Current Status of COMS AMV in NMSC/KMA Eunha Sohn, Sung-Rae Chung, Jong-Seo Park Satellite Analysis Division, NMSC/KMA soneh0431@korea.kr COMS AMV of KMA/NMSC has been produced hourly since April 1, 2011. It utilizes three consecutive COMS images with interval time of 15 minutes and is produced over observation area of extended northern hemisphere (ENH) which includes tropical area of southern hemisphere. COMS AMV has been also tested for NWP application from August, 2011. One of this work, error characteristics and statistics of COMS AMV for each channel were analyzed in terms of different latitude, level, height assignment method, and quality indicator. This paper introduced the results of COMS AMV including accuracy and error characteristics since the operational service of COMS started and demonstrated about the recent activities of COMS mesoscale AMV in terms of QI and validation. 1. COMS observation and COMS AMV algorithm COMS has three different observation modes. : Full Disk (FD), Extended Northern Hemisphere (ENH) and local area as shown in figure 1. COMS produces Full Disk image every 3 hours and ENH image every 15 minutes. And LA image can be obtained between ENH and Full Disk, here, here like this. COMS observes ENH 4 times an hour except for the time band of FD observation, which is overtaken every 3 hours. Therefore hourly COMS AMV has been produced with ENH images The Algorithm of COMS AMV is based on previous one which had been operated using the MTSAT data. Table 1 shows the specification defined in COMS AMV estimation. However, we did many works to improve AMV accuracy, in particular the modification of height assignment and target classification criteria in WV AMV computation can remove low-level AMVs. <Fig. 1 COMS observation modes>
<Table1 Specifications for COMS AMV estimation> 2. Characteristics of COMS AMV errors Figure 2 shows annual variation of the accuracy for IR AMV at high level. In winter of Northern Hemisphere (December through March), the AMV has slower bias than any other season due to strongest jet stream in high level. Conversely, the AMV in Southern Hemisphere shows relatively smaller slow bias in the same period because of weaker jet stream in summer of Southern Hemisphere. In winter season of Southern Hemisphere (July and August), the magnitude of slow bias seems to be comparable that in winter of Northern Hemisphere. Spatial and vertical distributions of high-level IR AMV s monthly averaged biases compared with UM wind are shown in figure 3 for July 2011 and January 2012. The slow bias of larger than 5 m/s (-5 m/s) can be shown in the latitudinal band of 20 ~ 30 degree in both winter Hemispheres, while zero bias shows in tropical area with lower wind speed for both. The accuracy of KMA AMV tends to contain statistically distinct seasonal variation which is dependent on the magnitude of wind speed. According to the vertical bias distribution, the height of maximum of AMV bias varies according to the latitude and season. In tropical area, slow bias seems to be almost zero, or small positive bias reveals for all levels regardless of season. There are discontinuities of bias value at 650hPa level, which results from applying height correction of surface inversion layer to this level. It has been known that uncertainty of height assignment plays a major role in the accuracy of AMV, and several studies have overtaken by resolving the problem, disagreement of vector tracking and height in algorithm, among AMV producers. KMA also continues to try to reduce bias over the region with strong wind and evaluate the performance by using UM data. Accuracy of KMA AMV tends to contain statistically the distinct seasonal variation which is dependent on the magnitude of wind speed. Consequently, region with strong wind has slow bias as compared with UM wind. Such a slow bias seems to appear due to difference of wind direction not difference of wind speed between AMV and UM wind. In the vertical bias distribution of IR AMV on July 2011 and January 2012, maximum height of AMV shows different results for latitude and season, which are influenced by tropopause. Tropical area approaches almost zero bias or small positive bias for all
levels regardless of season. However, levels above 350 hpa over mid-latitude area have slower bias for other levels. Signal of bias also changes from negative to positive at around 100 hpa. There are also discontinuities of bias value at level of 650 hpa, which results from applying height correction for winds at surrounding levels of surface inversion layer (a) High level IR COMS AMV (b) High level WV COMS AMV (c) Low level IR COMS AMV (d) Low level VIS COMS AMV <Fig. 2 Annual variation of bias and RMSVD of high level IR AMV from April 2011 to January 2012> <Fig. 3 Bias (upper) and RMSVD (bottom) of COMS IR high level AMV in terms of space and height during the period of July and January, 2012>
3. Recent activities for COMS AMV 3.1 Estimation of mesoscale COMS AMV National Institute of Meteorological Research/KMA had developed a mesoscale AMV retrieval algorithm using 1 km resolution (0.67 μm ) images from MTSAT-1R observation. To extract mesoscale AMVs with high quality and accuracy, various sensitivity tests have been conducted for main components and NIMR algorithm had been optimized. NIMR has applied the mesoscale AMV algorithm to COMS images. The target size and grid interval are used as 24 kmⅹ24 km and the time interval of satellite images are selected as 15 minutes. As another condition in target selection, the location of the target is centered at the grid location but optimized in a search area around the grid location. Targets are determined to have relatively high contrasts in candidate targets around the grid location. The mesoscale AMV algorithm uses across-correlation method and determines the search area with the simulated winds from UM. Within the search areas, the best matched areas are selected as the regions with maximum values of crosscorrelation coefficients. Global forecast fields from UM N512L70 have been used with 60 km horizontal resolution and 70 vertical layers, which are exploited with a radiative transfer model to assign heights of vectors, determine search-areas, and control quality of vectors. To reduce the error of height assignments in low levels, the inversion-level correction and cloud-base correction are employed in mesoscale AMV algorithm. 3.2 Comparison of mesoscale AMV and operational AMV Figure 4 is the example of mesoscale AMVs and operational AMVs using visible channel images for 0000 UTC on July 30, 2011, which are filtered with a QI above 0.85. Compared with the operational AMVs using visible channel images with 4km resolution the number of vectors increases by about three times in mesoscale AMVs. In low levels, the number of mesocale AMVs is about 2.5 times as many as the operational AMVs and the mesoscale AMVs depict comparatively well cyclonic flows. NIMR validated AMVs with NWP winds and radiosonde observation data for 0000 UTC on July 30, 2011(Table 2). Most of vectors are derived in oceans, where available radiosonde data are sparse. When NWP winds are utilized in this case, mesoscale AMV algorithm can extract more vectors with high accuracy than operational AMV algorithm using visible channel images with 4 km resolution. Particularly in low levels, the speed- BIAS and vector-rmse of mesoscale AMVs decrease by 71% and 15%, respectively. <Fig. 4 Comparison of visible channel (a) mesoscale AMVs and (b) operational AMVs for 0000UTC on July 30, 2011 (QI>0.85) The color of AMVs indicates the vector s height and only 25% of all vectors are displayed>
<Table 2. Preliminary comparison of validation results between mesoscale AMV and operational AMV (0000UTC, July 30. 2011) > 3.3 Optimization of QI methods for COMS AMV The quality of the current AMVs is leveled as quality indicator (QI), which consider consistency and homogeneity between the AMVs and simulated winds from numerical forecast model. This quality control (QC) method is suitable for synoptic scale motions while mesoscale AMVs with large gradient in space and time tend to be treated as vectors of low QI values. To improve QC method for mesoscale AMVs, sensitivity for QI threshold values and a new methodology is investigated. As a QI threshold gets higher from 0.5 to 0.85, Vector-RMSE decreases but Speed-BIAS increases. In addition, relatively low speed AMVs with high accuracy are removed out with use of a higher QI threshold. When the expected error (EE) is applied with a minimum QI value of 0.5, Vector-RMSE and Speed-BIAS decrease and more low speed AMVs with high accuracy remain. Therefore, QI ( 0.5) and EE ( 4) could be utilized together to reduce errors of mesoscale winds. <Fig. 5 Comparisons of quality control (QC) methods for mesoscale AMVs for February 2010. AMVs are extracted to satisfy individual QC condition: (a) QI 0.5 (left), (b) EE 4, (c) QI 0.85, and (d) QI 0.5 and EE 4 >
3. 4 Preliminary validation of the height assignment in mesoscale AMVs Mesoscale AMVs using 1km resolution visible channels can derive relatively more vectors with high resolution. These AMVs are involved in various cloud types and their heights should be assigned carefully. In order to re-evaluate HA approaches of mesoscale AMVs, CALIPSO and CloudSAT satellites could be utilized and the cloud-top and cloud-base heights from those satellites are compared with AMV heights (Figure 6). When a minimum QI value of 0.5 and a maximum EE value of 4 are applied, the bias and RMSE for height assignment (HA) are reduced and more vectors with high accuracy are derived in upper-level. These QC conditions tend to filter AMVs with poorly assigned heights and improve the accuracy of mesoscale AMVs. <Fig. 6 Validation of height assignment accuracy for mesoscale AMVs using CALIPSO cloud mask (0600 UTC 27 February, 2010). AMVs are extracted to satisfy individual QC condition: (a) QI 0.5 (left), (b) EE 4, (c) QI 0.85, and (d) QI 0.5 and EE 4 > 4. Conclusions NMSC/KMA has been producing and monitoring operationally COMS AMV since April 1, 2011. NWP center/kma also started using COMS AMV for operational UM NWP model running in the end of Dec. 2011. COMS AMV still needs to increase accuracy and vector number by improving height assignment and QI method etc. Mesoscale AMV showed better features in terms of accuracy and vector number than operational AMV in the preliminary results. NMSC/KMA has prepared to disseminate COMS AMV via GTS to other centers. But exact time is not determined.