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This study addresses the impact of satellite altimetry data processing on sea level studies at regional scale, with emphasis on the influence of various geophysical corrections and satellite orbit on the structure of the derived interannual signal and sea level trend. The work focuses on the analysis of TOPEX data for a period of over twelve years, for three regions in the North Atlantic: Tropical (0°≤φ≤25°), Sub-Tropical (25°≤φ≤50°) and Sub-Arctic (50°≤φ≤65°). For this analysis corrected sea level anomalies with respect to a mean sea surface model have been derived from the GDR-Ms provided by AVISO by applying various state-of-the-art models for the geophysical corrections. Results show that sea level trend determined from TOPEX altimetry is dependent on the adopted models for the major geophysical corrections. The main effects come from the sea state bias (SSB), and from the application or not of the inverse barometer (IB) correction. After an appropriate modelling of the TOPEX A/B bias, the two analysed SSB models induce small variations in sea level trend, from 0.0 to 0.2 mm/yr, with a small latitude dependence. The difference in sea level trend determined by a non IB-corrected series and an IB-corrected one has a strong regional dependence with large differences in the shape of the interannual signals and in the derived linear trends. The use of two different drift models for the TOPEX Microwave Radiometer (TMR) has a small but non negligible effect on the North Atlantic sea level trend of about 0.1 mm/yr. The interannual signals of sea level time series derived with the NASA and the CNES orbits respectively, show a small departure in the middle of the series, which has no impact on the derived sea level trend. These results strike the need for a continuous improvement in the modelling of the various effects that influence the altimeter measurement.

Sea level change has significant environmental, social and economical impacts. The importance of the estimates of the present rate of sea level change, for example for assessment of climate models or for coastal protection design, requires high accuracy and a realistic assessment of the involved uncertainties.

Sea level measurements are available from tide gauges at coastal locations, which measure sea level relative to a vertical reference on land and from satellite missions with radar altimeters. Satellite altimetry provides high quality measurements of absolute sea level, yielding an invaluable dataset for the analysis of sea level temporal and spatial variability. The main advantage of satellite altimetry over tide gauges is the ability to measure absolute sea level, independently of land movements, and the possibility to eliminate mass redistribution signals due to its nearly global spatial coverage.

Since the launch of the European Space Agency (ESA) satellite ERS-1 in 1991 and the joint NASA/CNES mission TOPEX/Poseidon (T/P) in 1992, two main data sets of continuous satellite altimeter measurements became available for sea level studies. The ERS-1 has been continued by ERS-2 in 1995 and Envisat in 2002. In December 2001 the T/P follow-on mission, Jason-1, has been launched, while TOPEX/Poseidon remained fully operational until January 2006, although in a different orbit since August 2002. In spite of the enormous advances in ERS data improvement, particularly in the accuracy of the radial orbit, it is recognised that the TOPEX/Poseidon mission revolutionised sea level research, providing sea surface height (SSH) measurements with an unprecedented accuracy, overall better than 5 cm for a single pass and better than 2 cm at the global scale, [

Compared to tide gauge measurements satellite altimetry has the advantage of providing nearly global and repetitive measurements of the sea surface height relative to a well defined geocentric reference system. The ESA missions cover the ocean every 35 days (except for the periods corresponding to ERS-1 ice and geodetic phases) within the latitude band -81°.5 ≤ φ ≤ 81°.5; T/P and Jason-1 cover the ocean approximately every 10 days within the latitude band -66° ≤ φ ≤ 66°.

While tide gauge and satellite altimetry are in essence independent data sets, the calibration of altimeter data using tide gauge measurements has helped to discover various problems in the measurements, both of instrumental and processing nature. As a consequence, the various altimeter data sets, including T/P, have undergone a continuous improvement over time. According to [

The first estimates of sea level trends using satellite altimetry were made using data from the Seasat and Geosat missions, [

The determination of sea level variability from satellite altimetry is influenced by many factors. Amongst the most important are sensor characteristics and long-term stability, and the methods used in the altimeter data processing. The latter can be divided in two major steps: the computation of corrected along track sea level anomalies (SLA) and the estimation methodology used to model sea level variation.

This study focuses on the impact of satellite altimeter data processing on sea level studies with emphasis on the influence of various geophysical corrections and the orbit field in the structure of the derived interannual signal and sea level trend, including regional dependencies.

Measurement of sea surface heights with high precision requires precise orbit determination of the spacecraft and the application of accurate geophysical corrections to the raw range estimate from the travel time of the altimeter pulse, accounting for the interactions of the altimeter signal with the atmosphere and the sea surface. The sea surface height error budget for the TOPEX altimeter is summarised in [

Differences in the methodology for processing altimetry data and in the corrections applied are of secondary concern for many applications using altimetry data, but are crucial for sea level change monitoring, since even small differences in the models used or processing approaches can seriously impact the sea level change estimates and corresponding uncertainties. In particular, the inverse barometer (IB) correction and the sea state bias (SSB) correction are major potential error sources in altimetry measurements. SSB errors may be as large as 5 cm in the absolute SSH and between 1 and 2 cm in the time varying altimeter signal [

Due to the potential impact of the altimetry data processing on the resulting sea level change estimates, it would be expected that sea level change studies would include a detailed description of the data used, of the geophysical and instrumental corrections applied and of eventual bias and drift corrections. However some sea level change analysis, particularly regional studies, fail to provide such a description of the altimetry data, for example, [

The knowledge of the influence of each geophysical correction on the altimeter measurement is fundamental for sea level studies; the most important impacts are driven by corrections which will induce drifts or long period variations. Therefore this study concentrates on the analysis of the effect on sea level trend of three major geophysical corrections: the wet tropospheric correction, the sea state bias and the inverse barometer effect. The last two are still the main error sources in satellite altimetry. In addition, the influence of the orbit field on sea level variation is also investigated.

This study focuses on the analysis of sea level change in the North Atlantic for the latitude range 0°-65°N and longitude range 80°W-7°W from TOPEX/Poseidon data (

The choice of these three regions is not accidental. Previous studies have shown that these regions possess distinct characteristics in terms of sea level variability [

For T/P data the base products available to users are the Geophysical Data Records (GDR) provided by the Archivage, Validation et Interprétation des données des Satellites Océanographiques (AVISO) or the Physical Oceanography Distributed Active Archive Center (PO.DAAC). There are several versions of the GDR products and it was found that the PO.DAAC products do not exactly mirror the corresponding AVISO products. The altimeter data used in this study are the “

The decision to use data only from the T/P mission aims to remove effects of possible bias and drifts between data sets of different satellites, which also have an impact on sea level variation. With more than twelve years of operation T/P represents the reference altimeter mission with the longest and most accurate records.

The inclusion of Poseidon data would introduce additional variability related with the bias between the two instruments, their different sea state bias effect and ionospheric corrections (dual-frequency for TOPEX versus DORIS for Poseidon). The number of full Poseidon cycles for this period, which have not been used is 30 (cycles 20, 31, 41, 55, 65, 79, 91, 97, 103, 114, 126, 138, 150, 162, 174, 180, 186, 197, 209, 216, 224, 234, 243, 256, 266, 278, 289, 299, 307, 361). There are also a number of cycles with mixed TOPEX and Poseidon data. Apart from cycles 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, all at the beginning of the mission, for which the percentage of Poseidon measurements in the study region ranges from 10% to 22%, a number of additional 23 cycles were also found to have portions of passes with Poseidon data. However these were only a few measurements, with an average of 0.5% and a maximum of 2%. Even considering this small percentage, these Poseidon measurements were discarded.

During the analysed period the T/P mission experienced two major changes, [

From the raw altimeter measurement until the corrected SSH above a reference ellipsoid or the corresponding sea level anomaly (SLA) with respect to a known mean sea surface (MSS), the most common variable used in sea level studies, a number of corrections have to be applied to data, to account for the interactions with the atmosphere and with the sea surface.

The corrected sea surface height is computed as follows:

Using the NASA JGM3 orbit, [

The cycle dependent drift effect in the TOPEX Microwave Radiometer (TMR) wet tropospheric correction, including the yaw state correction to the TMR measured Brightness Temperatures (TBs), has been modelled according to the Scharroo model, [

The SSB correction was derived from the Chambers model [

The TOPEX dual-frequency ionospheric correction has been smoothed using a second order Butterworth filter, [

The ocean tide model used was the NAO99b model, [

In the computation of DataSet1 no inverse barometer correction has been applied.

Sea level anomalies (SLA) have been derived from the corrected sea surface height values by removing the mean sea surface from the GSFC00.1 model, [

For the purpose of analysing the effect of different models in the geophysical corrections and different orbit solutions other data sets have been derived using the same methodology. In each data set all geophysical corrections applied to DataSet1 have been kept except one, as described below. This information is summarised in

_{atm} is the surface atmospheric pressure in mbar, computed from the dry tropospheric correction (dry_corr) field provided in the GDR-Ms, using

_{atm} is the surface atmospheric pressure in mbar, computed according to ^{-1} cut-off frequency.

For each of the above described data sets, the along track sea level anomalies for each TOPEX cycle have been gridded using a 0.5° spacing. For each non empty 0.5° block the along-track SLA values are replaced by the median value at the centre of the block.

Time series of spatially aggregated sea level anomalies for the Tropical, Sub-Tropical and Sub-Arctic Atlantic regions are obtained through spatial averaging of SLA values for each cycle with equi-area weighting by a cosine function of latitude to account for the relative geographical area represented by each block. As mentioned before, only TOPEX measurements have been used. The 30 full Poseidon cycles were discarded and replaced by interpolated values; missing TOPEX cycles (118, 432, 433) were also replaced by interpolated values from adjacent cycles. Considering the 7 datasets and the three mentioned regions a total number of 21 time series of SLA values were generated.

Flexible and robust non-parametric estimates of sea level inter-annual variability are obtained using STL, acronym for “Seasonal-Trend decomposition based on Loess”, [

Sea level linear trend is finally estimated for each time series, using an ordinary least squares (OLS) adjustment to each interannual curve, [

The process described in the previous section has been applied to each of the 21 time series. In this section the results obtained for each series are presented and discussed.

The differences between the various data sets and their dependence on the region are shown in detail in

The sea level trends have been computed both from the interannual signal and from the original SLA time series after removing the seasonal signal (sinusoidal cycle at the annual and semi-annual frequencies). The results obtained for the linear trends (mm/yr) and respective standard errors of the regression coefficient (mm/yr), obtained by OLS fit to each time series are presented in

To highlight the different results obtained for each data set,

In the next four sub-sections the results for the effects of the sea state bias, radiometer wet tropospheric correction, inverse barometer correction and the orbit field will be discussed in detail. Before discussing the results it should be emphasised that all 21 time series have been processed using exactly the same methodology in order to highlight the effects of using different geophysical corrections and orbit fields.

The development of SSB models for the various altimeter missions has been a subject of major research. The sea state bias correction is composed of several parts: the electromagnetic (EM), the skewness and the tracker biases. The EM is theoretically only a function of the frequency of the radar altimeter EM pulse. The skewness and the tracker bias are due to inaccurate tracking of the waveform by the on-board tracker and also depend on the sea state and on the altimeter frequency [

Most of the methods which have been used for determining the SSB correction fall in two main categories: parametric and non-parametric methods. The SSB is estimated by fitting an empirical model on altimeter-derived SSH differences, either at crossover points or along collinear tracks.

Parametric methods often assume an empirical relation between SSB, SWH and WS of the form

Non-parametric techniques have been developed for estimating the SSB from altimeter data themselves and applied to most satellite missions including TOPEX [

Scharroo et al. have been developing the so-called hybrib methods [

The Chambers SSB [

Both the Chambers and the Labroue models used in this study have been developed as separate models for TOPEX A and TOPEX B, derived from data from each of the instruments, aiming to take into account the different response of the TOPEX A and B altimeters to the sea state. The two TOPEX A models and their differences are shown in

Our first comparisons of time series of sea level anomalies derived with different SSB models, without applying any a priori TOPEX A/B bias, showed small but clear biases of few millimetres between the two altimeters. It has been pointed out by various authors [

Motivated by these results we developed a simple method to determine the TOPEX A/B relative bias for the SSB models used in this study. Using the same corrections applied to DataSet1 and DataSet2 (apart from the TOPEX A/B bias) global mean sea level values were computed using an equal-area weighted average [

The derived biases were added to the TOPEX B SSB values in the generation of both DataSet1 and DataSet2. Equivalently, they could have been subtracted from the corresponding TOPEX B sea level anomalies. Note that, since all remaining datasets used in this study (DataSet3 to DataSet7) use the same SSB (Chambers) model, the same TOPEX A/B bias of 2 mm has been applied to all of them.

Our results show that, after applying the above mentioned biases, the pattern of the amplitude of the cycle mean differences between the Chambers and the Labroue models (

The seasonal and latitudinal dependence is explained by the fact that both SSB corrections are empirical models, functions of the significant wave height (SWH) and wind speed (WS), both measured by the altimeter. Both fields have a strong annual variation with intensity and variance increasing with latitude, particularly during the boreal winter months. This is clearly shown in

The regional dependence of the SSB does not only influence sea level variations. Dong and coworkers [

According to

The GDR-Ms provide the radiometer wet tropospheric correction (wet_cor) as computed from the measured TBs of the 3 TMR channels (18 GHz, 21 GHz and 37 GHz). Several authors have reported drifts in the TBs of some of these channels and published correction models to account for these effects in the wet tropospheric path delay, [

Ruf [

Apart from refining the 18 GHz model by using a piecewise linear function of the TBs, Scharroo et al. also derived drift models for the other two channels (21 GHz and 37 GHz). In addition, their model also takes into account the yaw state corrections to the TBs of all the three TMR channels.

Our results show that the effect on the North Atlantic sea level variation of using the two TMR drift models (Scharroo and Ruf) is of 0.1 mm/yr (

The effect of the TMR drift on sea level studies is highly dependent on the length of the analysed altimeter time series. Most TMR drift models, including the two analysed here, consider that the TMR drift after 1999-2000 is approximately constant. Therefore, the longer the time series the smaller the effect will be. In

In spite of instrumental problems in the onboard microwave radiometers, affecting the wet tropospheric delay derived from the TBs measured by these instruments, provided these are properly modelled and accounted for, the radiometer derived wet_cor is still more accurate than the corresponding ECMWF model correction. For TOPEX, the TMR model provides a path delay correction with the accuracy better than 1.1 cm, even under the worst conditions of variable clouds and wind, [

Results show that the inverse barometer is the correction with the largest impact in the shape of the SLA time series for a given region (

In this study three different IB models have been compared, one that makes use of a constant reference atmospheric pressure (CRP) of 1013.3 mbar (DataSet4), one that uses a variable reference surface atmospheric pressure (VRP), interpolated from global average values obtained from the CNES/CLS (DataSet5) and the so-called combined-MOG2D model (DataSet6). In the computation of the VRP model, the values of ^{-1} cut-off frequency. However it was found that the smoothing does not affect the mean sea level determination since the maximum difference in the values of

The pattern of the differences between the combined-MOG2D and the VRP models presented in

In global studies IB corrected SLA time series tend to give smaller sea level trends than the corresponding non IB-corrected, [

Atmospheric pressure variability exhibits a strong latitudinal dependence with the lowest values at tropical latitudes (∼2 mbar) and the highest values (∼15 mb) at polar latitudes, [

The interannual components for the Subarctic region show a significantly different behaviour for IB-corrected and not corrected datasets. The sea level series for which the IB correction has not been applied departs significantly from the IB-corrected series for three distinct periods: near 1995, near 2000 and after mid-2003. These periods are associated with significant anomalies in the atmospheric patterns over the North Atlantic which influence the inverse barometer response of sea level.

The North Atlantic Oscillation (NAO) affects the sea surface height through a direct hydrostatic response to changes in the pressure field and indirectly through wind, ocean circulation and heat flux forcings [

The period after mid-2003 shows a striking decrease in sea level in the non IB-corrected data sets and a significant departure from the IB-corrected values, but seems not to be directly related to the NAO. However, the northern mid latitudes experienced abnormal weather in the summer of 2003 which was not diagnosed by the NAO indices but was captured by the Northen Annular Mode (NAM) index, [

^{2} relative to the NASA orbit [

It has been reported [

ITRF effects on mean sea level have been discussed by [

Our results confirm that the pattern of the differences between the two GDR-M orbits for the North Atlantic has a regional dependence, with the amplitude of the differences increasing with latitude (

The orbit differences affect the shape of the interannual signal (

Works mainly done within the scope of Jason-1 calibration/validation studies have pointed out differences between the two satellites, which seem to be related with some unidentified problems in TOPEX data. At the SWT meetings in November 2003 and November 2004 geographically correlated SSH differences between T/P and Jason-1 were reported, which seem correlated with the radial velocity. Clear hemispherical patterns were also found in TOPEX B SWH between ascending and descending passes. Further work needs to be performed in order to identify if the cause of these discrepancies is either on TOPEX or Jason-1 data and if it is related with orbital or tracker errors.

Recent orbit solutions based on GRACE derived gravity fields seem to substantially reduce the T/P orbital error as compared to the JGM3 orbits provided on the GDR-Ms. The availability of these improved orbits to users will certainly contribute to a further reduction of the overall error budget in altimeter data and will be an important contribution to long-term sea level variation studies.

In this paper the influence on sea level change of various geophysical corrections and the orbit solution used in the altimeter processing has been analysed for three regions in the North Atlantic using TOPEX data for a twelve years period.

Although the most common approach to model sea level variation is fitting a linear model to the SLA time series, we believe that the information present in the interannual variability curves is crucial to understand the variety of factors that affect sea level. A description of sea level interannual variability has been obtained from a robust non-parametric method, enhancing the underlying structure in the altimetry time series. Although the methodology is known to be unstable at the edges of the time series, comparisons of the linear trends obtained directly from the deseasoned SLA time series and from the corresponding interannual components are very similar, indicating that the impact of potential edge effects on the results is negligible.

Results show that all analysed geophysical corrections affect sea level trend determined by satellite altimetry.

All instrumental effects that will induce long-term effects in the measurements, such as the drift in the TMR radiometer, will seriously affect sea level trend results. Even a small difference in the modelling of this drift, such as the difference between the Scharroo and Ruf TMR models has an impact on sea level trend, in particular for short time series. Although this effect might have a small regional variation related with the spatial distribution of the three TBs measured by TMR and used in the wet_cor algorithm, the mean effect in the North Atlantic is 0.1 mm/yr.

Due to the dependence of the parametric and non-parametric SSB models on geophysical parameters such as SWH and WS the difference between the Chambers and the Labroue TOPEX SSB models shows regional variations in the sea level. However, these regional variations are dominated by an annual cycle which disappears in the interannual signal of the corresponding series. Results show that the difference between the two models, after an appropriate modelling of the TOPEX A/B bias, has a small impact on sea level trend of about 0.0 to 0.2 mm/yr for the North Atlantic region. It should be noted that two of the major groups who provide corrected altimeter sea level anomalies, PO.DAAC and AVISO, have been using different SSB models; PO.DAAC is using the Chambers model for the generation of the TPSSHA Product 189 (with a -3 mm bias added to TOPEX B SLA), while AVISO is using a previous version of the Labroue model in the generation of the CORSSH products (TOPEX A/B bias not specified). Therefore users who make use of these two data sets will derive different sea level trends even if they employ the same methodology in their analyses.

Considering the present models available for the inverse barometer effect the type of model does not have any impact on long-term sea level variation, particularly for the most recent VRP and combined-MOG2D models. However, results from non IB-corrected and IB-corrected SLA time series are very different, particularly in the highest latitude regions. In this case the difference in sea level change can reach 0.9 mm/yr.

Although it is recognized that the NASA orbit is more accurate and stable than the CNES solution, the impact of the orbit choice for the North Atlantic sea level variation is a small departure in the middle of the times series, which has a negligible impact on the linear sea level trend. The change of TOPEX orbit in 2002 did not have any impact either in any of the analysed geophysical corrections and orbit fields or on the derived sea level change.

Altimetry-derived sea level trends are particularly sensitive to the length of the time series. Different sea level trends are obtained depending on the overall period considered to produce the estimate. The length of the available records is still short and therefore sea level trend estimates are significantly affected by decadal variability and phenomena such as the North Atlantic Oscillation (NAO) or ENSO, [

This work focused on the impact of three major geophysical corrections and the orbit field. Other geophysical corrections could also have been analysed which have also an impact on sea level change, such as the ionospheric correction. For the present dual-frequency altimeters this correction is determined with an accuracy of 1.1 cm, [

It is not the purpose of this paper to validate any of the adopted geophysical models or satellite ephemeris, but rather to study their influence on sea level studies. The results strike the importance of calibration/validation studies to access both instrument stability and models accuracy. Although there is no ideal method to perform such a task, techniques such as single and multi-mission crossover analysis, collinear track analysis and comparisons with tide gauges provide useful and complementary information on data quality.

This work has been supported by program POCTI through the Centro de Investigação em Ciências Geoespaciais (CICGE). The authors wish to thank Sylvie Labroue for providing the look-up tables of the most recent version of the nonparametric SSB models and Loren Carrère for making available the global grids of the combined-MOG2D IB correction.

Study area. The background shows the GSFC00.1 mean sea surface.

SLA for DataSet1.

SLA differences for the Tropical region.

SLA differences DataSet1-DataSet2 (different SSB models: Chambers – Labroue).

SLA differences DataSet1-DataSet3 (different wet_cor models: Scharroo - Ruf).

SLA differences DataSet1-DataSet7 (different orbits)

NAO index.

Geophysical corrections and models used in the processing of TOPEX altimeter data (DataSet1).

GSFC00.1, [ | |

NASA JGM3, [ | |

ECMWF, [ | |

Scharroo TMR model (yaw state and drift effect on TMR TBs applied, [ | |

TOPEX: dual-frequency (filtered, [ | |

Chambers model, [ | |

Applied, [ | |

NAO99, [ | |

Applied, [ | |

Not Applied |

Geophysical corrections and models used in the processing of the various TOPEX altimeter data sets. The box corresponding to the correction that has been changed with respect to DataSet1 is shaded.

Scharroo TMR model, [ |
Chambers model, [ |
Not Applied | NASA | |

Scharroo TMR model, [ |
Labroue model, [ |
Not Applied | NASA | |

Ruf TMR model,[ |
Chambers model, [ |
Not Applied | NASA | |

Scharroo TMR model, [ |
Chambers model, [ |
Constant reference atmospheric pressure of 1013.3, [ |
NASA | |

Scharroo TMR model, [ |
Chambers model, [ |
Non-constant reference surface atmospheric pressure. [ |
NASA | |

Scharroo TMR model, [ |
Chambers model, [ |
Combined-MOG2D model [ |
NASA | |

Scharroo TMR model, [ |
Chambers model, [ |
Not Applied | CNES |

Linear trends (mm/yr) and corresponding standard errors (mm/yr) obtained by OLS fit to the time series of the interannual SLA signal.

trend | s. e. | trend | s. e. | trend | s. e. | |
---|---|---|---|---|---|---|

3.24 | 0.08 | 1.97 | 0.11 | 3.26 | 0.18 | |

3.07 | 0.08 | 1.86 | 0.11 | 3.22 | 0.18 | |

3.35 | 0.08 | 2.10 | 0.11 | 3.38 | 0.18 | |

3.14 | 0.08 | 1.82 | 0.09 | 4.14 | 0.13 | |

2.99 | 0.08 | 1.68 | 0.08 | 3.99 | 0.12 | |

3.00 | 0.08 | 1.69 | 0.08 | 4.01 | 0.13 | |

3.25 | 0.09 | 1.99 | 0.09 | 3.19 | 0.17 |

Linear trends (mm/yr) and corresponding standard errors (mm/yr) obtained by OLS fit to the original SLA time series (after removing the seasonal signal).

trend | s. e. | trend | s. e. | trend | s. e. | |
---|---|---|---|---|---|---|

3.22 | 0.20 | 2.04 | 0.40 | 3.40 | 0.74 | |

3.05 | 0.20 | 1.93 | 0.40 | 3.37 | 0.72 | |

3.33 | 0.21 | 2.16 | 0.41 | 3.52 | 0.73 | |

3.14 | 0.17 | 1.83 | 0.18 | 4.14 | 0.23 | |

3.00 | 0.16 | 1.68 | 0.17 | 4.00 | 0.23 | |

3.00 | 0.16 | 1.69 | 0.17 | 4.02 | 0.22 | |

3.23 | 0.20 | 2.05 | 0.40 | 3.33 | 0.74 |

Linear trends differences (mm/yr) between various datasets (obtained by OLS fit to the time series of the interannual SLA signal).

0.17 | 0.11 | 0.04 | |

-0.11 | -0.13 | -0.12 | |

0.10 | 0.15 | -0.88 | |

0.25 | 0.29 | -0.73 | |

0.24 | 0.28 | -0.75 | |

-0.01 | -0.02 | 0.07 | |

-0.15 | -0.14 | -0.15 | |

-0.01 | -0.01 | -0.02 |