Multi-mission Satellite Altimetry Research Articles

Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes

Multi-mission satellite altimetry (e.g., ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic-scale view of ocean variations in past decades. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared to its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise for coastal and lake observations. The article studies the performance of the Sentinel-3 Level-2 sea level altimetry products in the coastal areas of the Baltic Sea and on two lakes of Estonia. The Sentinel-3 data were compared with (i) collocated Global Navigation Satellite System (GNSS) ship measurements, (ii) the Estonian geoid model (EST-GEOID2017) together with sea-level anomaly corrections from the tide gauges, and (iii) collocated buoy measurements. The comparisons were carried out along seven Sentinel-3A/B tracks across the Baltic Sea and Estonian lakes in 2019. In addition, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level products and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis outcomes were compared with measurements from Estonia’s 21 tide gauges and the buoy deployed offshore. Our results showed that the uncertainty of the Sentinel-3 Level-2 altimetry product was below decimetre level for the seacoast and the selected lakes of Estonia. Results from CMEMS Level-3 altimetry products showed a correlation of 0.83 (RMSE 0.18 m) and 0.91 (RMSE 0.27 m) when compared against the tide gauge measurements and the NEMO model, respectively. The overall performance of the altimetry products was very good, except in the immediate vicinity of the coastline and for the lakes, where the accuracy was nearly three times lower than for the open sea, but still acceptably good.

Accuracy assessment of global ocean tide models in the South China Sea using satellite altimeter and tide gauge data

In this study, to meet the need for accurate tidal prediction, the accuracy of global ocean tide models was assessed in the South China Sea (0°–26°N, 99°–121°E). Seven tide models, namely, DTU10, EOT11a, FES2014, GOT4.8, HAMTIDE12, OSU12 and TPXO8, were considered. The accuracy of eight major tidal constituents (i.e., Q1, O1, P1, K1, N2, M2, S2 and K2) were assessed for the shallow water and coastal areas based on the tidal constants derived from multi-mission satellite altimetry (TOPEX and Jason series) and tide gauge observations. The root mean square values of each constituent between satellite-derived tidal constants and tide models were found in the range of 0.72–1.90 cm in the deep ocean (depth>200 m) and 1.18–5.63 cm in shallow water area (depth<200 m). Large inter-model discrepancies were noted in the Strait of Malacca and the Taiwan Strait, which could be attributable to the complicated hydrodynamic systems and the paucity of high-quality satellite altimetry data. In coastal regions, an accuracy performance was investigated using tidal results from 37 tide gauge stations. The root sum square values were in the range of 9.35–19.11 cm, with the FES2014 model exhibiting slightly superior performance.

The Prediction of Oceanic Mesoscale Eddy Properties and Propagation Trajectories Based on Machine Learning

Mesoscale eddies play an important role in ocean circulation, material energy exchange and variation of ocean environments. Machine learning methods can efficiently process massive amounts of data and automatically learn the implicit features, thus providing a new approach to eddy prediction research. Using the mesoscale eddy trajectory data derived from multimission satellite altimetry, we propose relevant machine learning models based on long short-term memory network (LSTM) and the extra trees (ET) algorithm for the prediction of eddy properties and propagation trajectories. Characteristic factors, including attribute features and past eddy displacements, were exploited to construct prediction models with high effectiveness and few predictors. To study their effects at different forecasting times, we separately trained the models by rebuilding the corresponding relationship between eddy samples and labels. In addition, the variation characteristics and the predictability of eddy properties and propagation trajectories were discussed from the prediction results. Cross-validation shows that at different prediction times, our method is superior to previous methods in terms of the mean absolute error (MAE) of eddy properties and the root mean square error (RMSE) of propagation. The stable variation in eddy properties makes the prediction more dependent on the historical time series than that of a propagation forecast. The short-term propagation prediction of eddies contained more noise than long-term predictions, and the long-term predictions revealed a more significant trend. Finally, we discuss the effect of eddy properties on the prediction ability of the eddy propagation trajectory.

Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images

Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.

Geostrophic currents in the northern Nordic Seas from a combination of multi-mission satellite altimetry and ocean modeling

Abstract. A deeper knowledge about geostrophic ocean surface currents in the northern Nordic Seas supports the understanding of ocean dynamics in an area affected by sea ice and rapidly changing environmental conditions. Monitoring these areas by satellite altimetry results in a fragmented and irregularly distributed data sampling and prevents the computation of homogeneous and highly resolved spatio-temporal datasets. In order to overcome this problem, an ocean model is used to fill in data when altimetry observations are missing. The present study provides a novel dataset based on a combination of along-track satellite-altimetry-derived dynamic ocean topography (DOT) elevations and simulated differential water heights (DWHs) from the Finite Element Sea ice Ocean Model (FESOM) version 1.4. This innovative dataset differs from classical assimilation methods because it substitutes altimetry data with the model output when altimetry fails or is not available. The combination approach is mainly based on a principal component analysis (PCA) after reducing both quantities by their constant and seasonal signals. In the main step, the most-dominant spatial patterns of the modeled differential water heights as provided by the PCA are linked with the temporal variability in the estimated DOT from altimetry by performing a principal component synthesis (PCS). After the combination, the annual signal obtained by altimetry and a constant offset are re-added in order to reference the final data product to the altimetry height level. Surface currents are computed by applying the geostrophic flow equations to the combined topography. The resulting final product is characterized by the spatial resolution of the ocean model around 1 km and the temporal variability in the altimetry along-track derived DOT heights. The combined DOT is compared to an independent DOT product, resulting in a positive correlation of about 80 %, to provide more detailed information about short periodic and finer spatial structures. The derived geostrophic velocity components are evaluated by in situ surface drifter observations. Summarizing all drifter observations in equally sized bins and comparing the velocity components shows good agreement in spatial patterns, magnitude and flow direction. Mean differences of 0.004 m s−1 in the zonal and 0.02 m s−1 in the meridional component are observed. A direct pointwise comparison between the combined geostrophic velocity components interpolated onto the drifter locations indicates that about 94 % of all residuals are smaller than 0.15 m s−1. The dataset is able to provide surface circulation information within the sea ice area and can be used to support a deeper comprehension of ocean currents in the northern Nordic Seas affected by rapid environmental changes in the 1995–2012 time period. The data are available at https://doi.org/10.1594/PANGAEA.900691 (Müller et al., 2019).

Variability of sea surface topography in coastal area (study case: Indonesia)

The deviation of the sea surface from the geoid reference surface is known as the Sea Surafce Topography (SST). SST is caused by several physical phenomena such as ocean wave, tides, current and atmospheric pressure loading, and the main application of SST is to determine ocean circulation. To get the SST, we use Sea Surface Height (SSH) and Mean Dynamic Topography (MDT) based on 30 years of sea-level data derived from multi-mission of satellite Altimetry and geoid height (Undulation) from Global Gravitation Model EGM-2008. Indonesia is an archipelagic country in the equatorial region that lies between Indian and Pacific oceans. Its geographical setting will affect variability of Sea Surface Topography (SST) in the Indonesian Sea. The anomaly from SST is one of the oceanic parameters that play a crucial role in the ocean dynamic and its possible hazards. We found that the characteristics differ considerably from one place to another, depending on the bathymetric depth and type of sea. The mean SSH varies from about 40 meters and rises to the east up to a height of 80 meters. Likewise, the MDT value increased from 0.5 meters to the east to a height of 1.2 meters. In closed, narrow and shallow seas, the long time-series of those surfaces are less sensitive to the effects of El Niño and La Niña as well as global sea level rise. On the other hand, global sea level rise significantly affects the characteristic of both surfaces over open and deep seas. We hope by using more spatially dense data from multi-mission satellite altimetry, we can see more detail about the local phenomena that influence SST. By studying the variability of SST from more than one cycle (18.6 years) it is expected that it can be used to understand the dynamics of the coastal area in Indonesia.

Hybrid model combining empirical mode decomposition, singular spectrum analysis, and least squares for satellite-derived sea-level anomaly prediction

ABSTRACT In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods.

Sea level trend over Malaysian seas from multi-mission satellite altimetry and vertical land motion corrected tidal data

Rise in sea levels is one of the disastrous effects of climate change. A relatively small increase in sea level could affect natural coastal systems. In a study of long-term changes in sea level and measurements of postglacial rebound, monitoring vertical land motion (VLM) is of crucial interest. This study presents an approach to estimate precise sea level trends based on a combination of multi-sensor techniques in the Malaysian region over 19 years. In this study, satellite altimeters (SALT) were used to derive absolute sea levels (ASLs). Tide gauge (TG) stations along the coast of Malaysia were utilised to derive the rate of relative sea levels using sea level changes and VLMs. To obtain ASL at TGs, VLM at these stations were computed using Global Positioning System (GPS), Persistent Scatterer Interferometric Synthetic Aperture Radar (PS InSAR), and SALT minus TG. The computed VLMs mostly show similarities in signs rather than magnitude. The findings from the multi-sensor techniques showed that regional sea level trends ranged from 2.65 ± 0.86 mm/yr to 6.03 ± 0.79 mm/yr for chosen sub-areas, with an overall mean of 4.47 ± 0.71 mm/yr and overall subsidence. This information is expected to be valuable for a wide variety of climatic applications and for studying environmental issues related to flooding and global warming in Malaysia.

Satellite-Based Offshore Wind Energy Resource Mapping in Malaysia

Development and application of offshore wind turbine farms have been increasing, particularly in the developed countries, because of their high power rating, high yield energy, high offshore wind, and unlimited space in the offshore. However, the poor data and simplistic methodologies of the previous assessments result in insufficient estimates of the wind energy potential. Thus, this study provides an assessment of the offshore wind energy resources in Malaysia using multi-mission satellite altimetry data. The satellite altimetry data was extracted from Radar Altimeter Database Systems located at GNSS and Geodynamics Laboratory, Universiti Teknologi Malaysia. The data were validated by buoy measurements from two offshore sites, as indicated by the high correlation coefficient of 0.88. Further, the offshore wind energy resource mapping data in Malaysia identified three areas in Peninsular Malaysia and Borneo as potential areas for offshore wind energy development.

Four decades of Antarctic surface elevation changes from multi-mission satellite altimetry

Abstract. We developed a multi-mission satellite altimetry analysis over the Antarctic Ice Sheet which comprises Seasat, Geosat, ERS-1, ERS-2, Envisat, ICESat and CryoSat-2. After a consistent reprocessing and a stepwise calibration of the inter-mission offsets, we obtained monthly grids of multi-mission surface elevation change (SEC) with respect to the reference epoch 09/2010 (in the format of month/year) from 1978 to 2017. A validation with independent elevation changes from in situ and airborne observations as well as a comparison with a firn model proves that the different missions and observation modes have been successfully combined to a seamless multi-mission time series. For coastal East Antarctica, even Seasat and Geosat provide reliable information and, hence, allow for the analysis of four decades of elevation changes. The spatial and temporal resolution of our result allows for the identification of when and where significant changes in elevation occurred. These time series add detailed information to the evolution of surface elevation in such key regions as Pine Island Glacier, Totten Glacier, Dronning Maud Land or Lake Vostok. After applying a density mask, we calculated time series of mass changes and found that the Antarctic Ice Sheet north of 81.5∘ S was losing mass at an average rate of -85±16 Gt yr−1 between 1992 and 2017, which accelerated to -137±25 Gt yr−1 after 2010.

Observing water level extremes in the Mekong River Basin: The benefit of long-repeat orbit missions in a multi-mission satellite altimetry approach

Single-mission altimetric water level observations of rivers are spatially and temporally limited, and thus they are often unable to quantify the full extent of extreme flood events. Moreover, only missions with a short-repeat orbit, such as Envisat, Jason-2, or SARAL, could provide meaningful time series of water level variations directly. However, long or non-repeat orbit missions such as CryoSat-2 have a very dense spatial resolution under the trade-off of a repeat time insufficient for time series extraction. Combining data from multiple altimeter missions into a multi-mission product allows for increasing the spatial and temporal resolution of the data. In this study, we combined water level data from CryoSat-2 with various observations from other altimeter missions in the Mekong River Basin between 2008 and 2016 into one multi-mission water level time series using the approach of universal kriging. In contrast to former multi-mission altimetry methods, this approach allows for the incorporation of CryoSat-2 data as well as data from other long or non-repeat orbit missions, such as Envisat-EM or SARAL-DP. Additionally, for the first time, data from tributaries are incorporated. The multi-mission time series including CryoSat-2 data adequately reflects the general inter-annual flood behaviour and the extreme floodings in 2008 and 2011. It performs better than single-mission time series or multi-mission time series based only on short-repeat orbit data. The Probability of Detection of the floodings with the multi-mission altimetry was around 80% while Envisat and Jason-2 single-mission altimetry could only detect around 40% of the floodings correctly. However, small flash floods still remain undetectable.

Contemporary sea level changes from satellite altimetry: What have we learned? What are the new challenges?

Since the early 1990s, high-precision multi-mission satellite altimetry has provided a 25-year-long sea level record from which global mean sea level rise and superimposed interannual and regional variability can be derived. Most recent results show that the global mean sea level is rising at a mean rate of 3.1 ± 0.3 mm/yr since January 1993. A clear acceleration is also visible on this 25-year time span, estimated to 0.10 mm/yr2. Mapping of spatial trend patterns continue to show deviation from the global mean rise in a number of regions. However, as the altimetry record lengthens, the ratio of regional trends to the global mean rise tends to decrease, with a factor of amplification of only 2, compared to 3 to 4 some years ago. Estimates of thermal expansion from Argo and ocean mass change from GRACE show that over the GRACE and Argo time span (since 2005) the sea level budget is almost closed. Assessment of the sea level budget over the entire altimetry era (since 1993) based on estimates of individual mass components for the glaciers and the ice sheets provides some upper bound for the still poorly known contribution from water storage on land. At regional scale, ocean thermal expansion is still the main cause of the spatial trend patterns observed by satellite altimetry. However, removing the steric component reveals residual signal that still needs interpretation. In the remaining of this review, we briefly discuss future sea level changes and associated coastal impacts. Finally, we address the issue of remaining gaps in sea level studies, in particular the need for producing coastal sea level products from dedicated satellite altimetry processing of sea level data in global coastal zones.

Long term variation of sea level anomaly (September 1992-January 2017) in the Indonesian sea from multi-mission satellite altimetry data

Sea level anomaly (SLA) is one of the oceanic parameters that play a crucial role in the ocean dynamics. In the Indonesian region, spatio-temporal characteristic of SLA is significantly affected by local topographic features of the region. In this research, we investigate long-term variation of SLA over the region based on Topex/Poseidon, Jason-1 and Jason-2 data, during the period of Sept 1992 to January 2017. After removing some geophysical effects, time series of SLA values at each point of the collinear tracks are analyzed. The result show the long term MSSH in Indonesia area have increase from western part of Indonesia to eastern part with range -40 m to 80 m. The long term SST in Indonesia have almost same trend with the deviation from geoid undulation full degree with short wavelength type, and this phenomena represent the local effect such as shallow bathymetry and close sea. And for long term SLA, Indonesia shows positive value that the anomaly increasing with variable from western part to eastern part of Indonesia from 2.5 mm/year until 7 mm/yr.

Description and assessment of regional sea-level trends and variability from altimetry and tide gauges at the northern Australian coast

This paper aims at providing a descriptive view of the low-frequency sea-level changes around the northern Australian coastline. Twenty years of sea-level observations from multi-mission satellite altimetry and tide gauges are used to characterize sea-level trends and inter-annual variability over the study region. The results show that the interannual sea-level fingerprint in the northern Australian coastline is closely related to El Niño Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO) events, with the greatest influence on the Gulf Carpentaria, Arafura Sea, and the Timor Sea. The basin average of 14 tide-gauge time series is in strong agreement with the basin average of the altimeter data, with a root mean square difference of 18 mm and a correlation coefficient of 0.95. The rate of the sea-level trend over the altimetry period (6.3 ± 1.4 mm/yr) estimated from tide gauges is slightly higher than that (6.1 ± 1.3 mm/yr) from altimetry in the time interval 1993–2013, which can vary with the length of the time interval. Here we provide new insights into examining the significance of sea-level trends by applying the non-parametric Mann-Kendall test. This test is applied to assess if the trends are significant (upward or downward). Apart from a positive rate of sea-level trends are not statistically significant in this region due to the effects of natural variability. The findings suggest that altimetric trends are not significant along the coasts and some parts of the Gulf Carpentaria (14°S-8°S), where geophysical corrections (e.g., ocean tides) cannot be estimated accurately and altimeter measurements are contaminated by reflections from the land.

Thirteen years of subglacial lake activity in Antarctica from multi-mission satellite altimetry

ABSTRACTThe ability to detect the surface expression of moving water beneath the Antarctic ice sheet by satellite has revealed a dynamic basal environment, with implications for regional ice dynamics, grounding-line stability, and fluxes of freshwater and nutrients to the Southern Ocean. Knowledge of subglacial activity on timescales important for near-term prediction of ice-sheet fluctuations (decadal to century) is limited by the short observational record of NASA's Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry mission used to generate the last continent-wide survey (2003–08). Here, we use synthetic aperture radar-interferometric-mode data from ESA's CryoSat-2 radar altimetry mission (2010–present), which samples 45 of the ICESat-derived subglacial lakes, to extend their time series to the end of 2016. The extended time series show that there have been surface-height changes at 20 of the 45 lakes since 2008, indicating that some of these features are persistent and potentially cyclic, while other features show negligible changes, suggesting these may be transient or nonhydrological features. Continued monitoring of active lakes for both height and velocity changes, as well as developing methods for identifying additional lakes, is critical to quantifying the full distribution of active subglacial lakes in Antarctica.

Mean dynamic topography over Peninsular Malaysian seas using multimission satellite altimetry

The development of satellite altimeters (SALTs) has brought huge benefits, among which is the ability to more adequately sense ocean-surface topography. The radar altimeter database system was used to capture and process ENVISAT, CRYOSAT-2, SARAL, JASON-1, and JASON-2 SALT data of 5 years between 2011 and 2015. The time series of monthly multimission SALT data showed an estimated sea level trend of 1.0, 2.4, 2.4, 3.6, and 12.0 mm/year at Gelang, Port Kelang, Kukup, Cendering, and Keling. The correlation analysis for the selected tide gauge stations produced satisfying results of R-squared with 0.86, 0.89, 0.91, and 0.97 for Cendering, Sedili, Gelang, and Geting, respectively. The ITG-Grace2010s geoid model was used to compute the mean dynamic topography (MDT) and plot to a grid of 0.25 deg for the Malacca Strait and South China Sea of Peninsular Malaysia, with Keling, Port Kelang, Geting, Sedili, and Johor Bahru tide gauge stations having values determined by interpolation to be 1.14, 1.19, 1.26, 1.88, and 2.91 m, respectively. MDT is computed from the SALT with respect to Port Kelang, the north–south sea slope ranges between −0.64 and 0.29 m/50 km and −0.01 and 0.52 m/50 km along the east and west coasts of Peninsular Malaysia, respectively.

Spatiotemporal densification of river water level time series by multimission satellite altimetry

AbstractLimitations of satellite radar altimetry for operational hydrology include its spatial and temporal sampling as well as measurement problems caused by local topography and heterogeneity of the reflecting surface. In this study, we develop an approach that eliminates most of these limitations to produce an approximately 3 day temporal resolution water level time series from the original typically (sub)monthly data sets for the Po River in detail, and for Congo, Mississippi, and Danube Rivers. We follow a geodetic approach by which, after estimating and removing intersatellite biases, all virtual stations of several satellite altimeters are connected hydraulically and statistically to produce water level time series at any location along the river. We test different data‐selection strategies and validate our method against the extensive available in situ data over the Po River, resulting in an average correlation of 0.7, Root‐Mean‐Square Error of 0.8 m, bias of −0.4 m, and Nash‐Sutcliffe Efficiency coefficient of 0.5. We validate the transferability of our method by applying it to the Congo, Mississippi, and Danube Rivers, which have very different geomorphological and climatic conditions. The methodology yields correlations above 0.75 and Nash‐Sutcliffe coefficients of 0.84 (Congo), 0.34 (Mississippi), and 0.35 (Danube).

DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry

Abstract. Satellite altimetry has been designed for sea level monitoring over open ocean areas. However, for some years, this technology has also been used to retrieve water levels from reservoirs, wetlands and in general any inland water body, although the radar altimetry technique has been especially applied to rivers and lakes. In this paper, a new approach for the estimation of inland water level time series is described. It is used for the computation of time series of rivers and lakes available through the web service "Database for Hydrological Time Series over Inland Waters" (DAHITI). The new method is based on an extended outlier rejection and a Kalman filter approach incorporating cross-calibrated multi-mission altimeter data from Envisat, ERS-2, Jason-1, Jason-2, TOPEX/Poseidon, and SARAL/AltiKa, including their uncertainties. The paper presents water level time series for a variety of lakes and rivers in North and South America featuring different characteristics such as shape, lake extent, river width, and data coverage. A comprehensive validation is performed by comparisons with in situ gauge data and results from external inland altimeter databases. The new approach yields rms differences with respect to in situ data between 4 and 36 cm for lakes and 8 and 114 cm for rivers. For most study cases, more accurate height information than from other available altimeter databases can be achieved.

Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project

Abstract. Sea level is one of the 50 Essential Climate Variables (ECVs) listed by the Global Climate Observing System (GCOS) in climate change monitoring. In the past two decades, sea level has been routinely measured from space using satellite altimetry techniques. In order to address a number of important scientific questions such as "Is sea level rise accelerating?", "Can we close the sea level budget?", "What are the causes of the regional and interannual variability?", "Can we already detect the anthropogenic forcing signature and separate it from the internal/natural climate variability?", and "What are the coastal impacts of sea level rise?", the accuracy of altimetry-based sea level records at global and regional scales needs to be significantly improved. For example, the global mean and regional sea level trend uncertainty should become better than 0.3 and 0.5 mm year−1, respectively (currently 0.6 and 1–2 mm year−1). Similarly, interannual global mean sea level variations (currently uncertain to 2–3 mm) need to be monitored with better accuracy. In this paper, we present various data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on "Sea Level" during its first phase (2010–2013), using multi-mission satellite altimetry data over the 1993–2010 time span. In a first step, using a new processing system with dedicated algorithms and adapted data processing strategies, an improved set of sea level products has been produced. The main improvements include: reduction of orbit errors and wet/dry atmospheric correction errors, reduction of instrumental drifts and bias, intercalibration biases, intercalibration between missions and combination of the different sea level data sets, and an improvement of the reference mean sea surface. We also present preliminary independent validations of the SL_cci products, based on tide gauges comparison and a sea level budget closure approach, as well as comparisons with ocean reanalyses and climate model outputs.

Ocean Wind Speed Characteristic Over Malaysian Seas From Multi-Mission Satellite Altimeter During Monsoon Periods

The need for precise measurement of wind speed and growth of interest in offshore wind power has led to development of many measurements technique. This paper presents a study of wind speed characteristics during monsoon periods (north-east monsoon and south-west monsoon) over Malaysian seas using multi-mission satellite altimetry data from year 1993 to 2011. The study area covers in this study are Malacca Straits, South China Sea, Sulu Sea and Celebes Sea. From the result, the strongest winds are between Novembers to February, but on average, December is the strongest recorded wind speed at most locations. The South China Sea is the roughest region throughout the year compare to the other sea. It was concluded that using altimetry data, we can solve the disadvantage of conventional measurement in terms of spatial data distributions.