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Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics
No full textInternational audienceOcean eddies affect large-scale circulation and induce a kinetic energy cascade through their non-linear interactions. However, since global observations of eddy dynamics come from satellite altimetry maps that smooth eddies and distort their geometry, the strength of this cascade is underestimated. Here, we use deep learning to improve observational estimates of global surface geostrophic currents and explore the implications for the cascade. By synthesizing multi-modal satellite observations of sea surface height (SSH) and temperature, we achieve up to a 30% improvement in spatial resolution over the community-standard SSH product. This reveals numerous strongly interacting eddies that were previously obscured by smoothing. In many regions, these newly resolved eddies lead to nearly an order-of-magnitude increase in the upscale kinetic energy cascade that peaks in spring and is strong enough to drive the seasonality of large mesoscale eddies. Our study suggests that deep learning can be a powerful paradigm for satellite oceanography
Mapping The Co2 Total Column Retrieval Performance From Shortwave Infrared Measurements: Synthetic Impacts Of The Spectral Resolution, Signal-To-Noise Ratio, And Spectral Band Selection
No full textInternational audienceSatellites have been providing spaceborne observations of the total column of CO2 (denoted XCO2) for over two decades now, and, with the need for independent verification of Paris Agreement objectives, many new satellite concepts are currently planned or being studied to complement or extend the instruments that already exist. Depending on whether they are targeting natural and/or anthropogenic fluxes of CO2, the designs of these future concepts vary greatly. The characteristics of their shortwave infrared (SWIR) observations notably explore several orders of magnitude in spectral resolution (from λ/Δλ ∼ 400 for Carbon Mapper to λ/Δλ ∼ 25 000 for MicroCarb) and include different selections of spectral bands (from one to four bands, among which there are the CO2-sensitive 1.6 µm and/or 2.05 µm bands). The very nature of the spaceborne measurements is also explored: for instance, the NanoCarb imaging concept proposes to measure CO2-sensitive truncated interferograms, instead of infrared spectra like other concepts, in order to significantly reduce the instrument size. This study synthetically explores the impact of three different design parameters on the XCO2 retrieval performance obtained through optimal estimation: (1) the spectral resolution, (2) the signal-to-noise ratio (SNR) and (3) the spectral band selection. Similar performance assessments are completed for the exactly defined OCO-2, MicroCarb, Copernicus CO2 Monitoring (CO2M) and NanoCarb concepts. We show that improving the SNR is more efficient than improving the spectral resolution to increase XCO2 precision when perturbing these parameters across 2 orders of magnitude, and we find that a low SNR and/or a low spectral resolution yield XCO2 with vertical sensitivities that give more weight to atmospheric layers close to the surface. The exploration of various spectral band combinations illustrates, especially for lower spectral resolutions, how including an O2-sensitive band helps to increase the optical path length information and how the 2.05 µm CO2-sensitive band contains more geophysical information than the 1.6 µm band. With very different characteristics, MicroCarb shows a CO2 information content that is only slightly higher than that of CO2M, which translates into XCO2 random errors that are lower by a factor ranging from 1.1 to 1.9, depending on the observational situation. The performance of NanoCarb for a single pixel of its imager is comparable to those of concepts that measure spectra at low SNR and low spectral resolution, but, as this novel concept would observe a given target several times during a single overpass, its performance improves when combining all the observations. Overall, the broad range of results obtained through this synthetic XCO2 performance mapping hint at the future intercomparison challenges that the wide variety of upcoming CO2-observing concepts will pose
Aerosol-Calibrated Matched Filter Method for Retrievals of Methane Point Source Emissions Over the Los Angeles Basin
No full textInternational audienceMethane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20-year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol-Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol-induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD)
Attribution of the Extreme 2022 Summer Drought along the Yangtze River Valley in China Based on Detection and Attribution System of Chinese Academy of Sciences
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Atmospheric Gravity Waves: Processes and Parameterization
No full textInternational audienceAtmospheric predictability from subseasonal to seasonal time scales and climate variability are both influenced critically by gravity waves (GW). The quality of regional and global numerical models relies on thorough understanding of GW dynamics and its interplay with chemistry, precipitation, clouds, and climate across many scales. For the foreseeable future, GWs and many other relevant processes will remain partly unresolved, and models will continue to rely on parameterizations. Recent model intercomparisons and studies show that present-day GW parameterizations do not accurately represent GW processes. These shortcomings introduce uncertainties, among others, in predicting the effects of climate change on important modes of variability. However, the last decade has produced new data and advances in theoretical and numerical developments that promise to improve the situation. This review gives a survey of these developments, discusses the present status of GW parameterizations, and formulates recommendations on how to proceed from there
Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global Storm-Resolving Model
No full textInternational audienceCirrus dominate the longwave radiative budget of the tropics. For the first time, the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different microphysical schemes within nudged global storm-resolving simulations from a single model, is quantified. Nudging allows us to compute radiative biases precisely using coincident satellite measurements and to fix the large-scale dynamics across our set of simulations to isolate the influence of microphysics. We run 5-day simulations with four commonly-used microphysics schemes of varying complexity (SAM1MOM, Thompson, M2005 and P3) and find that the tropical average longwave CRE varies over 20 W m−2 between schemes. P3 best reproduces observed longwave CRE. M2005 and P3 simulate cirrus with realistic frozen water path but unrealistically high ice crystal number concentrations which commonly hit limiters and lack the variability and dependence on frozen water content seen in aircraft observations. Thompson and SAM1MOM have too little cirrus
How general is the Jensen-Varadhan large deviation functional for 1D conservation laws?
No full textInternational audienceStarting from a microscopic particle model whose hydrodynamic limit under hyperbolic space-time scaling is a 1D conservation law, we derive the large deviation rate function encoding the probability to observe a density profile which is a non entropic shock, and compare this large deviation rate function with the classical Jensen-Varadhan functional, valid for the totally asymmetric exclusion process and the weakly asymmetric exclusion process in the strong asymmetry limit. We find that these two functionals have no reason to coincide, and in this sense Jensen-Varadhan functional is not universal. However, they do coincide in a small Mach number limit, suggesting that universality is restored in this regime. We then compute the leading order correction to the Jensen-Varadhan functional
Correction: A new insight into monsoon intraseasonal variability as revealed from distinct wind-precipitation regimes over the southwest coast of India
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Multi-platform high resolution in situ observations for understanding mesoscale and sub-mesoscale processes and their role in the air-sea exchanges: Experiences and prospects from the EUREC4A-OA/ATOMIC field experiment
No full textInternational audienceIn January-February 2020, the EUREC4A-OA/ATOMIC experiment took place in the Northwest Tropical Atlantic with the overall goal of understanding the role of fine-scale processes in internal ocean dynamics and air-sea interaction. Four oceanographic ships, the French Atalante, the German Maria S. Merian and Meteor, and the US Ron Brown, were closely coordinated with airborne observations and autonomous ocean platforms (gliders, ©Saildrones, Argo floats, and drifters) to simultaneously measure the ocean and atmosphere from east of Barbados to the northern border of French Guyana. The multiple observations of the ocean, atmosphere, and their interface have revealed more complex ocean dynamics than expected, in particular a strong interaction between the Amazon River outflow (despite its reduced winter discharge), the North Brazil Current (NBC), and several mesoscale eddies (including the highly energetic NBC rings). This leads to even richer submesoscale dynamics that shape an important fraction of the air-sea exchange of heat, momentum, and CO2, and efficiently isolates the NBC northward flow waters from intense and continuous interactions with the atmosphere. Owing to the many complementary observations from ships and autonomous platforms, we have been able to quantify some of these processes, including the diurnal cycle and the 3D dynamics of different mesoscale eddies, as well as to map and quantify different terms of the air-sea fluxes and their impacts on the marine atmospheric boundary-layer water budget. The results have been widely used not only to validate numerical simulations of the region, but also to guide their analyses and to improve various numerical parameterizations.The collection of these observations was the result of an important international coordination between many different groups of ocean and atmospheric scientists. In addition, the special strategy for targeted data collection of meso- and submesoscale processes relied on daily planning of the field experiment and on detailed analysis of the near-real-time satellite data and the observations already obtained during the experiment, which was essential for providing the right snapshots of the ocean and atmosphere for the quantification of many processes. The lessons learned from this experiment will be implemented and extended in the upcoming major high-resolution oceanographic endeavor, the WHIRLS experiment, which will take place in June-July 2025, southwest of Africa
Constrained and unconstrained stable discrete minimizations for p-robust local reconstructions in vertex patches in the de Rham complex
No full textInternational audienceWe analyze constrained and unconstrained minimization problems on patches of tetrahedra sharing a common vertex with discontinuous piecewise polynomial data of degree p. We show that the discrete minimizers in the spaces of piecewise polynomials of degree p conforming in the H1, H(curl), or H(div) spaces are as good as the minimizers in these entire (infinite-dimensional) Sobolev spaces, up to a constant that is independent of p. These results are useful in the analysis and design of finite element methods, namely for devising stable local commuting projectors and establishing local-best/global-best equivalences in a priori analysis and in the context of a posteriori error estimation. Unconstrained minimization in H1 and constrained minimization in H(div) have been previously treated in the literature. Along with improvement of the results in the H1 and H(div) cases, our key contribution is the treatment of the H(curl) framework. This enables us to cover the whole De Rham diagram in three space dimensions in a single setting