Derivation of AMVs from single-level retrieved MTG-IRS moisture fields

Derivation of AMVs from single-level retrieved MTG-IRS moisture fields Laura Stewart MetOffice Reading, Meteorology Building, University of Reading, Reading, RG6 6BB Abstract The potential to derive AMVs from single-level retrieved MTG-IRS moisture fields is investigated in this paper. Using hyper-spectral observations simulated from high-resolution model fields, MTG-IRS humidity profiles are generated at constant pressure levels in the troposphere. A typical feature tracking algorithm is applied to both the model humidity fields and the retrieval humidity fields, to derive quality controlled winds. Winds derived from tracking model fields are shown to well-represent the true model wind field especially in the mid-troposphere; winds derived from tracking retrievals are shown to be of good quality but limited in quantity. To increase the amount of wind information available from tracking retrievals, we smooth the fields using a Gaussian multi-scale representation. Using smoothed retrievals in the tracking algorithm results in more winds and a slight improvement in the wind errors for larger target box sizes. 1 Introduction The infrared sounder on board Meteosat Third Generation (MTG-IRS) will provide data at an unprecedented resolution, for use in high resolution numerical weather prediction (NWP). The MTG-IRS instrument will be a Michelson interferometer infrared sounder, sampling the spectral ranges 700-1210cm 1 and 1600-2175cm 1 at a spectral rate of 0.625cm 1. The instrument will observe the European domain with a return time of 30 minutes at a horizontal resolution of approximately 4km. It is expected that the spatial and temporal frequency of the data will enhance the retrieval of fine-scale humidity structures present in the troposphere. Atmospheric motion vectors (AMVs) are traditionally derived by tracking cloud or water vapour features in image sequences from visible, infra-red and water vapour channels. One of the main sources of error in their derivation is the accurate assignment of the winds to their correct height. An alternative method of measuring winds from satellites is to track water vapour features in retrievals of moisture fields from high-resolution hyper-spectral data, such as that provided by MTG-IRS. This method avoids the need for an explicit height assignment, since the retrieval fields are already assigned a constant pressure level. Velden et al [6] and Wanzong et al [7] have previously studied the potential to track features in retrieved moisture fields using simulated data sets based on the GIFTS (Geostationary Imaging Fourier Transform Spectrometer) instrument. Results showed the proposed technique allowed AMV production at multiple vertical levels, and demonstrated a good agreement between retrieval tracked winds and those tracked using the Weather Research and Forecasting (WRF) model fields. However, it was concluded that tracking algorithm improvements were necessary to fully exploit this new capability. Mannstein et al [4] also demonstrated the successful derivation of AMVs from forecast fields of atmospheric moisture using the Lokal-Modell 1

LM (LMK). However, the quality of these winds was inhibited by strong convective events. This paper presents recent and ongoing research on the potential to derive AMVs from single-level retrieved MTG-IRS moisture fields. Using Met Office UKV convective scale model fields, we simulate hyper-spectral observations at the MTG-IRS resolution and use these to generate high-resolution retrievals of moisture fields. By comparing against the model wind field and the wind field derived from tracking model fields, we can evaluate the quality of the retrieval tracked winds under a variety of assumptions. 2 Methodology Hyper-spectral observations are simulated using convective scale models fields from a Met Office UKV model run at 1.5km resolution on 70 levels over a domain of 450km 450km (300 300 grid points). The UKV model is chosen for the simulations because it explicitly represents convection in the model dynamics and microphysics. The case study used in the model run is representative of convective initiation starting with predominantly clear-sky conditions and was previously used in the MTG-IRS Simulation Project [5]. The Met Office fast radiative transfer model RTTOV 8.7 uses the model fields to calculate radiances in the infrared spectrum as would be observed by the hyper-spectral sounder IASI. We use the instrument IASI as a proxy for MTG-IRS because the final MTG-IRS RTTOV coefficient file is not yet available, and the spectral resolution is comparable with that of MTG-IRS. Using point spread functions provided by EUMETSAT and described in [5], the radiances can be mapped from the UKV model grid points onto the MTG-IRS projection; this reduces the grid domain to 111 57 pixels compared with the previous domain size of 300 300 pixels. High-resolution profiles of temperature and humidity will be one of the primary products to be retrieved from MTG-IRS. In this work we use the NWP SAF Met Office 1DVar code to generate retrievals of atmospheric moisture on 43 RTTOV pressure levels. The code applies Bayesian optimal estimation theory to minimise a cost function penalising for distance from the hyper-spectral observations and a coarser resolution background state taken from the 12km resolution Met Office North Atlantic and Europe (NAE) model. Noise of 0.2K is added to all the observations, and the assumed observation errors are described by the error covariance matrix R used in 1DVar retrievals at the Met Office. The background errors are calculated explicitly using the model and background fields. The AMV algorithm reads in a triplet of moisture fields at 30 minute intervals; these can be model moisture fields or retrieval moisture fields. The first image is divided into a number of target boxes, and for each target box it s position in the second image is determined using a Euclidean distance technique. Once the best matching window has been identified, a correlation and contrast check are performed, and a wind is derived. The derived winds are subject to an automated quality control scheme as described in [2], and the process is then repeated for the second and third image. We then compare these derived winds to the true model wind field. 3 Feature tracking in model humidity fields Before we use retrieved MTG-IRS humidity fields in the tracking algorithm, we first investigate its suitability when model fields are used. Tracking features in model fields will allow us to 2

ascertain if the tracking software works successfully, and will provide an idea of the amount of quality wind information retrievable under favourable conditions. Using a triplet of model images at 30 minute intervals, we run the feature tracking code at 9 pressure levels in the troposphere: 882hPa, 840hPa, 795hPa, 749hPa, 702hPa, 656hPa, 610hPa, 521hPa and 396hPa. The target box sizes considered are 6 6, 8 8, 10 10 and 12 12. The minimum correlation level is set at 0.6 and the QI threshold at 0.5. The maximum velocity and contrast thresholds are empirically derived for individual pressure levels. Figure 1 shows the average derived winds speeds (coloured lines) and the average true model wind (black line) over all pressure levels and choices of target box size. We observe that the derived winds are too fast below 700hPa, but are well-matched above 700hPa, with a slight tendency to be too slow. There is little variability in the mean wind speed with target box size. Operationally, feature tracked winds have a tendency to underestimate the true wind speed, so the results below 700hPa are surprising. However, given the resolution of the images we would only expect to track winds faster than approximately 2.5m/s; at these lower levels the model winds are often slower than this. Therefore retrieved winds at these levels for this case study are more likely to overestimate the true wind speed. Figure 1: Average wind speed of true model winds (black), and derived winds using target box sizes d = 6 6 (green), d = 8 8 (blue), d = 10 10 (red) and d = 12 12 (magenta). This result is further demonstrated in the mean speed bias (MSB) plots in Figure 2 (long dashed line). There is a negative bias for derived winds below 600hPa, and a positive bias above this level. A slight improvement in the magnitude of the negative bias is seen when using a larger target box size. Figure 2 also shows the mean magnitude vector difference (MMVD) for all pressure levels. There is a small variation in MMVD with target box size, with a slight improvement seen when using larger target boxes. For example, at 702hPa the MMVD for a 6 6 box is 2.58 compared to 1.96 for a 12 12 box. The large speed biases and MMVD values at the top of the troposphere are potentially a result of fast winds being tracked over a small domain, where movement of the target box is outside the domain boundaries. Between 800-500hPa, the MMVD values for the derived winds are comparable with the O-B values for operational winds derived from the WV7.3 and IR10.8 Meteosat-9 channels at these levels. Below 800hPa and above 500hPa, the MMVD values are typically 1-2m/s larger than the O-B statistics for operational winds, suggesting that additional quality control is potentially 3

needed at these levels. Figure 3 show the derived wind fields using 6 6 and 10 10 target box sizes against the true model wind field at 656hPa and 795hPa. At 656hPa, the derived winds give an accurate and well-distributed representation of the true model wind field. The larger target box size results in fewer winds, but with slightly smaller errors; however, the smallest target box size still provides predominantly good quality, accurate wind information. The MMVD errors are the smallest at this pressure level. At 795hPa, slower wind speeds mean feature tracking becomes more difficult; the large areas with no model winds indicate that the wind speeds are below 2.5m/s in these areas. The derived winds are more sparse at this level, and there is useful wind information mixed in with some spurious winds. The contrast and quality control checks eliminate most of the very anomalous winds, and so the remaining information is relatively accurate. However, the overestimation of wind speed is a problem. Figure 2: MSB (long dash) and MMVD (short dash) for derived winds using target box sizes d = 6 6 (green), d = 8 8 (blue), d = 10 10 (red) and d = 12 12 (magenta). Figure 3: Derived winds for d = 6 6 (green) and d = 10 10 (blue) and model winds (red) at 656hPa (left) and 795hPa (right). Wind vectors: 0-2.5m/s no barb, 2.5-5m/s short barb, 5m/s long barb. In conclusion, we have observed that feature tracking in high-resolution humidity fields is both feasible and beneficial. The best quality and quantity of winds are derived from tracking in the mid-troposphere (around 750-500hPa), although informative wind information is derivable at all levels. 4

4 Feature tracking in retrieval humidity fields We now investigate using retrieved humidity fields in the feature tracking algorithm, using the same variable and quality control specifications as were used when tracking model fields. Figure 4 shows the MSB and MMVD for retrieval tracked winds over all pressure levels and for all target box sizes. Compared to the equivalent plot for winds derived from tracking model fields (Figure 2), the information in this plot is much less consistent, and there is little identifiable variability with target box size. The MMVD values are again smallest in the mid-troposphere, although slightly larger than when model fields were used in the feature tracking; the values are now typically in the range [2,4] m/s compared to [2,3] m/s. The MSB are roughly in the range [-2,-1] m/s up to 610hPa, above which they become very large for some target box sizes. This is potentially because the wind field is very uniform at high levels, and in the retrieval imagery, several boxes along a trajectory may result in a good match. Using a larger target box size does not necessarily provide better results. Figure 4: MSB (long dash) and MMVD (short dash) for derived winds using target box sizes d = 6 6 (green), d = 8 8 (blue), d = 10 10 (red) and d = 12 12 (magenta). Looking at the derived wind field at 656hPa in Figure 5 we observe that fewer quality winds are derived when compared to tracking model fields. However, good quality information is still provided despite the reduced density. The MMVD for retrieval tracked winds at 656hPa is 2.65m/s for a 10 10 target box compared to 2.35m/s for model tracked winds, and the MSB is -1.87m/s compared to -1.19m/s. Not seen here, we observe very few winds in the lower troposphere where wind retrieval was previously an issue; at 882hPa and 749hPa no winds are derived for some target box sizes. We suspect that the lack of wind information derivable from tracking retrievals is partially due to the noisiness of the retrievals fields. Despite the over-estimation of observation error in the 1DVar retrieval, the column-wise nature of the retrieval does not guarantee a horizontal smoothness in the resultant humidity field. Although features and gradients of the true model humidity structure are well-represented in the humidity retrievals, the lack of smoothness in these structures is potentially inhibiting the use of the feature tracking algorithm. We investigate this in the next section. 5

Figure 5: Derived winds for truth tracked (green) and retrieval tracked (blue) at 656hPa when d=6 6 (left) and d=10 10 (right). 5 Feature tracking in smoothed retrieval humidity fields One potential method of utilising the retrieval structure without the interference of the inherent noise associated with a 1DVar retrieval is Gaussian multi-scale representation [3]. This technique is typically used in image analysis to study the contribution of different frequencies to the structure of the image, and has previously been used in vector generation from tracking in SEVIRI water vapour channels [1]. A Gaussian blur L(x, y) of a given image I(x, y) is the convolution of the image with a 2-dimensional Gaussian kernel G(x, y): where L(x, y) = G(x, y) I(x, y) G(x, y) = 1 x 2 +y 2 2πσ 2e 2σ 2 and where x and y are the distances from the kernel centre, and σ 2 is the variance. The kernel size dictates the number of points on the Gaussian function to use in the smoothing. The level of smoothing, or the range of frequencies removed, in determined by σ 2 ; the larger the value of σ 2, the smoother the image will be. By choosing an appropriate value of σ 2 we hope to reduce the noise in our retrieval fields without smoothing away fine-scale features and strong gradients. We apply a Gaussian multi-scale representation with a kernel size of 5 5 and σ 2 = 1 to the retrieval fields at all pressure levels. An example of how the original image at 795hPa is distorted is shown in Figure 6; the model and retrieval humidity fields are shown at the top and the smoothed images are shown at the bottom. Even for an optimal choice of sigma, smoothing the retrieval images will likely eliminate both noise and genuine features from the images. The smoothing process will reduce the contrast of the images, meaning the contrast thresholds are less likely to be reached. This will potentially result in the loss of good quality wind information. To study the impact of this, we will look at using smoothed retrievals in the feature tracking algorithm with the original contrast thresholds and also without any contrast thresholds. Figure 7 shows the percentage of quality winds generated when retrievals, smoothed retrievals and smoothed retrievals with no contrast check are used in the feature tracking algorithm with a 12 12 target box. Tracking smoothed retrievals provides noticeably more winds than tracking retrievals, with the largest impact seen at lower pressure levels. We suspect that 6

Figure 6: Truth (top left), retrieval (top right), smoothed with σ = 1 (bottom left) and smoothed with σ = 2 (bottom right) humidity fields at 795hPa. the lack of impact at higher pressure levels is a result of the contrast thresholds being set too high. When the contrast check is removed, we observed many more winds above 840hPa, with the largest impact seen at the higher pressure levels. The quality of the wind information is shown in the right image of Figure 7. For a 12 12 target box size, using smoothed retrievals in the feature tracking is nearly always an improvement on using the original retrievals. When no contrast check is used for the smoothed retrievals, the resultant winds have a slightly larger MMVD, but are still an improvement on tracking retrievals. There is also a reduction in the speed bias above 750hPa when the contrast check is eliminated. Figure 7: Percentage of quality winds (left) and MMVD and MSB (right) for retrievals (blue), smoothed retrievals (green) and smoothed retrievals with no contrast check (red) when d = 12 12. 7

Figure 8 shows the MMVD and MSB against target box size for smoothed retrievals (left) and smoothed retrievals without a contrast check (right). For both sets of winds, we observe that MMVD typically decreases with target box size. The MSB values are comparable for all target boxes below about 600hPa; above this, a 10 10 and 8 8 target box generates the best results. We conclude that target box size affects the impact of smoothing, with a larger target box size being preferable. Finally, Figure 9 shows all the derived winds fields at 656hPa for a 12 12 target box size. The left image compares the derived wind field when retrievals and smoothed retrievals are tracked. When few winds are available, using smoothed retrievals provides extra wind information (blue vectors) which is largely representative of the true wind field (red vectors). The right image compares the derived wind field when smoothed retrievals with (blue vectors) and without (green vectors) a contrast check are tracked. When the contrast check is eliminated, extra wind information is provided and there is an increased distribution of the information. The addition wind information is slightly less accurate than that derived from tracking with a contrast check, but is still of a reasonable quality. It is perhaps a future operational decision if the extra error in the wind field is acceptable for a large increase in the number of winds. Figure 8: MSB (long dash) and MMVD (short dash) for derived winds using smoothed retrievals with (left) and without (right) a contrast check, for target box sizes d = 6 6 (green), d = 8 8 (blue), d = 10 10 (red) and d = 12 12 (magenta). Figure 9: Left: Derived winds when tracking retrievals (green) and smoothed retrievals (blue) at 656hPa when d = 12 12. Right: Derived winds when tracking smoothed retrievals with (blue) and without (green) a contrast check at 656hPa when d = 12 12. 8

6 Summary and conclusions One of the main sources of error in traditional feature tracking techniques is the height assignment of the derived wind vectors. Feature tracking on single model pressure levels eliminates the need for this direct height assignment. The work discussed in this paper considered the potential to derive good quality AMVs from feature tracking in MTG-IRS single-level humidity retrieval fields. Using model fields in the feature tracking algorithm, we observed that good quality wind information was retrievable especially in the mid-troposphere (700-500hPa). A slight improvement was seen in the quality of the winds when using a larger target box size, although this resulted in fewer vectors. In the lower troposphere feature tracking was more difficult because of very slow moving winds. When humidity retrieval fields were used in the feature tracking algorithm, good quality wind information was derived, but it was limited in quantity especially at lower pressure levels. We surmised that the sparsity of derived vectors was potentially because of the noisiness of the retrieval fields, and considered the use of a Gaussian multi-scale representation to smooth the retrievals. Using smoothed retrievals in the tracking algorithm with a contrast check, resulted in more winds and a slight improvement in MMVD for larger target box sizes. When the contrast check is removed, more wind information is available, and the errors are comparable with those from tracking retrievals. However, the speed of the derived wind vectors is slightly less accurate than those derived under a contrast check. When using smoothed retrievals with or without a contrast check, a larger target box size is beneficial. In conclusion, we have demonstrated the successful application of single-level feature tracking in MTG-IRS retrieved humidity fields. Although some smoothing of the retrieval fields was required to improve the quantity of wind information available, this processing was shown to be undetrimental to the quality of the wind information. Care must be taken with the size of target box; larger target boxes resulted in smaller errors but fewer winds. It is perhaps beneficial to have a smaller target box and more information, if the errors are below acceptable thresholds. Also, when smoothing is performed it is helpful to choose a contrast check that will allow good quality wind information to be accepted despite the reduced resolution of the image. These are future considerations if single-level feature tracking is to be applied to the operational MTG-IRS data. References [1] Hernandez-Carrascal, A., On Tracer Selection and Tracking in WV Images, Proceedings of 10th Winds Workshop, Tokyo, Japan, 2010. [2] Holmlund K., C.S. Velden and M. Rohn, Enhanced Automated Quality Control Applied to High-Density Satellite-Derived Winds, Mon. Wea. Review, 129, 517-529, 2001. [3] Lindeberg T., Scale-space Theory: A Basic Tool for Analysing Structures at Different Scales, J. Appl. Stats., 21, 225-270, 1994. 9

[4] Mannstein H., L. Bugliaro, C. Keil and T. Zinner, Derivation of Atmospheric Motion Vectors from Forecast Model Fields of Atmospheric Moisture, EUMETSAT study contract EUM- SAT ITT 06/738, 2006. [5] Pavelin E.G., S.J. English and F.J. Bornemann, MTG-IRS Simulation Project: Final report, EUMETSAT study contract EUM/CO/08/4600000492/SAT, 2009. [6] Velden C.S., G. Dengel, R. Dengel and D. Stettner, Determination of wind vectors by tracking features on sequential moisture analyses derived from hyperspectral IR satellite soundings, Proceedings of 7th Winds Workshop, Helsinki, Finland, 2004. [7] Wanzong S., C.S. Velden, D.A. Santak and J.A. Otkin, Wind vector calculations using simulated hyperspectral satellite retrievals, Proceedings of 8th Winds Workshop, Beijing, China, 2006. 10