OPERATIONAL CLOUD MASKING FOR THE OSI SAF GLOBAL METOP/AVHRR SST PRODUCT

Transcription

1 OPERATIONAL CLOUD MASKING FOR THE OSI SAF GLOBAL METOP/AVHRR SST PRODUCT Lydie Lavanant, Philippe Marguinaud Loic Harang, Jérôme Lelay, Sonia Péré, Sabine Philippe Météo-France / DP / Centre de Météorologie Spatiale BP Lannion. France ABSTRACT The new EUMETSAT Polar Orbiter, METOP, carries an Advanced Very High Resolution Radiometer (AVHRR) that enables the detection and the characterization of the clouds over all areas. The Ocean and Sea Ice Satellite Application Facility (OSISAF) of EUMETSAT has developed an operational processing chain aiming to produce METOP derived SST fields globally in near real time. The cloud masking step which is a crucial part of the SST production is operationally assured by the MAIA version3 cloud mask developed at Météo-France (MF). This paper will review the successive steps of the OSISAF MAIA cloud mask production chain, show some examples of the products and analyse the preliminary validation results before and after METOP launch. METOP MAIA derived cloud characterizations are validated by comparison with interactively collected AVHRR targets by experienced nephanalysts and with mapped MSG2 operational production. 1. THE MAIA CLOUDMASK The MAIA package has been developed for several applications: SST production in the OSISAF operational chain Real-time MF Nowcasting cloud type imagery over sea and land from CMS NOAA HRPT acquisitions and METOP local (HRPT) and global (EUMETCAST) data. Sounding application: cloud identification and characterization in terms of number of cloud layers, cloud coverage, pressure and type inside the co-registered sounder (HIRS, IASI) footprint for atmospheric profile retrievals. The SST averaged in the sounder footprint is needed. Former MAIA versions are implemented in the NWPSAF AAPP package and this release will be part of AAPPv6. The main MAIA products are: Cloud Mask: it detects both cloud filled and cloud contaminated pixels and provides information on the presence of snow and sea ice, for day data Cloud Type: for all pixels identified as cloudy, detailed cloud analysis with information on the major cloud classes: fractional clouds, semitransparent clouds, high, medium, low opaque clouds including fog. Cloud top temperature for all pixels identified as opaque clouds Sea surface temperature for all pixels identified as clear water surfaces. Further processes are applied by the MF OSISAF team to the MAIA output cloud mask product to get high quality SST level 3 fields ( LeBorgne, 2007). The MAIA package is interfaced in input to AAPP level1b data (HRPT local acquisition stations) and to PFS leve1b data (global EUMETCAST data). The output format is the AAPP AVHRR level1c format (full resolution, satellite projection). The MAIA cloud mask itself is an independent library which can be invoked by different applications, provided the input arguments of the processed situation are correctly filled (vector of the 5 AVHRR channels, viewing geometry and position, local spatial texture). An additional output routine allows characterizing the clouds inside the sounder (HIRS, IASI) FOV for its implementation in the AAPP package. The software is written in fortran90 and installs under Unix and Linux.

2 The cloud detection algorithm is a succession of thresholds tests applied to every AVHRR situation to various combinations of the AVHRR channels. It follows the scientific algorithm developed in Derrien and LeGléau, A situation is said to be cloudy if one test is not satisfied. The series of tests applied depend on the surface type (land, sea or coast) and the solar zenith angle which determines the period of the day (day, twilight or night) and on the presence of specular reflection during the daytime (sunglint). Daytime period is defined by solar zenith angle less than 83 degrees, night-time period by solar zenith angle higher than 90 degrees and twilight if the angle is between 83 and 90 degrees. The surface type and elevation are input optional arguments of the library, and if not provided, the MAIA scheme makes their determination from the situation position using ancillary datasets. Depending on the surface type, daytime period and presence of sunglint, different series of tests and threshold values are invoked. The tests are done on different combination of channels, in brightness temperature, (ex: µm, µm, µm...), on local spatial texture of channels 1,2,4,3-4 computed on a 3*3 box centered on each AVHRR pixel, and on VIS/NIR bi-directional top of atmosphere reflectances at 0.6, 0.8 and 1.6 µm normalised for solar illumination. Some specific modules are defined for typical conditions, ex; Low Clouds in Sunglint is used for low clouds identification in sunglint areas. A SST is computed from T10.8µm and T12.0µm measurements using the split-window defined for SST calculation in the OSISAF and compared to the climatological SST. Most thresholds are determined in-line from satellite-dependent look-up tables using as input the viewing geometry (sun and satellite viewing angles), NWP forecast fields (surface temperature over land and total atmospheric water vapour content land and sea) and ancillary data (elevation, climatological minimum SST over sea and maximum reflectance over land). To save computing time and in coherence with the external information spatial resolution, the thresholds are computed at a spatial resolution defined by the user as a number of AVHRR pixels and lines (often 16x16). The look-up tables are computed off-line using the RTTOV fast forward model (Saunders, 1999) for IR channels over the ECMWF climatological profiles dataset and 6S (Vermote, 1997) for the visible channels. They are recomputed for each new satellite. Some thresholds are set empirically or satellite-dependent constants, determined from a long experience over the Europe acquisition area and from global training and validation targets data. Two sets of monthly global climatologies at a 0.1 degree resolution are provided with the software for the sea surface temperature over sea and the R0.8µm visible reflectance over land. A specific humidity climatology at a 5 degree resolution is also available but only used when the forecast is lacking. Elevation and surface type datasets are provided with the package at a 0.1 degree resolution. The extracted forecast values at the pixel position are the surface air temperature (only used over land), the total water vapour content (TWVC), the surface pressure, the air temperature at three standard levels (used for the cloud classification) and the geometric altitude. Two formats for reading forecasts are available: GRIB (WMO standard format) and ASCII. Over sea, the TWVC can be directly determined from co-registered AMSU-A regression (optional input argument) and in that case the forecast value is not used. The quality of the cloud detection process is assessed. When a decisive test have one of its key features close to a threshold, a low confidence flag is raised. A test is then applied to cloud contaminated pixels to check whether the cloud cover is opaque and completely fills the FOV. In that case, a cloud top temperature is computed. For clear pixels, the surface temperature is stored. When a situation is flagged cloudy, a further process is done to determine its cloud type. The input AVHRR channels vector goes through a classification tests sequence governed by its illumination (day, night, dawn), with the same philosophy for computing the thresholds. Ten cloud categories (identical to those defined in the NWCSAF) are defined: five opaque cloud classes according to their altitude: very low, low, medium, high and very high three semi-transparent classes according to their thickness: thick, mean and thin one class of semi-transparent clouds above lower clouds one fractional clouds class

3 Figure 1 shows an example over a 12h period of the METOP retrieved cloud type, sea surface temperature and cloud top temperature for opaque clouds. Figure 1: example for METOP AVHRR acquisitions 2007/08/15 00:00 12:00 of the retrieved MAIA cloud type (see colour convention on figure 5), the SST and cloud top temperature products 2. MAIA PRE-LAUNCH VALIDATION Before METOP launch, in order to secure a high quality cloud masking from the MAIA scheme, and to guarantee comparable quality with the NWCSAF and NOAA operational cloud masks, a special intercomparison and validation effort was initiated on NOAA data and on a global scale. First the MAIA cloud mask has gone through an intensive validation and inter-comparison to the NWC SAF PPS cloud mask scheme (Dybbroe, 2005) using world-wide NOAA/AVHRR interactive target datasets. Results of the inter-comparison can be found in Dybbroe and all (2006). It also has been inter-compared to the NOAA CLAVR-x (Heidinger,2005) and PPS softwares over a set of European NOAA/AVHRR passes. Table 1 is a summary of the inter-comparison done on 7 scenes acquired simultaneously at Meteo-France/CMS and SMHI and the corresponding archived NOAA LAC data for CLAVR-x. It concerns 6 full daytime NOAA18 HRPT summer scenes for 6 different days in July 2006 and one full NOAA17 HRPT winter case. The classification convention is the same for MAIA and PPS but is slightly different in CLAVR-x. To conduct the inter-comparison, we made the following assumptions: Cloud mask. Clear: CLAVR-x : clear, probably clear MAIA & PPS : cloud free sea, land, snow or sea ice covered Low clouds: CLAVR-x : Clouds with pressures greater than 680 hpa MAIA & PPS : Very-low, Low opaque clouds (T cloudtop >T 700hPa ), fractional Mid clouds: CLAVR-x : Clouds with pressures between 440 and 680 hpa MAIA & PPS : Mid, high opaque clouds ((T 500hPa +T tropo /2)<T cloudtop <T 700hPa ) High clouds: CLAVR-x : Clouds with pressures less than 440 hpa MAIA & PPS : Very high opaque, very thin, thin, thick cirrus, cirrus above others, fractional An example of the visual inspection for the NOAA :52 UTC LAC data is given in figure 5.

4 MAIA & CLARV-x PPS and CLARV-x MAIA and PPS SEA cloud mask agreement high cloud agreement mid cloud agreement low cloud agreement LAND cloud mask agreement high cloud agreement mid cloud agreement low cloud agreement Table 1:Statistical agreement over 7 NOAA orbits. 3. VALIDATION RESULTS OVER INTERACTIVE METOP TARGETS CLOUD MASK & TYPE After METOP launch, as part of the OSISAF Global METOP SST operational chain implementation at Météo-France, a special WP was set up to validate/ improve if necessary the cloud mask over sea, this step being a key point in the production of the SST. Also, it was the opportunity to secure a general high quality over land, which was not done during the METOP pre-launch validation study. From January to August 2007, more than 7200 sea and land world-wide targets have been collected with 2 purposes in mind: to be as representative as possible of all meteorological and background situations to systematically extract situations with incorrect MAIA results. Figures 2: Metop targets collected at CMS from January to august 2007 using the EUMETCast station. Bottom figures show the repartition of the targets with respectively the cloud type (see color definition on figure 5), the solar zenith angle and the surface temperature. The targets were interactively collected by experienced nephanalysists who were guided by other information sources (MSG acquisition/ NWP fields/ cloud radar..). Each target contains all information necessary to re-run the situation on demand (AVHRR obs, angles, atlas values, NWP background) which allow the test of new features and possible improvement of the software. Figure 2 shows the position of the targets used in this validation exercice. Clear situations are in blue, cloudy targets in green and sea-ice in red on the figures. Statistical indicators are derived from the following contingency tables:

5 Cloud detected Clear detected Cloud observed n a n b Clear observed n c n d PC= (n a +n d )/(n a +n b +n c +n d ) is the percentage of correct detections. It should be as large as possible. (1.-POD Cloud )= n b /(n a +n b ) is the rate of missed cloud observations, i.e. targets classified as cloudfree but observed cloudy. It should be as low as possible mainly for SST application. (1.-POD Clear )= n c /(n c +n d ) is the rate of missed clear observations, i.e. targets classified as cloudy but observed clear. It should be as low as possible. Statistical indicators are associated with changes in latitudes conditions (Nordic for 55N-90N and 90S- 55S, Mid-latitude for 20N-55N and 55S-20S, Low-latitude for 20S-20N), scene background (sea or sea-ice) and illumination conditions (day, night, twilight, sunglint).however, more interactive targets are needed for twilight situations. Each target is a 5x5 pixels extraction in a full scene but the performances are only analysed at the center pixel because of the local texture calculation. Figures 3 show the statistical performances of MAIA on the interactive targets separately over sea and land. Table 3 shows which type of clouds have been missed by the software. Figures 3: MAIA performances on all targets. Left-top figure shows the percentage of correct detections for all targets and left-bottom for the high-confidence targets. Right-top figure shows the rate of missed clear observations and the right-bottom figure the rate of missed cloudy situations. Very Low Low Medium High VHigh Semi-tr Semi-tr Semi-tr Semi-tr Fractional thin medium thick above others sea 21 (4%) 13(1.2%) land 73 (32%) 28 (12%) (31%) Table 3: Observed type of missed clouds Results indicate a good MAIA cloud mask agreement mainly over sea and we found no need to update the software after METOP launch over sea. The targets dataset helps to find some minor bugs in the software for land conditions, already corrected in these statistics.

6 The MAIA software includes a mask confidence which is raised when one of the tests is close to a threshold. The low confidence flag concerns about 6% of the targets of the dataset and was mainly set up for clear targets extracted in the vicinity of clouds and for fractional cloud layers. For the high-level confidence mask situations: More than 98.5% (sea) and 92.5% (land) of clear and cloudy situations are correctly classified Only about 1% (sea)-8% (land) of clouds and 3% (sea)-4% (land) of clear situations are missed Over sea, cloudy situations missed by MAIA are mainly Very-Low opaque or fractional clouds No specific geographical location of bad classified situations (not seen here) This study pointed to some remaining detection problems : Snow-detection is still difficult Very-low opaque and thin semi-transparent cloud detection over land and snow (I.e; over Greenland) is difficult 4. COMPARISON TO SAF NWC MSG2 CLOUD TYPE The OSI SAF Global METOP/AVHRR MAIA cloud mask software is built on the experience gained from the NWC SAF MSG software. Main differences comes from the number of channels (SEVIRI includes a 8.7µm band and the 3.9µm +1.6µm can be used together), the different scanning geometry and the resolution of the pixel. A special study was set up to compare the OSISAF METOP and the NWCSAF MSG2 cloud masks. The aim was to verify the coherence between both schemes over all surface types without any human subjective selection, to remove possible remaining bugs and to indirectly benefit of the large validation efforts done for the NWCSAF MSG2 mask development. Matchups have been selected with the following conditions: less than 7 minutes between both acquisitions, a homogeneous cloud classification in a local 5x5 pixels box around the matchup for both MSG and METOP. Figure 4 and tables 4 and 5 show the position of the matchups and the comparison for six full days in August corresponding to matchup situations. A large agreement is observed between both masks (larger than 99% over sea). Mask desagreement is mainly observed for night data and at the edge of the MSG disk over sea. In this study, no sea ice/ snow matchups have been extracted and this exercice will be repeated in winter. Figure 4: MAIA / NWCSAF MSG2 matchups position over 6 full days. Situations with same cloud types are in green and disagreement in red. Sea Day + Night MetOp clear MetOp Cloudy MSG2 clear MSG2 Cloudy Land Day + Night MetOp clear MetOp Cloudy MSG2 clear 86 MSG2 Cloudy Table 4: Cloud Mask comparison for 6 full days (2007/08/ /08/15)

7 MetOp MetOp MetOp MetOp Table 5: Cloud Type results for 6 full days (sea + land) 2007/08/ /08/15. Low opaque corresponds to Very-low, Low and fractional clouds together. Cirrus corresponds to all semi-transparent together. 5. REFERENCES Derrien, M. and Le Gléau, H., MSG/SEVIRI cloud mask and type from SAFNWC, Int. J. of Remote Sensing, Vol. 26, No. 21, pp Dybbroe, A., Karlsson, K.-G. and Thoss, A., NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modelling - part one: Algorithm description, J. Appl. Meteor., Vol 44, No 1, pp Dybbroe A., S. Hörnquist, L. Lavanant and P. Marguinaud, Cloud masking for the O&SI SAF Global Metop/AVHRR SST Product. Proceedings of the 2006 EUMETSAT Meteorological Satellite conference Heidinger A., Validation of CLAVR-x cloud detection over ocean using AVHRR GAC sea surface temperature. Proceedings of the 2005 SPIE conference. Lavanant, L., MAIA v3 AVHRR cloud mask and classification. EUMETSAT contract documentation. Available at LeBorgne P., G. Legendre and A. Marsouin, 2007 Operational SST from METOP/AVHRR. Proceedings of the 2007 EUMETSAT Meteorological Satellite conference Saunders,R., M. Matricardi, and P. Brunel, 1999: An improved fast radiative transfer model for assimilation of satellite radiances observations. Quart. J. Meteor. Soc., 125, Vermote, E., Tanré D., Deuzé L., Herman M. and J.J. Morcrette, Second simulation of the satellite signal in the solar spectrum, 6S : an overview. IEEE Trans. Geosci. Remote Sens., 35, pp

8 CLAVR-x cloud type MAIA. CMS station PPS. SMHI station Unprocessed Cloudfree land Cloudfree sea Snow Sea ice Very low clouds Low clouds Medium level clouds High clouds Very high clouds Very thin cirrus Thin cirrus Thick cirrus Cirrus above low/medium Fractional clouds Unclassified Figure 5: Visual inspection on NOAA :52 UTC HRPT pass. Left-top figure is the RGB multi-composite image 1,2 and 4.