VALIDATION OF DUAL-MODE METOP AMVS

Transcription

1 VALIDATION OF DUAL-MODE METOP AMVS Ákos Horváth 1, Régis Borde 2, and Hartwig Deneke 1 1 Leibniz Institute for Tropospheric Research, Permoserstrasse 15, Leipzig, Germany 2 EUMETSAT, Eumetsat Allee 1, Darmstadt, Germany Abstract The goal of this study was to perform a preliminary validation of EUMETSAT s soon-to-be-operational dual-mode METOP AMV (atmospheric motion vector or wind ) product against other, well-established satellite-derived wind observations. The novel dual-mode wind retrieval technique takes advantage of the swath overlap between the METOP-A/METOP-B tandem, which flies in the same orbital plane but with a half orbit separation. Unlike traditional polar-orbiter wind retrieval methods that use image triplets for tracking (e.g., MODIS AMVs), the METOP dual-mode winds are extracted from a pair of METOP-A and METOP-B 10.8 µm images obtained ~50 minutes apart. Wind extraction from image pairs has two major advantages. First, it reduces tracking time and, thus, tracking uncertainty and noise. Second, it increases the overlap area considerably, allowing global wind retrievals from AVHRR/3 for the first time. This is particularly important in the latitude band, where dual-mode METOP winds help filling the AMV data gap between the coverage areas of geostationary and polar sensors. DATA AND METHODOLOGY A test data set of dual-mode METOP AMVs was provided by EUMETSAT for the 104-day validation period running from 20 October 2013 to 31 January The comparison data set consisted of geostationary AMVs from GOES-15 (135 W), GOES-13 (75 W), METEOSAT-10 (0 ), METEOSAT-7 (57.3 E), and MTSAT-2 (145 E), as well as polar-orbiter AMVs from MODIS and MISR, both on the Terra satellite. Geostationary and MODIS AMVs were obtained from the daily data archives of CIMSS (Cooperative Institute for Meteorological Satellite Studies), University of Wisconsin-Madison, while MISR AMVs were extracted from the monthly-aggregate Cloud Motion Vector Product (MI3MCMVN), available from the NASA ASDC (Atmospheric Science Data Center). For consistency with the other AMVs, the MISR geometric heights were converted to pressure heights using ERA-Interim daily geopotential height analysis. Note that METEOSAT-10 and METEOSAT-7 AMVs in the CIMSS archive were derived using the SSEC (Space Science and Engineering Center) rather than the EUMETSAT algorithm, although the QI values were calculated according to the EUMETSAT scheme (the MISR QIs are also based on the EUMETSAT scheme). For compatibility with the METOP 10.8 µm IR winds, only IR AMVs were used from the validation data sets, with the exception of MISR, which only provides daytime visible (red band) AMVs. Also note the different target sizes and tracking times of geostationary, dual-mode METOP, MODIS, and MISR AMVs, which nominally are 72 km / 30 min, 31 km / 50 min, 26 km / 200 min, and 17.6 km / 7 min, respectively. The collocation criteria recommended for AMV validation by the CGMS (Coordination Group for Meteorological Satellites) are 150 km horizontal, 25 mb vertical, and 90 minutes temporal separation [Velden and Holmlund, 1998]. These thresholds were derived to ensure matches between AMVs and spatially/temporally sparse radiosonde winds, the latter of which are the traditional validation data for satellite winds. However, when comparing two dense satellite wind data sets, the CGMS criteria often result in several (up to a couple of dozen) potential validation winds for a given test wind. We considered the following categories for the selection of the comparison wind match: (1) maximum QI, (2) minimum pressure difference, (3) minimum horizontal distance, (4) minimum time difference, (5) minimum direction difference, (6) minimum vector difference, and (7) single wind (i.e., there is only a single validation wind meeting the collocation criteria). The METOP wind validation statistics are sensitive to the choice of the comparison wind. Therefore, we created match files that

2 contain, for each METOP wind, all potential comparison winds within the CGMS collocation thresholds. This allows selecting the comparison winds and calculating validation statistics in different ways during post processing. We note that the assimilation of satellite AMVs in relatively coarse resolution forecast models faces a similar challenge and requires spatial thinning, which usually selects the AMV with the maximum QI. In addition to results for the standard CGMS criteria, we also derived comparison statistics using the tighter horizontal and temporal collocation thresholds of 50 km and 30 minutes. The tighter collocation criteria yielded somewhat improved METOP validation statistics, especially for mid-level winds, with the reduction in the allowed horizontal separation contributing the most to the improvement; this indicates a larger spatial than temporal variability of AMVs in our data. Because one of our goals was evaluating the METOP height assignment, with the exception of a few select cases the comparison statistics were calculated without imposing the 25-hPa constraint on vertical separation. Comparison statistics were derived separately for the globe, tropics (25 S-25 N), northern hemisphere (25 N-90 N), and southern hemisphere (25 S-90 S) as well as for low-level (> 700 hpa), mid-level ( hpa), and high-level (< 400 hpa) clouds. Results were also stratified by METOP height assignment method: EBBT (equivalent blackbody brightness temperature) or IASI (Infrared Atmospheric Sounding Interferometer) CO 2 -slicing, which is used for mid- and high-level semitransparent clouds, provided there is a IASI measurement within 5 km of the tracked cloud patch. Calculations were done for two different quality thresholds: QI 80 (typical value for data assimilation, Forsythe and Saunders [2008]) and QI 60, although results are only shown for the former because the latter yielded largely similar statistics. VERTICAL VARIATION OF COMPARISON STATISTICS The vertical variation of comparison statistics is shown in Figure 1, binned according to METOP height. For MISR only the low-level results are shown, because a significant portion of the remaining METOP-MISR wind pairs combined mid- to high-level METOP AMVs with low-level MISR AMVs in multilayer situations. (This was due to the fact that the MISR stereo matchers are designed to be more sensitive to low-level cloud layers, where contrast in reflectivity is usually the highest.) These heightmismatched METOP-MISR wind pairs could not be eliminated without comparing the imagery of the cloud elements tracked by the two different sensors, and, thus, introduced artifacts in the comparison statistics for mid- and high-levels [Lonitz and Horváth, 2011]. When standard collocation thresholds and maximum QI validation winds were used (Figure 1a), the METOP wind speed bias was typically within -3 and +1 m s -1 throughout the entire atmosphere against all comparison winds. METOP AMVs tended to be slower than validation AMVs, which, however, might simply indicate that METOP heights are on average lower than the heights of validation winds. The wind speed rmsd varied between 4 and 12 m s -1, being smallest at low levels and largest at mid levels (Figure 1b). For low-level METOP AMVs the validation against MISR yielded results that were comparable to or even slightly better than those against the other sensors. Using the tight collocation thresholds reduced the speed bias to within ±0.5 m s -1 at low levels, but had little effect on its magnitude at mid and high levels (Figure 1c). The speed rmsd, on the other hand, decreased by 2-3 m s -1 throughout the entire atmosphere (Figure 1d). The overall comparison was best when validation winds yielding the minimum vector difference were used (Figures 1e and 1f). The METOP speed bias was within ±2 m s -1 at all levels; typically it was less than ±1 m s -1. The reduction in speed rmsd was comparable to that using the tight collocation criteria.

3 (a) (b) standard max QI standard max QI (c) (d) tight max QI tight max QI (e) (f) standard min VD standard min VD Figure 1: Vertical variation of METOP wind speed bias (left column) and rmsd (right column) for (a and b) standard collocation and maximum QI comparison wind, (c and d) tight collocation and maximum QI comparison wind, and (e and f) standard collocation and minimum vector difference comparison wind.

4 GEOGRAPHIC VARIATION OF COMPARISON STATISTICS The geographic variation of METOP wind speed bias and rmsd compared to maximum QI geostationary winds is given in Figure 2, averaged for all pressure levels and height assignment methods. In the tropics/subtropics, METOP winds mostly had a fast (positive) bias except in the Saharan region. A slow (negative) bias was more common at mid latitudes: the southern oceans, the North Pacific, the eastern United States and the Atlantic storm track region. Due to the non-uniform geographic sampling, the full disk-mean bias tended to be negative. The rmsd showed a clearer zonal variation, typically being < 5 m s -1 in the tropics/subtropics and > 7 m s -1 at mid latitudes, with the largest values occurring over the eastern United States and Atlantic storm track region. Figure 2: Geographic distribution of METOP wind speed bias (top) and rmsd (bottom) compared to maximum QI geostationary validation AMVs. All pressure levels and height assignment methods were included and standard collocation thresholds were used.

5 The vertical-mean METOP wind speed bias and rmsd relative to minimum vector difference validation winds are given in Figure 3 (cf. Figure 2). Most areas that had a strong slow bias relative to maximum QI validation winds now had a much weaker slow bias or even a slight positive bias and the fast-bias regions also saw a bias reduction, resulting in a significantly smaller full disk-mean bias. The rmsd showed the same zonal variation as before, that is, being smaller in the tropics/subtropics than at mid latitudes; however, it reduced by 2-3 m s -1 on average, with the largest improvements occurring over mid-latitude oceans. Figure 3: Geographic distribution of METOP wind speed bias (top) and rmsd (bottom) compared to minimum vector difference geostationary validation AMVs. All pressure levels and height assignment methods were included and standard collocation thresholds were used.

6 The METOP wind speed bias and rsmd compared to minimum vector difference MODIS-Terra winds are depicted in Figure 4, separately for the Arctic and Antarctic. In the Arctic (winter) region there was a fairly sharp land-ocean contrast, with slow bias dominating over land (except the northern half of Greenland) and fast bias occurring over the ocean. The METOP slow bias over land was most likely related to the artifact of ground retrievals. In contrast, METOP showed a fast bias almost everywhere in the Antarctic. Figure 4: METOP wind speed bias (top) and rmsd (bottom) compared to minimum vector difference MODIS-Terra validation AMVs for the Arctic (left column) and Antarctic (right column). All pressure levels and height assignment methods were included, and standard collocation thresholds were used.

7 The land-ocean contrast was also apparent in wind speed rmsd over the Arctic. Slow-bias land areas were characterized by larger rmsd values, typically > 7 m s -1, than were fast-bias oceanic regions, typically < 7 m s -1. There were regional variations over the Arctic Ocean too, with smaller rmsd values in the eastern parts than in the western parts. In contrast, the rmsd values did not show such obvious regional variations in the Antarctic and were < 7 m s -1 in most areas. SUMMARY We compared ~3 months of dual-mode METOP AMVs with geostationary as well as MODIS-Terra and MISR polar-orbiter AMVs. The comparison statistics were sensitive to the particular choice of validation wind, because several potential validation winds met the CGMS horizontal and temporal collocation criteria for each METOP wind vector. In addition, the comparison against MISR was only meaningful for low-level winds due to the MISR stereo matcher s strong preference to tracking the highest-contrast boundary clouds. About 92% of all METOP AMVs employed the EBBT height assignment technique, which, thus, dominated the results averaged for the entire data set. The remaining ~8% of METOP AMVs used CO 2 -slicing heights from neighboring IASI footprints, usually representing winds above 600 mb. The range of vertical-mean wind speed difference ( bias ), rmsd, vector difference, and correlation were [0,-2.2] m s -1, [7.1,9.7] m s -1, [6.8,8.7] m s -1, and [0.77,0.89], respectively, when using maximum QI validation winds. The corresponding values for minimum vector difference validation winds were [0,-0.74] m s -1, [5.4,6.9] m s -1, [4.6,5.7] m s -1, and [0.89,0.93], respectively. The METOP AMVs had a slow (negative) overall bias in most regions, which mostly originated from winds above 20 m s -1. Compared to MODIS-Terra, however, METOP AMVs showed a fast (positive) bias for winds above 3 m s -1. Interestingly, the overall agreement tended to be best in the tropics. In terms of vertical variations, the comparison statistics were best at low-levels where the results for MISR were comparable to those for the other sensors. Low-level METOP AMVs usually had a fast bias. Mid-level METOP winds typically showed a fast bias too, but were relatively less numerous than either low- or high-level winds. In contrast, high-level METOP winds tended to have a slow bias in most regions, which was responsible for the vertical-mean slow bias. For marine boundary layer clouds that form in areas with frequent low-level inversions, the METOP heights were usually below mb while the CIMSS heights were significantly above this level. The retrieved wind speeds, however, agreed well in these regions, indicating that the instruments consistently tracked the same cloud features. Therefore, the most likely reason for the height discrepancy was the lack of inversion correction in the CIMSS algorithm, making the METOP heights more accurate. Contrarily, in the Artic (winter) region over snow- or ice-covered surfaces the METOP and MODIS- Terra heights agreed fairly well, but METOP often retrieved unrealistically weak (< 3 m s -1 ) winds, resulting in a strong negative speed bias. These spurious METOP ground retrievals were probably the result of the wind retrieval scheme locking in on static ground targets, while the completely independent height assignment scheme returning correct heights from cloudy pixels. This METOP artifact and the resulting negative speed bias only affected Arctic land areas; the METOP speed bias relative to MODIS-Terra was overwhelmingly positive in the Arctic Ocean as well as the Antarctic. The Arctic also showed a land-ocean contrast in speed rmsd, with significantly larger values over land than ocean. The wind speed validation statistics for METOP AMVs with IASI heights were comparable to those for the all case. The comparison between METOP IASI and geostationary AMV heights was fairly good, with a METOP low bias of < 30 mb and rmsd of ~70 mb. Compared to MODIS-Terra heights, on the other hand, METOP IASI heights were ~40 mb higher with a larger rmsd of ~95 mb.

8 ACKNOWLEDGMENTS This work was carried out under EUMETSAT Tender No We thank Olivier Hautecoeur and Règis Borde of EUMETSAT for their patience and useful suggestions. We also thank CIMSS, University of Wisconsin-Madison, for providing help with the geostationary and MODIS AMVs in their Tropical Cyclones archive. REFERENCES Forsythe, M., and R. Saunders, (2008) Third analysis of the data displayed on the NWP SAF CMV monitoring website. NWP SAF Tech. Doc. NWPSAF-MO-TR-022 Vers. 1.2, Met Off., Reading, U. K. Lonitz, K., and Á. Horváth, (2011) Comparison of MISR and Meteosat-9 cloud-motion vectors. J. Geophys. Res., 116, D24202, doi: /2011jd Velden, C. S., and K. Holmlund, (1998) Report from the working group on verification and quality indices (WG III). Paper presented at Fourth International Winds Workshop, EUMETSAT, Saanenmöser, Switzerland.