Deep convective cloud systems are central to the global water and energy cycle, and yet their representation in climate models remains challenging. This study explores the potential of machine learning to classify and characterize cloud structures inside cloud systems using radiometric measurements from the planned C²OMODO (Convective Core Observation through MicrOwave Derivative in the trOpics) mission. The relationships between cloud structure (anvil, stratiform, convective, and deep convective) and geophysical variables such as ice water path as well as integrated vertical ice momentum are investigated using a gradient boosting algorithm both for classification and regression purposes. The gradient boosting classification method achieves an overall accuracy (True Positive) above 70%. Retrievals of the geophysical variables yield R² ranging from 0.60 to 0.99. Furthermore, it is shown that applying a prior classification of the scenes improves the performances of the retrieval. This study highlights the potential of the forthcoming C²OMODO mission in advancing our understanding of convective systems and paves the way for in-depth studies on alternative or refined classification schemes and inputs, which could deliver even better results.

Convective systems are responsible for energy transfers between the lower and upper atmospheric layers and play a fundamental role in the water cycle, weather and climate evolution. With a tandem of high resolution and wide swath all-sky atmospheric sounders, the C2OMODO concept allows the estimation of vertical dynamics within deep convective systems.

Convective systems are responsible for energy transfers between the lower and upper atmospheric layers and play a fundamental role in the water cycle, weather and climate evolution. With a tandem of high resolution and wide swath all-sky atmospheric sounders, the C2OMODO concept allows the estimation of vertical dynamics within deep convective systems.

Since 2018, for the first time, space measurements of colocated wind vectors and wave spectral characteristics are available thanks to the French/Chinese CFOSAT mission, which carries a wind scatterometer (SCAT) and a wave scatterometer (SWIM). Four years after its launch, CFOSAT data processing has been improved to reach a high level quality, leading to a reprocessing of the whole mission dataset. This paper focuses on the CFOSAT SWIM data reprocessing, the product performance, now homogeneous over mission lifetime, the complementarity with SAR observations and some scientific contributions to oceanography.

For the first time, co-located wind vectors and wave spectral characteristics are available thanks to the French/Chinese CFOSAT mission, which includes a wind scatterometer SCAT and a wave scatterometer SWIM. Three years after its launch, CFOSAT data is thoroughly qualified and various scientific work has been undertaken. This paper focuses on CFOSAT SWIM data, its performance and scientific contribution to oceanography, coastal and sea ice study.

In the continuity of the existing SAPHIR microwave radiometer on-board Megha-Tropiques, in operation since 2011, a new generation atmospheric sounder is currently under study. SAPHIR-NG is designed to provide enhanced observations of clear sky water vapor and profiles of hydrometeors. The scientific objectives and requirements, as well as the instrumental implementation are described.

SAPHIR-NG is a new generation atmospheric sounder concept based on the existing SAPHIR instrument, onboard Megha-Tropiques, which has been operating since 2011. Its improvements with respect to SAPHIR are presented hereafter, both in terms of scientific objectives and instrumental architecture.

The Chinese-French oceanography satellite, CFOSAT, was launched on October 2018. Two Ku-band scatterometers are on-board: SCAT for the wind observation and SWIM for the wave observation. After a first phase mainly dedicated to validation and identification of improvement possibilities, the ground processing and products generated were upgraded. This paper presents the main evolutions implemented and their positive impacts on the SWIM data quality.

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70-500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.

The accuracy of sea surface parameters retrieved from altimeter missions is predominantly governed by the choice of the so-called “retracking” algorithm, i.e., the model and inversion method implemented to obtain the surface parameters from the backscattered waveform. For continuity reasons, the choice of space agencies is usually to apply the same retracker from one satellite mission to the other to ensure long-time homogeneous series. In this article, taking the opportunity of a new configuration of the nadir pointing measurements onboard the recently launched China France Oceanography Satellite (CFOSAT) with the Surface Waves Investigation and Monitoring (SWIM) instrument (Hauseret al.,2020), the retracking method was upgraded, by implementing a novel algorithm, called “Adaptive” retracker. It combines the improvements brought by Poissonet al.,(2018) for the estimation of surface parameters from peaked waveforms over sea ice, improvements in the way the instrumental characteristics are considered in the model (mispointing, point target response) and a more accurate consideration of speckle statistics. In this article, we first show from simulations carried out in the instrumental configuration of SWIM that the Adaptive algorithm has better accuracy and performance than the classical MLE4 algorithm. Then, the geophysical parameters obtained with real data from SWIM are analyzed with comparisons to reference data sets (model and products from altimeters). We show that this new algorithm has several benefits with respect to the classical MLE4 method: no need of lookup tables to correct biases, significant noise reduction on all geophysical variables especially the significant wave height, and performance of inversion over a large set of echo shapes, resulting from standard oceanic scenes as well as highly specular conditions such as over bloom or sea ice.

The Chinese-French oceanography satellite, CFOSAT, was launched on October 2018. Two Ku-band scatterometers are on-board: SCAT for the wind observation and SWIM for the wave observation. This paper presents the most recent results on the SWIM data quality analysis a few months after the end of the CAL/VAL phase.

The China-France Oceangraphy SATellite (CFOSAT) is an innovative satellite mission with wind and waves measurements on oceans. This paper aims to evaluate the first results of the assimilation of the wave data provided by CFOSAT in the wave model MFWAM. Model runs are implemented during the Calibration/Validation phase of the mission. The results are compared to the wave data from altimeters and buoys. The first results are promising and indicate a significant improvement of wave heights in the different ocean basins (high latitudes, intermediate latitudes and the tropics). Azimuthal cut-off sensitivity tests for the SWIM wave spectra are also examined in this study. We also discussed the impact of combined wave spectra and the ones from the incidence angles of SWIM (6, 8 and 10°). In other respects this study also investigate the complementary use of SWIM and SAR from CFOSAT and SAR from Sentinel-1 for combined assimilation in the wave model MFWAM

This paper presents the goal, organization and first results of the calibration and validation activities devoted to the Ku-band wave scatterometer SWIM in operation since the end of October 2018 after the launch of the China-France Oceanography SATellite CFOSAT.

The wave scatterometer, SWIM, will be on-board the Chinese French oceanographic mission, CFOSAT. It will complete the Chinese wind scatterometer, SCAT. They will provide the scientific community with joint wind and wave measurements for the very first time. The launch is now coming soon: autumn 2018. SWIM is a Ku-band real-aperture radar with 6 rotating fan-beams pointing near nadir. The main characteristics and performance of the instrument measured during the Satellite Integration Campaign, products and ground segment development are presented in this paper, as well as predicted performances estimated from simulations.

In this paper, the surface emissivity is retrieved over the Arctic sea ice/open seas using observations from the advanced microwave sounding unit window channels during the year 2009. The emissivity computation is performed using two contrasted surface assumptions: specular and Lambertian assumptions. The obtained sea ice surface emissivities are studied in this paper with a focus on the effect of the surface assumption. Some factors of variability of the obtained emissivities are analyzed: variability in space, in time, with the zenith angle, and with respect to the frequency. We show that the near-nadir surface emissivity and emissivity difference (obtained using two contrasted surface assumptions) could be used as an excellent proxy to detect ice/no ice regions. We also show that near-nadir sea ice emissivity at some selected frequencies and the combination of both high and low window frequencies could also be very useful to better characterize sea ice surface physical properties and provide additional information for existing sea ice classifications, as they bring relevant information about first year and multiyear sea ice properties and their seasonal evolution.