An automated processing system for cloud-top height and amount from ATSR(2) stereo

A pre-operational algorithm and associated IDL processing chain for fully automated retrieval of cloud-top height and amount from dual view ATSR(2) has been developed. This processing chain includes ingest of SADIST formatted data especially all orbital position and camera viewing angles, automated stereo matching and transformation into ground co-ordinates above the WGS84 ellipsoid. Five different stereo matchers are currently included for testing: M2 (mean of the normalised differences within a patch), M3 (median of the normalised differences within a patch), 2D Nested Maximum ( a peak-valley correlation algorithm) PGotcha (a Pyramidal version of an in-house Adaptive Least Squares Correlation routine using sheetgrowing) and CDWT (a Complex Discrete Wavelet Transform). An assessment of these different stereo matchers is presented which indicates that M2 satisfies the requirement for high throughput (<1 minute/ATSR scene) and pixel-level accuracy with an acceptable blunder-rate (c.1-5%). Examples will be shown of the application of this processing scheme to a wide variety of different ATSR(2) images. The scheme has been applied to the retrieval of cloud amount through the use of a ground Digital Elevation Model and initial results indicate better cloud masking than existing schemes based on radiometry. A companion paper (Muller, Dundas, Vogt, Clothiaux, 1999) describes the quality assessment procedure in more detail. This work is supported by the EU under the Fourth Framework Programme CLOUDMAP Project (Contract No. ENV4 CT970399) and by ESA under data grant AO3-422. Introduction One of the greatest current uncertainties in Global Climate Models is the role of clouds, particularly Cirrus and Marine StCu, in the Earth’s radiation balance to determine whether clouds will enhance or decrease the effects of global warming. This study mainly focuses on natural Cirrus clouds and artificial Cirrus clouds (condensation trails of air planes) given the fact that these kind of clouds play a crucial role in a (local) increase of the so-called ‘greenhouse’ according to the current state of knowledge. Existing cloud climatologies, such as ISCCP [Rossow and Schiffer , 1991] or the HIRS/TOVS CO split window [Wylie and Menzel , 1991] do not provide the necessary accuracy either to test the effects of clouds, particularly broken clouds, on GCM forecasts or to assess the consequences of “global warming” on changing Ci & MrStCu cloud amounts Previous HIRS/TOVS data suggest that upper-level cloud amounts have increased since 1982. It has been hypothesized that contrails may contribute towards this increase as shown below in Figure 1 for a time series from 1983-1996 [Menzel et al. , 1996]. Figure 1. 13 years of cloud amount observations from ISCCP and HIRS/TOVS which indicates that upper-level cloud amounts are increasing. After [Menzel et al., 1996]. Inspection of Figure 1, however, shows a sharp discontinuity between the ISCCP observations and the ones from HIRS/TOVS which is partly due to the different definitions of cloud amount (loc. cit.) and partly due to the different IfoV of the AVHRR/geostationary (for ISCCP) and HIRS/TOVS instruments. (A)ATSR(2) (referred to hereafter as ATSR) will provide an unprecedented time series of 15 years of continuous cloud observations from the same instrument, albeit that it will be limited to the same local time and to observations at sparser time sampling than either ISCCP or HIRS/TOVS. [Hasler, 1981; Minzer et al. , 1978] first presented results on the use of stereo photogrammetry to retrieve cloud-top heights albeit from sidewards overlaps on two geostationary satellites. ATSR owing to its unique conical scanning geometry [ Prata et al. , 1990] can be employed to retrieve stereoscopic heights as first proposed by [ Lorenz, 1985]. More recently, [Prata and Turner, 1997] showed the application of a u t o m a t e d d i g i t a l s t e r e o photogrammetry to ATSR1 images. A fully automated processing chain has been developed by the authors for retrieving cloud-top heights from ATSR images from GBT SADIST2 formatted da ta us ing d ig i ta l s te reo photogrammetry. This includes an assessment of different stereo matching algorithms on ATSR. The processing chain is a macro within IDL with the stereo matcher being compiled from the original C code. Processing Chain The processing chain consists of an IDL routine called ATSR2_to_CTH. This routine takes as input arguments a SADIST2 file and a parameter file which includes the values for the appropriate stereo matcher and outputs a height file in WGS84 ellipsoid co-ordinates together with the appropriate matcher metric. The parameter file specifies which spectral bands are to be matched as well as the patch size (all the stereo matchers are area-based) and search radius to restrict the search in the forward view to the range of expected cloud-top heights. Five different stereo matchers have been tested but only two, M2 [Mandanayake and Muller, 1995] and M3 [Diner et al., 1996], have been implemented to date within the processing chain. Work continues on implementing the other three: 2D Nested Maximum (loc. cit.), PGotcha [Day et al. , 1992] and CDWT [Magarey, 1997]. M2 (multi-point matcher) is an areabased stereo matcher. It works by designating the imagery from one camera as the reference image and the other as the comparison image. A region, the patch size, is chosen in the reference image and a set of comparison patches within a search window is established in the comparison image. The matching metric is then computed by taking all the values in each patch and subtracting the mean values within the patch from each pixel to yield a normalised brightness. Then the absolute difference in the reference normalised patch minus the normalised values in the comparison patch averaged over the number of points being matched is tested against a threshold. The M2 metric is SM2= 1/Npts |(R(xi,yj) -<R>)-(C(xi,yj) -<C>) | |(Rmax -Rmin]*[Cmax –Cmin]) | SIGMAm2 (equation 1) where R(xi,yj) is the reference pixel values at ( i,j) C(xi,yj) is the corresponding value in the comparison image Rmax, Rmin are the maximum and minimum values within the reference image respectively Cmax, Cmin are the maximum and minimum values within the comparison patch respectively Npts is the number of points begin matched <R> is the average value within the reference patch <C> is the average value within the comparison patch i,j are the relative indices within the patches for summation and SIGMAm2 = 1/Npts | [R((xi,yj)<R>] | | [ Rmax Rmin ] | (equation 2) The quantity SIGMAm2 is an estimate if the average uncertainty in the numerator of equation 1. For a reference patch the x and y values of disparity are those for which SM2 is smaller than or equal to threshold TM2. If SM2 > TM2 the disparity is discarded. If multiple matches from M2 satisfies the threshold criterion, the best match for a patch is defined as the one that minimises SM2. This is done by setting a secondary threshold AM2 where AM2=f . SM2min where f > 1. If the best match is the only one for which SM2 < AM2 it is classed as a successful match. M3 is similar to M2 except medians rather than then means are used. SM3 ≤ TM3 are regarded as a successful match. TM3 and TM2 maybe different. The reader is referred to the references quoted for details of the other matchers. For ideal stereoscopic pairs, the two images should be taken at the same time. The time difference between the acquisition of the forward and the nadir is swaths 120 seconds. Equation 3 [Prata and Turner , 1997] is used for calculating the heights, where height, H is given by H = YDIS/(tan(ANGLEf)-tan(ANGLEn)) (equation 3)