HOMOGENEOUS VALIDATION SCHEME OF THE OSI SAF SEA SURFACE TEMPERATURE PRODUCTS

Transcription

1 HOMOGENEOUS VALIDATION SCHEME OF THE OSI SAF SEA SURFACE TEMPERATURE PRODUCTS Pierre Le Borgne, Gérard Legendre, Anne Marsouin, Sonia Péré Météo-France/DP/Centre de Météorologie Spatiale BP 50747, Lannion, France Abstract The Ocean and Sea Ice Satellite Application Facility (OSI SAF) is producing Sea Surface Temperature (SST) from polar orbiter satellites: METOP and NOAA, and geostationary satellites: GOES-East and METEOSAT. An homogeneous validation scheme has been developed and is applied automatically to all OSI SAF SST products since This validation scheme relies on in situ SST measurements collected daily with a 5-day delay. A blacklist of buoys updated every 10 days is used to eliminate erroneous buoy measurements from the validation statistics. The validation scheme calculates SST error statistics and plots graphs and error maps, separately for daytime and nighttime, globally and for rather small geographic areas, at three temporal resolution: daily, over 10 days and monthly. Many results are produced automatically for operational monitoring and others are made off line for dedicated studies. In 2010, the geostationary results are worst than the polar orbiter results, due to strong negative errors in the Tropical Atlantic, with two reasons contributing: a less efficient 2-channel algorithm for geostationary satellites instead of a 3-channel algorithm for polar satellites and a higher weight of the Tropical Atlantic area in METEOSAT-9 and GOES-13 statistics than in METOP-2 statistics over the global ocean. 1. INTRODUCTION The OSI SAF has been producing SST fields from polar orbiter and geostationary satellites since The first processing chains developed at the Centre de Météorologie Spatiale (CMS) of Météo-France were relative to GOES-E, METEOSAT and NOAA satellites (local acquisition). A new processing chain has been developed in 2007 for METOP in order to process the Advanced Very High Resolution Radiometer (AVHRR) data at full resolution over the globe. This chain, which is also used for NOAA satellites, has an improved cloud mask control and associates a quality level to each SST value. The processing chain of the geostationary satellites has been fully revisited in 2009 and is now consistent with the METOP chain. This enables us to develop an homogeneous validation scheme applicable to all satellites processed by OSI SAF. This text summarizes the SST operational processing, describes the validation scheme and presents results obtained on the OSI SAF SST products. 2. SST OPERATIONAL PROCESSING This section summarizes the main characteristics of the polar orbiter and geostationary satellites processing chains (see Le Borgne, 2007 and Le Borgne, 2010A, respectively for more details). The various satellites (METOP, NOAA, METEOSAT and GOES-E) carry the same type of instrument, i.e. a multi-channel infrared and visible radiometer, so the processing scheme is similar for all of them. It includes the following main steps: a) preprocessing: a threshold based cloud mask is calculated at full resolution, this mask being the MAIA cloud mask for polar orbiter (Lavanant, 2007) and the Nowcasting SAF cloud mask for geostationary satellites (Derrien and Le Gléau, 2005). b) cloud mask control: several tests are applied on each pixel that concern the local gradient, the local

2 SST, occurrence of aerosol, occurrence of ice and, for geostationary satellites only, the temporal variation. Each test is defined by two values associated to a test indicator varying from 0 to 100. The range limited by the two values corresponds to a potential problem (test indicator in ]0,100[ ), one side of the range corresponds to a critical problem (test indicator=100) and the other side to no problem (test indicator=0). The test indicators are then averaged to obtain the so-called cloud mask indicator. This step adds extra masking to the original cloud mask (preprocessing) in the polar orbiter scheme, but not in the geostationary scheme. c) SST calculation: SST is calculated by classical multi-channel formulas, where the coefficients have been calculated by applying a radiative transfer model to a data base of cloud free radio soundings. The daytime ( 0 < 90, where 0 is the solar zenith angle) formulas use 11 m and 12 m channels (there is no daytime SST for GOES-E that has no 12 m channel). The night time ( 0 > 110) formulas use 11 m, 12 m and 3.7 m channels for METOP and NOAA, 11 m and 12 m channels for METEOSAT (channel 3.9 m has not proved useful) and channels 11 m and 3.9 m for GOES-E. At twilight ( 0 in [90,110]), the SST is calculated by a weighted mean of the daytime and night time formulas. d) quality level: a quality level is associated to each pixel with the following values: 0 : unprocessed, 1 : cloudy, 2: bad, 3: suspect, 4: acceptable, 5 : excellent. The quality level value is derived from the cloud mask indicator and the satellite zenith angle. The quality level 2 significantly differ for polar orbiter (few cases) and for geostationary satellites (numerous cases, most of them would have been considered as cloudy by the polar orbiter scheme). The bias correction method, presented in Le Borgne et al., 2010A, is not yet included in the geostationary processing scheme, so this paper concern SST products without any bias correction. 3. VALIDATION SCHEME 3.1 Match-up Data Base Match-up Data Bases (MDB) are built separately for each satellite processed by the OSI SAF. MDB collects in situ SST measurements from ship, moored or drifting buoys, available through the Global Telecommunication System (GTS) and the coincident full resolution satellite information, within H hours from the in situ measurement (H=3 for polar orbiter and H=1 for geostationary satellites). The satellite information is extracted in a MxM pixel box centered on the measurement location (M=21 for polar orbiter and M=5 for geostationary satellites). The coverage of the box by clear pixels must be larger than 10%. The MDB includes the in situ measurements (platform ID, SST, auxiliary measures) and all the variables used in the SST processing. A MDB file is built daily with a 5-day delay to insure a good collection of the in situ data. There are three types of MDB: MDB1, in netcdf format, which contains satellite data for all pixels of the validation box, MDB2, in XML format, which contains restricted satellite data: values at the box center and statistics over the clear pixels of the box (mean and standard-deviation or histogram), MDB3 in ASCII format, which contains the same information as MDB2, but for a subset of parameters. The MDB1 files are intended for more detailed studies, while the MDB3 files are used for the OSI SAF operational validation. 3.2 Principles for validation statistics In order to calculate validation statistics, the MDB cases are selected as follows: drifters only, boxes containing only sea pixels (this reject coastal data), only the cases where there is a SST value at the central pixel of the validation box cases where the absolute value of the difference between the in situ measurement and the climatology exceeds 5 K are eliminated, if several in situ measurements are available for the same satellite pixel, the closest in time is retained With the above principles, the SST error is defined as the SST at the central pixel of the validation box minus the in situ measured SST, which is closest in time. Night-time, daytime and twilight cases are considered separately, as they correspond to different SST and cloud mask algorithms and because of a possible diurnal warming. The statistics are calculated at

3 three temporal resolutions: daily, to detect rapidly major problems of the processing chain, over 10 days, commonly used for most figures and plots and monthly, to obtain a better spatial coverage (figure 1 shows the nighttime cases for METOP-2 in March 2010). Figure 1: nighttime validation cases for METOP-2 in March 2010 (symbol color is related to the SST error) The statistics are calculated for each quality level value. As shown in figure 2 for METOP-2 and METEOSAT-9, similar results are obtained for quality level values 3,4,5, while a lower bias and a higher standard deviation are obtained for value 2, this effect being stronger for METEOSAT-9, which has many more cases at quality level 2 (see section 2). So most validation results are calculated for cases having a quality level in [3,5], which is also the range recommended to users. Figure 2: METOP-2 and METEOSAT-9 SST error with respect to the quality level (called cfl in legends) 3.3 Buoy blacklist In 2009, two sets of buoys in Pacific Ocean, off California and off Japan, have shown highly erroneous values during several weeks, inducing artifacts in the statistics calculated over the North Pacific. This lead us to define a specific scheme in order to detect such erroneous buoys. This scheme has been tested on the whole year 2009 (off line) and is now used routinely. A so-called buoy blacklist is built (and used ) according to the following principles: only night-time data are used satellites taken into account are METOP, NOAA18, NOAA19 the blacklist is updated every 10 days (at J+5) two 10-day periods are considered, period N-1 and N (ending at day J); a buoy is blacklisted if bias > 1.5C for two satellites on one period or for one satellite on both periods; the blacklisting interval may be period N-1 or period N or both for the near-real time statistics from J+1 to J+9, the buoys blacklisted until day J are eliminated.

4 86 buoys were blacklisted in 2009, only 33 buoys in 2010 (January-August). The most important event in 2010 was the occurrence of three erroneous buoys (13547, 13548, 13551) in February-April, west of Dakar (Senegal). Figure 3 shows that the blacklist efficiently removes erroneous data from METOP-2 nighttimes validation cases in this particular event. The blacklist is controlled and sometimes modified, interactively, every 3 months, using plots of the SST error and values, such as presented in figure 3. Buoy (left) is an easy example, where numerous data clearly define the problematic period, while buoy has fewer data that are more difficult to interpret (the buoy was perhaps erroneous in July-August, but not detected). Figure 3: METOP-2 nightimes validation cases, 2010/02/01 to 2010/03/31, without any blacklist (left) and with the operational blacklist (right). Symbol colors are the same as in figure 1. Figure 4: Examples of blacklisted buoys. SST error (top figure) or SST (bottom figure) is in green for METOP, in light blue for METEOSAT and in orange for GOES-E, in situ SST is in black; the period when the buoy is blacklisted is shown in red. An attempt has been made to add METEOSAT into the building scheme of the blacklist, but this has induced a lot of false alarms in the Tropical Atlantic, due to the strong negative errors of this satellite in this area. Adding other satellites is indeed useful and this should be achieved by changing the blacklist scheme or, hopefully, reducing METEOSAT error in Tropical Atlantic. 4. RESULTS 4.1 Overall error statistics Table 1 gives SST error statistics of all OSI SAF processed satellites, calculated on the whole area seen by each satellite. Some 2009 results are presented to show the interannual variability, but we will focus on the 2010 results. Among polar orbiter satellites, METOP-2 results are more significant since they are global. METOP-2 shows a negligible bias and standard deviation of 0.47 by night and 0.61 by day. The worse METOP-2 daytime results are due to a less efficient SST algorithm (2 channels by day instead of 3 channels by night). This explanation is also responsible for the geostationary satellites worse results, together with a higher weight of the Tropical Atlantic area in METEOSAT-9 and GOES-13 statistics than in the global METOP-2 statistics. METEOSAT-9 and GOES-13 show similar nighttime performances, this being by chance as they have different geographical patterns (see figure 5).

5 night time day time satellite period bias (C) st dev (C) nb cases bias (C) st dev (C) nb cases METOP Jan - 31 Dec METOP Jan 31 Aug NOAA-18 * Mar - 31 Dec NOAA-18 * Jan 31 Aug NOAA-19 * Jan 31 Aug METEOSAT May 31 Aug GOES May 31 Aug * European seas only Table 1: SST error statistics for all OSI SAF processed satellites. Figure 5: SST error, averaged on 4 months (May-August) for GOES-13 (nighttime), METEOSAT-9 (nighttime) and METOP-2 (nighttime and daytime)

6 4.2 Geographical and temporal variation Figure 5 shows the geographical variation of the SST error, averaged on 4 months (May-August) for GOES-13, METEOSAT-9 and METOP-2. The three satellites show a the negative bias in the Tropical Atantic. This problem is well known (Le Borgne et al, 2010A, Marullo et al, 2010) and due to limitations of the multispectral algorithms (Merchant et al 2009A) but seems especially strong this year, as shown below. GOES-13 shows unexplained high errors values in two areas: west of California (also problematic with METOP-2) and east of Canada (strong SST gradients, important cloudiness and no blacklist available as there is no nighttime METOP-2 data at this period of the year). METEOSAT-9 nighttime and daytime (not shown) errors that correspond to the same SST algorithm, are very similar. METOP-2 nighttime and daytime errors differ significantly with no simple explanation, except the different SST algorithms. In order to quantify the SST error geographically and temporally, geographical areas have been defined (figure 6). The validation statistics of each satellite are calculated globally and on each area. This allows to monitor the geographical and temporal variation of the error. As shown in figure 7, the global METOP-2 error is constant but most of the regional problems vary with time: the negative error in Tropical Atlantic was strong from January to May 2010, then decreased and disappeared only in August. Figure 6: geographical areas used for OSI SAF SST validation Figure 7: nightime (left) and daytime (right) METOP-2 SST bias for all geographical areas on a one year period. The temporal resolution is a 10-day deriod. Each curve is referenced by the abbreviated name of the zone, see figure 6.

7 4.3 Interannual and inter satellite comparisons The METOP-2 validation statistics have been (re)calculated on the whole period, starting on July 1 st 2007, in order to allow interannual comparisons. Several satellites can be easily compared on a same period and same area. Both are useful to assess a problem such as the errors in Tropical Atlantic. Figure 8 presents the METOP-2 SST bias on the most problematic areas in Tropical Atlantic, showing that the 2010 event is obviously the strongest one encountered over the 3 years. Figure 9 presents the GOES-13, METOP-2 and METEOSAT-9 nighttime results on the Brazil area (bra). In June-July, the error is about -0.3C for METOP-2, -0.6C for GOES-13 and -0.9C for METEOSAT-9, then all satellite errors are reduced in August. The satellites are also compared (by pairs) for two areas that are not problematic, Atlantic North West (anw) and Atlantic North East (ane) Figure 8: nighttime METOP-2 SST bias over three areas in Tropical Atlantic from July 2007 to August 2010 Figure 9: GOES-13, METOP-2 and METEOSAT-9 night time results from May to August CONCLUSION An homogeneous validation scheme has been developed and is routinely applied to all SST products of the OSI SAF. In situ measurement are collected on the GTS with a 5-day delay. A blacklist is updated automatically every 10-day which permit to eliminate erroneous buoys. The 2010 validation results are good for METOP-2 with a negligible bias and a standard deviation of 0.47 by night and 0.61 by day. They are worst for METEOSAT-9 and GOES-13 with a bias of and a standard deviation of 0.64, this being related to the strong negative errors that occurred in Tropical Atlantic in 2010.

8 To solve the regional bias issue, we propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of the classical multi-spectral algorithms. The simulated errors, used as correction terms, reduce significantly the regional biases and standard deviation of the differences with drifting buoy measurements (Le Borgne et al., 2010B). An optimal estimation method (Merchant et al B) will be also experimented. The validation scheme will be a useful tool to assess how both methods improve the SST validation results. REFERENCES Derrien M. and H. Le Gléau (2005), MSG/SEVIRI cloud mask and type from SAFNWC, International Journal of Remote Sensing, 26, 21, Lavanant, L., (2007) Operational cloud masking for the O&SI SAF global METOP SST production, proceedings of the 2007 EUMETSAT conference, Amsterdam, The Netherlands, September Le Borgne, P., G. Legendre and A. Marsouin (2007), Operational SST retrieval from METOP/AVHRR, proceedings of the 2007 EUMETSAT conference, Amsterdam, The Netherlands, September Le Borgne, P., G. Legendre, A. Marsouin, H. Roquet and C. Merchant (2010A), OSI SAF SEVIRI SST new processing chain: method, operational and experimental products, proceedings of the 2010 EUMETSAT conference, Cordoba, Spain, September Le Borgne, P., A. Marsouin, F. Orain and H. Roquet (2010B), Operational sea surface temperature bias adjustment using AATSR data, accepted, Remote Sensing of Environment. Le Borgne, P., H. Roquet and C. J. Merchant (2010C), Estimation of sea surface temperature from the Spinning Enhanced Visible and Infra Red Imager, improved using numerical weather prediction, accepted, Remote Sensing of Environment. Marullo, S., R. Santolieri, V. Banzon, R.H. Evans and M. Guarracino (2010), A diurnalscycle resolving sea surface temperature product for the tropical Atlantic, J. Geophys. Res., 115, C05011, doi: /2009JC005466, Merchant, C. J., A.R. Harris, H. Roquet and P. Le Borgne (2009A), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer. Geophys. Res. Lett, 36, L17604, doi: /2009gl Merchant C. J., P. Le Borgne, H. Roquet and A. Marsouin (2009B), Sea surface temperature from a geostationary satellite by optimal estimation, Remote Sensing of Environment, 113 (2),