*Article* **Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain**

**Daniele Ciani 1,***∗***, Rosalia Santoleri 1, Gian Luigi Liberti 1, Catherine Prigent 2, Craig Donlon <sup>3</sup> and Bruno Buongiorno Nardelli <sup>4</sup>**


Received: 11 July 2019; Accepted: 31 July 2019; Published: 3 August 2019

**Abstract:** We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

**Keywords:** sea surface salinity; microwave remote sensing; CIMR; copernicus marine service

## **1. Introduction**

The salinity of the ocean is a crucial parameter to investigate the water cycle, the ocean dynamics and the marine biogeochemistry from the global to the regional scale. It was classified as an Essential Climate Variable in the context of the Global Climate Observing System (GCOS) programme. Indeed, salinity affects both the sea water density and the marine carbonate chemistry (alkalinity), making it a fundamental variable to investigate the thermohaline global circulation, the local surface and deep

circulation, the water mass transformation and the uptake of carbon by the ocean including ocean acidification, e.g., [1–4].

In the past, salinity has suffered from poor observational coverage, being uniquely sampled via point-wise in situ observations, impacting the accurate assessment of its spatial variability and dynamics, in particular for the construction of gap-free optimally interpolated fields, e.g., the in situ-Based Reanalysis of the Global Ocean Temperature and Salinity (ISAS) [5]. The three-dimensional ISAS dataset was recently improved using quasi-global ARGO floats observations [6]. In addition, the characterization of the sea-surface salinity (SSS) has benefited from satellite observations and on their synergy with in situ measurements, e.g., [7–9].

In the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), a daily, mesoscale resolving SSS multi-year gap-free Level-4 analysis (L4 hereinafter) product was developed by Buongiorno Nardelli et al. 2016 [10] and Drogheiet al. 2018 [11]. The approach of [10,11], unlike [5–9], relies on a multi-dimensional (multivariate) optimal interpolation (OI) algorithm that combines both Soil Moisture Ocean Salinity (SMOS) satellite retrievals and in situ salinity measurements with satellite sea-surface temperature information. This product is distributed operationally in near real time in late 2018. Despite the success of this product, there are no missions planned to secure continuity of satellite SSS measurements after the SMOS and Soil Moisture Active Passive (SMAP) missions, which could impact both the accuracy and the effective resolution of the CMEMS SSS operational products.

The Copernicus Imaging Microwave Radiometer (CIMR) satellite mission [12] is being developed as a High Priority candidate Mission (HPCM) in the context of the European Copernicus Expansion Programme. To address the needs of Copernicus and the Integrated European Policy for the Arctic, the CIMR mission will carry a wide-swath (>1900 km) conically scanning multi-frequency microwave radiometer. CIMR measurements will be made over a forward scan arc followed 260 s later by a second measurement over a backward scan arc. Polarised (H and V) channels centered at 1.414, 6.925, 10.65, 18.7 and 36.5 GHz are included in the mission design under study (full Stokes parameters are foreseen). The real-aperture resolution of the 6.925/10.65 GHz channels is <15 km and 5/4 km for the 18.7/36.5 GHz channels, respectively. The 1.414 GHz channel will have a real-aperture resolution of 60 km (fundamentally limited by the size of the 8 m deployable mesh reflector). However, all channels will be oversampled allowing gridded products to be generated at much better spatial resolution. Channel NEdT is 0.2–0.8 K with a goal absolute radiometric accuracy of 0.5 K. Radio Frequency Interference (RFI) will be mitigated on-board the satellite using dedicated processors. CIMR will fly in a dawn-dusk orbit providing 95% global all weather coverage every day with one satellite and complete (no hole-at-the-pole) sub-daily coverage of the polar regions. CIMR will operate in synergy with the EUMETSAT MetOp-SG(B) mission so that over the polar regions (>60◦N and 60◦S) collocated and contemporaneous measurements between CIMR and MetOp Microwave Imager and SCA scatterometer measurements will be available within ±10 min. Thus, CIMR is designed to provide global mesoscale-to-submesoscale resolving observations of sea-surface temperature, sea-surface salinity and sea-ice concentration. The availability of the CIMR SSSs with global coverage would represent an opportunity to continue ingesting satellite SSS measurements in the CMEMS multivariate OI, potentially allowing to improve the interpolated product effective spatial resolution and accuracy with respect to L4 analyses built from in situ observations alone. The present study is thus focused at demonstrating this potential.

We will implement an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR Global Operational model outputs [13] and relying on the present-day version of the CMEMS SSS processing chain. In particular, the multivariate OI algorithm will be first run ingesting only synthetic in situ SSS extracted from the MERCATOR outputs. In a second run, the OI processing will rely on both synthetic in situ and CIMR observations, the latter also built from the MERCATOR model outputs accounting parametrically for the expected CIMR accuracy. Finally, the L4 SSS resulting from the two versions of the OI algorithm will be validated against the original MERCATOR global SSS. The impact of the future CIMR SSS in the OI processing will be demonstrated and quantified throughout the paper.

In Section 2, we present the datasets and methods used to carry out the study. This section illustrates both the principles of the OI algorithm as well as the details on the input-data processing and the structure of the OSSE. We go on presenting the main results of the OSSE in Section 3. In the end, Section 4 will discuss the main results and illustrate the main conclusions and perspectives of our study.

## **2. Materials and Methods**

This section contains the description of the dataset used to carry out the study, the principles of the CMEMS L4 SSS processing chain and the structure of the OSSE for evaluating the potential of the future CIMR SSS remote measurements.

#### *2.1. Data*

	- CIMR SSS observations, running the OI algorithm and finally assessing the quality of the L4 SSS maps given by the OI processing chain (see Sections 2.2, 2.4 and 3).

#### *2.2. Processing Chain Description*

The CMEMS SSS operational chain combines in situ and satellite-derived observations using the multidimensional OI technique originally introduced by Buongiorno Nardelli 2012 [15] and based on the findings of Bretherton et al. 2016 [16]. Presently, this processing chain provides global L4 weekly SSS mapped over a 1/4◦ regular grid. The algorithm is based on the assumption that SSS and SST covary at spatial scales smaller than those characterizing atmospheric fluxes. This allows us to extract useful dynamical information from the high-pass filtered satellite L4 SSTs for use with the SSS fields.The L4 SSTs are generally obtained by merging microwave and infrared observations, the latter having resolutions O(1 km) [17]. and provide guidance to the OI system when interpolating SSS fields resulting in an enhanced effective resolution compared to simple space-time interpolation approaches. The technique, originally developed to interpolate in situ SSS, was successively adapted to ingest satellite observations from SMOS and finally calibrated to compute dynamically consistent SSS/SSD datasets [10,11,18]. The theoretical framework of the SSS multivariate OI is briefly illustrated below, referring the reader to [10,11,15,18] for more details.

The optimal SSS analysis (SSSanalysis) is given by a weighted sum of the SSS observations anomalies (SSSobs) with respect to a first guess background field (SSSbackground). The weights provide an unbiased estimate (i.e., it has the same mean as the true field) with the minimum expected estimate error (in a least squares sense):

$$\overline{\text{SSS}}\_{\text{analyys}} = \overline{\text{SSS}}\_{\text{background}} + \text{C}(\text{R} + \text{C})^{-1}(\overline{\text{SSS}}\_{\text{obs}} - \overline{\text{SSS}}\_{\text{background}}).\tag{1}$$

In Equation (1), C is the background error covariance matrix and R, assumed diagonal, is the observations error covariance matrix (here defined by constant values per each observation type/platform). In the implementation described by [10,11], the background field is provided by an analysis built from in situ observations alone through a classical OI. More recently, in the framework of CMEMS, the background field estimate was modified by computing a first round of space-time OI based on in situ input data relying on a monthly climatology. The structure of the background error covariance matrix, C, is given by Equation (2):

$$\mathbf{C}(\Delta \mathbf{r}, \Delta \mathbf{t}, \Delta \text{SST}) = \boldsymbol{\varepsilon}^{-(\Delta \mathbf{r}/\mathcal{L})^2} \boldsymbol{\varepsilon}^{-(\Delta \mathbf{t}/\tau)^2} \boldsymbol{\varepsilon}^{-(\Delta \mathbf{S} \mathbf{T}/\mathcal{T})^2} \boldsymbol{\varepsilon} \tag{2}$$

where:


This covariance model approximates a multivariate approach that includes both SSS and SST in the state vector used to build the observation matrix (that is then used to estimate the covariance matrix C). In practice, the multivariate covariance model gives more weight to observations found on the same isothermal of the interpolation point compared to observations found at the same spatial and temporal separation but characterized by different SST values. The L4 SSTs, due to their high spatial resolution, accurately describe the thermal signature of the ocean mesoscale features at global scale. Their ingestion in the OI algorithm has already shown to improve L4 SSS effective resolution (e.g., [11]). Moreover, the processing chain relies on a dataset of pseudo in situ SSS observations to overcome the sparseness of avaliable in situ SSS. The pseudo-observations are extracted from the background field (1 every 16 grid boxes) and are used as additional input. In coastal areas (at distances < 200 km from the coast), the pseudo-observations are taken from the climatological background. This strategy guarantees the homogeneity of the L4 SSS spatial resolution in case of prolonged (in time) or extended (in space) input data gaps during the interpolation process.

In the present study, the input data for the OI processing, i.e., the in situ, satellite, pseudo SSS and high resolution L4 SST, are all derived from the MERCATOR numerical simulations (see also Section 2.3). In Equation (1), the constant values included in the observation error covariance matrix R are divided by the signal variance, thus representing the different noise-to-signal levels for each type of input data. These values have been found through dedicated tuning experiments and are 0.5 for the pseudo observations, 0.1 for the synthetic CIMR satellite SSS and 0.05 for the synthetic in situ observations. The mean value used for CIMR may appear too optimistic (only twice the error associated with synthetic in situ) and could be optimized by considering the expected latitudinal dependence of CIMR error. This dependence, however, is not presently handled by the CMEMS processing chain and is thus left to future developments.

#### *2.3. OSSE Description*

Selecting one year (2016) of daily SSS data from the CMEMS MERCATOR global operational model, we subsampled the SSS fields in two ways, according to the following purposes:


The processing chain is run in two different configurations: once ingesting the synthetic in situ SSS alone and, in a second run, the in situ plus the CIMR observations. In both cases, the result of the processing, i.e., the multivariate OI algorithm, is a global map of L4 SSS. Finally, the SSS maps obtained in the two configurations is compared with the original MERCATOR SSS fields, our benchmark. The metrics of the comparison are the root mean square error (RMSE) and the power spectral density (PSD). A flowchart of the OSSE is provided in Figure 1. The details on the simulation of the synthetic data are given in Section 2.4.

**Figure 1.** Workflow of the observing system simulation experiment.
