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Scatterometer wind vector products for application in meteorology and oceanography
Journal of Sea Research 74 (2012) 16–25
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Journal of Sea Research journal homepage: www.elsevier.com/locate/seares
Scatterometer wind vector products for application in meteorology and oceanography Jur Vogelzang ⁎, Ad Stoffelen KNMI, Wilhelminalaan 10, 3732 GK de Bilt, The Netherlands
a r t i c l e
i n f o
Article history: Received 8 September 2011 Received in revised form 13 April 2012 Accepted 4 May 2012 Available online 11 May 2012 Keywords: Wind Scatterometer ASCAT Sea Ice
a b s t r a c t Scatterometers measure the radar backscatter from wind-generated cm-size gravity-capillary waves and provide high-resolution vector wind fields over the oceans. All-weather scatterometer observations have proven accurate and important for the forecasting of dynamical and severe weather. Oceanographic applications have been initiated since scatterometers provide unique forcing information on the ocean eddy scale. With the launch of the Advanced Scatterometer (ASCAT) on board MetOp-A in 2006, and the foreseen launch of its successors MetOp-B and MetOp-C in 2012 and 2017, respectively, scatterometer measurements are expected to continue until 2022 at least. In this paper the principles of scatterometer wind measurement are reviewed. The quality of scatterometer winds in terms of resolution and accuracy is assessed using statistical methods. Future product improvements are indicated. It is expected that in this decennium the number of operational scatterometers will increase substantially, leading to improved temporal sampling. This opens the way for new data products that will be useful for applications in meteorology and oceanography. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Space-borne scatterometers provide unique global ocean surface vector wind products at high resolution. Since they operate at microwave (radar) frequencies, these instruments are not hindered by cloud cover and are hence able to reveal atmospheric phenomena such as meteorological polar front disturbances and tropical cyclone winds. Fig. 1 shows a classical example of a train of Rossby waves detected by the scatterometer on board the European Remote Sensing satellite (ERS2) but missed by numerical weather prediction models, resulting in an erroneous forecast next day in England and the Netherlands. Scatterometer wind data are assimilated in meteorological models like that of the European Centre for Medium-Range Weather Forecasts (ECMWF). They are used for nowcasting applications like storm warnings (Brennan et al., 2009; Sienkiewicz et al., 2010) as well as for climate scale studies (Moore and Renfrew, 2005). Case studies have demonstrated that scatterometer winds are useful in the prediction of extra-tropical cyclones (Stoffelen and van Beukering, 1997) as well as tropical cyclones, e.g., Isaksen and Stoffelen (2000). Scatterometer winds have proved to be very relevant in driving ocean circulation
⁎ Corresponding author. Tel.: + 31 30 2206827; fax: + 31 30 2210407. E-mail address: [email protected] (J. Vogelzang). 1385-1101/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.seares.2012.05.002
models, e.g., Chelton et al. (2004), Tokmakian (2005), Blanke et al. (2005), Liu and Xie (2006), which in turn plays a major role in the climate system and marine biology. The aim of this paper is to give an overview of past, present, and future scatterometer missions, and to discuss the quality of scatterometer wind data in terms of resolution and accuracy. Some attention will be paid to sea ice detection, but land applications (soil moisture) are outside the scope of this paper. Here, we follow the approach of Stoffelen (2008). More detailed information on scatterometry can be found in Stoffelen (1998a) and Portabella (2002), as well as on the KNMI scatterometer pages at www.knmi. nl/scatterometer. Section 2 gives a short historical overview of space-borne wind scatterometry. Section 3 will focus on the details of two common types of scatterometer instruments, the first with side-looking fanbeams and the other with a rotating pencil-beam. Instrument design affects observation geometry and operating frequency which, in turn, determine to a large extent the wind retrieval capabilities. In Section 4 the geophysical interpretation of the scatterometer measurements is discussed. The inversion problem is nonlinear, posing a number of challenges to scatterometer wind data processing. Section 5 shows how these problems are tackled in practice. The quality of the wind products in terms of resolution and accuracy is presented in Section 6. Section 7 briefly discusses sea ice detection. The paper ends with a presentation of future scatterometer missions and processing improvements. These will enable the production of high-level wind products.
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Table 1 Past and present scatterometer missions. Instrument Satellite
Mission duration
Agency
Radar band
SASS SCAT NSCAT SeaWinds
June 1978–Oct1978 July 1991–July 2011 Aug 1996–June 1997 June 1999–Nov 2009 April 2003–Oct 2003 Oct 2006 onwards Sept 2009 onwards
NASA ESA NASDA NASA NASDA/NASA/CNES EUMETSAT ISRO
Ku C Ku Ku
ASCAT OSCAT
SeaSat ERS1/2 ADEOS-1 QuikSCAT ADEOS-2 MetOp-A OceanSat-2
C Ku
section. An overview of future missions is given in Section 8 at the end of this paper. 3. Scatterometer instruments
Fig. 1. ERS-2 scatterometer winds (red) on 28 August 2006 13:00 Z showing a train of atmospheric waves in the North Atlantic at 25W and 40N. Yellow arrows and dots are quality-flagged ERS-2 scatterometer cells. The blue and purple arrows depict simultaneous model winds that generally do not resolve such weather phenomena. The METEOSAT Infra-Red background image is consistent with the scatterometer surface winds.
2. History The first scatterometer in space was the SeaSat-A Scatterometer System (SASS) operated by the National Aeronautics and Space Administration (NASA) in 1978. Although the SeaSat mission lasted for only three months, the SASS data clearly demonstrated the potential of scatterometer winds (see Pierson (1983) for more details). A follow-on design named NSCAT was mounted on the Japanese Advanced Earth Observation Satellite (ADEOS-1) and launched in 1996 (JPL, 1997). After nine months of successful operation the mission ended abruptly in June 1997 after a complete power loss. The QuikSCAT program was planned in order to fill the gap caused by the loss of ADEOS-1, and in June 1999 the SeaWinds scatterometer was launched. A good introduction to the instrument and its mission is given by Hoffman and Leidner (2005), while more details can be found in Leidner et al. (2000) and JPL (2006). SeaWinds operated successfully until November 23, 2009, long after its expected lifetime. The Japanese-American-French ADEOS-2 satellite also carried a SeaWinds scatterometer. It was launched in December 2002, but failed October 2003. Meanwhile, the European Space Agency (ESA) launched the European remote Sensing satellites in 1991 and 1995, respectively. Both satellites carried a scatterometer (ESA, 1993), denoted SCAT here, and the ERS-2 mission was ended on July 5, 2011, after 16 years of operation. SCAT paved the way for the first operational meteorological scatterometer instrument, the Advanced Scatterometer (ASCAT) on board MetOp-A (Figa-Saldaña et al., 2002). It was launched on 19 October 2006, and will be followed by MetOp-B in 2012 and MetOp-C in 2017, thus aiming for 15 years of operational scatterometer services. Finally, The Indian Space Research Organization (ISRO) launched the OSCAT scatterometer on board the Oceansat-2 satellite on September 23, 2009. The quality of the retrieved wind vectors is almost the same as that for QuikSCAT (Stoffelen and Verhoef, 2011). However, this is not (yet) an operational mission, so ASCAT is currently the only operational scatterometer. Table 1 gives an overview of past and present scatterometer missions. Details of the instruments will be discussed in the next
So far, two main types of scatterometer instruments have been operated from space: one with side-looking fan-beams and the other with a rotating pencil-beam. For more details and further references the reader is referred to Stoffelen (1998a) and Portabella (2002). The characteristics of a scatterometer instrument are to a large extent determined by its measurement geometry, which in turn depends on incidence angle and azimuth angle. The incidence angle is the angle between the incoming radar radiation and the vertical of the scattering surface. The azimuth angle is the angle between the ground projection of the radar look direction and some reference direction (for instance the North or the satellite ground track direction). Side-looking fan-beam scatterometers use three static antennas that illuminate a swath on the ocean surface perpendicular to the satellite ground track. As the satellite platform propagates in space a strip of the ocean surface is illuminated. The incidence angle varies along this strip, but the azimuth angle is fixed. Since no moving parts are involved in the antenna design, space‐borne fan-beam scatterometers can be operated at relatively large radar wavelengths (C-band, λ ≈ 5 cm) that are not sensitive to rain. Examples of side-looking fan-beam scatterometers are SCAT on ERS-1 and ERS-2 (ESA) and ASCAT on MetOp (EUMETSAT). Their measurement geometry is shown in Fig. 2. These instruments emit and receive vertically polarized microwaves, because this generally provides the highest radar backscatter and wind sensitivity, giving a good signal-to-noise ratio. A drawback is that the vertically polarized backscatter saturates at wind speeds of about 40 m/s, thus restricting the range of wind speeds measurable. To double the swath, ASCAT has three antennas looking to the left hand side of the satellite and three antennas looking to the right hand side (Figa-Saldaña et al., 2002). Rotating pencil-beam scatterometers use an elevated parabolicdish antenna that rotates along a vertical axis, so it illuminates a circular strip under the satellite with constant incidence angle. The size of the strip, of course, depends on the incidence angle. As the satellite moves along its orbit the circular strip passes over the ocean and a point on the ocean within reach of the radar beam is illuminated from two different azimuth angles whose values depend on the position on the strip. Pairs of measurements with azimuth angle difference near 0° or near 180° have a measurement geometry that is less favorable for wind retrieval. By using both vertical and horizontal polarizations a total number of four independent measurements is obtained within the combined swath. The spatial resolution of the system is determined by the antenna size, which in turn is limited by cost considerations, notably for a rotating antenna. Therefore spaceborne operation of rotating pencil-beam scatterometers has been limited to small radar wavelengths (Ku-band, λ ≈ 2 cm) that are sensitive to rain droplets. On the other hand, Ku-band scatterometers are more sensitive for low wind speeds b 3 m/s (Spencer et al., 1997). Examples of rotating pencil-beam scatterometers are SeaWinds
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Fig. 2. Measurement geometry for side-looking fan-beam scatterometer. Left: SCAT; right: ASCAT.
on QuikScat (USA) and OSCAT on OceanSat-2 (India). The SeaWinds measurement geometry is shown in Fig. 3. 4. Geophysical interpretation Scatterometers measure the normalized radar cross section of the ocean surface, σ0, which depends primarily on the following factors: – radar frequency and polarization; – incidence angle and azimuth angle;
Fig. 3. Measurement geometry for rotating pencil-beam scatterometer.
– surface roughness that in turn depends mainly on local wind speed and direction; – the reflectivity of the surface; – presence of land, sea ice, and surface slicks. Radar frequency and polarization are fixed parameters of the scatterometer instrument, while incidence and azimuth angles are determined by the observation geometry. Among the other parameters, local air-sea momentum exchange has turned out to be the dominant process that determines the radar return from the ocean surface and which has been represented by equivalent neutral wind speed and direction, as these are readily available from other sources for calibration and validation purposes (Portabella and Stoffelen, 2009). In order to derive geophysical parameters from scatterometer measurements, one needs a geophysical model function that returns the radar cross section as a function of all parameters listed above. In principle, there are two approaches to find the geophysical model function. In the fundamental approach the whole process chain, from wave generation by wind to radar backscatter from the ocean surface, is modeled (Janssen et al., 1998). In the empirical approach, a form of the geophysical model function is empirically investigated (Stoffelen, 1998a) and adopted with free parameters that are fitted to observations (Hersbach et al., 2007; Wentz et al., 1984). The advantage of the fundamental approach is that it directly relates to the physical understanding. However, the empirical functions reproduce the observed radar cross sections faster and more accurately, due to the complexity of both the nature of the ocean surface as a function of wind speed and electromagnetic scattering from the ocean surface. Therefore practical applications rely on empirical geophysical model functions. The geophysical model function is easiest to understand for sidelooking fan-beam scatterometers like SCAT and ASCAT that measure at one frequency, one polarization, and at three incidence/azimuth angle combinations (of the fore, mid, and aft beams). A full scatterometer measurement then consists of a triplet of radar cross sections measured by the fore, mid, and aft antennas. The geophysical model function, in a three-dimensional measurement space spun up by these three cross section axes, takes the form of a folded conical manifold with two sheets. Fig. 4 shows the function named CMOD5.n, the one currently in operational use, for wind vector cell 42 in the 12.5-km swath for wind speeds from 0 to 30 m/s. The distance along the major
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Fig. 5. Scatterometer wind processor scheme. The input consists of the measured radar cross sections (σ0 values) and supporting information (time, location, etc.).
Fig. 4. The currently operational geophysical model function CMOD5.n at wind vector cell 42 in the 12.5-km ASCAT swath in measurement space spanned by the cross sections measures by the fore, mid, and aft antennas. The wind speed ranges from 0 to 30 m/s. The black dots are individual triplets of measurements.
axis of the cone is a measure for the wind speed, with zero wind at the origin. The cone surface is shown in blue, while the black dots are triplets of individual measurements. Generally, the measurements are in close proximity to the cone manifold. Individual measurements may lie a certain distance away from the cone, due to radar speckle noise or due to disturbing effects, such as wind variability, rain, sea ice, or land contamination. The distance of a measurement from the cone surface is used for quality control. If it exceeds a certain limit the measurement is flagged. Otherwise it is proportional to the probability that the wind speed and direction corresponding to the point on the cone are correct.
that generally yields more than one solution. These ambiguous solutions are commonly referred to as ambiguities. Next, the distance to the geophysical model function surface of each of the ambiguities is controlled for the presence of rain, land, or sea ice. If the ambiguities are accepted each distance is transformed into a probability for the particular ambiguity being the correct solution according to pi ∝ exp(− di2), with p the probability, d the distance of the measurement to a point on the geophysical model function, and i the index counting the ambiguities. If the wind data are to be assimilated into meteorological models, the ambiguous solutions and their probabilities can be provided; the data assimilation system will sort out which ambiguity fits best to the model state. If the winds are to be used as stand alone product, for nowcasting applications or as input into oceanographic models, the “best” wind solution must be chosen. This process is known as ambiguity removal. Several methods for ambiguity removal have been proposed, e.g. Stoffelen (1998a), Hoffman et al. (2003) and references therein. The KNMI processors feature two-dimensional variational ambiguity removal which consists of two steps (Vogelzang et al., 2009): 1. construction of an analysis wind field from the ambiguous solutions and a short-range model forecast (usually from ECMWF); 2. selection of the ambiguity closest to the analysis. The scatterometer processors are run at KNMI to provide nearreal-time winds. The following products are made operationally:
5. Scatterometer wind processing EUMETSAT set up a system of Satellite Application Facilities (SAF's) providing software, data products, and user support. All oceanic scatterometer facilities are coordinated by KNMI within the framework of the SAF for Numerical Weather Prediction (NWP SAF) and the Ocean and Sea Ice SAF (OSI SAF). Land applications (soil moisture) are under the responsibility of the Hydrology SAF (H SAF). KNMI also leads higher-level gridded ocean vector wind products within the European Union MyOcean project (www. MyOcean.eu). KNMI developed operational scatterometer wind processors for QuikScat (SeaWinds Data Processor, SDP) and for ASCAT and ERS (ASCAT Wind Data Processor, AWDP). SDP and AWDP are freely available upon registration at the NWP SAF web site. A processor for OSCAT (OWDP) is running experimentally. The general structure of the KNMI wind data processors is shown in Fig. 5. After reading in the data and initializing the wind processor's data structures, the wind speed and direction that give the best match through the geophysical model function to the measured radar cross section triplet are determined. This is a nonlinear inversion problem
1. ASCAT winds on 25 km grid size; 2. ASCAT winds on 12.5 km grid size; 3. ASCAT coastal wind product on 12.5 km grid size. Furthermore, an experimental OSCAT wind product on a 50 km grid is available. All wind products are displayed on the web, see Fig. 6 for an example. The operational products are disseminated to the international user community by internet and by dedicated meteorological systems like EUMETCast (www.eumetsat.int/Home/ Main/DataAccess/EUMETCast.). 5.1. ASCAT coastal product It should be noted here that the grid size of the scatterometer wind product does not equal the spatial resolution, i.e., the size of the smallest details discernable. To understand this it is necessary to go deeper into the details of the radar processing. Scatterometer wind processing starts with gridded radar cross sections. These are obtained by averaging all individual radar cross sections that fall in an area representative for the grid cell. Fig. 7 shows this process
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Fig. 6. ASCAT global wind speeds on 12.5 km grid for September 1, 2011. Only the ascending passes are shown. The colors indicate the wind speed range.
schematically for ASCAT with 25 km grid size. For ASCAT with 12.5 km grid size all dimensions have to be divided by 2. The solid ellipses in Fig. 7 represent the individual radar footprints of the fore beam, the dashed ellipses those for the aft beam. Those of the mid beam are left out for reasons of clarity. All individual cross sections that fall within the solid box of 100 km by 100 km are averaged, weighted with a Hamming filter to minimize noise as indicated in Fig. 7. The procedure is repeated for each grid cell. It is clear that the resolution of the wind product is of the order of two times the grid size. The ASCAT coastal product is obtained in a similar way, except that the individual cross sections are averaged over a circular area
Fig. 7. ASCAT level 1 radar cross section averaging.
with a radius of 15 km. Moreover, an unweighted average is taken. By using a high resolution land/sea mask it is possible to reject land contaminated measurements much more selectively than is possible with gridded radar cross sections. As a result, wind closer to the coast may be obtained. The radius of 15 km was chosen such that the ASCAT coastal product has the same error characteristics as the standard ASCAT product on a 12.5 km grid.
6. Quality of scatterometer wind products The quality of scatterometer wind products can be assessed by comparing the scatterometer winds with model predictions or with buoy measurements. Some older examples are the studies by Freilich and Dunbar (1999), Ebuchi (1999), and Atlas et al. (1999) for NSCAT, and those by Portabella and Stoffelen (2001), Draper and Long (2002), Ebuchi et al. (2002), and Bourassa et al. (2003) for SeaWinds. Results for SCAT can be found in Stoffelen (1998a) and references therein. In an operational environment such monitoring is performed continuously. The KNMI wind products are compared to ECMWF model forecasts and buoy measurements. Also the frequency with which various quality flags are set is monitored, and the results are displayed on the monitoring pages of the web site www.knmi.nl/ scatterometer. Also large meteorological institutes running a global weather model continuously monitor differences between the model analysis and the various measurements assimilated into it, e.g. ECMWF (www.ecmwf.int/products/forecasts/d/charts/ monitoring/satellite/wind/scatt) and Meteo-France (www.meteo. fr/special/minisites/monitoring/SATELLITE/SCATT/scatt). However, comparison of scatterometer winds with buoy data and meteorological model predictions only gives relative information on the accuracy, because all systems contain errors and because the systems have not been rigorously intercalibrated. Moreover, the systems measure at different spatial resolutions, so intercomparison requires careful consideration of the spatial representation error which gives the variance of the signal measured by one system but missed by the other. This will be discussed in more detail in the next two paragraphs.
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6.1. Resolution The spatial resolution can be estimated from variance spectra of the zonal and meridional wind components, u and v, respectively (Chelton et al., 2006; Freilich and Chelton, 2006; Patoux and Brown, 2001; Vogelzang et al., 2011). Fig. 8 shows spectra from all operational ASCAT-12.5 wind measurements from January 2009 (blue curves) and for the collocated ECMWF forecast winds (red curves). The scatterometer spectra are in agreement with a spatial resolution of about 25 km. Only at the smallest scale (highest wave number) the wind spectra for the scatterometer tend to become horizontal, indicating loss of resolution. The ECMWF forecasts were exported on a grid size of approximately 60 km and interpolated bilinearly in space and quadratically in time to the scatterometer grid. Fig. 8 also shows that at spatial scales below 1000 km (spatial frequency above 10− 6 m− 1) the ECMWF spectra fall off much faster than the scatterometer spectra. This is because the ECMWF model suppresses small scales that may not be beneficial for the prediction at medium time range. As a consequence, the resolution of the ECMWF model above the open ocean in regions with valid scatterometer winds is much larger than its grid size. Note that the spectra also differ slightly at the largest scales (smallest spatial resolutions). This is because the scatterometer winds and the ECMWF forecasts have not yet been fully intercalibrated, as will be discussed in the next section. 6.2. Accuracy and calibration As stated before, comparison of scatterometer winds with buoy data or with meteorological model predictions only gives relative information on the accuracy, because all systems contain errors and because the systems have not been rigorously intercalibrated. Both accuracy and intercalibration may be assessed using triple collocations of buoy measurements, scatterometer winds, and model predictions. The triple collocation method was introduced by Stoffelen (1998b). Assuming that linear calibration suffices, and that the errors are independent of wind speed and direction and (for the scatterometer)
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wind vector cell number, the error model can be solved, yielding absolute values for the measurement accuracies of each system. In this study it was also shown that these assumptions are valid in a global statistical sense. The triple collocation method has been shown to give consistent results. Vogelzang et al. (2011) applied the triple collocation method with representation errors obtained from the difference between ASCAT and ECMWF wind spectra of January 2011. The results for the error variances for two ASCAT wind products at the scatterometer spatial resolution are given in Table 2, using buoy measurements as absolute calibration reference. The results in Table 2 pertain to the six months period from December 2010 to May 2011. The accuracy in the error variances is estimated to be of the order of 0.05 m 2s − 2. The results in Table 2 show that the ASCAT wind products on a 12.5 km grid contain more noise than the product on 25 km (0.07 and 0.14 m 2s − 2 for u and v, respectively). However, the 12.5 km products compare better to the high resolution buoy measurements than the 25 km product and worse to the low resolution ECMWF forecasts. This indicates that the ASCAT 12.5 km products contain more small-scale information. The decrease in difference with the buoy measurements of the ASCAT 12.5 km products compared to that of the 25 km product is 0.17 m 2s − 2 for u and 0.18 m 2 s − 2 for v. This difference is due to an increase of information content of the 12.5 km products over that of the 25 km product, and this increase is larger than the increase in measurement noise given above. Note that the triple collocation method needs data sets that extend over a long period of time, because there are relatively few buoys that give reliable wind information. Therefore, a period of six months is about the minimum needed. Also, the wind statistics should ideally cover all seasons, as the atmospheric wind and stability conditions vary over time and influence the statistical results. Moreover, the buoys are mostly located in the tropical oceans and off the coasts of Europe and North America, providing uneven sampling over the globe.
7. Sea ice products Space‐borne scatterometers operate from a polar orbit, so they cover the northern and southern pole regions every revolution (which takes about 90 min, depending on the precise height of the satellite). Sea ice has a large radar cross section, so it is important to filter it out, in order not to contaminate the wind retrieval. In the past forecasts of the sea surface temperature from the ECMWF model were used, but this led to a too conservative ice screening. Moreover, the extent of sea ice cover is an important geophysical parameter itself, so effort has been made to improve the sea ice model and screening. At KNMI a sea ice screening based on Bayesian statistics has been developed (Belmonte Rivas and Stoffelen, 2011). In measurement space the sea ice points lie close to a line in the proximity of the wind cone, indicating dependency on a single geophysical parameter
Table 2 Triple collocation results for the error variances at scatterometer resolution for the period December 2010 up to and including May 2011. Product
Fig. 8. Wind spectra for the zonal wind component u (left hand panel) and the meridional component v (right hand panel) for ASCAT winds on a 12.5 km grid (blue curves) and collocated ECMWF forecasts (red curves).
Nr. of collocs
Buoy
ECMWF
Scatterometer
εu εv εu εv εu εv (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2)
ASCAT-25 12,512 1.54 ASCAT13,312 1.37 12.5 ASCAT 14,197 1.37 coastal
1.64 1.46
1.93 2.22
2.10 2.37
0.34 0.41
0.44 0.58
1.42
2.34
2.43
0.41
0.61
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called ice age. A sea ice and water discrimination procedure has been developed for QuikScat. The algorithm has been adapted for C-band and Fig. 9 shows the sea ice extent over the Arctic and Antarctic on September 11, 2011, as measured with ASCAT. It now runs experimentally at KNMI for ASCAT as a last step before operationalization. 8. Conclusions and outlook 8.1. Product improvements As already noted in Section 6.2, the triple collocation method provides (linear) calibration coefficients with respect to buoy measurements and absolute error variances. However, due to the long data sets needed, it has been assumed so far that the scatterometer errors are independent of both wind speed and wind vector cell. It is known that this is only true approximately. Longer data sets and combination with other analysis methods will be employed in order to improve ASCAT wind products by better calibration and to refine the description of the ASCAT measurement errors and calibration. In Section 5.1 it was shown that the coastal product is made by regridding the full resolution radar cross section data. This opens the possibility to perform regridding on a finer grid, thus enabling wind products at higher resolution. Fig. 10 shows an example of such a product for ASCAT data over a tropical cyclone recorded on January 26, 2010 northeast of the Philippines. The top panel image is obtained with standard coastal processing with an averaging radius of 15 km. The bottom panel image is obtained with coastal processing on a 6.25 km grid with an averaging radius of 7.5 km. The quality control flag (orange arrows) has been switched off when making the 6.25 km product, because this flag requires tuning to the output data. The variational quality control flag (purple arrows) was left intact, although this also requires specific tuning. Nevertheless, the 6.25 km product reveals all kinds of small-scale features in the cyclone, like convergence zones that look realistic but need validation. An ultra-high resolution ASCAT product (6.25 km grid size or smaller, if feasible) is foreseen to become operational in 2013.
As stated in Section 5, the default ambiguity removal procedure for KNMI wind processors makes an analysis of the ambiguous scatterometer winds using the ECMWF model forecasts as background. It is known from theory that such an analysis is optimal when the error characteristics of the scatterometer and background winds are well known. The scatterometer winds are spatially uncorrelated, so it is sufficient to know their error variance (for instance from triple collocation). The errors in the model winds are spatially correlated, so here the full covariance matrix is needed. The problem is simplified by assuming homogeneity and isotropy. The error covariances are modeled by structure functions. So far, ambiguity removal uses simple Gaussian structure functions, but recently Vogelzang and Stoffelen (2011) have shown how to obtain structure functions numerically from correlated differences between scatterometer wind and ECMWF background. Fig. 11 shows a result above a front recorded on January 2, 2009 around 5:06 UT off the west coast of Canada. The upper panel shows the wind field obtained with default Gaussian structure functions, the bottom panel with numerical structure functions. The front is associated with a sharp change in wind direction. Using default Gaussian structure functions, the analysis fails to describe the small‐scale features of the front because both the ECMWF background and the structure function are too smooth. As a result, a relatively large area is flagged by the ambiguity removal quality control (purple arrows) that signals large differences between observation and background. Moreover, there are some ambiguity removal problems, visible as erratic and opposing (↑↓ ↑) flow patterns. When using numerical structure functions only a few cells are flagged and the ambiguity removal problems are solved, resulting in a much narrower frontal zone. The numerical structure functions have a much larger range than the Gaussian functions employed operationally. As a result, the calculations become much more time demanding. Therefore, use in nearreal-time operations or in reprocessing needs further optimization. 8.2. Future scatterometer missions Fig. 12 gives an overview of (planned) scatterometer missions from 2008 to 2020. The C-band ASCAT series of instruments will be operational until 2022. A follow-on mission (EPS-SG) is under study
Fig. 9. Arctic (left) and Antarctic (right) sea ice extents for 14 September 2011 from ASCAT measurements. Land is black, sea is blue, and sea ice is grey. Light gray indicates young sea ice, dark grey old. The white dot in the centre of each image indicates the North Pole and South Pole, respectively.
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130oW
130oW 130oW
Fig. 10. Tropical cyclone recorded by ASCAT on 26 January 2010 around 0:36 UT northeast of the Philippines with 12.5 km grid size (top panel) and 6.25 km grid size (bottom panel). Orange arrows indicate rain, but rain flagging has been switched off for the 6.25 km product. Purple arrows indicate a large difference between scatterometer observation and ECMWF forecast. The images are centered at 19°N, 129°E and cover an area of 2° × 2°.
130oW Fig. 11. ASCAT-12.5 wind field on January 2, 2009 around 5:06 UT off the west coast of Canada. Purple arrows indicate a large difference between observation and analysis. The area shown measures 5° × 5° centered around 43N, 129°W. The image in the top panel is obtained with default Gaussian structure functions, that is in the bottom panel with numerical structure functions.
now. Scatterometers operating at Ku‐band are designed or proposed by China (HY-2A and HY-2B), India (ScatSat), and Russia (MeteorM3). Also new scatterometer types are developed. The Chinese/French CFOSat features a rotating fan‐beam scatterometer at Ku‐band. Such an instrument will collect radar cross sections at a range of azimuth angles, and it will be a challenge to get as much information as possible out of these data. Another new class of instruments is formed by the dual-frequency scatterometers (GCOM W2, GCOM W3, FY-3E, and Indian operational scatterometer series). Such instruments may allow rain measurements from the comparison of the radar return at both frequencies.
wind information is interpolated to a geographical grid, and at level 4 information from various measurement systems is merged into a single product on a geographical grid at fixed times. Such products are expected to become important as input into high resolution models. However, such products can only be made if detailed observations are made at high time frequency. ASCAT has a coverage of about 80% of the Earth per day (EUMETSAT, 2011). To increase temporal coverage, more scatterometers are needed. If all planned scatterometer missions indicated in Fig. 12 are realized, wind information products at high spatial and temporal scale are within reach and we may enter a “golden age” of scatterometry.
8.3. Level 3 and level 4 products
Acknowledgments
So far, KNMI produces level 2 winds, i.e., winds on a grid (in space and time) determined by the satellite swath geometry. At level 3 the
The authors thank their colleague Jeroen Verspeek for providing Fig. 4. The NWP SAF scatterometer wind processors for SeaWinds
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Fig. 12. Overview of scatterometer missions © CEOS VC 24/2/'11.
(SDP) and ASCAT (AWDP) are freeware. They can be obtained upon registration at www.nwpsaf.org. Access to near-real time scatterometer wind products can be obtained upon registration at www.osi-saf.org. The NWP SAF and OSI SAF are sponsored by EUMETSAT.
References Atlas, R., Bloom, S.C., Hoffmann, R.N., Brin, E., Ardizzone, J., Terry, J., Bungato, D., Jusem, J.C., 1999. Geophysical validation of NSCAT winds using atmospheric data and analyses. Journal of Geophysical Research 104C, 11,405–11,424. Belmonte Rivas, M., Stoffelen, A., 2011. New Bayesian algorithm for sea ice detection with QuikSCAT. IEEE Transactions on Geoscience and Remote Sensing 49, 1894–1901, http://dx.doi.org/10.1109/TGRS.2010.2101608. Blanke, B., Speich, S., Bentamy, A., Roy, C., Sow, B., 2005. Modelling the structure and variability of the southern Benguela upwelling using QuikSCAT wind forcing. Journal of Geophysical Research 110, C07018, http://dx.doi.org/10.1029/2004JC002529. Bourassa, M.A., Legler, D.M., O'Brien, J.J., Smith, S.R., 2003. SeaWinds validation with research vessels. Journal of Geophysical Research 108C, 3019, http://dx.doi.org/ 10.1029/2001JC00108. Brennan, M.J., Hennon, C.C., Knabb, R.D., 2009. The operational use of QuikSCAT ocean vector winds at the National Hurricane Center. Weather and Forecasting 24, 621–645. Chelton, D.B., Schlax, M.G., Freilich, M.H., Milliff, R.E., 2004. Satellite measurements reveal persistent small-scale features in ocean winds. Science 303, 978–983. Chelton, D.B., Freilich, M.H., Sienkiewitcz, J.M., Von Ahn, J.M., 2006. On the use of QuikSCAT scatterometer measurements of surface winds for marine weather prediction. Monthly Weather Review 134, 2055–2071. Draper, D.W., Long, D.G., 2002. As assessment of SeaWinds on QuikSCAT wind retrieval. Journal of Geophysical Research 107C, 3212, http://dx.doi.org/10.1029/ 2002JC001330. Ebuchi, N., 1999. Statistical distribution of wind speeds and directions globally observed by NSCAT. Journal of Geophysical Research 104C, 11,393–11,403. Ebuchi, N., Graber, H.C., Caruso, M.J., 2002. Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. Journal of Atmospheric and Oceanic Technology 19, 2049–2062. ESA, 1993. ERS user handbook, revision 1. European Space Agency SP-1148. EUMETSAT, 2011. ASCAT product guide. Report EUM/OPS-EPS/MAN/04/0028, issue v4C. ois www.eumetsat.org/WEBOPS/eps-pg/ASCAT/ASCAT-PG-0TOC. Figa-Saldaña, J., Wilson, J.J., Attema, E., Gelsthorpe, R., Drinkwater, M.R., Stoffelen, A., 2002. The advanced scatterometer (ASCAT) on METOP: a follow-on for European scatterometers. Canadian Journal of Remote Sensing 28, 404–412. Freilich, M.H., Chelton, D.B., 2006. Wavenumber spectra for Pacific winds measured by the SeaSat scatterometer. Journal of Physical Oceanography 16, 741–757. Freilich, M.H., Dunbar, R.S., 1999. The accuracy of NSCAT 1 vector winds: comparisons with National Data Buoy Center buoys. Journal of Geophysical Research 104C, 11,231–11,246. Hersbach, H., Stoffelen, A., de Haan, S., 2007. An improved C-band scatterometer geophysical model function: CMOD-5. Journal of Geophysical Research 112C, http://dx.doi.org/10.1029/2006JC003,743.
Hoffman, R.N., Leidner, S.M., 2005. An introduction to the near-real-time QuikSCAT data. Weather and Forecasting 20, 476–493. Hoffman, R.N., Leidner, S.M., Henderson, J.M., Atlas, R., Ardizzone, J.V., Bloom, S.C., 2003. A two-dimensional variational analysis method for NSCAT ambiguity removal: methodology, sensitivity, and tuning. Journal of Atmospheric and Oceanic Technology 20, 585–605. Isaksen, L., Stoffelen, A., 2000. ERS scatterometer wind data impact on ECMWF's tropical cyclone forecasts. IEEE Transactions on Geoscience and Remote Sensing 38, 1885–1892. Janssen, P.A.E.M., Wallbrink, H., Calkoen, C.J., van Halsema, D., Oost, W.A., Snoeij, P., 1998. A summary of the VIERS-1 scatterometer model. Physics and Chemistry of the Earth 23, 575–579, http://dx.doi.org/10.1016/S0079_1946(98)00072_x. JPL, 1997. NASA scatterometer science data product user's manual, version 1.1. Jet Propulsion Laboratory D-12985. JPL, 2006. QuikSCAT science data product user's manual, version 3.0. Jet Propulsion Laboratory D-18053. Leidner, M., Hoffman, R., Augenbaum, J., 2000. SeaWinds scatterometer real-time BUFR geophysical data product, version 2.2.0. NOAA/NESDIS. Liu, W.T., Xie, X., 2006. Measuring ocean surface wind from space, In: Gower, J. (Ed.), Remote Sensing of the Marine Environment, Manual of remote Sensing, third edition. American Society for Photogrammetry and Remote Sensing, vol. 6, pp. 149–178. Moore, G.W., Renfrew, I.A., 2005. Tip jets and barrier winds: a QuikSCAT climatology of high wind speed events around Greenland. Journal of Climate 18, 3713–3725. Patoux, J., Brown, R.A., 2001. Spectral analysis of QuikSCAT surface winds and twodimensional turbulence. Journal of Geophysical Research 106D, 23,995–24,005. Pierson, W.J., 1983. Highlights of the Seasat-SASS program: a review. In: Allan, T.D. (Ed.), Satellite Microwave Remote Sensing. Ellis Horwood Ltd., pp. 69–86. Portabella, M., 2002, Wind field retrieval from satellite radar systems. Thesis. University of Barcelona, Spain. Portabella, M., Stoffelen, A., 2001. Rain detection and quality control of SeaWinds. Journal of Atmospheric and Oceanic Technology 18, 1171–1183. Portabella, M., Stoffelen, A.C.M., 2009. On Scatterometer Ocean Stress. Journal of Atmospheric and Oceanic Technology 26 (2), 368–382, http://dx.doi.org/10.1175/ 2008JTECHO578.1. Sienkiewicz, J., Brennan, M.J., Knabb, R., Chang, P.S., Cobb, H., Jelenak, Z.J., Ahmad, K.A., Soisuvarn, S., Kosier, D., Bancroft, G., 2010. Impact of the loss of QuikSCAT on NOAA MWS marine warning and forecast operations. First International Ocean Vector Winds Science Team meeting, Barcelona, Spain, May 18–20, 2010. Spencer, M.W., Wu, C., Long, D.G., 1997. Tradeoffs in the design of a spaceborne scanning pencil beam scatterometer: application to SeaWinds. IEEE Transactions on Geoscience and Remote Sensing 35, 115–126. Stoffelen, A., 1998a. Scatterometry. Thesis, University of Utrecht, Te Netherlands. Stoffelen, A., 1998b. Toward the true near-surface wind speed: error modeling and calibration using triple collocation. Journal of Geophysical Research 103 (C4), 7755–7766. Stoffelen, A., 2008. Scatterometer applications in the European seas. In: Barale, V., Gade, M. (Eds.), Remote sensing of the European seas. Springer, Germany. Stoffelen, A., van Beukering, P., 1997. The impact of improved scatterometer winds on HIRLAM analyses and forecasts. BCRS report 1.1 OP-04, BCRS, Delft, The Netherlands/ HIRLAM technical report 31. IMET, Dublin, Ireland.
J. Vogelzang, A. Stoffelen / Journal of Sea Research 74 (2012) 16–25 Stoffelen, A., Verhoef, A., 2011. OCEANSAT-2 scatterometer winds and sea ice. Proc. EUMETSAT Meteorological Satellite Conference, 5–9 Sept. 2011, Oslo, Norway ( www.eumetsat.int/ Home/Main/AboutEUMETSAT/Publications/ConferenceandWorkshopProceedings/ 2011/SP_2011114105950658.). Tokmakian, R., 2005. An ocean model's response to scatterometer winds. Ocean Modelling 9, 89–103. Vogelzang, J., Stoffelen, A., 2011. NWP model error structure functions obtained from scatterometer winds. IEEE Transactions on Geoscience and Remote Sensing, http://dx.doi.org/10.1109/TGRS.2011.2168407.
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Vogelzang, J., Stoffelen, A., Verhoef, A., de Vries, J., Bonekamp, H., 2009. Validation of two-dimensional variational ambiguity removal on SeaWinds scatterometer data. Journal of Atmospheric and Oceanic Technology 26, 1229–1245. Vogelzang, J., Stoffelen, A., Verhoef, A., Figa-Saldaña, J., 2011. On the quality of highresolution scatterometer winds. Journal of Geophysical Research C10033, http:// dx.doi.org/10.1029/2010JC006640. Wentz, F., Peterherych, S., Thomas, L., 1984. A model for ocean radar cross section at 14.6 GHz. Journal of Geophysical Research 89 C, 3689–3704.
Contents lists available at SciVerse ScienceDirect
Journal of Sea Research journal homepage: www.elsevier.com/locate/seares
Scatterometer wind vector products for application in meteorology and oceanography Jur Vogelzang ⁎, Ad Stoffelen KNMI, Wilhelminalaan 10, 3732 GK de Bilt, The Netherlands
a r t i c l e
i n f o
Article history: Received 8 September 2011 Received in revised form 13 April 2012 Accepted 4 May 2012 Available online 11 May 2012 Keywords: Wind Scatterometer ASCAT Sea Ice
a b s t r a c t Scatterometers measure the radar backscatter from wind-generated cm-size gravity-capillary waves and provide high-resolution vector wind fields over the oceans. All-weather scatterometer observations have proven accurate and important for the forecasting of dynamical and severe weather. Oceanographic applications have been initiated since scatterometers provide unique forcing information on the ocean eddy scale. With the launch of the Advanced Scatterometer (ASCAT) on board MetOp-A in 2006, and the foreseen launch of its successors MetOp-B and MetOp-C in 2012 and 2017, respectively, scatterometer measurements are expected to continue until 2022 at least. In this paper the principles of scatterometer wind measurement are reviewed. The quality of scatterometer winds in terms of resolution and accuracy is assessed using statistical methods. Future product improvements are indicated. It is expected that in this decennium the number of operational scatterometers will increase substantially, leading to improved temporal sampling. This opens the way for new data products that will be useful for applications in meteorology and oceanography. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Space-borne scatterometers provide unique global ocean surface vector wind products at high resolution. Since they operate at microwave (radar) frequencies, these instruments are not hindered by cloud cover and are hence able to reveal atmospheric phenomena such as meteorological polar front disturbances and tropical cyclone winds. Fig. 1 shows a classical example of a train of Rossby waves detected by the scatterometer on board the European Remote Sensing satellite (ERS2) but missed by numerical weather prediction models, resulting in an erroneous forecast next day in England and the Netherlands. Scatterometer wind data are assimilated in meteorological models like that of the European Centre for Medium-Range Weather Forecasts (ECMWF). They are used for nowcasting applications like storm warnings (Brennan et al., 2009; Sienkiewicz et al., 2010) as well as for climate scale studies (Moore and Renfrew, 2005). Case studies have demonstrated that scatterometer winds are useful in the prediction of extra-tropical cyclones (Stoffelen and van Beukering, 1997) as well as tropical cyclones, e.g., Isaksen and Stoffelen (2000). Scatterometer winds have proved to be very relevant in driving ocean circulation
⁎ Corresponding author. Tel.: + 31 30 2206827; fax: + 31 30 2210407. E-mail address: [email protected] (J. Vogelzang). 1385-1101/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.seares.2012.05.002
models, e.g., Chelton et al. (2004), Tokmakian (2005), Blanke et al. (2005), Liu and Xie (2006), which in turn plays a major role in the climate system and marine biology. The aim of this paper is to give an overview of past, present, and future scatterometer missions, and to discuss the quality of scatterometer wind data in terms of resolution and accuracy. Some attention will be paid to sea ice detection, but land applications (soil moisture) are outside the scope of this paper. Here, we follow the approach of Stoffelen (2008). More detailed information on scatterometry can be found in Stoffelen (1998a) and Portabella (2002), as well as on the KNMI scatterometer pages at www.knmi. nl/scatterometer. Section 2 gives a short historical overview of space-borne wind scatterometry. Section 3 will focus on the details of two common types of scatterometer instruments, the first with side-looking fanbeams and the other with a rotating pencil-beam. Instrument design affects observation geometry and operating frequency which, in turn, determine to a large extent the wind retrieval capabilities. In Section 4 the geophysical interpretation of the scatterometer measurements is discussed. The inversion problem is nonlinear, posing a number of challenges to scatterometer wind data processing. Section 5 shows how these problems are tackled in practice. The quality of the wind products in terms of resolution and accuracy is presented in Section 6. Section 7 briefly discusses sea ice detection. The paper ends with a presentation of future scatterometer missions and processing improvements. These will enable the production of high-level wind products.
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Table 1 Past and present scatterometer missions. Instrument Satellite
Mission duration
Agency
Radar band
SASS SCAT NSCAT SeaWinds
June 1978–Oct1978 July 1991–July 2011 Aug 1996–June 1997 June 1999–Nov 2009 April 2003–Oct 2003 Oct 2006 onwards Sept 2009 onwards
NASA ESA NASDA NASA NASDA/NASA/CNES EUMETSAT ISRO
Ku C Ku Ku
ASCAT OSCAT
SeaSat ERS1/2 ADEOS-1 QuikSCAT ADEOS-2 MetOp-A OceanSat-2
C Ku
section. An overview of future missions is given in Section 8 at the end of this paper. 3. Scatterometer instruments
Fig. 1. ERS-2 scatterometer winds (red) on 28 August 2006 13:00 Z showing a train of atmospheric waves in the North Atlantic at 25W and 40N. Yellow arrows and dots are quality-flagged ERS-2 scatterometer cells. The blue and purple arrows depict simultaneous model winds that generally do not resolve such weather phenomena. The METEOSAT Infra-Red background image is consistent with the scatterometer surface winds.
2. History The first scatterometer in space was the SeaSat-A Scatterometer System (SASS) operated by the National Aeronautics and Space Administration (NASA) in 1978. Although the SeaSat mission lasted for only three months, the SASS data clearly demonstrated the potential of scatterometer winds (see Pierson (1983) for more details). A follow-on design named NSCAT was mounted on the Japanese Advanced Earth Observation Satellite (ADEOS-1) and launched in 1996 (JPL, 1997). After nine months of successful operation the mission ended abruptly in June 1997 after a complete power loss. The QuikSCAT program was planned in order to fill the gap caused by the loss of ADEOS-1, and in June 1999 the SeaWinds scatterometer was launched. A good introduction to the instrument and its mission is given by Hoffman and Leidner (2005), while more details can be found in Leidner et al. (2000) and JPL (2006). SeaWinds operated successfully until November 23, 2009, long after its expected lifetime. The Japanese-American-French ADEOS-2 satellite also carried a SeaWinds scatterometer. It was launched in December 2002, but failed October 2003. Meanwhile, the European Space Agency (ESA) launched the European remote Sensing satellites in 1991 and 1995, respectively. Both satellites carried a scatterometer (ESA, 1993), denoted SCAT here, and the ERS-2 mission was ended on July 5, 2011, after 16 years of operation. SCAT paved the way for the first operational meteorological scatterometer instrument, the Advanced Scatterometer (ASCAT) on board MetOp-A (Figa-Saldaña et al., 2002). It was launched on 19 October 2006, and will be followed by MetOp-B in 2012 and MetOp-C in 2017, thus aiming for 15 years of operational scatterometer services. Finally, The Indian Space Research Organization (ISRO) launched the OSCAT scatterometer on board the Oceansat-2 satellite on September 23, 2009. The quality of the retrieved wind vectors is almost the same as that for QuikSCAT (Stoffelen and Verhoef, 2011). However, this is not (yet) an operational mission, so ASCAT is currently the only operational scatterometer. Table 1 gives an overview of past and present scatterometer missions. Details of the instruments will be discussed in the next
So far, two main types of scatterometer instruments have been operated from space: one with side-looking fan-beams and the other with a rotating pencil-beam. For more details and further references the reader is referred to Stoffelen (1998a) and Portabella (2002). The characteristics of a scatterometer instrument are to a large extent determined by its measurement geometry, which in turn depends on incidence angle and azimuth angle. The incidence angle is the angle between the incoming radar radiation and the vertical of the scattering surface. The azimuth angle is the angle between the ground projection of the radar look direction and some reference direction (for instance the North or the satellite ground track direction). Side-looking fan-beam scatterometers use three static antennas that illuminate a swath on the ocean surface perpendicular to the satellite ground track. As the satellite platform propagates in space a strip of the ocean surface is illuminated. The incidence angle varies along this strip, but the azimuth angle is fixed. Since no moving parts are involved in the antenna design, space‐borne fan-beam scatterometers can be operated at relatively large radar wavelengths (C-band, λ ≈ 5 cm) that are not sensitive to rain. Examples of side-looking fan-beam scatterometers are SCAT on ERS-1 and ERS-2 (ESA) and ASCAT on MetOp (EUMETSAT). Their measurement geometry is shown in Fig. 2. These instruments emit and receive vertically polarized microwaves, because this generally provides the highest radar backscatter and wind sensitivity, giving a good signal-to-noise ratio. A drawback is that the vertically polarized backscatter saturates at wind speeds of about 40 m/s, thus restricting the range of wind speeds measurable. To double the swath, ASCAT has three antennas looking to the left hand side of the satellite and three antennas looking to the right hand side (Figa-Saldaña et al., 2002). Rotating pencil-beam scatterometers use an elevated parabolicdish antenna that rotates along a vertical axis, so it illuminates a circular strip under the satellite with constant incidence angle. The size of the strip, of course, depends on the incidence angle. As the satellite moves along its orbit the circular strip passes over the ocean and a point on the ocean within reach of the radar beam is illuminated from two different azimuth angles whose values depend on the position on the strip. Pairs of measurements with azimuth angle difference near 0° or near 180° have a measurement geometry that is less favorable for wind retrieval. By using both vertical and horizontal polarizations a total number of four independent measurements is obtained within the combined swath. The spatial resolution of the system is determined by the antenna size, which in turn is limited by cost considerations, notably for a rotating antenna. Therefore spaceborne operation of rotating pencil-beam scatterometers has been limited to small radar wavelengths (Ku-band, λ ≈ 2 cm) that are sensitive to rain droplets. On the other hand, Ku-band scatterometers are more sensitive for low wind speeds b 3 m/s (Spencer et al., 1997). Examples of rotating pencil-beam scatterometers are SeaWinds
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Fig. 2. Measurement geometry for side-looking fan-beam scatterometer. Left: SCAT; right: ASCAT.
on QuikScat (USA) and OSCAT on OceanSat-2 (India). The SeaWinds measurement geometry is shown in Fig. 3. 4. Geophysical interpretation Scatterometers measure the normalized radar cross section of the ocean surface, σ0, which depends primarily on the following factors: – radar frequency and polarization; – incidence angle and azimuth angle;
Fig. 3. Measurement geometry for rotating pencil-beam scatterometer.
– surface roughness that in turn depends mainly on local wind speed and direction; – the reflectivity of the surface; – presence of land, sea ice, and surface slicks. Radar frequency and polarization are fixed parameters of the scatterometer instrument, while incidence and azimuth angles are determined by the observation geometry. Among the other parameters, local air-sea momentum exchange has turned out to be the dominant process that determines the radar return from the ocean surface and which has been represented by equivalent neutral wind speed and direction, as these are readily available from other sources for calibration and validation purposes (Portabella and Stoffelen, 2009). In order to derive geophysical parameters from scatterometer measurements, one needs a geophysical model function that returns the radar cross section as a function of all parameters listed above. In principle, there are two approaches to find the geophysical model function. In the fundamental approach the whole process chain, from wave generation by wind to radar backscatter from the ocean surface, is modeled (Janssen et al., 1998). In the empirical approach, a form of the geophysical model function is empirically investigated (Stoffelen, 1998a) and adopted with free parameters that are fitted to observations (Hersbach et al., 2007; Wentz et al., 1984). The advantage of the fundamental approach is that it directly relates to the physical understanding. However, the empirical functions reproduce the observed radar cross sections faster and more accurately, due to the complexity of both the nature of the ocean surface as a function of wind speed and electromagnetic scattering from the ocean surface. Therefore practical applications rely on empirical geophysical model functions. The geophysical model function is easiest to understand for sidelooking fan-beam scatterometers like SCAT and ASCAT that measure at one frequency, one polarization, and at three incidence/azimuth angle combinations (of the fore, mid, and aft beams). A full scatterometer measurement then consists of a triplet of radar cross sections measured by the fore, mid, and aft antennas. The geophysical model function, in a three-dimensional measurement space spun up by these three cross section axes, takes the form of a folded conical manifold with two sheets. Fig. 4 shows the function named CMOD5.n, the one currently in operational use, for wind vector cell 42 in the 12.5-km swath for wind speeds from 0 to 30 m/s. The distance along the major
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Fig. 5. Scatterometer wind processor scheme. The input consists of the measured radar cross sections (σ0 values) and supporting information (time, location, etc.).
Fig. 4. The currently operational geophysical model function CMOD5.n at wind vector cell 42 in the 12.5-km ASCAT swath in measurement space spanned by the cross sections measures by the fore, mid, and aft antennas. The wind speed ranges from 0 to 30 m/s. The black dots are individual triplets of measurements.
axis of the cone is a measure for the wind speed, with zero wind at the origin. The cone surface is shown in blue, while the black dots are triplets of individual measurements. Generally, the measurements are in close proximity to the cone manifold. Individual measurements may lie a certain distance away from the cone, due to radar speckle noise or due to disturbing effects, such as wind variability, rain, sea ice, or land contamination. The distance of a measurement from the cone surface is used for quality control. If it exceeds a certain limit the measurement is flagged. Otherwise it is proportional to the probability that the wind speed and direction corresponding to the point on the cone are correct.
that generally yields more than one solution. These ambiguous solutions are commonly referred to as ambiguities. Next, the distance to the geophysical model function surface of each of the ambiguities is controlled for the presence of rain, land, or sea ice. If the ambiguities are accepted each distance is transformed into a probability for the particular ambiguity being the correct solution according to pi ∝ exp(− di2), with p the probability, d the distance of the measurement to a point on the geophysical model function, and i the index counting the ambiguities. If the wind data are to be assimilated into meteorological models, the ambiguous solutions and their probabilities can be provided; the data assimilation system will sort out which ambiguity fits best to the model state. If the winds are to be used as stand alone product, for nowcasting applications or as input into oceanographic models, the “best” wind solution must be chosen. This process is known as ambiguity removal. Several methods for ambiguity removal have been proposed, e.g. Stoffelen (1998a), Hoffman et al. (2003) and references therein. The KNMI processors feature two-dimensional variational ambiguity removal which consists of two steps (Vogelzang et al., 2009): 1. construction of an analysis wind field from the ambiguous solutions and a short-range model forecast (usually from ECMWF); 2. selection of the ambiguity closest to the analysis. The scatterometer processors are run at KNMI to provide nearreal-time winds. The following products are made operationally:
5. Scatterometer wind processing EUMETSAT set up a system of Satellite Application Facilities (SAF's) providing software, data products, and user support. All oceanic scatterometer facilities are coordinated by KNMI within the framework of the SAF for Numerical Weather Prediction (NWP SAF) and the Ocean and Sea Ice SAF (OSI SAF). Land applications (soil moisture) are under the responsibility of the Hydrology SAF (H SAF). KNMI also leads higher-level gridded ocean vector wind products within the European Union MyOcean project (www. MyOcean.eu). KNMI developed operational scatterometer wind processors for QuikScat (SeaWinds Data Processor, SDP) and for ASCAT and ERS (ASCAT Wind Data Processor, AWDP). SDP and AWDP are freely available upon registration at the NWP SAF web site. A processor for OSCAT (OWDP) is running experimentally. The general structure of the KNMI wind data processors is shown in Fig. 5. After reading in the data and initializing the wind processor's data structures, the wind speed and direction that give the best match through the geophysical model function to the measured radar cross section triplet are determined. This is a nonlinear inversion problem
1. ASCAT winds on 25 km grid size; 2. ASCAT winds on 12.5 km grid size; 3. ASCAT coastal wind product on 12.5 km grid size. Furthermore, an experimental OSCAT wind product on a 50 km grid is available. All wind products are displayed on the web, see Fig. 6 for an example. The operational products are disseminated to the international user community by internet and by dedicated meteorological systems like EUMETCast (www.eumetsat.int/Home/ Main/DataAccess/EUMETCast.). 5.1. ASCAT coastal product It should be noted here that the grid size of the scatterometer wind product does not equal the spatial resolution, i.e., the size of the smallest details discernable. To understand this it is necessary to go deeper into the details of the radar processing. Scatterometer wind processing starts with gridded radar cross sections. These are obtained by averaging all individual radar cross sections that fall in an area representative for the grid cell. Fig. 7 shows this process
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Fig. 6. ASCAT global wind speeds on 12.5 km grid for September 1, 2011. Only the ascending passes are shown. The colors indicate the wind speed range.
schematically for ASCAT with 25 km grid size. For ASCAT with 12.5 km grid size all dimensions have to be divided by 2. The solid ellipses in Fig. 7 represent the individual radar footprints of the fore beam, the dashed ellipses those for the aft beam. Those of the mid beam are left out for reasons of clarity. All individual cross sections that fall within the solid box of 100 km by 100 km are averaged, weighted with a Hamming filter to minimize noise as indicated in Fig. 7. The procedure is repeated for each grid cell. It is clear that the resolution of the wind product is of the order of two times the grid size. The ASCAT coastal product is obtained in a similar way, except that the individual cross sections are averaged over a circular area
Fig. 7. ASCAT level 1 radar cross section averaging.
with a radius of 15 km. Moreover, an unweighted average is taken. By using a high resolution land/sea mask it is possible to reject land contaminated measurements much more selectively than is possible with gridded radar cross sections. As a result, wind closer to the coast may be obtained. The radius of 15 km was chosen such that the ASCAT coastal product has the same error characteristics as the standard ASCAT product on a 12.5 km grid.
6. Quality of scatterometer wind products The quality of scatterometer wind products can be assessed by comparing the scatterometer winds with model predictions or with buoy measurements. Some older examples are the studies by Freilich and Dunbar (1999), Ebuchi (1999), and Atlas et al. (1999) for NSCAT, and those by Portabella and Stoffelen (2001), Draper and Long (2002), Ebuchi et al. (2002), and Bourassa et al. (2003) for SeaWinds. Results for SCAT can be found in Stoffelen (1998a) and references therein. In an operational environment such monitoring is performed continuously. The KNMI wind products are compared to ECMWF model forecasts and buoy measurements. Also the frequency with which various quality flags are set is monitored, and the results are displayed on the monitoring pages of the web site www.knmi.nl/ scatterometer. Also large meteorological institutes running a global weather model continuously monitor differences between the model analysis and the various measurements assimilated into it, e.g. ECMWF (www.ecmwf.int/products/forecasts/d/charts/ monitoring/satellite/wind/scatt) and Meteo-France (www.meteo. fr/special/minisites/monitoring/SATELLITE/SCATT/scatt). However, comparison of scatterometer winds with buoy data and meteorological model predictions only gives relative information on the accuracy, because all systems contain errors and because the systems have not been rigorously intercalibrated. Moreover, the systems measure at different spatial resolutions, so intercomparison requires careful consideration of the spatial representation error which gives the variance of the signal measured by one system but missed by the other. This will be discussed in more detail in the next two paragraphs.
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6.1. Resolution The spatial resolution can be estimated from variance spectra of the zonal and meridional wind components, u and v, respectively (Chelton et al., 2006; Freilich and Chelton, 2006; Patoux and Brown, 2001; Vogelzang et al., 2011). Fig. 8 shows spectra from all operational ASCAT-12.5 wind measurements from January 2009 (blue curves) and for the collocated ECMWF forecast winds (red curves). The scatterometer spectra are in agreement with a spatial resolution of about 25 km. Only at the smallest scale (highest wave number) the wind spectra for the scatterometer tend to become horizontal, indicating loss of resolution. The ECMWF forecasts were exported on a grid size of approximately 60 km and interpolated bilinearly in space and quadratically in time to the scatterometer grid. Fig. 8 also shows that at spatial scales below 1000 km (spatial frequency above 10− 6 m− 1) the ECMWF spectra fall off much faster than the scatterometer spectra. This is because the ECMWF model suppresses small scales that may not be beneficial for the prediction at medium time range. As a consequence, the resolution of the ECMWF model above the open ocean in regions with valid scatterometer winds is much larger than its grid size. Note that the spectra also differ slightly at the largest scales (smallest spatial resolutions). This is because the scatterometer winds and the ECMWF forecasts have not yet been fully intercalibrated, as will be discussed in the next section. 6.2. Accuracy and calibration As stated before, comparison of scatterometer winds with buoy data or with meteorological model predictions only gives relative information on the accuracy, because all systems contain errors and because the systems have not been rigorously intercalibrated. Both accuracy and intercalibration may be assessed using triple collocations of buoy measurements, scatterometer winds, and model predictions. The triple collocation method was introduced by Stoffelen (1998b). Assuming that linear calibration suffices, and that the errors are independent of wind speed and direction and (for the scatterometer)
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wind vector cell number, the error model can be solved, yielding absolute values for the measurement accuracies of each system. In this study it was also shown that these assumptions are valid in a global statistical sense. The triple collocation method has been shown to give consistent results. Vogelzang et al. (2011) applied the triple collocation method with representation errors obtained from the difference between ASCAT and ECMWF wind spectra of January 2011. The results for the error variances for two ASCAT wind products at the scatterometer spatial resolution are given in Table 2, using buoy measurements as absolute calibration reference. The results in Table 2 pertain to the six months period from December 2010 to May 2011. The accuracy in the error variances is estimated to be of the order of 0.05 m 2s − 2. The results in Table 2 show that the ASCAT wind products on a 12.5 km grid contain more noise than the product on 25 km (0.07 and 0.14 m 2s − 2 for u and v, respectively). However, the 12.5 km products compare better to the high resolution buoy measurements than the 25 km product and worse to the low resolution ECMWF forecasts. This indicates that the ASCAT 12.5 km products contain more small-scale information. The decrease in difference with the buoy measurements of the ASCAT 12.5 km products compared to that of the 25 km product is 0.17 m 2s − 2 for u and 0.18 m 2 s − 2 for v. This difference is due to an increase of information content of the 12.5 km products over that of the 25 km product, and this increase is larger than the increase in measurement noise given above. Note that the triple collocation method needs data sets that extend over a long period of time, because there are relatively few buoys that give reliable wind information. Therefore, a period of six months is about the minimum needed. Also, the wind statistics should ideally cover all seasons, as the atmospheric wind and stability conditions vary over time and influence the statistical results. Moreover, the buoys are mostly located in the tropical oceans and off the coasts of Europe and North America, providing uneven sampling over the globe.
7. Sea ice products Space‐borne scatterometers operate from a polar orbit, so they cover the northern and southern pole regions every revolution (which takes about 90 min, depending on the precise height of the satellite). Sea ice has a large radar cross section, so it is important to filter it out, in order not to contaminate the wind retrieval. In the past forecasts of the sea surface temperature from the ECMWF model were used, but this led to a too conservative ice screening. Moreover, the extent of sea ice cover is an important geophysical parameter itself, so effort has been made to improve the sea ice model and screening. At KNMI a sea ice screening based on Bayesian statistics has been developed (Belmonte Rivas and Stoffelen, 2011). In measurement space the sea ice points lie close to a line in the proximity of the wind cone, indicating dependency on a single geophysical parameter
Table 2 Triple collocation results for the error variances at scatterometer resolution for the period December 2010 up to and including May 2011. Product
Fig. 8. Wind spectra for the zonal wind component u (left hand panel) and the meridional component v (right hand panel) for ASCAT winds on a 12.5 km grid (blue curves) and collocated ECMWF forecasts (red curves).
Nr. of collocs
Buoy
ECMWF
Scatterometer
εu εv εu εv εu εv (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2) (m2s− 2)
ASCAT-25 12,512 1.54 ASCAT13,312 1.37 12.5 ASCAT 14,197 1.37 coastal
1.64 1.46
1.93 2.22
2.10 2.37
0.34 0.41
0.44 0.58
1.42
2.34
2.43
0.41
0.61
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called ice age. A sea ice and water discrimination procedure has been developed for QuikScat. The algorithm has been adapted for C-band and Fig. 9 shows the sea ice extent over the Arctic and Antarctic on September 11, 2011, as measured with ASCAT. It now runs experimentally at KNMI for ASCAT as a last step before operationalization. 8. Conclusions and outlook 8.1. Product improvements As already noted in Section 6.2, the triple collocation method provides (linear) calibration coefficients with respect to buoy measurements and absolute error variances. However, due to the long data sets needed, it has been assumed so far that the scatterometer errors are independent of both wind speed and wind vector cell. It is known that this is only true approximately. Longer data sets and combination with other analysis methods will be employed in order to improve ASCAT wind products by better calibration and to refine the description of the ASCAT measurement errors and calibration. In Section 5.1 it was shown that the coastal product is made by regridding the full resolution radar cross section data. This opens the possibility to perform regridding on a finer grid, thus enabling wind products at higher resolution. Fig. 10 shows an example of such a product for ASCAT data over a tropical cyclone recorded on January 26, 2010 northeast of the Philippines. The top panel image is obtained with standard coastal processing with an averaging radius of 15 km. The bottom panel image is obtained with coastal processing on a 6.25 km grid with an averaging radius of 7.5 km. The quality control flag (orange arrows) has been switched off when making the 6.25 km product, because this flag requires tuning to the output data. The variational quality control flag (purple arrows) was left intact, although this also requires specific tuning. Nevertheless, the 6.25 km product reveals all kinds of small-scale features in the cyclone, like convergence zones that look realistic but need validation. An ultra-high resolution ASCAT product (6.25 km grid size or smaller, if feasible) is foreseen to become operational in 2013.
As stated in Section 5, the default ambiguity removal procedure for KNMI wind processors makes an analysis of the ambiguous scatterometer winds using the ECMWF model forecasts as background. It is known from theory that such an analysis is optimal when the error characteristics of the scatterometer and background winds are well known. The scatterometer winds are spatially uncorrelated, so it is sufficient to know their error variance (for instance from triple collocation). The errors in the model winds are spatially correlated, so here the full covariance matrix is needed. The problem is simplified by assuming homogeneity and isotropy. The error covariances are modeled by structure functions. So far, ambiguity removal uses simple Gaussian structure functions, but recently Vogelzang and Stoffelen (2011) have shown how to obtain structure functions numerically from correlated differences between scatterometer wind and ECMWF background. Fig. 11 shows a result above a front recorded on January 2, 2009 around 5:06 UT off the west coast of Canada. The upper panel shows the wind field obtained with default Gaussian structure functions, the bottom panel with numerical structure functions. The front is associated with a sharp change in wind direction. Using default Gaussian structure functions, the analysis fails to describe the small‐scale features of the front because both the ECMWF background and the structure function are too smooth. As a result, a relatively large area is flagged by the ambiguity removal quality control (purple arrows) that signals large differences between observation and background. Moreover, there are some ambiguity removal problems, visible as erratic and opposing (↑↓ ↑) flow patterns. When using numerical structure functions only a few cells are flagged and the ambiguity removal problems are solved, resulting in a much narrower frontal zone. The numerical structure functions have a much larger range than the Gaussian functions employed operationally. As a result, the calculations become much more time demanding. Therefore, use in nearreal-time operations or in reprocessing needs further optimization. 8.2. Future scatterometer missions Fig. 12 gives an overview of (planned) scatterometer missions from 2008 to 2020. The C-band ASCAT series of instruments will be operational until 2022. A follow-on mission (EPS-SG) is under study
Fig. 9. Arctic (left) and Antarctic (right) sea ice extents for 14 September 2011 from ASCAT measurements. Land is black, sea is blue, and sea ice is grey. Light gray indicates young sea ice, dark grey old. The white dot in the centre of each image indicates the North Pole and South Pole, respectively.
J. Vogelzang, A. Stoffelen / Journal of Sea Research 74 (2012) 16–25
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130oW
130oW 130oW
Fig. 10. Tropical cyclone recorded by ASCAT on 26 January 2010 around 0:36 UT northeast of the Philippines with 12.5 km grid size (top panel) and 6.25 km grid size (bottom panel). Orange arrows indicate rain, but rain flagging has been switched off for the 6.25 km product. Purple arrows indicate a large difference between scatterometer observation and ECMWF forecast. The images are centered at 19°N, 129°E and cover an area of 2° × 2°.
130oW Fig. 11. ASCAT-12.5 wind field on January 2, 2009 around 5:06 UT off the west coast of Canada. Purple arrows indicate a large difference between observation and analysis. The area shown measures 5° × 5° centered around 43N, 129°W. The image in the top panel is obtained with default Gaussian structure functions, that is in the bottom panel with numerical structure functions.
now. Scatterometers operating at Ku‐band are designed or proposed by China (HY-2A and HY-2B), India (ScatSat), and Russia (MeteorM3). Also new scatterometer types are developed. The Chinese/French CFOSat features a rotating fan‐beam scatterometer at Ku‐band. Such an instrument will collect radar cross sections at a range of azimuth angles, and it will be a challenge to get as much information as possible out of these data. Another new class of instruments is formed by the dual-frequency scatterometers (GCOM W2, GCOM W3, FY-3E, and Indian operational scatterometer series). Such instruments may allow rain measurements from the comparison of the radar return at both frequencies.
wind information is interpolated to a geographical grid, and at level 4 information from various measurement systems is merged into a single product on a geographical grid at fixed times. Such products are expected to become important as input into high resolution models. However, such products can only be made if detailed observations are made at high time frequency. ASCAT has a coverage of about 80% of the Earth per day (EUMETSAT, 2011). To increase temporal coverage, more scatterometers are needed. If all planned scatterometer missions indicated in Fig. 12 are realized, wind information products at high spatial and temporal scale are within reach and we may enter a “golden age” of scatterometry.
8.3. Level 3 and level 4 products
Acknowledgments
So far, KNMI produces level 2 winds, i.e., winds on a grid (in space and time) determined by the satellite swath geometry. At level 3 the
The authors thank their colleague Jeroen Verspeek for providing Fig. 4. The NWP SAF scatterometer wind processors for SeaWinds
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Fig. 12. Overview of scatterometer missions © CEOS VC 24/2/'11.
(SDP) and ASCAT (AWDP) are freeware. They can be obtained upon registration at www.nwpsaf.org. Access to near-real time scatterometer wind products can be obtained upon registration at www.osi-saf.org. The NWP SAF and OSI SAF are sponsored by EUMETSAT.
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