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Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

Remote Sensing, 11 (8), ISSN:2072-4292

Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of km to a few km. Changes in the model horizontal resolution affect not only atmospheric transport, but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 km to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to extrapolate the results beyond the tested resolutions. Finally, we show that the high resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser resolution models. These representativeness errors need to be considered when assimilating in situ data and high resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high resolution CO2 simulations provided by the CAMS in real-time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

To monitor on a continuous basis the vast and harsh Arctic environment, considering the sparse population and the lack of transport links, space technologies are definitely essential tools including Earth observation, navigation and communication satellites. DG GROW asked for an Expert Group in spring 2017 with the mandate to update and/or complete the review and analysis of the Users’ needs, thus allowing the Commission to assess the relevance of the development of a "Copernicus expansion mission" dedicated to Polar and Snow monitoring., JRC.D.6-Knowledge for Sustainable Development and Food Security

To monitor on a continuous basis the vast and harsh Arctic environment, considering the sparse population and the lack of transport links, space technologies are definitely essential tools including Earth observation, navigation and communication satellites. DG GROW asked for an Expert Group in spring 2017 with the mandate to update and/or complete the review and analysis of the Users’ needs, thus allowing the Commission to assess the relevance of the development of a "Copernicus expansion mission" dedicated to Polar and Snow monitoring., JRC.D.6-Knowledge for Sustainable Development and Food Security

The research undertaken in this thesis focuses on the assimilation of remotely sensed data for continental surface monitoring. The analysed variables are root-zone soil moisture content and above-ground vegetation biomass. The data cover the period 2001 to 2004 and were collected over fallow at the SMOSREX experimental site, south-west of Toulouse, France. Four assimilation techniques (EKF, EnKF, 1D-VAR and T-VAR) were implemented into the Météo-France SVAT model ISBA-A-gs and their performances compared to each other. It was found that the 1D-VAR is the most effective technique, for both quality of the analyses and lower computational cost. This particular technique was applied in a joint assimilation of surface soil moisture and leaf are index observations, as well as of remotely sensed measurements collected over the site. The assimilation improves the control simulation, if a dynamic wilting point is introduced (this is particularly useful during strong drought periods). The impact of a high level of uncertainty in the precipitation data is also evaluated.; Le travail réalisé dans le cadre de cette thèse porte sur l'assimilation de données de télédétection pour le suivi des surfaces continentales. Les variables analysées sont l'état hydrique du sol et la biomasse de la végétation, sur la jachère du site expérimental SMOSREX, de 2001 à 2004, au sud-ouest de Toulouse. Quatre méthodes d'assimilation (EKF, EnKF, 1D-VAR et T-VAR) ont été mises en œuvre dans le modèle ISBA-A-gs de Météo-France, et comparées. La méthode 1D-VAR est la plus performante, aussi bien pour la qualité des analyses qu'en temps de calcul. Cette méthode a été appliquée à l'assimilation simultanée des observations de l'humidité de la surface et de l'indice foliaire, ainsi qu'aux mesures de télédétection réalisées sur le site. L'assimilation améliore la simulation de contrôle, à condition d'introduire un point de flétrissement dynamique (cela est particulièrement utile pendant les périodes de forte sécheresse). Les effets d'une incertitude sur les précipitations sont évalués.

Ponencia presentada en: I Congreso de la Asociación Española de Climatología “La climatología española en los albores del siglo XXI”, celebrado en Barcelona del 1 al 3 de diciembre de 1999. [ES]La sencilla relación lineal propuesta por Angstrom-Prescott en 1924 entre la irradiación solar global relativa H/Ho, y la insolación relativa n/N, ha sido sometida, con el transcurso de los años, a numerosos procedimientos experimentales, casi todos ellos tratando un período de varios años. En el Laboratorio de Enseñanza de Física del Medio Ambiente de la Universidad de Valencia, se ha estudiado esta relación utilizando datos correspondientes a días salteados de tres meses, prácticamente coincidentes con el período estacional de otoño. Sin embargo, los resultados finales coinciden en gran medida con los correspondientes a trabajos hechos sobre un período de varios años, lo cual puede significar que, en determinadas ocasiones, las medidas hechas sobre un período estacional, pueden ser representativas de las correspondientes a una media anual o de varios años. [EN]The simple linear relationship proposed by Angstrom-Prescott in 1924 between relative global solar irradiance, H/Ho, and relative insolation, n/N, has undergone, along the years, a number of experimental applications, almost always dealing with periods of several years. In the teaching Environmental Physics Laboratory of the University of Valencia we have studied this relationship using data corresponding to days within a period of three months, practically coinciding with the autumn season. In spite of that, the final results obtained largely coincide with those from other studies carried out over periods of several years. This means that in some occasions, the measurements carried out over a seasonal penod may also be representative of those corresponding to annual means over a number of years.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort., Remote Sensing, 10 (12), ISSN:2072-4292

This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future., Remote Sensing of Environment, 244, ISSN:0034-4257