Copernicus Imaging Microwave Radiometer Research Articles

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors

AbstractThe monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters.

An Antenna Pattern Correction Algorithm for Conical Scanning Spaceborne Radiometers: The CIMR Case

The rapid evolution of the effects observed in various areas of our planet related to climate change poses urgent questions about the knowledge of the state of the polar area and requires satellite acquisitions with fine spatial resolution and high accuracy to develop advanced products. The Copernicus Imaging Microwave Radiometer (CIMR) mission, based on a multifrequency microwave radiometer and designed to observe the ocean, sea ice, and Arctic environment, requires brightness temperature measurements with a total absolute uncertainty of 0.5 K and a spatial resolution of 5 km. This constraint demands very large reflectors with a gain value of tens of decibels. Mechanical constraints will be attained by using a mesh reflector, which guarantees the required resolution but with the drawback of a radiation pattern characterized by many grating lobes that contaminate the value of the brightness temperature associated with the boresight position. In this article, an antenna pattern correction (APC) is proposed to correct these effects. The algorithm takes advantage of an iterative formulation based on the Jacobi Method, providing a suitable correction that depends on the chosen spatial resolution. The APC algorithm was tested at both K- and Ka-bands with similar performance. Here, only the results from the latter are shown, as its antenna pattern is the most challenging among CIMR.

A model predictive control for attitude stabilization and spin control of a spacecraft with a flexible rotating payload

Many Earth Observation missions, implementing space-based microwave sensing techniques for collecting surface information, employ spinning sensors to cover large swaths of terrestrial areas, thus improving the rate at which global maps of those measured data are generated. These spacecraft (as Soil Moisture Active Passive (SMAP) developed by NASA or Copernicus Imaging Microwave Radiometer (CIMR) currently under development by Thales Alenia Space) consist of a main non-spun platform and a rotating part composed of an antenna boom, a deployable reflector and a rotation mechanism. As the reflector is designated to rotate about the nadir axis producing conically scanned antenna beams with precise surface incidence angle, the payload pointing accuracy needs to be addressed at both spin subsystem and platform level. In this work, a representative model of the dynamic behaviour of SMAP satellite is developed as a study case to design the proposed control strategies; in particular, a SMAP-like payload structural model is built using FEM commercial codes. The spacecraft is equipped with a Reaction Wheels Assembly (RWA) to accomplish both momentum compensation for the spun element and three-axis attitude control and a motor for the spin mechanism. The objective of the study is to develop the spacecraft control architecture in the frame of Model Predictive Control (MPC) theory. MPC refers to a class of algorithms in which the control action is obtained by computing an open-loop optimal sequence of control moves over a predefined time horizon; moreover, the ability to set constraints on process inputs and outputs directly in the problem formulation allows to account for actuators’ limits. In the study two operative phases of the satellite are addressed: the Spin-up, in which the 6-meter diameter antenna is spun-up to the operative condition of 14.6 RPM, and the Science Phase, in which precise nadir pointing and stability of the flexible system must be kept for acquiring high-resolution measurements. To this purpose, control–structure interaction between attitude/spin control system and flexible dynamics, as well as system’s imbalances, are carefully addressed by the proposed control architecture. The nonlinear in-orbit dynamics of the flexible spacecraft is then used to evaluate the performance of the MPC controller in terms of pointing accuracy and robustness to uncertainties.

Year-round sea ice and snow characterization from combined passive and active microwave observations and radiative transfer modeling

Satellite microwave observations from 1.4 to 36 GHz already showed sensitivity to several geophysical parameters of sea ice such as Sea Ice Concentration (SIC), Sea Ice Thickness (SIT) or snow depth. The main goal of this article is to provide a realistic and comprehensive characterization of the sea ice and its snow cover that explains the microwave observations during a whole year using a radiative transfer model. For this purpose, we construct a unique dataset of passive microwave observations, to mimic the future Copernicus Imaging Microwave Radiometer (CIMR), along with the active microwave scatterometer data (ASCAT). CIMR database is used to classify sea ice microwave signatures in their spectral dimension with a machine learning technique while ASCAT data are used to help interpret the results of the classification. Classification results are then interpreted with a state-of-art sea ice and Snow Microwave Radiative Transfer model (SMRT) for all highlighted signatures and all seasons. Results make it possible to identify the specific behaviors from the observation co-variabilities for SIC, SIT, and snow structure. Our analysis underlined the role of the depth hoar over multi-year ice, for the interpretation of scattering signals in winter. Scattering signals that appear in late summer are explained by the presence of superimposed ice. This characterization will benefit from future advances in SMRT development, as well as the improved observations of future satellite missions.

An Enhanced Resolution Brightness Temperature Product for Future Conical Scanning Microwave Radiometers

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \mathrm {\,\,km} \times 3 \mathrm {\,\,km}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36 \mathrm {\,\,km} \times 36 \mathrm {\,\,km}$ </tex-math></inline-formula> . Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~1.9 times finer than the measured field when using the FWD+BWD combination.

Preliminary Studies on CIMR Antenna Pattern Brightness Temperature Compensation

Spaceborne microwave radiometry provides an essential contribution to monitoring the Earth with varying spatial resolution both related to the reflector dimension and the frequency of operation. The ESA's Copernicus imaging microwave radiometer (CIMR) mission aims at collecting the geophysical observables at a spatial resolution ranging from 60 km in L band to 4 km in Ka band. This goal can be achieved by equipping CIMR with a large unfurlable mesh reflector antenna. A limitation of the antenna design is that the antenna pattern includes grating lobes that contaminate the scene measurement with contributions originated far from the nominal footprint. This effect introduces inaccuracies in brightness temperature measurements, particularly when facing radiometric discontinuities, e.g., near the coastlines and sea ice edges, which can be greater than the mission required maximum of 0.5 K. The aim of this article is to assess a technique which will be able to correct the effects of antenna pattern and obtain reliable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_B</i> measurements. The analyzed simple technique is based on a regularized deconvolution of the antenna pattern to reconstruct the actual brightness temperatures. The technique was tested over a synthetic scenario that mimics both steep and smooth variations in spatial and thermal domains.

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

AbstractThe Copernicus Imaging Microwave Radiometer (CIMR) is currently being implemented by the European Space Agency (ESA) as a Copernicus Expansion Mission primarily designed to observe the Polar Regions in support of the Integrated European Policy for the Arctic. It is a conically scanning microwave radiometer with polarized channels centered at 1.414, 6.925, 10.65, 18.7, and 36.5 GHz and channel NEΔT between 0.2 and 0.7 K. A large rotating deployable mesh reflector will provide real‐aperture resolutions ranging from 60 (1.4 GHz) to 5 km (36.5 GHz). To evaluate CIMR retrieval performance, a simplified end‐to‐end simulation of the mission has been carried out. The simulation includes important processes and input parameters, such as test geophysical datasets, forward models, an instrument simulator, and retrieval algorithms to derive the key mission geophysical products. The forward modeling is tested by producing Brightness Temperatures (TBs) from 4 global scenes. A comparison with current observations of the open ocean and sea ice at similar frequencies confirmed the realism of the simulations. The produced top‐of‐atmosphere TBs are converted to Antenna brightness Temperatures (TAs), taking into account the instrument design, and are then inverted to retrieve Sea Ice Concentration (SIC), Sea Surface Temperature (SST), and Sea Surface Salinity (SSS). Evaluating the retrieval performance showed that the simulated CIMR instrument can provide SST, SSS, and SIC measurements with precisions and spatial resolutions conforming with the mission requirements. The evaluation also highlighted the challenges of observing the Arctic environment and put in perspective CIMR capabilities compared with current instruments.

Dual-Band Piecewise-Linear Profile Feed-Horns for the Copernicus Imaging Microwave Radiometer Mission

This letter presents the design strategy and the experimental results on the Ku/Ka dual-band feed-horn developed within the Phase A/B1 study of the Copernicus imaging microwave radiometer, which is one of the new six expansion missions of the European Copernicus flagship program on Earth observation operating in five frequency channels. The radiometer requirements translate into a dense focal plane composed by compact and lightweight dual-band feed horns. The optimum illumination was derived in terms of Bloch waves and the applicability of piecewise-linear profile feed-horns for its synthesis in the case of two highly separated Ku--/Ka--frequency bands (1:1.95) was investigated. The presented feed-horn design was validated up to a technological readiness level of 6. The reported performances at antenna level for the complete Ku-/Ka-band feed cluster demonstrate the feasibility of dual-band smooth-wall feed-horns focal-planes meeting all the Earth observation requirements in terms of footprints, beam- and wide-beam efficiencies, and cross-polarization.

Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission

Abstract. Across spatial and temporal scales, sea-ice motion has implications for ship navigation, the sea-ice thickness distribution, sea-ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status quo (processing from daily averaged maps of brightness temperature). We document that the “swath-to-swath” (S2S) approach results in many more (2 orders of magnitude) sea-ice drift vectors than the “daily map” (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 h winter Arctic sea-ice drift is 0.9 km for S2S vectors and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geolocation uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms and making the user community ready to fully take advantage of such a product.

Technical note: A sensitivity analysis from 1 to 40 GHz for observing the Arctic Ocean with the Copernicus Imaging Microwave Radiometer

Abstract. The Copernicus Imaging Microwave Radiometer (CIMR) is one of the high-priority missions for the expansion of the Copernicus program within the European Space Agency (ESA). It is designed to respond to the European Union Arctic policy. Its channels, incidence angle, precision, and spatial resolutions have been selected to observe the Arctic Ocean with the recommendations expressed by the user communities. In this note, we present the sensitivity analysis that has led to the choice of the CIMR channels. The famous figure from Wilheit (1979), describing the frequency sensitivity of passive microwave satellite observations to ocean parameters, has been extensively used for channel selection of microwave radiometer frequencies on board oceanic satellite missions. Here, we propose to update this sensitivity analysis, using state-of-the-art radiative transfer simulations for different geophysical conditions (Arctic, mid-latitude, tropics). We used the Radiative Transfer Model (RTM) from Meissner and Wentz (2012) for the ocean surface, the Round Robin Data Package of the ESA Climate Change Initiative (Pedersen et al., 2019) for the sea ice, and the RTM from Rosenkranz (2017) for the atmosphere. The sensitivities of the brightness temperatures (TBs) observed by CIMR as a function of sea surface temperature (SST), sea surface salinity (SSS), sea ice concentration (SIC), ocean wind speed (OWS), total column water vapor (TCWV), and total column liquid water (TCLW) are presented as a function of frequency between 1 and 40 GHz. The analysis underlines the difficulty to reach the user requirements with single-channel retrieval, especially under cold ocean conditions. With simultaneous measurements between 1.4 and 36 GHz onboard CIMR, applying multi-channel algorithms will be facilitated, to provide the user community with the required ocean and ice information under arctic environments.

An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics

Satellite passive microwave observations from 1.4 to 36 GHz already showed sensitivity to vegetation parameters, primarily through the calculations of the Vegetation Optical Depth (VOD) at individual window frequencies, separately. Here we evaluate the synergy of this frequency range for vegetation characterization, through the estimation of two vegetation parameters, its foliage and the photosynthesis activity as described by the Normalized Difference Vegetation Index (NDVI), and its woody components and carbon stock as described by the Above Ground Carbon (AGC), using different combinations of channels in the considered frequency range. Neural network retrievals are trained on these two vegetation parameters (NDVI and AGC), for several microwave channel combinations, including the future Copernicus Imaging Microwave Radiometer (CIMR) that will observe simultaneously in window channels from 1.4 to 36 GHz, for the first time. This methodology avoids the use of any assumptions in the complex interaction between the surface (vegetation and soil) and the radiation, as well as any ancillary observations, to propose a genuine and objective evaluation of the information content of the passive microwave frequencies for vegetation characterization. Our analysis quantifies the synergy of the microwave frequencies from 1.4 to 36 GHz, and shows the expected potential of the CIMR instrument for the monitoring of vegetation parameters in tropical environments, as compared to current instruments. It also confirms that the 1.4 GHz observations have a high sensitivity to AGC, as compared to the other single frequencies up to 36 GHz, at least under tropical environments.

Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm

Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm3/cm3 to 0.037 cm3/cm3, than the multi-frequency approach (ubRMSD from 0.028 cm3/cm3 to 0.089 cm3/cm3) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm3/cm3 to 0.023 cm3/cm3) than the multi-angular version (ubRMSD from 0.026 cm3/cm3 to 0.034 cm3/cm3) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended.

Impact of channel selection on SST retrievals from passive microwave observations

Two retrieval algorithms developed as a part of the European Space Agency Climate Change Initiative (ESA-CCI) project are used to assess the effects of withholding observations from selected frequency channels on the retrieved subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and to evaluate a Copernicus Imaging Microwave Radiometer (CIMR) like channel configuration.The first algorithm is a statistical regression-based retrieval algorithm, while the second is a physically based optimal estimation (OE) algorithm. A database with matching satellite and drifting buoy observations is used to test the performance of each channel configuration using both retrieval algorithms to identify the most optimal channel selection for accurate SST retrievals. The evaluation against in situ observations allows identification of the strengths and weaknesses of the two retrieval algorithms, and demonstrates the importance of using in situ observations to evaluate existing theoretical retrieval uncertainty studies. Overall, the performance increases as expected when more channels are included in the retrieval. In particular, more channels allow a better performance for the range of different observing conditions (e.g. cold waters). The two retrieval algorithms agree that for a three-channel configuration, the 6, 10, 18 GHz (V and H polarization) is better than the 6, 10, 23 GHz configuration (V and H polarization). This is demonstrated for different geographical regions and throughout all seasons. Of the different combinations tested here, it is evident that withholding observations from the 23 and 36 GHz channels from the retrieval has the least impact on the SST performance. Overall, this analysis shows that the CIMR like channel configuration performs very well when compared to an AMSR-E like constellation using both retrieval algorithms.

Editorial for the Special Issue: “Ten Years of Remote Sensing at Barcelona Expert Center”

This book celebrates the ten year anniversary of the Barcelona Expert Center by presenting recent contributions related to the topics on which the team has been working during those years. The Barcelona Expert Center’s expertise covers a wide variety of remote sensing fields, but the main focus of the research is on the SMOS data processing and its ocean, land, and ice applications. This book contains 14 scientific papers addressing topics that go from the description of the new data processing algorithms that are implemented in the last version of the operational SMOS level 1 processor to scientific applications derived from SMOS: results on the sea-surface salinity assimilation in coastal models, synergies of the sea-surface salinity with temperature and chlorophyll and their impact on the better retrieval of ocean surface currents, quality assessment of SMOS-derived sea ice thickness, sea-surface salinity, and soil moisture products, among others. Moreover, one of the papers verifies the potential of the future Copernicus Imaging Microwave Radiometer (CIMR) mission within the CMEMS sea-surface salinity (SSS) operational production after the SMOS era.

Ice Concentration Retrieval from the Analysis of Microwaves: Evaluation of a New Methodology Optimized for the Copernicus Imaging Microwave Radiometer

A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high frequencies have different spatial resolutions, and a scheme is developed to benefit from the low errors of the low frequencies and the high spatial resolutions of the high frequencies. This effort is specifically designed for the Copernicus Imaging Microwave Radiometer (CIMR) project, equipped with a large deployable antenna to provide a spatial resolution of ∼5 km at 18 and 36 GHz, and ∼15 km at 6 and 10 GHz. The algorithm is tested with Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, for a clear scene over the north polar region, with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) estimates and the Ocean Sea Ice—Satellite Application Facilities (OSI SAF) operational products. Several algorithm options are tested, and the study case shows that both high spatial resolution and low errors are obtained with the IceCREAM method. It is also tested for the full polar regions, winter and summer, under clear and cloudy conditions. Our method is globally applicable, without fine-tuning or further weather filtering. The systematic use of all channels from 6 to 36 GHz makes it robust to changes in ice surface conditions and to weather interactions.

Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

Sea Ice and Atmospheric Parameter Retrieval From Satellite Microwave Radiometers: Synergy of AMSR2 and SMOS Compared With the CIMR Candidate Mission

AbstractResearch on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the polar regions where uncertainties about the interactions between sea ice, ocean, and atmosphere are driving ongoing monitoring efforts. The Copernicus Imaging Microwave Radiometer (CIMR) is an European Space Agency (ESA) candidate mission which promises to offer high resolution, low uncertainty observation capabilities at the 1.4, 6.9, 10.65, 18.7, and 36.5 GHz frequencies. To assess the potential impact of CIMR for sea ice parameter retrieval, a comparison is made between retrievals based on present AMSR2 observations and a retrieval using future CIMR equivalent observations over a data set of validated sea ice concentration (SIC) values. An optimal estimation retrieval method (OEM) is used which can use input from different channel combinations to retrieve seven geophysical parameters (sea ice concentration, multi‐year ice fraction, ice surface temperature, columnar water vapor, liquid water path, over ocean wind speed, and sea surface temperature). An advantage of CIMR over existing radiometers is that it would provide higher spatial resolution observations at the lower frequency channels (6.9, 10.65, and 18.7 GHz) which are less sensitive to atmospheric influence. This enables the passive microwave based retrieval of SIC and other surface parameters with higher resolution and lower uncertainty than is currently possible. An information content analysis expands the comparison between AMSR2 and CIMR to all retrievable surface and atmospheric parameters. This analysis quantifies the contributions to the observed signal and highlights the differences between different input channel combinations. The higher resolution of the low frequency CIMR channels allow for unprecedented detail to be achieved in Arctic passive microwave sea ice retrievals. The presence of 1.4 GHz channels on board CIMR opens up the possibility for thin sea ice thickness (SIT) retrieval. A combination of collocated AMSR2 and SMOS observations is used to simulate a full CIMR suite of measurements, and the OEM is modified to include SIT as a retrieval parameter. The output from different retrieval configurations is compared with an operational SIT product. The CIMR instrument can provide increased accuracy for SIC retrieval at very high resolutions with a combination of the 18.7 and 36.5 GHz channels while also maintaining sensitivity for atmospheric water vapor retrieval. In combination with the 1.4 GHz channels, SIT can be added as an eighth retrieval parameter with performance on par with existing operational products.

Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer

Sea surface temperatures (SSTs) derived from passive microwave (PMW) observations benefit global ocean and SST analyses because of their near-all-weather availability. Present PMW SSTs have a real aperture-limited spatial resolution in excess of 50 km, limiting the spatial fidelity with which SST features, reflecting ocean dynamics, can be captured. This contrasts with the target resolution of global analyses of 5 to 10 km. The Copernicus Imaging Microwave Radiometer (CIMR) is a mission concept under consideration as a high-priority candidate mission for the expansion of the Copernicus space programme. This instrument would be capable of real aperture resolution &lt; 15 km with low total uncertainties in the range 0.4–0.8 K for channels between 1.4 and 36.5 GHz, and a dual-view arrangement that further reduces noise. This paper provides a comparative study of SST uncertainty and feature resolution with and without the availability of CIMR in the future SST-observing satellite constellation based on a detailed simulation of CIMR plus infrared observations and the processing of global SST analyses with 0.05° final grid resolution. Simulations of CIMR data including structured errors were added to an observing system consisting of the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and the Advanced Very High Resolution Radiometer (AVHRR) on MetOp-A. This resulted in a large improvement in the global root-mean-square error (RMSE) for SST from 0.37 K to 0.21 K for January and 0.40 K to 0.25 K for July. There was a particularly noticeable improvement in the performance of the analysis, as measured by the reduction in RMSE, for dynamical and persistently cloudy areas. Of these, the Aghulas Current showed an improvement of 43% in January and 48% in July, the Gulf Stream showed 70% and 44% improvements, the Southern Ocean showed 57% and 74% improvements, and the Maritime Continent showed 50% and 40% improvements, respectively.

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath &gt;1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn–dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.