle is varied to provide measurements at different altitudes. 
Digital returns are oriented along radial scans 
11 
Rawinsonde balloons provide measurements of temperature, altitude, dewpoint 
and winds at 40 mandatory pressure levels from over l0a stations in the U.S. 
every 12 hours. 
3. Registration Requirements 
Data registration processing for severe storms studies tends to have both 
multi-temporal a'id multispectral aspects. As mentioned in the discussion of 
the VISSR, multi-temporal imagery from a single sensor is frequently used to 
extract measurements of atmospheric motion and cloud growth. 
observations are often registered and overlayed on visible imagery from a 
single time for comparison with cloud features and are reqistered and 
intqrated with other sources (e.g., ground based observations) for comparison 
or integration with models. Examples of multi-source image registration 
include COES-west to GOES-east for direct stereo cloud top measurements, and 
digital radar to GOES visible and IR for comparison of rainfall and doppler 
velocities with cloud structure. mese multi-source observations from 
differe!;t times are then registered to produce time lapse displays and 
temporal analyses. Because of its frequent coverage, its ability to show 
mesoscale features, and the cotcputiancl time required to remap sequences of 
images to a cOmmOn projection, the GOES imagery is generally used ds the 
The resulting 
100 
refereice coordinate system or nap base for these analyses. 
relativ,. importance, the accurao; and remapping considerations of GOES 
sensors will be addressed further in the next subsection. Next, some 
considerations involvinq point source and gridded field registration will be 
discusst=u. Finally, in the next Section, the current multi-source 
combinations will be slmmarized. 
Because of its 
3.1 G U S  Data Registration 
As described in Section 2.1, the absolute navigation accuracy from current 
'IISSR and VAS landnark processing is on the order of 1 to 2 pixels (1-3 km). 
Errors may be intrcduced by: 
- operator errors in the identification and correlation of 
landnark, 
- geometric irregularities in the image, and 
- differences betweenl2 the oblate spheroid model and the actual 
earth's surface. 
For registration of successive image frames, relative errors are typically on 
the order of a single pixel. 
equates to an error of approximately 0.5 nJsec for 30 minute interval data and 
3 d s e c  for 5 minute interval data. 
In measurement of cloud drift winds, this 
Stereo measurements of cloud top heights are made b.1 remapping 512 x 512 image 
sectors from one GOES satellite to the projection of another. 
orbit/attitude coefficients define a (latitude, longitude) to (line, pixel) 
remappiq function and its inverse. 
grid, with bilinear iiiterpolation used to relate corresponding locations 
between grid points. 
registered images to align cloud shadows and other features. 
relative error is 3n the arder of a 
height error on the order of 0.5 km. 
?he 
This is used to generate a remapping 
Absolute georeferencirq errors are reduced by shifting 
pixel, with a resulting absoll;te cloud 
The resulting 
Oigital radar data is. registered to GOES Visible and IR imagery chrough a t m  
step process. Data in radial scan orientation (B scan) is first resampled to 
an earth latitude, lonqitude grid. Plane earth, low elevation and short range 
approximations are used where possible to reduce camputation time iii 
transforming from the radar to earth coordinate system. 
introduce an error on the order of 0.5 km (one half a GOES visible pixel). 
Next, the image in this earth latitude, longitude projection is remapped 
to the GOES projection. Bilinear interpolation is used to compute 
psitions between user specified grid pints (usually at 20 pixel spacing). 
These approximations 
11 
Polar orbiter data is not generally registered to GOES data for severe storms 
observations, prtially aae to the disparate time scales and partially due to 
the representation o f  the polar orbiter image locztion information as 
latitude, lonqltude values at sampled locations along scan lines. m e  latter 
makes the (lire, pixel) t o  (latitude, longitude) transformation trivial, but 
makes the inverse transformation difficult to solve. It is therefore easier 
to register data using t h e  polar orbiter image as a base. 
i o i  
3.2 Point Source and Gridded Field Registration 
Point source or station observations (e.g., winds from VISSR, soudings froiri 
VAS, and rawinsondes) have know, locations atid can easily be overlayed on maps 
or GOES imagery. However, further analysis and comparison with image features 
frequently requires gridded fields for contouring or diagnostic (e.g., 
divergence) ccmputation. Furthermore, integrated analysis of multiple 
parameters requires that all parameters be represented in the same grid 
locatio.%. A number of techniques are used to interpolate between point 
source observations to compte values at intermediate grid points. 'These 
techniques include weighting values by inverse distance within a search area, 
eliminating pints that are shadowed by closer pints, and interactive schemes 
that recompute weights based on residual eriors between original point values 
and values interpolated from the gridded field. 
and algorithm parameters affects the degree to which the gridded fidd 
represents features in the data. 
The selection of technique 
4.0 S m a r y  and Rocmendations 
Current multi-source and multi-temporal registration requirements are 
sumnarized below: 
Mult i-ttinporal 
- GOES VISSR and VAS image frames to first image of a sequence 
- Station observations and gridded fields to GOES image and 
to map projections 
Multi-source 
- GOES VISSH/VAS - west satellite projection to east satellite 
projection (or the reverse) 
- Digital radar to GOES VISSR/VAS 
- Station observations and qridded fields to GOES VISSR/VAS 
Because the GOES image projection generally serves as a cMmOn base, 
rqistrat.ion accuracy requirements are generally 1 km (1 visible pixel). 
Registratior of the above data for research case studies generally allows 
sufficient time for the remapFing process. 
as will be performed on the Centralized Storm Information System (CSIS), 
the rqistration and overlay must be prformed in minutes. 
13 For nowcasting applications 
While the above capabilities for data registration are fairly powerful, 
limitations still exist: 
(1) 
clear whether improved models or procedures can improve this. Furthermore, 
non-linearities are observed in short interval imaJPs that bre not four.d in 30 
minute data and are therefore not modeled in the navigatiofi function. 
Absoluts errors on GOES VISSR image are as high as 6 pixels. It is not 
(2)  Stereo GOES image pairs are currently offset interactively to compensate 
for absolute navigation errors. 
the two images may improve the registration accuracy and thus the accuracy of 
cloud height measurements. 
Registration using relative control points in 
( 3 )  The incorporation of a new data source (e.g., a polar orbiting sounder) 
into B ~ I  analysis requires the developnent of additional software to identify 
and extr-t image control points and to perform the necessary resampling and 
remappiry to the reference map projection. It is also extremeiy difficult to 
experiment with map projections, resolutions, and resampling functions. 
interactive "geographic information system" is needed that allows selection of 
map projection, resolution, data sources, and resampling function for each 
source. Image 7eorefercncing function implementations are needed for 
diffarent data sources that embody aproximations for computational efficiency 
whiie maintaining reqdired geogramic awulacy. Finally, the system interface 
should provide for straightforward addition of new sources ir. terms of format 
and image location parameters. 
An 
103 
References 
1. 
2. 
3. 
4. 
5. 
5. 
7. 
8. 
9. 
10. 
11. 
12. 
13. 
A. F. Hasler, W. C. Skillman, W. E. Shenk, and J. Steranka, "In Situ 
Aircraft Verification of the Quality of Satellite Cloud Winds over Oceanic 
Regions", Journal of Applied Meteorology, Vol.  18, No. 11, Novemkr 1979. 
A. F. Hasler and R. F. Adler, "Cloud Top Sturcture of Tornadic 
lhunderstorm from 3 minute Interval Stereo Satellite Images Compared 
with Radar and Other Observations", Conference on Radar Pkteorolqy, 
April 1980. 
€I. Wntgomery, "VAS Instrumentation for Future GdES Mission", 
Geosynchronous Meteorological Satellite Data Semirar, C S K  Y-931-76-87, 
March 1976. 
C. T. Mottershead and D. R. Phillips, "Image Navi.gatiot-1 
Geosynchronous Meteorological Satellites", Preprints oi 
Aerospace and Aeronautical Meteorology and Symposium on- _--- .: Sensing 
from Satellites, pp. 260-264, 1976. 
-s:iference on . -  
Dennis Phillips and Eric Smith, "Geosynchronous Satellite Navigatio:i 
Model", University of Wisconsin, Janurary 1974. 
C. E. Velz, "Orbit arid Attitude State Recoveries from Landmark Data", 
Proceedings of AAS Astrodynamics Specialist Conference, paper No. AAS 
75-058, July 1975. 
A. J. F'uchs, C. E. Velz, and C. C. Goad, "Orbit and Attitude State 
Recoveries from b.ndmark Data", Journal of Astronautical Scie;ces, 
1975, pp. 369-381. 
R. Nankervis, D. Koch, H. Sielski, D. Hall, "Absolute Image 
Registration for Geosynchronous SaLellites", G S K ,  1379. 
E. Rodgers, H. Siddingaiah, A. Chang, E. Wilheit, "A Statistical Technique 
for Determining Rainfall over LanC Employing Nirrbus-6 ESMR iVleasurmentE", 
Fourth NASA Weather and Climate Program Science Review, January 1979. 
iJoAA Polar Orbiter Data (TIRE-N and NOAA-6) Users Guide (Preliminary 
Version), NOAA National Climatic Center, Satellite Data Services 
Di v is ion, December 1979. 
L. Chen, M. Faghmous, and K. Ghosh, AOIPS RADPAK System Description 
and Users Guide, General. Software CorpratLn, GSC-m8102, Aqusti981. 
A. F. Hasler, "Stereographic Obszrvztions from Geosynchronous 
Satellites: An Improtant New Tool for the Atmospheric Sciences", 
Bulletin of the American Meteorologic31 Society, Vol. 62, No. 2, 
February 1981. 
CenLralized Storm Information Systerr (CSIS) Implementation Plan, 
University of Wisconsin Space Science and Engineering Center, Madison, 
Wixonsin, May 1981. 
104 


Data registration and integration requirements for severe storms research

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server