Improvements to MISR stereo motion vectors

Improvements to MISR stereo motion vectors

Ákos Horváth1

Received 11 March 2013; revised 26 April 2013; accepted 3 May 2013; published 10 June 2013.

[1] This study evaluated the effects of recent major modifications to the Multiangle ImagingSpectroRadiometer (MISR) wind retrieval algorithm by repeating an earlier yearlongcomparison with Meteosat-9 cloud motion vectors. Algorithm upgrades included anarea-based stereo matcher, enhanced quality control, georegistration corrections to focal planedistortions, and increased retrieval resolution. The new winds had better quality at all levels,yielding a particularly large increase in coverage at middle and high levels. The upgrades hadan overall neutral impact on the E-W wind comparison statistics, which were already quitegood in the preceding MISR data set. The comparison statistics for the more error-proneN-S wind, however, improved significantly on all scales—global, zonal, and regional—andthroughout the entire atmosphere. Both the negative N-S wind bias and root mean squaredifference decreased, and correlation increased substantially, with middle and high levels,and tropics and subtropics experiencing the largest improvements. Subpixel georegistrationcorrections reduced cross-swath variations in N-S wind and height by half. As the net effectof these improvements, the error characteristics of the previously more uncertain N-S windcomponent became comparable to those of the E-W wind component at low and middlelevels. Despite the substantial reductions in N-S wind errors, which are highly correlated withstereo height errors, the MISR – Meteosat-9 height comparison did not generally improve,strongly suggestingMeteosat-9 height assignment errors as the primary driver of discrepancy.

Citation: Horvath, A. (2013), Improvements to MISR stereo motion vectors, J. Geophys. Res. Atmos., 118,5600–5620, doi:10.1002/jgrd.50466.

1. Introduction

[2] The Multiangle Imaging SpectroRadiometer (MISR)measures reflected solar radiation in nine distinct directions:at nadir and oblique angles of 26�, 46�, 60�, and 70�,distributed along track, both forward and aft relative to flightdirection of the Terra satellite. Cloud motion and associatedheight are derived simultaneously by tracking cloud patternsover a 3.5 min interval in the nadir, 46�, and 70� views,separately for the forward and aft camera triplets [Horváthand Davies, 2001a]. Compared to cloud motion vectors(CMVs) from geostationary or polar-orbiter imagers, MISRstereo motion vectors (SMVs) offer potentially more accurateheights thanks to the purely geometric retrieval technique,which is insensitive to radiometric calibration drift andrequires no ancillary data; however, precise coregistration ofthe multiangle views is crucial [Moroney et al., 2002]. TheCMV height assignment, on the other hand, interprets thecloud radiometric signature in the infrared window, CO2, orwater vapor channels using radiative transfer calculationsand forecast temperature profiles [Nieman et al., 1993]. As aresult, CMV heights are prone to substantial errors in broken,

semitransparent, or multilayer clouds, and in the case of a low-level temperature inversion [Garay et al., 2008].[3] The first-generation SMVswere evaluated against limited

sets of GOES-10 (Geostationary Operational EnvironmentalSatellite) CMVs [Horváth and Davies, 2001b] and radar windprofiler data [Marchand et al., 2007]. As predicted during thedesign of the stereo algorithm, MISR winds were found to bemore accurate in the east-west direction than in the north-southdirection, because aliasing between cloud motion and heightparallaxes in the latter complicates the retrieval. However,retrieval errors were often higher than the prelaunch RMSestimate of ~4m s�1 for wind, and ~400m for height.Subsequently, Davies et al. [2007] introduced upgrades tothe original algorithm. Image coregistration was improved tosubpixel accuracy for all cameras by matching sea-ice patternsand land-surface features. This enabled a reliable SMVestimate from the aft camera triplet as well, which had beenpreviously plagued by excessive registration uncertainties inthe 70� aft view. Subpixel parallax assessment and tighterquality control based on forward-aft SMV consistency werealso implemented. As shown by comparisons to model winds[Davies et al., 2007], and an extended set of wind profilerobservations [Hinkelman et al., 2009], these second-generation SMVs had significantly improved quality, broadlycomparable to prelaunch estimates, but this came at theexpense of greatly reduced coverage.[4] The latest assessment by Lonitz and Horváth [2011]

evaluated one year of second-generation SMVs againststate-of-the-art Meteosat-9 CMVs. This study provided themost comprehensive error characterization of MISR winds,

1Leibniz Institute for Tropospheric Research, Leipzig, Germany.

Corresponding author: Á. Horváth, Leibniz Institute for TroposphericResearch, Permoserstrasse 15, D-04318, Leipzig, Germany. ([email protected])

©2013. American Geophysical Union. All Rights Reserved.2169-897X/13/10.1002/jgrd.50466

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JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 5600–5620, doi:10.1002/jgrd.50466, 2013

including their geographic and vertical error distribution,identified regions and cloud types where stereo heights werea definite improvement over CMV heights, but also discov-ered a systematic SMV bias across the MISR swath. Thecross-swath bias was among several issues specificallyaddressed by the second major overhaul of the MISR windretrieval algorithm incorporated into the Level 2 cloudproduct software. The upgraded cloud product is now avail-able for the entire mission starting from 1 March 2000.[5] The current paper describes the main algorithm modifi-

cations and their effects on SMVs. I used the data set of col-located SMVs and CMVs from Lonitz and Horváth [2011],but replaced the second-generation SMVs with the latestversion, allowing a direct comparison of my results withthose of Lonitz and Horváth [2011]. The upgrades to thestereo algorithm significantly improved not only SMV-CMV comparison statistics, but also the coverage of SMVs.[6] The remainder of the paper is organized as follows:

Section 2 describes the MISR upgrades and comparison meth-odology. Section 3 documents resulting improvements toMISR winds as compared to Lonitz and Horváth [2011].Finally, I offer a summary and concluding remarks in section 4.

2. Stereo Algorithm Upgrades andComparison Method

2.1. Stereo Algorithm Upgrades

[7] The principle of the MISR wind retrieval is described inHorváth and Davies [2001a]. Technical details of its practicalimplementation are given in Diner et al. [1999], while thoseof the latest version are provided by Mueller et al. [2013].Here, I summarize only the most significant algorithm upgradesimplemented since the study of Lonitz and Horváth [2011].[8] Coarse feature-based stereo matcher has been replaced

with sophisticated area-based technique. For cloud tracking,all previous versions of the MISR wind algorithm used theNested Maxima (NM) feature matcher, which tracked localreflectance maxima [Muller et al., 2002]. This stereo matcherwas fast but had sparse coverage with typically 1–2% of databeing matched, its accuracy was only to within 2 pixels, andthe distribution of returned disparities exhibited a long tail.These limitations necessitated use of a relatively large(70.4� 70.4 km2) retrieval domain to obtain the ~100samples required to map the disparity field reasonably well.In addition, Nested Maxima often failed in relatively feature-less cirrus clouds, especially in the most oblique 70� views.The new algorithm uses a hierarchical sum-of-absolute-differences area-based matcher with much better accuracyand lower failure rate. A three-level pyramidal scheme isemployed, sampling image pairs at successively finer resolu-tions of 1100, 550, and 275m. This multiresolution approachimproves run time by decreasing the dimensions of thesearch window at the down-sampled upper levels and usinga reduced search window at the highest resolution bottomlevel; thus, eliminating the need to stereo match entire imagesat full 275m resolution.[9] Thanks to the vastly improved coverage of the hierar-

chical sum-of-absolute-differences matcher, the operationalwind retrieval resolution has been increased from 70.4 to17.6 km. As section 3.8 demonstrates, meaningful SMVscan be obtained even at 4.4 km resolution in high-texturecloud scenes.

[10] Analysis of measured disparities has also beenimproved. The previous algorithm used two separate 2-D(cross-track, along-track) disparity histograms, one each forthe 70�–46� and 46�–nadir camera pairs. The new approachforms a single, joint, 4-D disparity histogram for both direc-tions and camera pairs simultaneously, allowing a more accu-rate estimation of the dominant disparity signal from themost-populated histogram bin.[11] Corrections for focal plane distortions have been

implemented. Lonitz and Horváth [2011] noticed a cross-swath bias in SMVs, resulting in less accurate retrievals onthe eastern side of the MISR swath than on the western.This cross-swath artifact was traced back to focal planedistortions unaccounted for in the MISR Camera GeometricModel used during Level 1 processing. The new algorithmincludes subpixel georegistration corrections in Level 2processing to mitigate cross-swath bias; the effect of thesecorrections on SMVs is discussed in section 3.7.[12] The quality control scheme has been completely

revamped. The quality of an individual SMV was character-ized in the old scheme by the WindQuality (WQ) flag, whichtook integer values from 0 to 4 representing ever more strin-gent limits on the maximum allowed forward-aft differencesin wind speed, height, and direction. In contrast, the QualityIndicator (QI) of the new MISR algorithm varies smoothlybetween 0 and 100, and is calculated more in line with theEUMETSAT (European Organization for the Exploitationof Meteorological Satellites) quality control scheme[Holmlund, 1998; Holmlund et al., 2001]. The new schemekeeps the forward-aft vector comparison, which amounts toa temporal consistency test, but also adds a spatial consis-tency test of neighboring SMVs. A further modification re-laxes the requirement for successful wind retrievals in bothforward and aft camera triplets. While the previous data setonly reported the average of forward and aft vectors if bothexisted, the new data set also includes cases with a single(forward or aft) valid retrieval, provided this vector passesthe spatial consistency test with respect to neighboring vec-tors in the opposite (aft or forward) direction.[13] For the old SMVs, the product guidelines advised

using retrievals with WQ= 3 (“good”) or WQ = 4 (“best”)only. For the new SMVs, the MISR team uses a thresholdof QI ≥ 50 [Mueller et al., 2012], while geostationary windmonitoring guidelines recommend a higher threshold ofQI ≥ 80, given a EUMETSAT-type quality control scheme[Forsythe and Saunders, 2008]. In section 3.2, I analyzethe dependence of wind comparison on quality indices,which can help in selecting the adequate SMV quality thresh-old for a specific application until the mapping between theMISR and EUMETSAT QI schemes is better understood.[14] A new flag has been introduced to filter out cloud-free

“ground retrievals.” Unlike traditional CMV methods, theMISR wind algorithm does not apply a priori cloud target se-lection, and obtains retrievals in cloud-free land scenes as well.Such ground retrievals were not flagged in prior versions of thedata set and, thus, had to be screened out by ad hoc methods inpostprocessing [Hinkelman et al., 2009; Lonitz and Horváth,2011]. Adopting the logic of previous investigators, the newstereo algorithm now flags near-surface, low-speed retrievalsunlikely to be associated with advecting clouds. Thisflag, called MotionDerivedCloudMask, classifies retrievals ashigh/low-confidence cloud or high/low-confidence clear.

HORVÁTH: IMPROVED MISR WIND RETRIEVALS

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[15] Cross-track winds are now also reported at 1.1 km res-olution, in addition to the 17.6 km standard product. It is wellestablished that the accuracy of MISR cross-track winds issignificantly better than that of along-track winds, the latterof which suffer from aliasing between cloud height and cloudmotion parallaxes [Marchand et al., 2007; Hinkelman et al.,2009; Lonitz and Horváth, 2011]. Taking advantage of thisincreased accuracy, the new SMV data set now also providescross-track wind components at 1.1 km resolution. Such finescale component vectors can be used to investigate cloud dy-namical processes, as demonstrated by Wu et al.’s [2010]study on the inner-core dynamics of tropical cyclones.[16] The improved capabilities of the new wind algorithm

are demonstrated in Figure 1 for an extra-tropical cyclonein the South Atlantic. Here, the 70.4 km SMVs were fromthe final version F08_0017 of the old TC_STEREO product,also used in Lonitz and Horváth [2011], while the 17.6 kmSMVs were from the new and renamed TC_CLOUD productversion F01_0001. (For consistency with Lonitz and Horváth[2011], MISR wind is still labeled in this paper as stereo mo-tion vector or SMV, although the newMISR product uses thecustomary label cloud motion vector or CMV, similar to theMeteosat-9 product.) The narrow MISR swath only alloweda maximum of six 70.4 km SMVs across the track, and alarge number of the retrievals were of low quality (see theshorter vectors in Figure 1a). In contrast, the improvedSMVs clearly had better coverage, and revealed much finerdetails of the wind field.

2.2. Comparison Method

[17] This study was based on the Lonitz and Horváth[2011] data set, comprising ~200,000 MISR – Meteosat-9wind pairs from the year 2008. The original data set includedground-filtered MISR SMVs with WQ ≥ 3 from theTC_STEREO product version F08_0017, and Meteosat-9CMVs with QI ≥ 80 [EUMETSAT, 2011], which werematched using spatial and temporal collocation criteriaexceeding those recommended by the Coordination Group

for Meteorological Satellites [Velden and Holmlund, 1998].In the current analysis, I simply replaced the old 70.4 kmSMVs with the new 17.6 km SMVs from the TC_CLOUDproduct version F01_0001, but retained the Meteosat-9CMVs. The old MISR ground filter was also replaced, andhere I only considered retrievals flagged by the improvedground filter as high-confidence cloudy.[18] As shown in Figure 1, there were a maximum of 16

new SMVs for any given old SMV. When analyzing theevolution of quality indices, I replaced each old SMV bythe highest-quality new SMV within the corresponding orig-inal wind domain. For the analysis of SMV-CMV compari-son statistics, however, all 17.6 km SMVs within a 70.4 kmdomain and above a certain QI threshold (0, 50, or 80) wereaveraged. This averaging was carried out to reduce represen-tativeness biases in the comparison, because Meteosat-9winds correspond to a nominal area of 72� 72 km2 at thesubsatellite point, which is more comparable to the originalMISR domain size.

3. Results

3.1. Multiangle Imaging SpectroRadiometerGround Retrievals

[19] Analysis of MISR ground retrievals provides a usefulself-consistency validation to obtain minimum error bars onSMVs, because ground has no motion. The comparison ofoverall statistics between previous and current data sets issummarized in Table 1. The vF08_0017 results are fromLonitz and Horváth [2011], and correspond to retrievals withWQ ≥ 3 in the Meteosat-9 full disk area. For vF01_0001 data,calculations were carried out separately for the Meteosat-9full disk and the entire globe using three different QI thresh-olds: 0 (all retrievals), 50, and 80. Here, I present onlyvF01_0001 results for their native 17.6 km resolution,because averaging to 70.4 km yielded similar statistics.[20] For “wind” components within the Meteosat-9 full disk,

the new algorithm showed only slight general improvement,

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Figure 1. MISR winds for path 191, orbit 45232, blocks 115–118: (a) 70.4 km vF08_0017 SMVs and (b)17.6 km vF01_0001 SMVs. (a) The longer vectors indicate WQ ≥ 3 (“good” or “best”) winds, while theshorter vectors indicate lower quality winds. (b) Winds with QI ≥ 50. The white square marks a70.4� 70.4 km2 vF08_0017 domain.


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because previous ground retrievals were already fairly accurate.The E-W component was practically unbiased, with a rootmean square difference (RMSD) of ~0.5m s�1. The N-Scomponent also had a small bias, changing from �0.13m s�1

to ~0.3m s�1, but a larger RMSD of ~1.7m s�1. (Larger N-Serrors are a general feature of MISR, caused by the aliasing ofmotion and height parallaxes in the along-track direction.) Thedependence on QI was rather weak, indicating the quality ofvF01_0001 retrievals is generally much better and moreuniform than that of vF08_0017 retrievals (see also section 3.2.)[21] Height bias also remained small, changing from

+11m to �30m, which corresponded to the change in signand magnitude of the N-S “wind” bias. (Height and N-Scomponent errors are correlated with a sensitivity of approx-imately �70 to �80m per +1m s�1.) The largest improve-ment occurred in height RMSD, which reduced by half,from 331 to ~160m. The reduction in height RMSD wasnot due to the smaller domain size of vF01_0001 retrievals,because averaging to 70.4 km resolution resulted in similarimprovements; rather, it reflected the denser and moreuniform sampling of a domain by the new area-based stereomatcher compared to the Nested Max matcher, the latter ofwhich sparsely samples only the brightest ~100 points withina 70.4� 70.4 km2 area.[22] Extending analysis to the entire globe slightly wors-

ened the statistics, especially for the E-W component. Thiswas due to the inclusion of cloud motion retrievals in polarregions, where distinguishing between low-level clouds andsnow/ice covered surfaces was problematic, potentially lead-ing to false ground flags. High-latitude cloudy SMVs affectmore the E-W component than the N-S component, partlybecause the dominant winds are zonal, and partly becausethe E-W direction corresponds to the more error-pronealong-track direction in polar regions. (Whereas at low tomiddle latitudes the E-W component is more in the cross-track direction while the N-S component is more in thealong-track direction.)[23] Global distributions of ground retrieval “wind” biases

are mapped in Figure 2. The E-W biases were mostly within�1m s�1 except in Greenland and Antarctica, where positive(westerly) biases could exceed 2m s�1, possibly due to kata-batic wind. The N-S biases were generally larger, typicallybeing within �2m s�1, and tended to be more often positive(southerly) than negative (northerly). In polar regions, how-ever, the N-S bias could be smaller than the E-W bias,reflecting the changed heading of the MISR ground track.In fact, for stereo retrievals the sharpest contrast in accuracy

is between the cross-track and along-track directions, plottedin Figures 2c and 2d. The cross-track biases were smallest,rarely exceeding �0.25m s�1, even at high latitudes.Conversely, the along-track biases were largest, resemblingN-S biases at low to middle latitudes and E-W biases inpolar regions.[24] In Figure 3, corresponding retrieved mean surface ele-

vations are compared with the digital elevation model (DEM)from the MISR Ancillary Geographic Product. Agreementbetween retrieved heights and the DEMwas excellent, signif-icantly improved since the previous stereo algorithm. InvF08_0017 data, 75% of height differences were ≤150m,and 89% were ≤300m [Lonitz and Horváth, 2011]. ForvF01_0001 these values increased to 95 and 99%, respec-tively, in line with reduced height RMSD given in Table 1.Note the largest height differences were partly due to errorsin the MISR DEM. In certain areas DEM errors are ashigh as several tens of meters, which introduce “wind”retrieval errors through increased coregistration uncertainties(V. Jovanovic, personal communication, 2013).[25] Finally, I compared vertical profiles of ground

retrieval “wind” biases between the data sets, as plotted inFigure 4. Extending analysis from the Meteosat-9 full diskto the entire globe increased the encountered surface eleva-tion range from 0–3.5 km to 0–6.5 km. (Polar regions above70� latitude were excluded to reduce the impact of falseground flags.) When results were binned according to DEMground elevation, the most noticeable change was a slightlyincreased N-S component bias in the new data. Apart fromthat, both “wind” components in both data sets showed prac-tically no vertical variation. Binning “winds” by theirretrieved heights, however, introduced a systematic decreasewith height in vF08_0017 data, especially for the N-S com-ponent. This vertical rearrangement was due to a negativecorrelation between N-S (along-track) “wind” errors andheight errors, whereby negative N-S component biases resultin positive height biases, and vice versa. The aliasingbetween “wind” and height errors was much less apparentin vF01_0001 retrievals, only affecting lowest and highestelevation bins. As shown in Lonitz and Horváth [2011],stereo matcher and, thus, wind error distributions can be wellmodeled by the sum of a Gaussian distribution and a uniformdistribution representing blunders (gross wind and heighterrors). The reduced error aliasing reported above indicatedless frequent blunders in the upgraded algorithm. Indeed,the fraction of ground retrieval N-S “wind” errors exceeding5m s�1, which cause height errors larger than the 500m bin

Table 1. MISR Ground Retrieval Statisticsa

E-W Bias(m s�1)

E-W RMSDb

(m s�1)N-S Bias(m s�1)

N-S RMSDb

(m s�1)Height Bias

(m)Height RMSDb

(m)SampleNumber

vF08_0017 �0.06 0.70 �0.13 1.95 11 331 147,602WQ ≥ 3vF01_0001 0.02 / 0.15 0.54 / 1.24 0.34 / 0.34 1.91 / 2.07 �33 / �29 175 / 211 5,438,051 / 13,102,480QI ≥ 0vF01_0001 0.00 / 0.11 0.49 / 1.05 0.30 / 0.30 1.73 / 1.84 �31 / �26 161 / 192 5,023,555 / 11,626,276QI ≥ 50vF01_0001 �0.01 / 0.08 0.46 / 0.85 0.34 / 0.34 1.65 / 1.68 �38 / �32 148 / 169 2,999,110 / 6,528,446QI ≥ 80

aThe vF08_0017 results are from Lonitz and Horváth [2011], corresponding to theMeteosat-9 full disk. For vF01_0001 data results are given separately forthe Meteosat-9 full disk and the entire globe.

bRoot mean square difference.


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size, dropped from 2.5% in vF08_0017, to 0.5% invF01_0001.

3.2. Dependence on Quality Indicators

[26] As described in section 2, the vF08_0017 MISRquality flag takes integer values between 0 and 4, withWQ ≥ 3 representing high-quality retrievals. The Meteosat-9quality index ranges from 0 to 100, with guidelines prescrib-ing a threshold of QI ≥ 80 for validation purposes [Forsytheand Saunders, 2008]. The new vF01_0001 MISR productemploys a EUMETSAT-type quality control scheme with arecommended high-quality threshold of QI ≥ 50 [Muelleret al., 2013], although here I used a high-quality thresholdof QI ≥ 80 for consistency with Meteosat-9. In the following,I analyze the vertical variation of retrieval quality in eachdata set, and dependence of the SMV-CMV comparison onquality indices. The results presented below might guide fu-ture investigators in striking the appropriate balance betweenSMV quality and coverage for their particular applications.[27] The distribution of quality indices as a function of

height is represented by box-whisker plots in Figures 5a,5b, and 5c, with corresponding vertical profiles of high-quality fractions given in Figure 5d. The quality ofvF08_0017 SMVs systematically shifted to lower values asheight increased, with a large drop between low andmiddle levels, and a weaker decrease above 3 km.Correspondingly, the average fraction of high-qualityretrievals was 58% at low levels (< 3 km), 33% at middlelevels (3–7 km), and 18% at high levels (> 7 km). The qualityof vF01_0001 SMVs also decreased with height; however, itwas significantly better than that of vF08_0017 SMVs at allaltitudes. In vF01_0001, midlevel winds had only slightlyworse quality than the best low-level winds, and the largestquality decrease occurred at high levels. The average fractionof high-quality retrievals at low, middle, and high levels was80%, 70%, and 47%, respectively, representing a factor of1.4, 2.1, and 2.6 increase in coverage compared tovF08_0017. The fact that high-level winds were the mostuncertain in both vF08_0017 and vF01_0001 reflected thegeneral difficulties posed to the stereo technique by cloudsat these altitudes. Tracking relatively featureless and low-contrast cirrus between the most oblique and nadir viewscan be challenging even for the new, area-based stereomatcher. Image matching difficulties might also arise fordeep convective clouds, because the oblique cameras mostlyview cloud sides, while the nadir camera views cloud tops. Inaddition, the MISR assumption of no vertical wind breaksdown in rapidly developing convective clouds, resulting inlarge retrieval errors.[28] In contrast, for Meteosat-9 the quality of high-level

CMVs was comparable to, or even slightly better than, thatof low-level CMVs. The most noticeable deteriorationoccurred at middle levels, especially between 3 and 5 km,where not only the quality, but also the number, of retrievalsdecreased significantly. As discussed in Forsythe andDoutriaux-Boucher [2005] and Lonitz and Horváth [2011],CMV heights are more uncertain in this layer, because it ishere where the CO2-slicing technique dominating at higherlevels transitions to the brightness temperature-based methodfavored at lower levels. There is also a nonlinear shift inheight assignment from cloud top to cloud base at thesealtitudes. This increased uncertainty/variability in height

Figure 2. Mean 1� � 1� “wind” component bias forMISR vF01_0001 ground retrievals with QI ≥ 80: (a)east-west, (b) north-south, (c) cross-track, and (d) along-track. In Figures 2c and 2d the ground track of MISRpath 209 is indicated as a guide. Also note the differentscale in Figure 2c.


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assignment can lower CMV QI through spatial and temporalheight consistency tests included in the EUMETSAT qualitycontrol scheme [Holmlund, 1998; Holmlund et al., 2001]. Asa result, the average fraction of high-quality CMVs was 62%at low levels, 47% at middle levels, and 66% at high levels.[29] In summary, SMV quality significantly improved

from vF08_0017 to vF01_0001, with middle and highlevels showing the largest relative increases in coverage,by a factor of 2 to 3. At high levels, nevertheless,Meteosat-9 CMVs still had a better average QI than MISRSMVs. At low and middle levels, on the other hand, theQI of new stereo winds was better than that of geostationarywinds. (Note, however, that the precise mapping between

MISR and Meteosat-9 QI values representing comparablequality will require further experiments.)[30] Next, I investigated the dependence of SMV-CMV

comparison statistics on the various quality indices.Figures 6a and 6b show the number of matched pairs andwind RMSD in the more problematic N-S direction as a func-tion of MISR vF08_0017 WQ and Meteosat-9 QI. Apartfrom minor discrepancies due to a slightly different groundfilter used in the current study, these were essentially thesame results as those obtained by Lonitz and Horváth[2011]. The comparison statistics were particularly sensitiveto MISR WQ, suggesting more frequent blunders in SMVsthan in CMVs. The N-S wind RMSD decreased by a factorof 4 between worst and best SMV bins; however, this cameat the price of a comparable reduction in coverage. Thedependence on the MISR quality indicator was also ratheruneven, with a very sharp drop in N-S wind RMSD betweenWQ = 2 (“uncertain”) and WQ = 3 (“good”) winds.[31] The N-S wind RMSD corresponding to 70.4 km mean

vF01_0001 SMVs is plotted in Figure 6c. Here all 17.6 kmMISR winds (0≤QI≤100) were included in the mean, andfor consistency SMV-CMV pairs were binned according tothe original vF08_0017WQ values (amounting to a triple collo-cation between vF08_0017 SMVs, vF01_0001 SMVs, andMeteosat-9 CMVs). The variation with MISR qualityindex was now fairly smooth and the N-S wind RMSDdecreased by half for previously low-quality MISR SMVs.The improvements for high-quality vF08_0017 MISR windswere more modest, indicating an already tight quality controlin the original “good” and “best” wind categories. These com-parison statistics, however, could be further improved by im-posing a threshold on the vF01_0001 winds included in theaveraging. For example, the N-S wind RMSD range betweenthe worst and best bins in Figure 6c reduced from 7.31–3.66ms�1 to 6.10–2.67ms�1 and 5.45–2.62ms�1 when a thresholdof QI≥ 50 or QI≥ 80 were applied, respectively.[32] To demonstrate the better coverage of the new MISR

winds, in Figure 6d I binned SMV-CMV pairs according tothe QI of the highest-quality 17.6 km vF01_0001 SMV withinan original 70.4 km vF08_0017 domain. Comparison withFigure 6a indicated a 2.5-fold increase in number of wind pairsin the best bin, from 1.42� 105 for SMVvF08_0017 WQ=4 andCMV QI≥ 80 to 3.57� 105 for SMVvF01_0001 QI≥80 andCMV QI≥ 80. This estimate of coverage improvement agreedwell with the analysis shown in Figure 5.[33] The N-S wind RMSD corresponding to the binning in

Figure 6d is plotted in Figure 6e. Although, as before, thecomparison significantly improved for previously low-quality SMVs, the RMSD values in higher-quality bins herewere 30–50% larger than the ones for the 70.4 km averagesshown in Figure 6c. This was partly due to an increase inrepresentativeness error when comparing 17.6 km resolutionSMVs to nominally 72 km resolution Meteosat-9 CMVs.Indeed, RMSD reduced when vF01_0001 SMVs wereaveraged to 70.4 km and binned according to their mean QIas in Figure 6f, but differences were still slightly larger thanin Figure 6c. (Note that Figures 6c and 6f contain the exactsame SMV-CMV pairs, but the former used vF08_0017WQ, while the latter vF01_0001 mean QI for binning.) Forexample, the RMSD in the best bin was 3.66m s�1 inFigure 6c (for SMVvF08_0017 WQ = 4 and CMV QI ≥ 80)and 4.05m s�1 in Figure 6f (for SMVvF01_0001 mean-

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Figure 3. Mean 1� � 1� ground elevation from (a) MISRvF01_0001 ground retrievals with QI ≥ 80 and (b) the digitalelevation model (DEM) in the Ancillary Geographic Product,and (c) MISR-DEM ground elevation difference.


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QI ≥ 80 and CMV QI ≥ 80). This suggested the old MISR“best” wind category had slightly tighter quality control thanthe new one, albeit at the cost of much-reduced coverage.

3.3. Full-Disk Mean Comparison

[34] Full-disk annual-mean SMV-CMV comparison statis-tics are given in Tables 2, 3, and 4 for low- (< 3 km), middle-(3–7 km), and high-level (> 7 km) clouds. The vF08_0017results are essentially the same as in Lonitz and Horváth[2011], with only minor differences due to the differentground filter and height bins (geometric vs. pressure height)used in the current study. The vF01_0001 results correspondto 70.4 km average SMVs calculated separately for three dif-ferent QI thresholds.[35] Compared to Meteosat-9 CMVs, vF08_0017 SMVs

showed negative biases in both components at all levels, thatis, MISR winds had weaker westerlies and southerlies(or stronger easterlies and northerlies). The magnitude ofthe E-W wind bias was typically <1m s�1 with an RMSDbetween 2 and 4m s�1. The N-S wind bias and RMSD werelarger, and steadily increased with height, from�0.8m s�1 to�4.3m s�1, and from 3.5m s�1 to 9.2m s�1, respectively.[36] In vF01_0001 data, the already quite good E-W wind

statistics showed only small changes. The magnitude of the biasdecreased to <0.5m s�1; the RMSD and correlation, however,remained practically unchanged. The only notable exceptionwas a 0.5–0.8m s�1 (15–20%) increase in RMSD at middlelevel. Recall that this is the layer where Meteosat-9 height as-signment techniques are the least consistent, as indicated bythe reduced CMV quality in Figure 5. Therefore, the slightly in-creased E-W wind scatter at middle level might have been in-dicative of CMV height uncertainties more than anything else.[37] In contrast, the N-S wind component showed clear

and substantial improvement in new stereo data at all heights.The negative bias was eliminated at low level, and reducedby 2.5–3.0m s�1 at middle and high levels. The RMSD alsodecreased by 0.8–0.9m s�1 at low level, and 3–4m s�1 athigh and middle levels, accompanied by significant increasesin correlation. As a result, N-S wind errors became

comparable to E-W wind errors at low and middle levels,and only at high level did a significant contrast in uncertaintyremain between wind components.[38] The reduction in negative N-S wind bias led to a general

decrease in vF01_0001 SMV heights. At low and high levelsthe new MISR heights still exceeded Meteosat-9 heights,but the mean SMV-CMV height difference decreased from430–630m to 200–300m. At middle level, however, the meanheight difference did not decrease in magnitude, but simplychanged sign from +335m to �[260–350] m; that is, the newSMV heights were, on average, lower than CMV heights. TheRMSD and correlation values indicated that agreement betweenMISR and Meteosat-9 heights remained generally poor, and atmiddle and high levels, even deteriorated compared tovF08_0017 data. Lack of height comparison improvements cor-responding to MISR N-S wind improvements stronglysuggested the dominance of Meteosat-9 height assignment er-rors in the SMV-CMV height differences.[39] It is also worth noting that the MISR QI threshold had

little influence on wind comparison statistics at low and mid-dle levels, and most affected results at high level. This indi-cated an across-the-board SMV improvement for low-leveland midlevel cloud types, and revealed that MISR qualitycontrol best captures retrieval uncertainties in cirrus anddeep convection.[40] To analyze sampling differences between the old and

new MISR data sets, in Table 5 I divided the original MISRvF08_0017 SMV – Meteosat-9 CMV matched pairs into twocomplementary groups: one with and one without correspond-ing high-quality (QI≥ 80) vF01_0001 SMVs (triple collocationversus double collocation). The second group contained origi-nal wind pairs that were removed from a given height bin inthe new data set, mostly by improved quality control, but insome cases by virtue of the new SMV height falling into a dif-ferent height category. The comparison statistics were at alllevels better for wind pairs with corresponding high-qualityvF01_0001 SMVs than for pairs without, confirming the over-all positive effect of the new quality control scheme. The con-trast in wind difference statistics between the two groups was

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Figure 4. Vertical variation of MISR ground retrieval E-W “wind” bias (asterisks) and N-S “wind” bias(diamonds) as a function of (a) true DEM ground elevation and (b) MISR-retrieved ground elevation, forvF08_0017 (grey, orange) and vF01_0001 (black, red) data. Horizontal bars indicate standard deviations,separately for “winds” larger/smaller than the bin mean. The vF08_0017 results are from Figure 3 in Lonitzand Horváth [2011] and correspond to retrievals with WQ ≥ 3 within the Meteosat-9 full disk, while thevF01_0001 results refer to retrievals with QI ≥ 80 over the entire globe within 70�S–70�N.


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relatively small for low-level clouds, but it was substantial formidlevel and high-level clouds. For example, the midlevel E-W wind mean difference, N-S wind mean difference, andRMSD doubled between the groups.[41] Height difference statistics, on the other hand, were

fairly comparable between groups for low- and high-levelwinds, showing significant contrast only for midlevel winds.At middle level, the vF01_0001 data set preferentiallyremoved wind pairs in which the vF08_0017 MISR heightslargely overestimated Meteosat-9 heights (by 702m on aver-age); as discussed above, these data pairs also showed greatlyincreased wind differences. The rest of the midlevel MISRvF08_0017 SMVs showed good height agreement withMeteosat-9 CMVs, with a mean difference of only 93m.These sampling differences between vF08_0017 andvF01_0001 MISR winds help explain changes in the SMV-CMV mean height differences given in Tables 2, 3, and 4.For low- and high-level winds, the SMV-CMV mean heightdifference decreased by 200–300m between old and newMISR data sets, due mainly to a decrease in new SMV

heights resulting from an increased southerly wind compo-nent (or reduced negative N-S wind bias). For midlevelwinds, the change in the sign of the SMV-CMV mean differ-ence implied a much larger, 600–650m reduction in newSMV heights. As Table 5 indicates, however, the largechange in the midlevel SMV-CMV mean height differencewas a combination of two factors: a decrease of 200–300min new SMV heights—similar to low- and high-levelwinds—and preferential exclusion by the new QI schemeof wind pairs with large vF08_0017 height overestimations.[42] To further demonstrate MISR improvements from

vF08_0017 to vF01_0001, Figure 7 plots SMV-CMV vectordifferences in a wind rose. As discussed by Lonitz andHorváth [2011], vector differences increase in the 14�/194�direction, which corresponds to Terra/MISR along-track di-rection. Dominant features of the observed pattern could beexplained by retrieval sensitivities to stereo matching errors,especially in the D camera. The vF08_0017 difference distri-bution was asymmetric with a larger frequency peak along14� than along 194�, causing the negative SMV bias

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Figure 5. Height dependence of retrieval quality. (a, b, and c) Vertical profiles of quality index distributionsforMISR vF08_0017,MISR vF01_0001, andMeteosat-9 winds. The left and right edges of a box are the 25th

and 75th percentiles, the line within a box is the median, and the� sign is the mean.Whiskers represent the 5th

and 95th percentiles. The number of winds in a given height bin is also indicated. (d) Vertical variation of thefraction of high-quality retrievals defined asWQ≥ 3 for MISR vF08_0017 SMVs (blue), andQI≥ 80 for bothMISR vF01_0001 SMVs (red) and Meteosat-9 CMVs (black).


5607

(a)

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MISR vF01_0001all QI, averaged to 70.4-km

Figure 6. SMV-CMV comparison versus quality indices. (a and b) Sample number and N-S wind RMSD for 70.4 kmvF08_0017 SMVs. (c) N-S wind RMSD for vF01_0001 SMVs averaged to 70.4 km and binned by vF08_0017 WQ, as inFigures 6a and 6b. (d and e) Sample number and N-S wind RMSD for the highest-quality 17.6 km vF01_0001 SMVs in70.4 km domains. (f) N-S wind RMSD for vF01_0001 SMVs averaged to 70.4 km and binned by the mean QI.


5608

(Figure 7a). The weak but clear dependence of height differ-ences on N-S wind differences was also indicative of theerror correlations in MISR retrievals (Figure 7b). Thedifference distribution for vF01_0001 data, on the otherhand, was fairly symmetric (Figure 7c), with only a fainttrace of the MISR error pattern (Figure 7d), thanks to themuch-reduced SMV bias and RMSD.

3.4. Meridional Variations

[43] Meridional variations in zonal-mean SMV-CMVcomparison statistics are shown in Figure 8, separately forlow-, middle-, and high-level clouds as determined byMISR height. The vF08_0017 results are practically the sameas in Lonitz and Horváth [2011], while the vF01_0001results refer to 70.4 km averages of SMVs with QI ≥ 80.The conclusions drawn below regarding the new stereo dataset are largely independent of QI threshold, because zonalmeans, similar to full-disk means, were sensitive toQI at highlevels only, and even there only moderately so.[44] Zonal-mean statistics for the more accurate E-W wind

component showed generally small changes between the old

and new MISR data sets. The largest improvements occurredin the N-S wind statistics at middle and high levels, althoughthe improvement at low level was also significant. The negativeN-SMISRwind bias greatly decreased in vF01_0001 data at alllatitudes, with particularly large, 2 to 4ms�1, reductions formiddle- and high-level clouds. The hemispheric asymmetry invF08_0017 N-S wind bias at middle level, whereby MISRbiases were 1 to 2ms�1 larger in the Southern Hemisphere thanin the Northern, was mostly eliminated in the new data set.These bias improvements were accompanied by significant re-ductions in N-S wind RMSD. As a result, the bias and RMSDin N-S winds became comparable to those in E-W winds atall latitudes for low- and middle-level clouds. For high-levelclouds, the N-S wind RMSDwas still 1.0–1.5m s�1 larger thanthe E-W wind RMSD; however, the large peaks at the equatorand 50�S/N in vF08_0017 data disappeared in vF01_0001 data.The N-S wind RMSD of the updated high-level retrievals nowshowed a smooth meridional variation, with a slight increasefrom equator to higher latitudes. Simultaneously, the N-S windcorrelation increased significantly in the tropics/subtropics(30�S–30�N), especially at middle and high levels.

Table 3. Full-Disk Annual-Mean Comparison of Meteosat-9 CMVs (QI ≥ 80) and MISR vF08_0017 SMVs (WQ ≥ 3) or vF01_0001SMVs for Mid-Level (3–7 km) Clouds, as Determined by MISR SMV Heighta

E-WMDb

E-WRMSDc

E-WCorr.d

N-SMDb

N-SRMSDc

N-SCorr.d

HeightMDb

HeightRMSDc

HeightCorr.d

SpeedMDb MVDe SDMVDf NRMSVDg

vF08_0017 �0.99 3.81 0.95 �3.04 7.06 0.72 335 2068 0.63 0.94 5.50 5.84 0.61WQ ≥ 3(13300)vF01_0001 �0.50 4.65 0.93 �0.56 4.22 0.86 �322 2464 0.49 �0.77 4.48 4.40 0.51QI ≥ 0(13361)vF01_0001 �0.50 4.55 0.94 �0.47 4.13 0.87 �349 2442 0.49 �0.75 4.38 4.31 0.50QI ≥ 50(13031)vF01_0001 �0.33 4.32 0.94 �0.41 4.05 0.86 �261 2290 0.51 �0.40 4.20 4.17 0.48QI ≥ 80(10059)

aFor vF01_0001 data, all 17.6 km SMVs exceeding a certain QI threshold (0, 50, or 80) were averaged within a 70.4 km domain.bMean difference (m s�1 or m).cRoot mean square difference (m s�1 or m).dCorrelation.eMean vector difference (m s�1).fStandard deviation about MVD (m s�1).gRoot mean square vector difference normalized by MISR wind speed.

Table 2. Full-Disk Annual-Mean Comparison of Meteosat-9 CMVs (QI ≥ 80) and MISR vF08_0017 SMVs (WQ ≥ 3) or vF01_0001SMVs for Low-Level (< 3 km) Clouds, as Determined by MISR SMV Heighta

E-WMDb

E-WRMSDc

E-WCorr.d

N-SMDb

N-SRMSDc

N-SCorr.d

HeightMDb

HeightRMSDc

HeightCorr.d


vF08_0017 �0.37 2.38 0.96 �0.81 3.49 0.85 437 1060 0.33 0.08 2.99 2.99 0.46WQ ≥ 3 (199831)vF01_0001 �0.15 2.47 0.96 0.00 2.62 0.90 225 1009 0.36 �0.12 2.32 2.76 0.39QI ≥ 0 (197453)vF01_0001 �0.15 2.50 0.96 0.01 2.62 0.90 217 1032 0.35 �0.12 2.32 2.78 0.40QI ≥ 50 (197362)vF01_0001 �0.17 2.56 0.95 0.00 2.67 0.90 194 1097 0.30 �0.11 2.38 2.83 0.41QI ≥ 80 (172311)



5609

[45] Although changes to the E-Wwind statistics were typ-ically small, resulting in a slight bias improvement, at certainlatitudes the RMSD increased, and the correlation decreasednoticeably: 30�–35�S for midlevel and 20�–25�S/N for high-level clouds. The deterioration of the E-W wind comparisonin these regions was probably related to CMV height assign-ment errors or sampling differences between vF08_0017 andvF01_0001. Recall, only high-confidence cloud retrievalswere considered from the new data set, but low-confidencecloud SMVs were excluded. Nevertheless, this issue war-rants further investigation.[46] The zonal-mean comparison for wind height differ-

ences between the new SMV and CMV did not improve,compared to the results in Lonitz and Horváth [2011]. In linewith full-disk mean results, MISR SMV heights and, thus,SMV-CMV height differences, decreased by a few hundredmeters at all latitudes, but otherwise meridional variations

remained largely unchanged. In terms of RMSD and correla-tion, height comparison even worsened at middle and highlevels. Some of these changes might have been due to the dif-ferent behavior of old and new stereo matchers in multilayercloud fields. The vF08_0017 Nested Max matcher is provento favor well-textured low-level clouds. Much less is knownabout the performance of the vF01_0001 area-based matcher,although it is expected to be less error-prone in cirrus thanNested Max. Therefore, it is possible that in certain cases,the new matcher tracked upper level clouds, while NestedMax and Meteosat-9 both tracked the lower levels, causingincreased height differences in the updated data set. Lack ofan overall improvement in wind height comparison mirroringthe vast improvements in MISR N-S wind retrievals,however, reinforced the notion that SMV-CMV heightdifferences were primarily driven by Meteosat-9 heightassignment errors.

Table 5. Full-Disk Annual-Mean Comparison of Meteosat-9 CMVs (QI ≥ 80) and MISR vF08_0017 SMVs (WQ ≥ 3) With or WithoutCorresponding High-Quality vF01_0001 SMVs in the Same MISR Height Bina

E-WMDb

E-WRMSDc

E-WCorr.d

N-SMDb

N-SRMSDc

N-SCorr.d

HeightMDb

HeightRMSDc

HeightCorr.d


Low Levelwith vF01_0001(169312)

�0.35 2.25 0.96 �0.80 3.36 0.86 444 1019 0.35 0.10 2.87 2.84 0.44

without vF01_0001(30519)

�0.48 3.01 0.94 �0.90 4.16 0.81 398 1261 0.28 �0.03 3.66 3.61 0.55

Mid Levelwith vF01_0001(8010)

�0.74 3.48 0.96 �1.97 4.96 0.83 93 1930 0.59 0.37 4.44 4.11 0.47

without vF01_0001(5290)

�1.35 4.27 0.94 �4.65 9.39 0.62 702 2261 0.61 1.81 7.10 7.48 0.75

High Levelwith vF01_0001(5510)

�0.54 3.03 0.98 �3.51 7.26 0.90 548 1307 0.78 1.14 6.22 4.82 0.35

without vF01_0001(6514)

�0.82 3.87 0.98 �4.94 10.60 0.83 702 1453 0.79 1.78 7.58 8.36 0.46

aHigh-quality vF01_0001 SMVs were defined as QI ≥ 80.bMean difference (m s�1 or m).cRoot mean square difference (m s�1 or m).dCorrelation.eMean vector difference (m s�1).fStandard deviation about MVD (m s�1).gRoot mean square vector difference normalized by MISR wind speed.

Table 4. Full-Disk Annual-Mean Comparison of Meteosat-9 CMVs (QI ≥ 80) and MISR vF08_0017 SMVs (WQ ≥ 3) or vF01_0001SMVs for High-Level (> 7 km) Clouds, as Determined by MISR SMV Heighta

E-WMDb

E-WRMSDc

E-WCorr.d

N-SMDb

N-SRMSDc

N-SCorr.d

HeightMDb

HeightRMSDc

HeightCorr.d


vF08_0017 �0.69 3.51 0.98 �4.29 9.22 0.86 631 1388 0.79 1.49 6.95 7.00 0.42WQ ≥ 3(12024)vF01_0001 �0.19 3.59 0.97 �1.56 6.57 0.90 248 1744 0.62 0.59 4.84 5.71 0.35QI ≥ 0 (9266)vF01_0001 �0.07 3.46 0.97 �1.29 5.09 0.94 262 1681 0.64 0.57 4.63 4.06 0.29QI ≥ 50 (8954)vF01_0001 0.27 3.84 0.97 �0.89 4.95 0.94 400 1831 0.60 0.98 4.71 4.13 0.29QI ≥ 80 (5741)



5610

3.5. Regional Variations

[47] Regional variations of changes in SMV-CMV compar-ison statistics between MISR vF01_0001 and vF08_0017 aregiven for the E-W and N-S wind components in Figures 9and 10, respectively. The vF01_0001 – vF08_0017 changein magnitude of SMV-CMV mean wind difference, RMSD,and correlation was calculated separately for low-, middle-,and high-level clouds. As shown, high-quality low-levelSMVs were found mostly over ocean, and to a lesser degreein Europe and East Africa, while high-quality land retrievalswere predominantly at middle and high levels.[48] Relative to Meteosat-9 CMVs, the MISR E-W wind

validation statistics showed both increased and decreased dif-ferences between new and old stereo algorithms, dependingon location (Figure 9). The distribution of the new-old vali-dation differences, however, was symmetric, centering onzero for all parameters at all levels, and yielding only smalloverall changes when averaged over the full-disk (seeTables 2, 3, and 4.) Changes in SMV-CMV absolute meanwind difference between vF01_0001 and vF08_0017 weretypically within �0.4, �0.9, and �1.0m s�1 for low-,middle-, and high-level clouds. Corresponding changes in

RMSD varied within �1.0, �1.7, and �1.6m s�1, whilecorrelation changes were usually within�10%. The smallestregional changes occurred in boundary layer clouds over thecentral and eastern South Atlantic (�0.25m s�1 for meandifference and RMSD, and �2.5% for correlation), whereold MISR winds showed generally the best performance[Lonitz and Horváth, 2011].[49] In contrast, the vF01_0001 N-S wind comparison

statistics significantly improved most everywhere and in allheight bins, as compared to vF08_0017 statistics(Figure 10). The SMV-CMV absolute mean wind differencedecreased by 1 to 2m s�1 or more, with middle and highlevels experiencing the larger reductions. Simultaneously,RMSD decreased by similar amounts in most locations, withthe correlation increasing by 10 to 20% or more, especially inthe tropics and subtropics (30�S–30�N). The only deteriora-tion of validation statistics was found in low-level winds,where the N-S mean wind difference noticeably increasedin two regions: (i) a narrow band over the Atlantic Ocean run-ning between continents, and, (ii) the oceanic ITCZ aroundthe Gulf of Guinea (5�S–5�N, 20�W–10�E). Compared toMeteosat-9, the low-level vF08_0017 MISR N-S winds were

Figure 7. Distribution of MISR SMV –Meteosat-9 CMV vector difference colored according to (a and c)frequency and (b and d) mean-difference-corrected height difference. Figures 7a and 7b are from Figure 10in Lonitz and Horváth [2011] and correspond to vF08_0017 SMVs with WQ ≥ 3, while Figures 7c and 7drefer to 70.4 km averages of vF01_0001 SMVs with QI ≥ 80. The Meteosat-9 CMVs have QI ≥ 80 in bothcases. Triangles indicate error vector directions due to stereo matching errors in the D along-track (red), Dcross-track (blue), B along-track (green), and B cross-track (cyan) positions.


5611

negatively biased everywhere, except these two regionswhere they showed a positive bias [Lonitz and Horváth,2011]. The vF01_0001 stereo algorithm made meridionalwinds generally more positive/southerly (or less negative/northerly) and, therefore, further increased positive MISRbias in these two regions, while reducing negative MISR biaseverywhere else.[50] As also discussed by Lonitz and Horváth [2011], re-

gion (i) corresponded to the oceanic MISR paths 207–213,which had only a few land tie points for georegistration.Consequently, these paths were more often flagged for uncer-tain geolocation, resulting in reduced sampling that likelycaused the increase in low-level N-S SMV bias comparedto other paths. For middle- and high-level clouds other errors,such as in stereo matching, were the dominant ones, whoselarge improvements overcompensated registration errors,

overall yielding reduced middle- and high-level SMV-CMV differences even in this oceanic region.[51] The SMVs in region (ii), on the other hand, were from

paths running over Europe and the Sahara, which contained alarge number of land tie points for georegistration. MISRbiases due to orbit-level geolocation uncertainties were, thus,unlikely to explain the increased N-S mean wind differencehere. Note low-level Meteosat-9 winds also show consider-ably larger observation-model differences in this region thanin surrounding marine areas. (See maps on the SatelliteApplication Facility on Numerical Weather PredictionAMV monitoring web site at http://www.nwpsaf.org.)Visible and near-infrared CMVs below 600 hPa over sea nearGuinea are even blacklisted in the UK Met Office NWPmodel; therefore, increased N-S wind difference in the Gulfof Guinea might actually be the result of comparing

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Figure 8. Meridional variation of MISR –Meteosat-9 annual (a, d, and g) mean wind difference, (b, e, and h)wind RMSD, and (c, f, and i) wind correlation, separately for low-, middle-, and high-level clouds, asdetermined by MISR height. Dotted and solid lines correspond to MISR vF08_0017 SMVs with WQ≥ 3 and70.4 km averages of vF01_0001 SMVs with QI≥80, respectively. The E-W and N-S wind components areplotted in black and red.


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http://www.nwpsaf.org

improved vF01_0001 SMVs to biased CMVs. Future studiescould perhaps investigate the effect of biomass burningsmoke and desert dust on tracer selection and height assign-ment, which could be partially responsible for CMV biasesin this area.[52] In accordance with full-disk and zonal mean results,

regional height differences generally did not improve invF01_0001 data, and for middle- and high-level clouds thecomparison even worsened. The only notable exceptionwas a reduction of a couple of hundred meters in the SMV-CMV mean height difference over low-level marine clouds,caused by a similar decrease in the new SMV heights.CMV heights, nevertheless, remained biased low by 500 to1000m in marine boundary layer clouds compared to

MISR, lidar, and model cloud heights, even after consideringcloud base adjustments in the Meteosat-9 retrievals. As dem-onstrated by Lonitz and Horváth [2011], this low bias inbrightness temperature-based CMV heights was likely dueto contributions from the warm sea surface in brokencloud scenes.

3.6. Vertical Variations

[53] Vertical variations in SMV-CMV comparison statis-tics are given in Figure 11 at a scale finer than the coarselow-, middle-, and high-level categories discussed previ-ously. In vF08_0017 data, the mean difference was negativefor both wind components at all levels except the lowermost.This was partly caused by the general error aliasing in stereo

Figure 9. Change in E-W wind absolute mean difference, RMSD, and correlation between MISRvF01_0001 SMV – Meteosat 9 CMV and MISR vF08_0017 SMV – Meteosat 9 CMV comparison, for(a, b, and c) low-level, (d, e, and f) middle-level, and (g, h, and i) high-level clouds, as determined byMISR height. Results are significant at the 95% confidence level and correspond to vF08_0017 SMVs withWQ ≥ 3 and 70.4 km averages of vF01_0001 SMVs with QI ≥ 80. Negative/positive values indicate a de-crease/increase in the validation statistics of vF01_0001 retrievals. Grid size is 2� � 2� for low-level clouds,but 4� � 4� for middle- and high-level clouds, given the much-reduced sampling in the latter two altitudebins.


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retrievals, whereby negative/positive N-S wind errors entailedoverestimation/underestimation of heights. (A similar verticalrearrangement due to binning byMISR height was also appar-ent in ground retrievals, as discussed in section 3.1.) The mag-nitude of the E-W wind mean difference was relatively smallat all levels, typically ≤1.5m s�1. The magnitude of the N-Swindmean difference, however, rapidly increased with height,from +2m s�1 at the surface to �8m s�1 at 14 km altitude.There was also a large local maximum in N-S wind meandifference at 3–4 km.[54] In vF01_0001 data, the E-W wind mean difference

remained negative at low and middle levels, but turned posi-tive at high level, with magnitude reducing to ≤0.5m s�1.The N-S wind mean difference remained negative every-where except the lowermost layer; however, its magnitudewas drastically reduced to ≤1.5m s�1. The stereo erroraliasing and local N-S wind difference maximum at 3–4 kmalso became less prominent in the new retrievals.[55] The E-W wind RMSD showed relatively small verti-

cal variation for both old and new SMVs. However, its value

slightly increased from 2–4m s�1 in vF08_0017, to 2–5ms�1 in vF01_0001, the largest increases of 0.5–1.0m s�1

occurring at ~4 km and above 12 km altitude. ThevF08_0017 N-S wind RMSD, on the other hand, stronglyincreased with height throughout the troposphere, from 3ms�1 to 12m s�1. The vertical variation of N-S wind RMSDin vF01_0001 was significantly reduced to 3–6m s�1, withmiddle and high levels experiencing the largest decreases.As a result of these improvements in N-S wind mean differ-ence and RMSD, MISR retrieval errors in the two wind com-ponents became comparable at low and middle levels, andthe meridional wind remained slightly less accurate than thezonal wind only at high level.[56] The E-W wind correlation tended to slightly increase

with height in both data sets, reflecting the stronger and pre-dominantly zonal flow at higher altitudes. Similar to E-Wwind RMSD, the E-W wind correlation also showed a minordeterioration for new vF01_0001 retrievals. The vF08_0017N-S wind correlations were smaller and had a more complexvertical pattern, with a large drop to 0.63 in the 3–4 km height

Figure 10. Same as Figure 9 but for the N-S wind component.


5614

range. In vF01_0001 data, the N-S wind correlation showedsimilar relative variation, but significantly increased at alllevels, typically by 5 to 15%.[57] In summary, the MISR –Meteosat-9 E-W wind mean

difference improved, but the RMSD and correlation slightlyworsened, especially at middle and high levels, forvF01_0001 SMVs. Rather than being indicative of increasedMISR errors, the slight worsening of E-W wind comparisonat middle and high levels, where wind shear and variabilityare strong, might have actually been due to improvementsin SMV heights relative to systematically biased CMVheights. The N-S wind statistics, on the other hand, showeddefinite and significant improvements throughout the entireatmosphere. A local deterioration in N-S wind comparisonwas still apparent at 3–4 km, although it was less dramaticfor new MISR retrievals. The larger differences in this layerwere most likely related to reduced CMV quality(Figure 5), and the shift in CMV height assignment fromcloud top to cloud base, which could have resulted inheight-mismatched SMV-CMV pairs.

3.7. Cross-Swath Variations

[58] Lonitz and Horváth [2011] noticed cross-swath biasesin vF08_0017 MISR data, whereby SMV errors were signif-icantly larger on the eastern side of the swath than on thewestern. These biases affected N-S wind and height the most,and were traced back to uncorrected lens aberrations duringL1B2 georectification and registration [Mueller et al.,2013]. Although MISR geolocation accuracies were wellwithin design specifications, the Camera Geometric Modeldid not fully account for focal plane distortions [Jovanovicet al., 2007]. While the residual optical distortion in thecross-track direction was corrected in vF08_0017, the distor-tion in the along-track direction was neglected. The resultingalong-track coregistration error was found to vary across theswath with a magnitude of �0.4 pixel for the most oblique70� views, and �0.1 pixel for the 46� views. ThevF01_0001 algorithm applies subpixel registration correc-tions in the along-track direction as well to further mitigatethe effect of optical distortions. These corrections werederived from cloud-free, terrain-referenced imagery, andshowed no variation throughout the mission time line.[59] The effect of vF01_0001 upgrades on the cross-swath

bias is depicted in Figure 12. The 70.4 km MISR domainswere numbered 1 through 8 from west to east across theswath; however, the edge domains were usually not observedby all nine cameras and, thus, contained no retrievals. Notethat registration corrections were actually applied at thenative 17.6 km resolution in the new algorithm, but here Ionly show the 70.4 km average results.[60] The cross-swath dependence of MISR ground retrieval

biases is plotted in Figure 12a. The magnitude of variation invF08_0017 E-W wind, N-S wind, and height was 0.6m s�1,2.6m s�1, and 210m, respectively. The vF01_0001 registra-tion corrections had a relatively small effect in the western halfof the swath (domains 2, 3, 4), but significantly improvedbiases in the eastern half (domains 5, 6, 7), especially for N-Swind and height. As a result, the amplitude of E-W windvariations reduced slightly to 0.4m s�1, and that of N-S windand height variations decreased by half to 1.3m s�1 and 105min the new retrievals. In both data sets, the cross-swath varia-tion of height bias mirrored that of N-S wind bias due to

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5615

coupling between these two parameters with a sensitivity of�70 to �80m per m s�1.[61] The cross-swath dependencies of SMV-CMV mean

differences given in Figure 12b were very similar to thoseof MISR ground retrieval biases. The vF01_0001 registrationcorrections had again the largest effect in the eastern half ofthe swath, yielding similar bias improvements as in groundretrievals. For the new SMVs, the E-W wind and N-S windmean difference had reduced west-to-east variation from�0.4m s�1 to +0.1m s�1, and from �0.9m s�1 to +0.5m s�1.Height differences generally decreased by 200–250m due toa decrease in vF01_0001 SMV heights, and cross-swathvariations also reduced to between 140–260m.[62] As shown in Figure 12c, the SMV-CMV cross-swath

variations could be almost completely eliminated in both datasets by subtracting the corresponding MISR ground retrievalbiases from Figure 12a. Swath-mean differences, however,were still smaller in vF01_0001 data, especially for N-S windand height. In addition, the ground-bias correction could onlybe applied in a statistical sense and, thus, for instantaneousretrievals vF01_0001 was clearly superior to vF08_0017, asdemonstrated in Figure 12b.

3.8. Vortex Street Case Study: 4.4 km SMVs

[63] The operational vF01_0001 SMVs are generated at animproved resolution of 17.6 km; however, the new stereoalgorithm is capable of retrieving wind vectors at even finerscales. This is demonstrated in Figure 13 for a von Kármánvortex street that formed in the wake of Jan Mayen Island,located in the Norwegian Sea. The ice-capped 2277mBeerenberg volcano often produces mesoscale vortices instratocumulus-topped flow downwind of the island. The vor-tex street imaged by MISR on 6 June 2001 (path 217, orbit7808, blocks 33–35), was particularly impressive, compris-ing eight pairs of alternately rotating vortices extending350 km southward of the volcano. Animating the nineseparate views clearly showed the eastern train of vorticesrotating clockwise and the western train of vortices rotatingcounter-clockwise. This highly textured cloud field was wellsuited to test the new algorithm’s ability to retrieve the finestructure of complex atmospheric flows.[64] Height-resolved stereo winds obtained at 4.4 km

resolution are overlaid on the MISR nadir image inFigure 13a. Here I plotted residual winds after subtractingupstream wind; that is, I used a coordinate system movingwith the mean flow. Retrievals outside the island wake indi-cated a mean upstream wind direction and speed of ~25�(north-northeast) and ~12m s�1, respectively. The residualwinds were clearly not random and captured the expectedflow pattern, including the counter-rotating vortices, reason-ably well. Deviations from a textbook vortex wind structurewere partly caused by assuming a spatially and temporallyconstant upstream wind estimated at the satellite overpasstime of 1252UTC, although the large-scale wind slightlychanged during vortex shedding, as indicated by the curva-ture of the vortex street.[65] The corresponding relative vorticity field is plotted

in Figure 13b. The clockwise and counter-clockwise ro-tating vortices were nicely collocated with local minimaand maxima in vorticity. The relative vorticity averagedover the vortices varied between 5 and 15� 10�4 s�1,tending to decrease with distance from the island, as

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5616

shown in Figure 14. The MISR-observed magnitude anddownwind trend of vorticity were in good agreement withthe idealized large eddy simulations of Heinze et al. [2012],which showed vortex size increasing and mean vorticity

decreasing downstream due to diffusion (see their Figure 6).These simulations also indicated the vortex core is ~0.2Kwarmer than its environment, and features a continuousupdraft fed by a convergent near-surface inflow of warm air.

(c)(b)(a)

Figure 13. ExperimentalMISR retrievals for the vonKármán vortex street off JanMayen Island at 1252UTCon 6 June 2001: (a) 4.4 km residual winds after mean wind removal, (b) relative vorticity field, and (c) wind-corrected 1.1 km cloud top heights. The 70.4 km wide and 348 km long image segment is from path 217, orbit7808, blocks 33–35. Cyan and orange stars mark the clockwise and counter-clockwise rotating vortex centers,and the yellow centerline is a third-order polynomial fit to the intervortex midpoints. The cross-street distance(H), the along-street distance (L), and the cross-wind island width (D) are also indicated.


5617

The updraft is associated with a divergent outflow at vortextop, which leads to a sinking inversion and additional temper-ature increase. Heinze et al. [2012] hypothesized this locallowering of the capping inversion to be responsible for thetypically cloud-free vortex eye; the gradual filling of vortexcenters, also visible in Figure 13a, could then be explainedby downstream decrease in vortex core temperature.[66] From the MISR nadir image I also calculated two

different dimensionless ratios describing vortex geometry:(i) the aspect ratio of cross-street distance between two halvesof the vortex streetH to the along-street distance between twoadjacent, like-rotating vortices L, and, (ii) the ratio of cross-street distance H to crosswind width of the island D, alsoknown as dimensionless width. These quantities were com-puted following the methodology of Young and Zawislak[2006], who analyzed island wake vortex streets inModerate Resolution Imaging Spectroradiometer imagery.First, the vortex center positions were determined as cen-troids of clear pixels within each vortex; the first three vorti-ces in the immediate lee of the island, and the very last vortexwere excluded because they lacked well-defined cloud-freecores, yielding 12 vortex centers in total (cyan and orangestars for clockwise and counter-clockwise rotation). Cross-street and along-street distances were then calculated relativeto a curved centerline obtained by fitting a third-order poly-nomial to the intervortex midpoints (yellow line). Finally, Icomputed two estimates for the crosswind width of the is-land: (i) at sea level from two coastal points (D1 = 14.6 km),and, (ii) at inversion level from the clearing in the cloud fieldaround the volcano (D2 = 10.2 km), which might have beenthe more relevant value.[67] From similarity theory and laboratory measurements,

von Kármán and Rubach [1912] derived an aspect ratio of

0.28, while Tyler [1930] reported a dimensionless width of1.2 for two-dimensional, inviscid, neutrally stratified flowaround a cylinder. Island wake vortices, in contrast, usuallyform in a highly viscous, well mixed, stratified boundarylayer capped by an inversion. For such three-dimensionalatmospheric flows, earlier satellite observations yieldedaspect ratios of 0.33 to 0.60, and dimensionless widths of~1; however, the estimates were rather sensitive to viewgeometry and pixel size. In a recent study based on high-resolution Moderate Resolution Imaging Spectroradiometerimagery, Young and Zawislak [2006] found that atmosphericvortex streets do follow geometric similarity theories, butwith larger values for the dimensionless ratios than thosepredicted for inviscid flow around a bluff body. From asample of 30 cases, they derived for the aspect ratio a 95%confidence interval of 0.36 to 0.47 with a mean value of0.42, when assuming a straight centerline. Using a curvedcenterline, which might be more relevant for this case, theobserved confidence interval and mean value were 0.30 to0.43 and 0.37, respectively. Similarly, the confidence intervaland mean value of dimensionless width were found to be1.23 to 2.00 and 1.62 without curvature, and 1.40 to 2.25and 1.83 with curvature.[68] In the current vortex street, cross-street distance

showed only small variation between 13.5 and 16.4 km witha mean of 14.8 km. The along-street distance, however, sys-tematically increased downstream of the island, from 32.0to 49.3 km, with a mean of 38.7 km. As a result, the aspectratio varied between 0.31 and 0.42, having a mean value of0.38, in good agreement with the Young and Zawislak[2006] estimates for curved centerlines. My overall estimatefor the dimensionless width was 1.02 or 1.45, depending oncrosswind island diameter used (D1 or D2). These valueswere considerably lower than those of Young and Zawislak[2006]; however, the dimensionless width ratio is a moreuncertain quantity due to difficulty determining the elevationat which island diameter is relevant to the flow.[69] Finally, the wind-corrected, 1.1 km resolution MISR

heights are given in Figure 13c. The satellite-measured peakelevation of 2023m represented a 254 m underestimation ofBeerenberg, well within the �400m uncertainty of instanta-neous retrievals. The southern/southwestern slopes of thevolcano were also well captured. Cloud-top heights weretypically between 600 and 1500m, and showed reasonablycoherent, although hard-to-interpret, fluctuations. This casestudy hopefully demonstrated that high-resolution stereowind and height retrievals can help describe the fine structureof atmospheric vortices, and might even aid the evaluation ofnumerical simulations.

4. Summary and Concluding Remarks

[70] The MISR stereo algorithm, which simultaneouslyretrieves cloud motion and associated height, recentlyunderwent a major overhaul, and the Level 2 cloud productwas reprocessed to the new standard for the entire Terramission starting from 1 March 2000. Upgrades included anarea-based stereo matcher, improved quality control modeledon the EUMETSAT scheme, subpixel registration correc-tions to mitigate cross-swath biases arising from focal planedistortions, and an increased retrieval resolution of 17.6 km.This paper documented the resulting improvements to winds

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5618

in the new TC_CLOUD vF01_0001 product compared to theprevious TC_STEREO vF08_0017 product. I repeated theMISR SMV – Meteosat-9 CMV comparison study ofLonitz and Horváth [2011], replacing vF08_0017 SMVswith their vF01_0001 counterparts in the ~200,000 collo-cated wind pairs obtained from one year’s worth of data.To facilitate a direct comparison with earlier results andreduce representativeness errors, the new MISR winds wereaveraged down from their native resolution to the previousstandard of 70.4 km.[71] The quality of the new winds was significantly better

than that of the old winds at all levels, although still tendedto decrease with height. The coverage of high-qualityretrievals, defined as the fraction of QI ≥ 80 for compatibilitywith Meteosat-9, increased by 40% at low level, and a factorof 2 to 3 at middle and high levels compared to vF08_0017.As a result, vF01_0001 SMVs achieved better coverage thanMeteosat-9 CMVs at low, and, especially middle level. High-level geostationary winds, however, retained their superiorcoverage, probably due to the inherent difficulties in stereomatching oblique and nadir MISR views of cirrus and deepconvective clouds. I also found that the new quality controlscheme best captured retrieval uncertainties in high-levelclouds, while the choice of QI threshold had relatively littleeffect on SMV-CMV comparison statistics in low- andmiddle-level clouds.[72] Upgrades had an overall neutral impact on E-W wind

comparison statistics, which were already quite good invF08_0017. N-S wind comparison statistics, however,improved significantly on global, zonal, and regionalscales, and throughout the entire atmosphere. The negativeMISR N-S wind bias was practically eliminated at low leveland reduced by 2.5 to 3.0m s�1 at middle and high levels.These bias improvements were accompanied by RMSDreductions of similar magnitude, and correlation increasesof 10 to 20% or more, especially at middle and high levelsand in tropics and subtropics (30�S–30�N). The vF01_0001subpixel registration corrections had their biggest positiveimpact in the eastern half of the MISR swath, where biaseswere largest in vF08_0017 data, reducing amplitude of N-Swind and height cross-track variations by half, to 1.4m s�1

and 120m. As the net effect of these improvements, N-Swind errors became comparable to E-W wind errors at lowand middle levels, and zonal wind remained slightly moreaccurate than meridional wind only at high level.[73] Although the vF01_0001 algorithm upgrades had an

overwhelmingly positive effect on N-S wind retrievals, thelow-level N-S component bias slightly increased in two spe-cific regions. For the oceanic paths 207–213, bias increasewas likely due to sampling issues arising from more frequentorbit-level geolocation uncertainties; it remains to be seen ifpaths with few land tie points for georegistration are similarlyaffected over the Pacific. In the Gulf of Guinea this explana-tion does not hold; however, low-level Meteosat-9 windsalso show large observation-model differences, and, as a re-sult, are often blacklisted in this region. Therefore, increasedN-S wind difference here might actually have been the resultof comparing improved SMVs to biased CMVs.[74] Despite substantial reduction in N-S wind errors,

which are highly correlated with stereo height errors, theMISR – Meteosat-9 height comparison did not improve.Heights were, on average, 200 to 300m lower in vF01_0001

than in vF08_0017, due to much-reduced negative N-S windbiases, but otherwise height differences remained similar toprevious results, pointing to CMV height assignment errorsas the primary driver of discrepancy. Attribution of theremaining wind and height differences to either MISR orMeteosat-9 retrieval errors will ultimately require a triplecollocation approach [Stoffelen, 1998], adding a third indepen-dent wind data set to the comparison.[75] The new algorithm’s potential for retrieving wind

fluctuations at scales even finer than the operational17.6 km was also demonstrated. Experimental 4.4 kmSMVs allowed a reasonable description of the fine structureof an island wake vortex street including its vorticity field,in good quantitative agreement with idealized large eddysimulations. Such high-resolution stereo wind and heightretrievals might be useful for in-depth dynamical casestudies of complex atmospheric flows.[76] The MISR instrument has been retrieving height-

resolved winds continuously for over a decade and is stillin excellent health. Its carrier, the Terra satellite, hassufficient propellant to keep a nominal 705 km 10:30A.M.equator crossing time orbit through spring 2018 (E. Moyerand D. Diner, personal communication, 2013). Scienceoperations can continue at 705 km altitude through June2020 by allowing a drift in equator crossing from 10:30A.M. to 10:15A.M. Options are also being evaluated to extendoperations even beyond 2020, when Terra could exit themorning satellite constellation and settle at a lower orbit.Thus, the MISR mission is expected to continue for severalmore years, eventually producing a 20-plus year climate datarecord. The current latency between data acquisition andproduct availability is 12 h, too long for routine data assimi-lation purposes. Experiments have, however, shown thepossibility of a 5 h latency or better, which might be adequatefor certain NWP applications. Studies investigating theimpact of stereo winds on forecast skill have already started.

[77] Acknowledgments. This work was partially funded by the HansErtel Centre for Weather Research (HErZ) initiative of the GermanWeather Service (DWD). I am indebted to Kevin Mueller, CatherineMoroney, and Veljko Jovanovic of the Jet Propulsion Laboratory,California Institute of Technology, for help with the operational MISR dataset as well as producing the experimental high-resolution retrievals of thevortex street case study. I also thank Katrin Lonitz of the Max PlanckInstitute for Meteorology, Hamburg, for computational support. Finally,the suggestions of three anonymous reviewers greatly improved the paper.

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Improvements to MISR stereo motion vectors

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