An Investigation of the On-board Microwave Radiometer of Satellite Altimetry for Studying the Atmosphere Variability

doi:10.21203/rs.3.rs-3200138/v1

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Research Article

An Investigation of the On-board Microwave Radiometer of Satellite Altimetry for Studying the Atmosphere Variability

https://doi.org/10.21203/rs.3.rs-3200138/v1

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published 23 Feb, 2024

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Since its first launching, the ability of satellite Altimetry in providing reliable and accurate ocean geophysical information of the sea surface height (SSH), significant wave height (SWH), and wind speed has been proven by numerous research, as it was designed for observing the ocean dynamics through nadir range measurement between satellite and the sea surface. However, to achieve high level accuracy, environmental and geophysical effects on the range measurement must be accurately determined and corrected, in particularly the effects from the atmospheric water vapor which can divert altimeter range up to 3–45 cm. Thus, satellite Altimetry is originally equipped by the on-board microwave radiometer to measure the water vapour content for correcting the range measurement. To our knowledge, no one has attempted to apply the on-board radiometer for atmospheric studies. In this present work, we attempt to optimize the on-board radiometer data for studying the atmosphere variability due to the El Niño–Southern Oscillation (ENSO) phenomena. We convert the on-board water vapor data into the precipitable water vapour (PWV), and we then investigate whether the derived PWV can capture the variability of ocean-atmosphere phenomena due to ENSO as accurate as the traditional Altimetry-derived sea level anomaly (SLA). Based on our analysis using the empirical orthogonal function (EOF), the results show convincing argument that Altimetry-derived PWV are reliable in examining the atmospheric fluctuation as the correlation of its primary principal component time series (PC1) with Oceanic Nino Index is higher (0.87) than SLA (0.80). The correlations between two dominant principal components (PC1 and PC2) of PWV and SLA are high, which are approximately 0.93 and − 0.67 for PC1 and PC2, respectively. These results may reinforce the confidence in the ability of satellite Altimetry for ocean-atmospheric studies.

Sea level

precipitable water vapour

satellite Altimetry

ocean-atmosphere interactions.

During the last three decades of radar Altimetry development, the milestones marked not only in monitoring and understanding ocean dynamics at mesoscales level and greater, but also in the study of coastal oceanography, inland water hydrology, and climate have surpassed expectations (Abdalla et al., 2021). It has been ascertained to be a valuable tool in providing ocean geophysical information of sea surface heights (SSHs), significant wave heights (SWHs), wind speed, etc. As reported in numerous researches, the accuracy of Altimetry satellite in measuring sea level variation is immaculate, which is up to 2–3 cm in the open ocean and up to 4 cm in the coastal region with the latest missions and retracking algorithms (Shum et al., 1995; Gómez-Enri et al., 2008; Valladeau et al., 2012; Abdullah et al., 2016; Cipollini et al., 2016; Abdullah, 2018). As the accuracy and precision of the altimeter measurements have significantly evolved, ocean variables, particularly sea level, derived from satellite Altimetry are utilised to study the signature of interannual, intraannual, and seasonal hydro-meteorological phenomena, for instance Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO) (Srinivas et al., 2005; Aparna et al., 2012; Sreenivas et al., 2012; Moon et al., 2015; Cheng et al., 2016; Lyu et al., 2017; Wang et al., 2018; Hamlington et al., 2020), Madden-Julian Oscillation (MJO) (Zhang et al., 2009; Oliver and Thompson, 2010, 2011; Rohith et al., 2019), and winter and summer monsoons (Chen et al., 2010; Saramul and Ezer, 2014; Trott and Subrahmanyam, 2019; Qu et al., 2022).

The primary trigger for these hydro-meteorological events is the complex interaction (coupling) between the atmosphere and ocean variabilities (Gupta et al., 2009; Jayawardena, 2015; Debele et al., 2019), that is generally due to the interchange energy of solar radiation at the overlapping boundary of the atmosphere and the ocean (Fedorov, 2008; Xie, 2009; Misra, 2014). While the influence of ocean-atmospheric coupling to the ocean part has been proven to be well perceived through satellite altimeter derived sea level, the atmospheric part is yet to be elaborated. According to the latest technology of satellite Altimetry, we believe that the satellite is capable to provide reliable data not only for the ocean dynamics, but also for the atmosphere. This is because satellite Altimetry is not only equipped with the radar system to measure the sea surface, but also it is equipped with the microwave radiometer to measure atmospheric water vapour. The radiometer measurement initially provides the wet path delay correction for the main altimeter range measurement (Eymard et al., 1994; Blanc et al., 1996; Fernandes et al., 2015; Fernandes et al., 2021). This perspective develops a new potential for satellite Altimetry to also be exploited to observe the atmosphere. However, study regarding the employment of altimeter microwave radiometer for atmosphere observation is still limited, albeit the effort to improve the accuracy of the satellite Altimetry microwave radiometer has been a focus on several research since the wet path delay (path delay caused by tropospheric water vapour) has a significant effect on Altimetry range measurement. The uncertainty of wet path delay or Wet Tropospheric Correction (WTC) from Jason-1 radiometer measurement is approximately 0.74 ± 0.15 cm (Brown et al., 2004). Apart from that, the accuracy of microwave radiometer measurement has been enhanced on each follow-on satellite mission (Brown, 2010; Maiwald et al., 2016; Fernandes and Lázaro, 2018). With this accuracy, the benefit of radiometer measurement should be acknowledged, not only to provide correction for the altimeter range, but also for the purpose of the atmosphere observation. Thus, the aim of this present study is to investigate the applicability of the on-board radiometer in studying the interannual ocean-atmospheric phenomena caused by ENSO and to compare the results against those derived from the sea surface measurements in the form of sea level anomaly (SLA).

The research area selected for this study is the Pacific Ocean as it has significant influence on climate variability, particularly over equatorial zone. In the interannual timescale, the ocean and atmosphere variability in the Pacific Ocean are predominantly influenced by anomalous climate mode of ENSO, which involves non-periodic fluctuation in winds, sea surface temperatures (SST), and air pressure of the underlying atmosphere (Southern Oscillation) across the equator over the tropical eastern Pacific Ocean (Philander, 1985; Holton and Dmowska, 1989; Timmermann et al., 2018). In addition, this area is selected because there are numerous previous studies on that area which can thoroughly validate our results, as the primary aim this study is merely to investigate the applicability of the on-board microwave radiometer to capture the atmospheric variability during ocean-atmosphere phenomena (in this case ENSO). It is not intended to elaborate a new physical signature of the event.

ENSO phenomenon relates to walker circulation due to the forces of pressure gradient caused by high-pressure area over the eastern Pacific and low-pressure system over Maritime Continent (Wang et al., 2000; Chang et al., 2004; Zhang et al., 2016). La Niña is caused by an extremely strong walker circulation, which results in colder ocean temperatures in the central and eastern tropical Pacific Ocean, owning to enhanced upwelling. During this phase, the trade winds are stronger, resulting warmer ocean temperature over western Pacific Ocean. Deep convection is more concentrated in Indonesia, thus resulting heavy rainfall over the region. At a certain point, the walker circulation experience weakening or reversal (which comprises trade winds), causing the upwelling of cool deep-sea water to dwindle or vanish. This situation causing the sea surface to reach temperatures above average, thus creating El Niño. During this phase, when the trade wind anomalously is weak, the sea temperature over the eastern part become warm. Thus, the deep convection of walker circulation moves to the east, resulting lower precipitation in Indonesia.

This phenomenon has major influence of the tropics and subtropics climates, particularly when this oscillations pattern become extreme (Wang et al., 2000; Chang et al., 2004; Han and Huang, 2009; Zhang et al., 2016). It causes intense weather, such as floods and droughts in several region in the world. Regions bordering the Pacific Ocean are the most affected, causing major problems particularly to the developing countries that depend on agricultures (e.g. Garden, 2014; Chang-Yang et al., 2016; Luo and Lau, 2020; Qian et al., 2020) and fisheries (e.g. Salinger, 2013; Kumar et al., 2014; Lehodey et al., 2020) as part of their economic resources.

This paper is divided into four Sections. Section 1: Introduction, is an introductory section which discuss the primary premise of the research, including the aim and expected outcomes of this study. Section 2: Data and Methodology, explain the attributes of Altimetry data used, including the observation period and missions involved. This section also includes the procedure used in deriving Altimetry derived SLA and PWV anomalies. Section 3: Results and Discussion, provides deliberate analysis and discussion regarding the variability of SLA and PWV during ENSO and the interrelation between those variables in the Pacific Ocean. Section 4: Conclusion and Remarks, summarises the findings of the research. The expected progress in the future study using Altimetry data for ocean-atmosphere interrelation are also discussed.

2.1. Altimetry Data Base

The data of 30 years from January 1993 to December 2022 from five (5) satellite missions: TOPEX, Jason-1, Jason-2, Jason-3, and Sentinel-6a are utilised to derive SLA and PWV. The timeline of all missions is shown in Fig. 1. The data are retrieved from Radar Altimeter Database System (RADS) and Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO). The temporal resolution of the data is 9.9 days and the sampling interval along track is ~ 6 km. The details description of the satellite’s tracks geometry is provided in Table 1. The spatial map of study area including satellite passes used in the study and highlighted passes and points used in across equator and along equator analysis, respectively, are indicated in Fig. 2. The derivation of SLA and PWV are provided in Section 2.2 and Section 2.3, respectively.

Table 1

Characteristics of orbit parameters for TOPEX, Jason-1, Jason-2, Jason-3, and Sentinel-6.
Main Characteristics
Ground track separation at Equator	315 km
Along-track sampling interval	~ 6 km
Number of passes per cycle	254
Number of passes used in this study	149
Number of footprints used in this study	184,933
Orbital velocity	7.2 km/s
Ground scanning velocity	5.8 km/s
Repeat cycle	9.9 days

2.2. Altimetry-Derived SLA

The basic concept of satellite altimeter operation is it measures height or distance between the satellite and the target surface using a nadir pointing microwave instrument called Radar Altimeter (Gommenginger et al., 2011). The measured distance (called “the range”) is then used to determine the value of sea surface height (SSH), which is the height between the instantaneous sea surface and reference ellipsoid, by computing the different value between the satellite position and the measured distance. Meanwhile, SLA is defined as the height between the instantaneous sea surface and mean sea surface height (MSS). The distinction between SSH and SLA is described in Fig. 3.

SLA from satellite Altimetry range measurement can be extracted as in Eq. (1) (Andersen and Scharroo, 2011).

$${h}_{SLA}=H-{R}_{retracked}-{Δ h}_{dry}-{Δ h}_{wet}-{Δ h}_{iono}-{Δ h}_{ssb}-{h}_{MSS}-{h}_{tides}-{h}_{atm}$$

(1)

where $H$ is the satellite altitude above reference ellipsoid. ${R}_{retracked}=c\frac{t}{2}$ is the computed range from travel time, $t$, observed by altimeter radar, and $c$ is the speed of light. ${R}_{retracked}$ is corrected observed range using waveform retracking algorithm, MLE-3 (Thibaut et al., 2010). ${Δ h}_{dry}$ is dry tropospheric correction, ${Δ h}_{wet}$ is wet tropospheric correction, ${Δ h}_{iono}$ is ionospheric correction, ${Δ h}_{ssb}$ is seas state bias correction, ${h}_{MSS}$ is mean sea surface height, ${h}_{tides}$ is tide corrections (including ocean tide, solid earth tide, pole tide, and tide loading) and ${h}_{atm}$ is dynamic atmospheric corrections (including high frequency fluctuation and inverse barometer correction). Details about the corrections and models used to derive SLA are tabulated in Table 1.

2.3. Altimetry-Derived PWV

The on-board microwave radiometer is used to derive three main elements in the atmosphere: water vapour, cloud liquid water, and wind driven variations on sea surface by collecting the radiations that are reflected by the ocean. These elements are utilised to derive wet tropospheric delay for range measured by altimeter radar.

The retrieval of wet tropospheric delay or correction, ${R}_{wet}$, involve two processing stages. The first stage is to calibrate the output of brightness temperature and to remove any correlation between the hardware physical temperature and the brightness temperature, which includes: (i) the correction of hardware losses from the system component to attain the antenna temperature; and (ii) the elimination of on-earth and off-earth side lobe contamination from the antenna temperature to get the brightness temperature (Brown et al., 2004). The second stage is to retrieve the wet tropospheric correction from brightness temperature. It begins with the following equation for estimating cloud liquid, ${L}_{z}$, and wind speed, $W$, from brightness temperature (Keihm et al., 1995):

$${L}_{z}={l}_{0}+\sum {l}_{f}{T}_{b}\left(f\right)$$

(2)

$$W={w}_{0}+\sum {w}_{f}{T}_{b}\left(f\right)$$

(3)

where ${T}_{b}\left(f\right)$ is the measured brightness temperature corresponds to the frequency, $f$, and ${l}_{0}, {l}_{f}, {w}_{0},$and ${w}_{f}$ are predetermined coefficients.

Subsequently, the path delay induced by water vapour is retrieved through two-step statistical inversion. The first step is computation of the initial estimate of the path delay value, ${PD}^{\left(1\right)}$, using coefficients associated with wind speed, which can be expressed as:

$${PD}^{\left(1\right)}={B}_{0}^{\left(1\right)}\left(w\right)\sum {B}_{f}^{\left(1\right)}\left(w\right)*\text{ln}\left[280-{T}_{b}\left(f\right)\right]$$

(4)

where ${B}^{\left(1\right)}$ are coefficients for the initial value associated with wind speed. The second step is computation of the final path delay value, ${PD}^{\left(2\right)}$, using coefficient associated with wind speed and the initial path delay value, and can be expressed as:

$${PD}^{\left(2\right)}={B}_{0}\left(w, {PD}^{\left(1\right)}\right)+\sum {B}_{f}\left(w, {PD}^{\left(1\right)}\right)*\text{ln}\left[280-{T}_{b}\left(f\right)\right]$$

(5)

where $B$ as coefficient of final value associated with wind speed and the initial path delay value. Thus, the total wet path delay, $WPD$, can be obtained by adding the cloud liquid component to the final path delay value, as shown in Eq. (6). The wet tropospheric correction, ${R}_{wet}$, which is a negative value of $WPD$ is estimated in Eq. (7):

$$WPD={PD}^{\left(2\right)}+1.6*{L}_{z}$$

(6)

$${R}_{wet}=-WPD$$

(7)

The acquired ${R}_{wet}$ value can then be used to compute the total water vapour concentration in a specific atmospheric column, referred to as $PWV$, as expressed in Eq. (8).

$$PWV= \frac{{10}^{6}}{{\rho R}_{v}\left(\left(\frac{{k}_{3}}{{T}_{m}}\right)+{k}_{2}^{{\prime }}\right)}*{R}_{wet}$$

(8)

where ${R}_{v}=4.61527{m}^{3}/kg.K$ is the specific gas constant for the water vapour, $\rho =997kg/{m}^{3}$ is the density of water, ${k}_{3}=3.7765*{10}^{-5}{K}^{2}/mbar$, ${k}_{2}^{{\prime }}=17K/mbar$, and ${T}_{m}$ is the temperature of atmosphere column. Since ${T}_{m}$ is not recorded by the altimeter satellite, its value is acquired using existing model, such as Global Pressure Temperature (GPT). This model is retrieved from European Centre of Medium Range Weather Forecast reanalysis v5 (ECMWF ERA5), which able to provide yearly data variation from earth pressure and temperature (Böhm et al., 2007).

Please note that, for Jason-1, Jason-2, and Jason-3 satellite mission, atmosphere water vapour content data are provided in the raw satellite data file in unit kg/m² and final PWV can be simply calculated by multiplying the data with water density value. However, for TOPEX and Sentinel-6a satellite missions, the data are not included in satellite data file, instead it is provided as wet tropospheric correction, ${R}_{wet}$. Therefore, to standardise the data, ${R}_{wet}$ is used to derive PWV for all missions.

The ${R}_{wet}$ has been through thorough validation using multiple independent datasets, including GNSS-derived path delay (Fernandes et al., 2010), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Model, Global Precipitation Measurement (GPM), etc (Fernandes et al., 2015; Fernandes and Lázaro, 2018). With the immaculate works the accuracy of wet tropospheric correction nowadays is up to 1–3 cm (Fernandes et al., 2021).

2.4. Extraction of Inter-annual Signal

To effectively analysis the Walker Circulation that associated with interannual phenomena (in this case, ENSO), annual harmonics and dominant climatology component for inter-seasonal phenomena must be eliminated from SLA and PWV time-series, to retrieve the variables’ anomaly.

As these inter-seasonal components depict the homogenous state without anomalous variation, it can be modelled by averaging long period data. The modelled components are the long-term mean and trends, and frequent seasonal oscillation for each variable, using the following equations:

$$\nabla M\left(t\right)=M\left(t\right)- \stackrel{-}{M}-T\left(t\right)-S\left(t\right)$$

(9)

where $\nabla M$ is the variable anomaly, $t$ is time, $M$ is the variable observed values, $\stackrel{-}{M }$is the variable long term mean, $T$ and $S$ are the trend and seasonal component of the variable time series, respectively. The elements for the trend and seasonal component are estimated using simple least square adjustment. The trend component is determined using the simple linear model and the seasonal component is determined by the first three harmonics of the annual cycle. $T$ and $S$ can be defined as follows:

$$T\left(t\right)= {a}_{tr}+ {b}_{tr}t$$

(10)

$$S\left(t\right)= \sum _{k=1}^{3}\left({a}_{k}\text{cos}\left(k\omega t\right)+ {b}_{k}\text{sin}\left(k\omega t\right)\right)$$

(11)

where ${a}_{tr}$and ${b}_{tr}$ are the constant and the gradient term of the linear trend component, respectively. $\omega$is the frequency of one year, which can be defined as $\frac{2\pi }{365.25 {day}^{-1}}$, thus, ${a}_{k}$and ${b}_{k}$for $k=\text{1,2}, \& 3$ are sine and cosine terms for the first three harmonics of the annual cycle. The period of data to resolve these components using least square adjustment is 30 years (January 193 to December 2022). The exact round number of years for the base period is necessary to minimise bias from non-complete annual harmonics cycle. However, decadal signals are neglected in the modelling and remain in the time-series, as its oscillation is insignificant and has not influence the pattern of interannual variability.

To analyse the influence of interannual phenomena to the SLA and PWV variabilities, for instance ENSO, annual harmonics and dominant climatology component are eliminated from SLA and PWV time-series. Subsequently, simple average is performed to extract monthly data from all the available data observed in the respective month. The seasonal signal modelling and elimination are applied to all observed data in all footprints used in the study. Figure 4 shows the derived SLA and PWV time-series overlaid with the modelled climatological components, and SLA and PWV anomaly after climatological components have been eliminated from the time-series, respectively.

3.1. Validation of Altimetry-derived PWV

To determine the capability of Altimetry-derived PWV in capturing the state of water content in the atmosphere, PWV derived from ECMWF wet tropospheric correction model are used to validate the Altimeter derived PWV. ${T}_{m}$ used in ECMWF derived PWV are similar to the values used in Altimeter derived PWV. The comparison is conducted on every footprint over the study region. Temporal resolution of both data are 9.9 days, corresponding to the Altimeter’s temporal resolution. For validation purposes, correlation coefficient between both datasets is computed and histogram of deviation of difference is plotted. The statistical results are indicated in Table 2 and Fig. 5.

Table 2

Statistical results of comparison between Altimetry derived PWV and ECMWF derived PWV
Main Characteristics	Values
Correlation Coefficient	0.976
Mean Different	0.371 mm
Standard Deviation of Difference	2.518 mm
Root Mean Square Difference	2.518 mm
Normalised Root Mean Square Difference	1.011 mm

There is a high positive correlation between the two datasets which yields correlation coefficient value of 0.976, showing that Altimetry derived PWV is in a good agreement with ECMWF derived PWV. The Root Mean Square Difference (RMSD) is 2.545 mm and the normalised (NRMSD) or the ratio of the RMSD to the standard deviation of difference (STDD) is 1.011 mm. The RMSD values indicate that the discrepancy between both datasets is insignificant. From the histogram of the difference (Fig. 5 (b)), the difference appears to be normally distributed with the symmetrical bell shape. The peak value is close to zero, signifies that the mean differences are extremely small. The discrepancies are probably caused by random error rather than error in derivations or caused by inaccurate data.

3.2. Variability of SLA and PWV during El Niño and La Niña

The intensity of ENSO is defined by the Oceanic Nino Index (ONI), which the average of three months rotation of ocean SST anomaly in the east-central tropical Pacific (5°N-5°S, 120°-170°W). El Niño and La Niña conditions are considered to be present when the ONI is higher than 0.5 and lower than − 0.5, respectively, which is warmer and cooler than average. Figure 6 depicts the variation of PWV and SLA from lower to upper latitude for ascending (Pass 82) descending (Pass 247) passes derived at ENSO region. The PWV and SLA are plotted during difference phases of ENSO (El Niño, neutral, and La Niña). The fluctuations of both variables are associate with ONI as it increases (decreases) during high ONI (low ONI) approaching the ENSO region. During the extreme El Nino with Nino value up to 2.71, the anomalies of PWV and SLA in the central Pacific increase up to 18 mm and 23 cm, respectively. Conversely, during La Nina event with negative Nino value − 1.77, PWV and SLA anomalies in the central Pacific decrease down to approximately − 7 mm and − 9 cm, respectively. Compared to the natural condition when Nino value − 0.01, the PWV and SLA anomalies are insignificant. The initial results evidently indicate that both PWV and SLA variables sensitive to ENSO and hence the variables can be further exploited to investigate the ocean-atmosphere interactions.

Theoretically, a walker circulation during ENSO season upholds an equilibrium state in the tropical ocean and atmosphere, in which during El Niño the eastern Pacific Ocean is characterised by higher sea level and deeper thermocline as the atmospheric pressure is low. Low atmospheric pressure is driven by relatively increasing precipitation (Ji et al., 2000; Lau and Yang, 2003; Tsonis et al., 2003; Gastineau et al., 2009; Stuecker et al., 2017). Whereas, in the Maritime Continent (western Pacific Ocean), the sea level is lower as the atmospheric pressure is higher resulted from decreasing precipitation. The opposite situation occurs during the La Niña season (Chang et al., 2004; Jia et al., 2016; Zhang et al., 2016). These patterns are apparent in the spatial plot of Pacific Ocean in Fig. 7 and Fig. 8, which depict the variation of SLA and PWV during the highest, neutral, and lowest ONI. The pole like gradient of SLA is more significant during both El Niño and La Niña seasons. During the El Niño the SLA anomalies increase up to 23 cm over centra Pacific and decrease down to -25 cm over western Pacific. On the contrary, during La Niña, the SLA anomalies increase up to 22 cm in the western Pacific and decrease down to -16 cm over the centra Pacific. Meanwhile for PWV, the gradient creates basin like pattern where the highest anomalies during the El Niño event increase up to 18 mm along the equator in the Central Pacific and decrease down to -14 mm in the region starting from Maritime Continent, spreading to the upper and lower latitude toward the central Pacific. Similar patter also shown during La Niña with the contradict increasing (up to 9 mm) and decreasing (down to -14 mm) anomalies regions.

Noted that the number of parameters that influence SLA variability are significantly higher than parameters that effect PWV, yielding the different in general pattern throughout the entire study region. However, the main concern is the changes in the convection and subsidence areas of walker circulation during ENSO event, which are in the central Pacific and Maritime Continent. As apparent in the Figures, the fluctuation of SLA and PWV anomaly during El Niño and La Niña are similar and highly correlated with ONI (Fig. 9), which is higher than 0.8 and lower than − 0.8. As expected, since ONI is determined based on the SST observed in the highlighted region (black box in the Figures) over the central Pacific, its correlation with SLA and PWV over this area is positive. As aforementioned, the nature of these variables over the Maritime Continent are extremely contradict to the central Pacific, particularly during extreme events (high and low ONI), resulting in negative correlations with ONI.

3.3. Principal Component Analysis

A standard Empirical Orthogonal Function (EOF) analysis has been performed on the Altimetry-derived SLA and PWV to identify and discrete the underlying dominant variations from timeseries of both SLA and PWV variables. The primary leading principal component (PC1) for PWV and SLA, which respectively explain 35% and 42% of the total variance, have generated significant contrast spatial pattern over central Pacific and Maritime Continent. However, SLA forms a pole like pattern, meanwhile for PWV, the mode is centred in the central Pacific. According to the spatial patterns, prompt analysis can consider that EOF PC1 of both SLA and PWV are associated with ENSO variability (Fig. 10). The argument is further supported by the high correlation coefficient of both PWV and SLA correspondent time series PC1 with ONI, which are 0.87 and 0.80, respectively (Fig. 11).

The spatial plot of secondary principal component (PC2), which explains 13% and 10% of the total variance for PWV and SLA, respectively, show similar pattern to EOF PC1 with the low coefficients shifted slightly eastward and westward, respectively (see Fig. 8). However, the correspondent time series have almost zero correlations with ONI, which are − 0.03 for PWV and 0.01 for SLA (Fig. 13 and Fig. 14). This is because there are apparent temporal disparities between PC2 oscillation and ONI. This indicates that there is likely to be ENSO propagation features in PC2 of PWV and SLA signals. Standard EOF is incapable in encapsulating propagating signal as a single mode, instead it captures the signals from several independent modes, resulting in low correlation of two similar variations of different phases.

However, the discrepancies are varied through the entire time series for both PWV and SLA. Thus, the time series is divided into several shorter period and only three (labelled as a, b, and c in Fig. 13 and Fig. 14) significant oscillations during extreme ENSO are selected for cross correlations analysis. The selected period of short time series is 1.5 years before and after (3 years in total) the maximum ONI of the ENSO cycle. This range is selected as the minimum period of ENSO cycle is 2.5–3 years (Tudhope et al., 2001; McPhaden et al., 2006). The cross correlations are computed to determine the temporal discrepancies that contribute to maximum correlations between the variables and ONI. The results in Fig. 13 and Fig. 14 show the temporal lags for maximum correlation.

The highest correlation for PWV and SLA are during ENSO cycle c, from May 2014 to April 2017, with maximum correlation − 0.95 by 2 months lag and 0.87 by 8 months lag, respectively. Meanwhile, the lowest correlation for PWV is during ENSO cycle A, from May 1996 to April 1999, with maximum correlation − 0.63 by 5 months lag and for SLA is during ENSO cycle B, from June 2008 to May 2011, with maximum correlation − 0.58 by -5 months lag. According to these results, when compared to ONI, temporal lags for PWV and SLA are not similar and inconsistence for every cycle. Thus, it is unreliable to draw a conclusive decision whether there are ENSO propagation features in PC2 of PWV and SLA signal. There is high probability that the signal is from local variation or other phenomena, which is not significantly related to ENSO.

Another dominant event that occurs in the Pacific Ocean is the Inter-Tropical Convergence Zone (ITCZ). As indicated in EOF pattern in Fig. 12, particularly for PWV, the positive values are concentrated in the upper latitude, between 3° – 25° in the central Pacific. Although ITCZ varies seasonally, and the seasonal oscillation have been removed from the data set, however, the seasonal variations remain.

The ITCZ is defined as a cloud belt created by a high concentration of uprising air driven by the convergence of north-easterly and south-easterly trade winds (Smith, 2003). The ITCZ position changes seasonally in response to the warmest area, theoretically it will travel to north during boreal summer and south during boreal winter. However, ITCZ in the eastern Pacific remains in the northern hemisphere during both seasons. This is driven by the equatorial asymmetry in SST and formation of stratus cloud in the southern Pacific, which probably prompted by the land-sea distribution (Xie and Philander, 1994; Xie, 1996).

3.4. Interrelation of SLA and PWV

The influence ENSO to the ocean variability utilising Altimetry-derived SLA have been studied by numerous researchers (Ji et al., 2000; Cazenave et al., 2012; Cheng et al., 2016) (Moon et al., 2015; Zhang et al., 2019) and have been proven to be the most accurate method, particularly for global scale. In this study, the oscillation of PWV has shown similar patterns as SLA in capturing ENSO propagation feature. The propagation of PWV associated well with SLA fluctuation shown by the high correlation values between both variables. Correlation coefficient between SLA and PWV has been computed using 30 years of interannual data to investigate the interrelation between the two variables throughout Pacific Ocean (Fig. 15). The results are satisfactory as the correlations are high (> 0.9) in both west and central Pacific regions. These results indicate that on-board microwave radiometer of Altimetry satellite is able to encapsulate atmospheric variabilities as efficient as ocean variations.

To further analyse the interrelation between SLA and PWV in encapsulate interannual variation related walker circulation, correlation coefficient of the primary (PC1) and secondary (PC2) leading principal component of both variables have been computed. The results (Fig. 16) show higher positive correlation for PC1 with correlation coefficient of 0.93 and high negative correlation for PC2 with correlation coefficient of -0.67.

In the previous analysis, it has been concluded that PC1 of PWV and SLA variables is highly correlated with ENSO propagation as the correlation coefficient of both variables and ONI are high (0.87 and 0.80, respectively), and PC2 is associated with ITCZ oscillation based on the EOF pattern, particularly for PWV. These results indicate that hydro-meteorological phenomena signatures are significantly captured in both Altimetry-derived PWV and SLA datasets. In addition, the comparison between SLA and PWV, PC1 and PC2 indicate high correlation, proving that both Altimetry-derived ocean and atmosphere variables have high interrelation. This confirm that satellite Altimetry not only reliable for ocean geophysical study, but also for atmospheric variability.

Comprehensive analyses have been conducted to identify and compare the interannual oscillation signature related to ENSO in altimeter-derived SLA and PWV time series. According to variability analysis across equator, the fluctuation of SLA and PWV associated well with ENSO oscillation as the increasing and decreasing anomalies spatially and temporally similar to ONI. Spatial plots also indicate that the variation of SLA and PWV during La Niña and El Niño are parallel to the equilibrium theory of walker circulation, which is SLA and PWV is higher in the central Pacific and lower in the Maritime Continent during El Niño and conversely during La Niña. The signature of ENSO in Altimetry-derived SLA and PWV variability for 30 years is undeniably substantial as the correlations between both variables with ONI at ENSO region are acceptably high (> 0.8).

A standard EOF analysis is performed to identify the variability modes of SLA and PWV that occur in the Pacific Ocean. Two dominant modes are identified with pole-like spatial characteristics. The primary component, PC1 for both variables fully explain the ENSO variations as they have high correlation with ONI, particularly for PWV with correlation coefficient of 0.87, and SLA with correlation coefficient of 0.80. However, for secondary component, PC2, there is no conclusive evidence that associate its oscillation with ENSO as almost zero correlations are found for both SLA and PWV. Based on the EOF pattern for PC2, the signal most probably associated with another dominant even in the Pacific Ocean, which is ITCZ.

As per these results, the capability of satellite Altimetry in observing the atmosphere through PWV derived from on-board microwave radiometer data is comparable, and even better in some cases with the altimeter-derived SLA. The variability of PWV is consistent with the ENSO interannual transition, temporally and spatially, showing that the ability of satellite Altimetry in observing atmosphere variability is as reliable and accurate as perceiving ocean geophysical oscillation. To examine the interrelation between SLA and PWV, spatial correlation coefficient using time series for along track data for the entire study region is computed. The results are surprisingly satisfied as the corelation values for the targeted areas (Maritime Continent and Central Pacific) are higher than 0.9. The correlation for primary and secondary principal components, PC1 and PC2 are also gratifying as the correlation values are 0.93 and − 0.67, respectively. These results reinforce the confidence in the ability of satellite Altimetry for ocean-atmospheric studies.

As the satellite Altimetry capability in providing accurate sea level measurement has been recognised many years ago, and the new potential of observing atmospheric water vapour has so far shows substantially satisfactory results, the aim to optimise the satellite by utilising synchronous altimeter-radiometer measurement to observe ocean-atmosphere coupling in support of hydro-meteorological hazard mitigation is highly feasible. Altimetry satellite might be not able to measure daily weather, particularly rainfall, which highly variable through time and space, as its highest temporal resolution is approximately 9–10 days and longer data post processing. However, it is reliable enough to monitor longer periodic phenomena, such as ENSO, Indian Ocean Dipole (IOD), Madden-Julian Oscillation (MJO), and the oscillation of Inter-Tropical Convergence Zone (ITCZ), which associate with monsoon.

Using both ocean and atmosphere measurement from satellite Altimetry not only present a new potential of the satellite itself, but also a way to overcome the drawback of previous ocean-atmosphere studies, which has temporal and spatial disparities as each variable were observed using different platforms at different time and locations.

AVISO	Archiving, Validation, and Interpretation of Satellite Oceanographic data
ECMWF	European Centre of Medium Range Weather Forecast
ECMWF ERA5	European Centre of Medium Range Weather Forecast reanalysis v5
ENSO	El Niño–Southern Oscillation
EOF	Empirical Orthogonal Function
GNSS	Global Navigation Satellite System
GPM	Global Precipitation Measurement
GPT	Global Pressure Temperature
IOD	Indian Ocean Dipole
ITCZ	Inter-Tropical Convergence Zone
MJO	Madden-Julian Oscillation
NRMSD	Normalised Root Mean Square Difference
ONI	Oceanic Nino Index
PC	Principal Component
PWV	Precipitable Water Vapour
RADS	Radar altimetry database system
RMSD	Root Mean Square Difference
SLA	Sea Level Anomaly
SSH	Sea Surface Height
SST	Sea surface temperature
STDD	Standard Deviation of Difference
SWH	Significant Wave Height
WTC	Wet Tropospheric Correction

Availability of data and materials

Satellite altimetry data and European Centre of Medium Range Weather Forecast (ECMWF) data are available in Radar Altimeter Database System (RADS) and Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) ftp site (https://www.aviso.altimetry.fr/en/data/data-access/ftp.html). Oceanic Nino Index (ONI) data are available at National Oceanic and Atmospheric Administration (NOAA) website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php).

Competing interests

The authors declare that they have no conflict of interest.

Funding

This research was funded by Lembaga Penelitian dan Pengabdian Masyarakat - Institut Teknologi Bandung (LPPM - ITB).

Authors’ contribution

DDW provide the initial ideas, design the research framework, and wrote the manuscript. NNA design the research framework, implement the research designed, perform data processing, and wrote the manuscript. IM and WK assist in supervise in data processing and analyses. ZAJT and MRA assisted in metrological discussion and analysis. FN assisted in data processing.

Acknowledgements

We would like to thank the Lembaga Penelitian dan Pengabdian Masyarakat – Institut Teknologi Bandung (LPPM – ITB) for financially supporting this research. We also thank the Radar Altimeter Database System (RADS) of TU Delft, Netherlands and Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) for providing Altimetry and ECMWF data used in this present study, as well as National Oceanic and Atmospheric Administration (NOAA) for providing the Oceanic Nino Index (ONI) data. NNA thanks to Bandung Institute of Technology for fully supporting her PhD studies under the scheme “Ganesha Talent Assistantship – International Fellowship (GTA-IF)”.

Authors’ information

NNA is a PhD student at Institut Teknologi Bandung, currently performing research regarding ocean-atmosphere interaction using solely satellite altimetry data over the tropic regions.

DDW is an associate professor in Geodesy with interests in application of satellite geodesy for Earth-Atmosphere observations.

IM is a Professor in Earthquake Geodesy with interest in natural hazard mitigation utilising geodetic space observations.

WK is an associate professor in Geodesy with interest in application of satellite geodesy for Earth-Atmosphere observations.

ZAJT is a senior lecturer in Geodesy with interest in geomorphology dan hydrometeorology study particularly over tropic region.

MRA is an assistant professor in Meteorology with interest in climate and atmospheric sciences as well as tropical dynamics.

FN is the graduate researcher working on altimetry data processing.

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An Investigation of the On-board Microwave Radiometer of Satellite Altimetry for Studying the Atmosphere Variability

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Abstract

Figures

1. Introduction

2. Data and Methodology

2.1. Altimetry Data Base

2.2. Altimetry-Derived SLA

2.3. Altimetry-Derived PWV

2.4. Extraction of Inter-annual Signal

3. Results and Discussions

3.1. Validation of Altimetry-derived PWV

3.2. Variability of SLA and PWV during El Niño and La Niña

3.3. Principal Component Analysis

3.4. Interrelation of SLA and PWV

4. Conclusions and Remarks

Abbreviations

Declarations

References

Supplementary Files

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