Use of SSM/I ice concentration data in the ECMWF SST analysis

Transcription

1 Meteorol. Appl. 5, (1998) Use of SSM/I ice concentration data in the ECMWF SST analysis P Fernandez*, G Kelly and R Saunders, EUMETSAT/ECMWF Fellowship Programme, ECMWF, Shinfield Park, Reading, RG2 9AX, UK * Permanent affiliation: I.N.M., Paseo de las Moreras s/n, Madrid, Spain The use of the SSM/I sea ice concentration data together with a sea surface temperature (SST) analysis in a numerical weather prediction (NWP) model is described. The ice concentration data provide a useful representation of the SST freezing-point isotherm while the SST field allows a quality control of the sea ice data. A land sea mask, an orography field and a climatological SST field are also used to improve the analysis of surface temperature in some inland seas and lakes. A monthly mean sea ice concentration climatology has been created from 13 years of the ECMWF re-analysis ice concentration dataset which was also derived from satellite multi-channel microwave radiometer data. Data assimilation experiments have shown a small positive impact in the southern hemisphere model forecast scores when this new surface field is used in the NWP model. 1. Introduction The sea surface temperature (SST) field of a numerical weather prediction (NWP) model defines the lower boundary conditions over oceans, inland seas and lakes. The temperature of the sea surface and the phase (i.e. water or ice) define the albedo, heat fluxes and surface stress at the ocean atmosphere interface, which can all directly influence the atmosphere. It is therefore important to have an accurate analysis of the surface for the optimal determination of the surface boundary conditions of an NWP model. The European Centre for Medium-Range Weather Forecasts (ECMWF) SST analysis was primarily based on the gridded global SST field produced by the National Center for Environmental Prediction (NCEP) in Washington DC (Reynolds & Smith, 1994). The position of the ice edge was defined by the SST isotherm of 1.7 C in the analysis. However, some differences have been observed when comparing this analysis with the sea ice concentration inferred from satellite data. These differences can be significant under certain conditions of rapid ice freezing/melting or large polynya events (i.e. open water surrounded by ice), especially during the melting seasons as illustrated in Figure 1. The operational use of SSM/I (Special Sensor Microwave/Imager) ice concentration data to improve the ECMWF SST field was proposed in The ECMWF re-analysis project had already created a sea ice concentration dataset with 1 latitude/longitude resolution between November 1978 and December 1991 from microwave satellite data and proved that the features of this dataset were more stable and realistic than those of sea ice masks represented by other SST analyses (Nomura, 1995). Figure 1. This image shows significant differences between the SSM/I ice edge (sea ice is white and land is light grey) and the sea surface temperature freezing isotherm of 1.7 C contoured in black. The limit between SSM/I sea ice and seawater is an ice concentration of 55%. Since November 1994 near-real time global SSM/I sea ice data have been retrieved daily from the National Environmental Satellite, Data and Information Service (NESDIS). The sea ice concentration data are provided in percentages (0 100%) at the pixel scale (50 km) and mapped on to a polar stereographic projection grid. This paper describes the use of the SSM/I data to provide a more accurate definition of the position of the 287

2 P Fernandez, G Kelly and R Saunders sea ice boundary and how it is integrated into the overall SST analysis at ECMWF. A 13-year monthly mean sea ice climatology inferred from microwave satellite data is also presented. Finally the impact of the new surface field on the atmospheric forecast model is documented. 2. SSM/I and surface temperature data 2.1. SSM/I sea ice product The first SSM/I instrument was launched on 19 June 1987 aboard the Defense Meteorological Satellite Program (DMSP) Spacecraft F8, orbiting in a circular sun-synchronous near-polar orbit at an altitude of approximately 833 km with an inclination of 98.8 and an orbital period of 102 min. This results in 14.1 full orbits per day with a swath width of 1400 km. Two small circular sectors of 280 km radius at the north and south poles are never measured owing to the orbit inclination. Data from the two satellites in operation during 1996, DMSP F-10 and DMSP F-13, are used for this study. The SSM/I is a seven-channel linearly polarised passive microwave radiometric system which measures atmospheric, ocean and terrain microwave brightness temperatures at 19.3, 37.0 and 85.5 GHz in both vertical and horizontal polarisations and at 22.2 GHz only in vertical polarisation. The SSM/I data are processed by the Fleet Naval Oceanography Command and the US Air Weather Service to obtain near real-time global maps of cloud water content, rain rates, water vapour over ocean, marine wind speed, sea ice location, age and concentration, snow water content and land surface type, moisture and temperature. The only parameter used for the SST analysis is the sea ice concentration computed from the SSM/I brightness temperatures by regression and mapped to a 50 km pixel grid. The NASA (National Aeronautics and Space Administration) team sea ice algorithm (Cavalieri & Gloersen, 1984; Steffen & Schweiger, 1991) hereafter referred to as the NASA algorithm, makes use of both frequency and polarisation information from three brightness temperature channels: 19 GHz V, 19 GHz H and 37 GHz V. Each channel is averaged to a 50 km grid cell from four adjacent 25 km grid cells. The retrieval method is based on the differences in microwave emissivity and polarisation between water and ice surfaces. The largest emissivity difference between ocean water and ice occurs at the longer wavelengths (19.3 GHz) whereas the difference between first-year (FY) ice and multi-year (MY) ice emissivities is larger at shorter wavelengths (37.0 GHz). The retrieved ice concentrations are the addition of inferred MY and FY ice expressed as a percentage. 288 Sea ice has a very complex structure: it is inhomogeneous, anisotropic, never in physical equilibrium and has a composition that includes solid ice crystals and salt precipitates, liquid brine solution and gaseous air pockets. The radiative emission at microwave frequencies is determined by the electrical properties of the ice and these are determined fundamentally by the concentration of brine. The depth from which most of the observed radiation emanates is controlled by the dielectric properties of the ice. The optical depth is of the order of the radiation wavelength for saline ice and substantially larger for desalinated ice. Thus, in the Arctic, for FY ice the microwave emission comes from the snow/ice interface and is not subject to scattering. Nevertheless, the extensive surface melting during the summer months allows an important amount of brine to be drained in summer and be replaced by air pockets. Thus, for MY ice the microwave emission comes from lower down and the net radiation is reduced by scattering caused mainly by the presence of the air pockets. In the Antarctic, the salinity profile of MY ice is closer to the Arctic FY ice, mainly because of the absence of extensive surface melting. The variability of the microwave emission between southern and northern hemispheres, owing to the difference in the salinity profile for Arctic and Antarctic sea ice, has to be considered in the algorithm. The NASA algorithm includes this difference by having different coefficients for each hemisphere. The coefficients are functions of a set of nine brightness temperatures, which are observed SSM/I radiances over areas of known ice-free ocean, FY sea ice and MY sea ice for each of the three SSM/I channels and for each hemisphere. The SSM/I sea ice data are available as orbital composites in files of 3 5 days before their nominal date. Data corresponding to a particular day and satellite are sometimes incomplete, but since day to day variations in ice concentration data are small, the use of data from several consecutive days can assure the coverage of the product. The ice concentration data are expressed as percentages, with a value of zero for water, and the land is flagged as missing data. Following the criterion adopted by Nomura (1995), the sea ice limit is taken as 55% and is permanently set to 100% north of 86 N, where there is a lack of data owing to the DMSP orbit. Limited validation of the SSM/I product has been performed using the manual sea ice coverage analysis for the Gulf of Bothnia provided by the Finnish Meteorological Service. The SSM/I ice concentration data agreed well with the analysed coverage for several different dates through the winter of Sea ice retrieval problems Some problems in the derived sea ice concentration have been detected in the routine monitoring of the SSM/I ice product. The radiometric signature of new

3 Use of SSM/I ice concentration data in the ECMWF SST analysis ice, close to open waters, is difficult for the algorithm to interpret, as are the melting ponds during the summer season. Weather effects can also give spurious ice data over the open sea. Quality control checks described below have been added to remove these sources of error from the data. (a) Antarctic glacial ice recently. For this application it would have been preferable for the land mask to include the ice shelves, as they are permanently ice covered. This error in the ice concentration representation can only be explained by the inability of the algorithm to discriminate between more than two radiometrically different sea ice types. To alleviate this problem, all glaciers and ice shelves have now been permanently set to ice in the analysis. Some areas consistently assigned as open water on parts of the Antarctic coast which should be ice have been identified. It was observed that the spurious water spots exactly matched the location of glaciers. This was first observed during October 1995 when some points were assigned as water on the ice shelves and over isolated points along the coast. As the shelves are extended perennial ice formations it was not possible to explain these features as polynyas. The SSM/I Antarctic land mask provided a good mask for these features until the end of September 1995, but it was then replaced by a land mask covering only the true land surface, which explains why this problem was only detected relatively The NASA algorithm allows for both FY and MY but not for new ice features, whose presence can be one of the main sources of error in the total ice concentration. New ice, most commonly found in leads, glaciers and coastal polynyas during winter, is characterised by microwave polarisation differences intermediate between open water and thick FY ice (Cavalieri & Gloersen, 1984). Larger areas of new ice within the sensor field of view will result in proportionally larger underestimates by the algorithm. This problem has to date not been observed in the northern hemisphere. An example is shown in Figure 2. The edges of the ice Figure 2. Land is light grey, open water data is dark grey and ice data are white. Open water areas can be seen over the Ross Ice Shelf, Ronne Ice Shelf and Larsen Ice Shelf in (a). The ice shelves and glaciers have now been permanently set to ice as shown in (b) after the checks have been applied. Figure 3. The shading is as defined in Figure 2. Melting pond areas flagged as missing (light grey) are present over the Arctic ice as shown in (a). These areas vary spatially and temporally across the Arctic and have to be set permanently to ice using a climatological criterion whose result is displayed in (b). 289

4 P Fernandez, G Kelly and R Saunders shelves, which become sea ice, are still determined from the SSM/I data. (b) Arctic melt ponds Some open water areas in the Arctic sea ice have been detected during the period from June until mid- August. These are melt ponds (Comiso & Kwok, 1996), which are radiometrically closer to open water and vary spatially and temporally across the Arctic. The NASA algorithm does not work over these melting areas and so they are flagged as missing. A check using a sea ice climatology has been added so that the missing data areas are now classified as ice or water according to the sea ice climatological data (see Figure 3). It is debatable how these areas should be classified as they are essentially areas of water, probably several centimetres deep on top of the ice sheet. The correct classification in this case will depend on the application. (c) Incorrect retrievals over open ocean The SSM/I ice concentration data often include false sea ice data over open oceans. The microwave emission from a specular ocean surface is a function of the frequency, viewing angle and polarisation. An increase of Table 1. The status of analysed data in the NCEP SST for inland seas and lakes Lakes considered North America Great Lakes Other Canadian Lakes Asia Black Sea Caspian Sea Aral Sea Lake Baikal Status Analysed with in situ data Not analysed Analysed with in situ data Analysed with AVHRR data Not analysed Not analysed the wind speed over the open ocean represents an increase in microwave emission at 19 GHz and it has been shown that the horizontal polarisations are almost twice as sensitive as the vertical polarisations to nearsurface winds (Gloersen & Barath, 1977). The contributions to microwave emission owing to atmospheric water vapour fluctuations can be neglected in polar regions, but they are significant at lower latitudes. Cloud liquid water and precipitation also affect the microwave emission over water. Although a weather filter (Gloersen & Cavalieri, 1986) included in the NASA algorithm has reduced the weather effect considerably, a substantial number of suspect data remain in the original SSM/I ice concentration fields. To remove these erroneous data, a check has been included to reject the ice concentration data where the SST is higher than a threshold of 1.0 C following the same criterion as the re-analysis ice concentration dataset (Nomura, 1995) Surface temperature fields The NCEP 1 1 SST gridded data are retrieved daily from NCEP. This blended SST analysis is derived from in situ and Advanced Very High Resolution Radiometer (AVHRR) data and is a seven-day running mean centred on four-days old (Reynolds & Smith, 1994). The SST fields cover all the oceans, but for inland seas and lakes the analysis varies considerably, as illustrated in Table 1. Hence, it is necessary to use a surface temperature (ST) climatological dataset over the lakes or inland seas not analysed. The climatological dataset used is the Alexander & Mobley (1974) monthly SST climate with an orographical correction for the lakes not at sea level. 3. Production of surface temperature analysis Africa Lake Victoria Other African Lakes Not analysed Not analysed Reprojection and interpolation of the sea ice and SST fields to the NWP model grid is necessary as the different input fields are on different geographical grids. Some other fields are also necessary to fill in areas not Table 2. The fields used in the SST analysis 290 Fields Projection Resolution Sea surface temperature (NCEP) Latitude/longitude 1.0 Surface temperature climatology Latitude/longitude 1.0 Ice concentration climatology Latitude/longitude 1.0 SSM/I ice concentration Polar stereographic 50 km/pixel Orography Latitude/longitude 1.0 Land sea mask Latitude/longitude 0.5 Orography Gaussian grid Desired resolution Land sea mask Gaussian grid Desired resolution Surface temperature first guess Gaussian grid Desired resolution

5 Use of SSM/I ice concentration data in the ECMWF SST analysis analysed. The fields which contribute to the analysis are listed in Table 2. To cope with missing data a date check in the NCEP 1 1 SST is made, and if the data are older than ten days, the system reverts to a climatological SST field. The next step is to convert all the fields to the same projection and resolution. This was found to be the best treatment of the SSM/I data whose pixel resolution is 50 km is to interpolate all gridded files to 0.5 latitude/longitude. An ice concentration climatological field obtained from the first 13 years of the ECMWF re-analysis ice concentration dataset is used if SSM/I data are not available or to classify the SSM/I sea ice concentration assigned as missing data. This dataset was derived from daily sea ice concentration data at 1 latitude/longitude resolution from November 1978 to December A combination of satellite multichannel microwave radiometer (SMMR) data and SSM/I data are the source for this dataset (Nomura, 1995) and allow monthly mean sea ice concentration fields to be computed as shown in Figures 4 and 5. The monthly mean climatological fields have to be interpolated in time for the appropriate date in each month. The classification of the grid points over sea is defined in Table 3, where TMPICE is the freezing point of sea water ( 1.70 C for a salinity of 30 ), TMPCON is a quality control threshold of 1.0 C established by Nomura (1995) and is a small value (i.e. 0.01). The NCEP SST threshold temperature TMPCON is used to eliminate the spurious SSM/I ice concentration data (section 2.2(c)) and the climatological ice concentration threshold of 55% allows the missing data in ice or water (section 2.2(b)) to be classified. After these simple but important quality control checks the SSM/I sea ice concentration data is assumed to be the reference for the definition of the freezing point isotherm. Grid points of sea ice in the SSM/I field will have a temperature under the freezing point of salt water ( 1.70 C) and water points will have a temperature above the sea freezing point. The next step is to interpolate this latitude/longitude file to a reduced Gaussian model grid for interfacing with the other atmospheric model fields. This can be done to any desired model resolution. The final step consists in substituting the analysed SST into the ST first guess field (from a six-hour forecast) with the aid of a sea/land mask following the scheme outlined in Table 4. The result is a new SST analysis at any ECMWF model resolution. 4. Results of forecast impact experiments Some data assimilation experiments have been performed for different seasons to test the impact of this new SST analysis on the atmospheric forecast model fields compared with the old analyses with ice coverage based only on SST. Three 13-day assimilation experiments were run to test the impact of both the use of SSM/I ice concentration information and also the derived ice concentration climatology. The dates of one of these experiments were February 1996, when the sea ice coverage is at a minimum in the southern hemisphere and a maximum in the northern hemisphere, and the differences between using satellite sea ice information (real time data or climatology) or not, to define the sea ice boundary, are likely to be important in both hemispheres. Three experiments were performed: one using SSM/I ice concentration data (ZNAX, the experiment name), a second one using satellite-derived ice concentration climatological data (ZN5U) and a third one as a control without any satellite sea ice data (ZN5T). The forecasts out to ten days for each experiment were verified by computing the root mean square (rms) difference between the forecast and corresponding verifying analysis of the geopotential height field at various levels in the atmosphere. This is one of the standard measures employed in assessing the impact of model changes on the forecast at ECMWF. The first experiment compared using SSM/I data versus no satellite ice data. A slightly positive impact in the model forecast scores was observed when comparing the two assimilation experiments, one using the SSM/I ice concentration data (ZNAX) and the NCEP SST data in the SST analysis, and the second using only the NCEP SST data in the SST analysis (see Figures 6(a) and 6(b)). The main difference between both processes is, as explained above, the definition of the ice edge location. For the 500 hpa geopotential height the difference between the rms errors for both experiments is not significant in the northern hemisphere but is slightly reduced for the five-day forecast in the southern hemisphere for the experiment using the SSM/I data. In order to compute the effect of the SSM/I ice concentration data in the successive forecasts at higher levels in the atmosphere, a study of the differences between the experiments for a single day has been carried out. The rms error for both experiments for 25 February 1996 is displayed in Figures 6(c) and 6(d). The impact on the 500 hpa geopotential is neutral for the northern hemisphere but from the fourth forecast day onwards in the southern hemisphere the rms errors are lower for the experiment using SSM/I data. Figure 7(a) shows the difference in surface temperature between both experiments. The main features are located over the Ross Sea, where the surface temperature field without ice concentration data has sea ice while the field with the SSM/I ice product has correctly assigned the area as water. As Table 4 shows, a first guess is used for defining the temperature of the land grid points and as the date of the study is at the end of the experiment 291

6 P Fernandez, G Kelly and R Saunders Figure 4. Monthly Arctic sea ice concentrations averaged over the 13 years of the re-analysis ice concentration dataset. The scale is expressed as a percentage from 55% to 75% in black, 75% to 95% in dark grey and 95% to 100% in grey. 292

7 Use of SSM/I ice concentration data in the ECMWF SST analysis Figure 5. Monthly Antarctic sea ice concentration averaged over the 13 years of the re-analysis ice concentration dataset. The scale is is expressed as a percentage from 55% to 75% in black, 75% to 95% in dark grey and 95% to 100% in grey. 293

8 P Fernandez, G Kelly and R Saunders Table 3. The scheme employed for the classification of grid points as sea ice or water. TMPICE is a threshold of 1.70 C, TMPCON is a threshold of 1.0 C and e is a small value of 0.01 SSM/I ice SST NCEP Result Comment YES ³TMPCON SST Bad SSM/I data YES TMPICE TMPICE -e Definite sea ice YES >TMPICE TMPICE -e Melting but still ice <TMPCON NO TMPICE TMPICE + e Bad SST data NO >TMPICE SST Open water ST Climatology Not analysed inland seas SSM/I ice CLIM ICE Result Comment MISSING >55% TMPICE -e Definite sea ice MISSING 55% SST Open water Table 4. The definition of the surface temperature in the model projection to derive the final SST field Land Open sea Old ice New ice These points defined by surface These points defined by These grid points are sea Sea ice points in the SST temperature first guess SST analysis after Table 3 ice in both fields and will analysis but sea-water in the keep the value of the surface temperature first guess surface temperature first will take a value e. guess Figure 6. Root mean square error (m) averaged over 13 days for the forecast 500 hpa geopotential height. Solid lines represent the SSM/I ice concentration data experiment and dashed lines the control experiment without SSM/I data. Panel (a) is for the northern hemisphere and (b) for the southern hemisphere. Panels (c) and (d) show similar plots for one day (25 February 1996). 294

9 Use of SSM/I ice concentration data in the ECMWF SST analysis Figure 7. Differences in the fields between the SSM/I ice concentration experiment and the control experiment for 25 February 1996 in the southern hemisphere: (a) surface temperature analysis differences in C, (b) 500 hpa differences in metres for the four-day forecast period from 25 February, (c) verifying analysis for the five-day forecast period valid for 1 March, (d) 500 hpa differences in metres for the five-day forecast period from 25 February, (e) five-day forecast of 500 hpa geopotential for the SSM/I experiment, and (f) five-day forecast of 500 hpa geopotential for the control experiment valid for 1 March. 295

10 P Fernandez, G Kelly and R Saunders period, this first guess has been changing during the period between the two experiments. The differences in the 500 hpa geopotential for the four- and five-day forecasts are displayed in Figures 7(b) and 7(d) respectively. Differences between 4 and 8 m are observed in the four-day forecast and between 8 and 16 m in the five-day forecast period and the latter are significant and mainly positive. The SSM/I experiment has smoother features in the 500 hpa geopotential (Figure 7(e)) than the control (Figure 7(f)) and is closer to the analysis verification chart (Figure 7(d)). The trough located west of 20 E has been smoothed and the trough areas over 40 W and 160 E have been deepened, as the verification analysis also shows. The experiment using the ice concentration climatological data in the SST analysis instead of the SSM/I data is also compared with the control experiment, with no satellite ice data. The impact on the forecasts for the northern and southern hemispheres was very similar to the results shown in Figure 6. If we compare the results for the real time SSM/I data experiment with the sea ice climatology, the forecast impact observed in the northern hemisphere is the same (i.e. neutral) but for the southern hemisphere a slight improvement for the forecasts when SSM/I data was used was apparent. One should expect the SSM/I data to give the best analysis overall, especially during periods when the sea ice boundary is far from the climatology. 5. Conclusions A scheme to make use of the SSM/I sea ice product to improve the sea surface temperature field of the ECMWF model analysis has been developed as described above and has been operational since 23 April The use of the SSM/I ice concentration data provides a better definition of the location of the edge of the sea ice, and the NCEP SST data provide some simple quality controls for the incorrectly assigned SSM/I sea ice points. Some problems observed in the sea ice product revealed by daily monitoring of the SSM/I ice concentration data have made it necessary to introduce some quality control checks of the product. A new definition of the temperature values over small lakes and inland seas has been completed using a more accurate land sea mask and the Alexander and Mobley monthly SST climate. Nevertheless there is a need for a more accurate climatology of lake surface temperatures not included in the NCEP analysis. Either satellite or in situ data could be used for this purpose. Finally, a monthly mean ice concentration field has been created using 13 years of microwave satellite data to be used as a backup if SSM/I data are not available. Some assimilation experiments have been performed to test the impact of the use of satellite ice concentration information in an NWP model. They have included both real time SSM/I ice concentration data used in the 296 SST analysis and a satellite-derived ice concentration climatological dataset. For the 500 hpa geopotential height, the forecast impact of the improved SST analysis is neutral in the northern hemisphere and slightly positive for the five-day forecast in the southern hemisphere. The impact of these SST changes was not as large as expected; however, some individual forecasts clearly benefited from the improved SST analysis. On a more regional scale the new surface fields can be important for better prediction of local weather phenomena. The weather over Scandinavian countries, for instance, is sensitive to whether the Baltic Sea is open or frozen in the winter. A more accurate description of this can lead to improved forecasts over these countries. Acknowledgments This work was carried out at ECMWF in the Satellite Section and the authors would like to thank EUMET- SAT for their fellowship support for P. Fernandez. We thank A. Simmons for his helpful suggestions and comments on this manuscript, E. Gerard and M. Tomassini for their help in the production of this manuscript, P. Viterbo for his suggestions during this study and A. Nomura, A. Hernandez and J. Haseler for their help in the software development. The SSM/I products used in this report were provided by NOAA/NESDIS the SST fields by NCEP. The assistance of the ECMWF reanalysis group in providing the climatological data is also gratefully acknowledged. References Alexander, R. C. & Mobley, R. L. (1974). 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