Land surface albedo from MSG/SEVIRI: retrieval method, validation, and application for weather forecast

The European Meteorological Satellite Organization (EUMETSAT) maintains a number of decentralized processing centers dedicated to different scientific themes. The Portuguese Meteorological Institute hosts the Satellite Application Facility on Land Surface Analysis (LSA-SAF). The primary objective of the LSA-SAF is to provide added-value products for the meteorological and environmental science communities with main applications in the fields of climate modeling, environmental management, natural hazards management, and climate change detection. Since 2005 data from Meteosat Second Generation satellite are routinely processed in near real time by the LSA-SAF operational system in Lisbon. Presently, the delivered operational products comprise land surface albedo and temperature, shortwave and long-wave downwelling radiation fluxes, vegetation parameters and snow cover. After more than ten years (1999-2010) of research, development, and progressive operational activities, a summary of the surface albedo product characteristics and performances is presented. The relevance of LSA-SAF albedo product is analyzed through a weather forecast model (ALADIN) in order to account for the inter-annual spatial and temporal variability. Results clearly show a positive impact on the 12-hour forecast of 2m temperatures

Identification of soil-cooling rains in southern France from soil temperature and soil moisture observations

International audienceAbstract. In this study, the frequency and intensity of soil-cooling rains is assessed using in situ observations of atmospheric and soil profile variables in southern France. Rainfall, soil temperature, and topsoil volumetric soil moisture (VSM) observations, measured every 12 min at 21 stations of the SMOSMANIA (Soil Moisture Observing System – Meteorological Automatic Network Integrated Application) network, are analyzed over a time period of 9 years, from 2008 to 2016. The spatial and temporal statistical distribution of the observed rainfall events presenting a marked soil-cooling effect is investigated. It is observed that the soil temperature at a depth of 5 cm can decrease by as much as 6.5 ∘C in only 12 min during a soil-cooling rain. We define marked soil-cooling rains as rainfall events triggering a drop in soil temperature at a depth of 5 cm larger than 1.5 ∘C in 12 min. Under Mediterranean and Mediterranean–mountain climates, it is shown that such events occur up to nearly 3 times a year, and about once a year on average. This frequency decreases to about once every 3.5 years under semi-oceanic climate. Under oceanic climate, such pronounced soil-cooling rains are not observed over the considered period of time. Rainwater temperature is estimated for 13 cases of marked soil-cooling rains using observed changes within 12 min in soil temperature at a depth of 5 cm, together with soil thermal properties and changes in VSM. On average, the estimated rainwater temperature is generally lower than the observed ambient air temperature, wet-bulb temperature, and topsoil temperature at a depth of 5 cm, with mean differences of −5.1, −3.8, and −11.1 ∘C, respectively. The most pronounced differences are attributed to hailstorms or to hailstones melting before getting to the soil surface. Ignoring this cooling effect can introduce biases in land surface energy budget simulations

Copernicus Global Land Operations - Scientific quality evaluation cross-cutting consistency - January-December 2019

The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land service to operate a multi-purpose service component that provides a series of biogeophysical products on the status and evolution of land surface at a global scale. Production and delivery of the variables take place in a timely manner and are complemented by the constitution of long-term time series. The most advanced indirect validation technique consists in integrating the products into a land surface model (LSM) using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM, able to represent the diurnal cycle of the surface fluxes together with the seasonal, inter-annual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS (LDAS-Monde) is able to assimilate SPOT-VGT and PROBA-V Leaf Area Index (LAI) and ASCAT surface soil moisture (SSM) satellite products at a global scale at a spatial resolution of at least 0.25° x 0.25°. This permits the active monitoring of LAI and SSM variables. A passive monitoring of Surface Albedo (SA), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Land Surface Temperature (LST) is performed (i.e., the simulated values are compared with the satellite products), as these quantities are not assimilated yet. The LDAS generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model), residuals (observations vs. analysis), and increments (analysis vs. model); (2) for SA, FAPAR and LST, metrics derived from the passive monitoring. In both cases, the Pearson correlation coefficient (R), the root mean square difference (RMSD), the standard deviation of difference (SDD), and mean bias skill scores are used. In this report, results are presented for the January-December 2019 period over Southern Africa and over the Murray-Darling basin. Note that the last data from SPOT-VGT were used on 13th May 2014. After this date, new LAI / FAPAR / SA products from PROBA-V are used. For LAI, over both Southern Africa and the Murray-Darling basin, the scores tend to present better values during the dry spells of 2019 than during previous years from 2010 to 2018. The RMSD scores of consolidated estimate of LAI Version 2 and LAI Version 1 are comparable in 2019. The impact on analyzed LAI of transitioning from SPOT-VGT to PROBA-V is neutral to positive. For FAPAR, over both Southern Africa and the Murray-Darling basin, the scores tend to present slightly better values during the dry spells of 2019 than during previous years from 2010 to 2018. Overall conclusions for FAPAR are similar to those for LAI. For SA, a striking result is that a large increase in the mean bias value is observed after the transition from SPOT-VGT to PROBA-V, of about 0.02 for both Southern Africa and for the Murray-Darling basin. There is a clear discontinuity in the SA time series, not observed for LAI nor for FAPAR. For SWI-001, the impact of the seasonal SSM CDF-matching performed prior the assimilation is particularly striking for Southern Africa. Without a seasonal CDF-matching, the original SSM information would be misleading over Southern Africa. For LST, the model tends to underestimate LST, especially at daytime. Over the Murray-Darling basin, the mean yearly bias is about -8°C in 2019 (a dry year), against -4°C in 2010 (a wet year). This result shows that daytime LST biases are more pronounced in dry conditions. Possible causes of the spatial, diurnal and seasonal patterns of the LST bias are hot-spot phenomenon (more sunlit than shaded elements are seen by the satellite), biases in the incoming solar and infrared radiation data used to force the model. LDAS analyses were also used to assess the accuracy of LAI and FAPAR observations, with respect to GCOS requirements. It is showed that small values of LAI observations tend to meet the GCOS requirements more often than large values of LAI observations and of FAPAR observations, for both Southern Africa and the Murray-Darling basin. Overall, low FAPAR values present more uncertainties than low LAI values

Copernicus Global Land Operations - Scientific quality evaluation cross-cutting consistency - January-December 2019

The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land service to operate a multi-purpose service component that provides a series of biogeophysical products on the status and evolution of land surface at a global scale. Production and delivery of the variables take place in a timely manner and are complemented by the constitution of long-term time series. The most advanced indirect validation technique consists in integrating the products into a land surface model (LSM) using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM, able to represent the diurnal cycle of the surface fluxes together with the seasonal, inter-annual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS (LDAS-Monde) is able to assimilate SPOT-VGT and PROBA-V Leaf Area Index (LAI) and ASCAT surface soil moisture (SSM) satellite products at a global scale at a spatial resolution of at least 0.25° x 0.25°. This permits the active monitoring of LAI and SSM variables. A passive monitoring of Surface Albedo (SA), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Land Surface Temperature (LST) is performed (i.e., the simulated values are compared with the satellite products), as these quantities are not assimilated yet. The LDAS generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model), residuals (observations vs. analysis), and increments (analysis vs. model); (2) for SA, FAPAR and LST, metrics derived from the passive monitoring. In both cases, the Pearson correlation coefficient (R), the root mean square difference (RMSD), the standard deviation of difference (SDD), and mean bias skill scores are used. In this report, results are presented for the January-December 2019 period over Southern Africa and over the Murray-Darling basin. Note that the last data from SPOT-VGT were used on 13th May 2014. After this date, new LAI / FAPAR / SA products from PROBA-V are used. For LAI, over both Southern Africa and the Murray-Darling basin, the scores tend to present better values during the dry spells of 2019 than during previous years from 2010 to 2018. The RMSD scores of consolidated estimate of LAI Version 2 and LAI Version 1 are comparable in 2019. The impact on analyzed LAI of transitioning from SPOT-VGT to PROBA-V is neutral to positive. For FAPAR, over both Southern Africa and the Murray-Darling basin, the scores tend to present slightly better values during the dry spells of 2019 than during previous years from 2010 to 2018. Overall conclusions for FAPAR are similar to those for LAI. For SA, a striking result is that a large increase in the mean bias value is observed after the transition from SPOT-VGT to PROBA-V, of about 0.02 for both Southern Africa and for the Murray-Darling basin. There is a clear discontinuity in the SA time series, not observed for LAI nor for FAPAR. For SWI-001, the impact of the seasonal SSM CDF-matching performed prior the assimilation is particularly striking for Southern Africa. Without a seasonal CDF-matching, the original SSM information would be misleading over Southern Africa. For LST, the model tends to underestimate LST, especially at daytime. Over the Murray-Darling basin, the mean yearly bias is about -8°C in 2019 (a dry year), against -4°C in 2010 (a wet year). This result shows that daytime LST biases are more pronounced in dry conditions. Possible causes of the spatial, diurnal and seasonal patterns of the LST bias are hot-spot phenomenon (more sunlit than shaded elements are seen by the satellite), biases in the incoming solar and infrared radiation data used to force the model. LDAS analyses were also used to assess the accuracy of LAI and FAPAR observations, with respect to GCOS requirements. It is showed that small values of LAI observations tend to meet the GCOS requirements more often than large values of LAI observations and of FAPAR observations, for both Southern Africa and the Murray-Darling basin. Overall, low FAPAR values present more uncertainties than low LAI values

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a subkilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017-2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

International audienceThis paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers

Utilisation de données satellitaires en hydro-météorologie : la recherche à Météo-France

La modélisation des processus de surface en relation avec le cycle du carbone et l’hydrologie superficielle, requiert l’utilisation de la télédétection pour spatialiser et piloter les modèles, et l’assimilation de données de télédétection dans différents domaines de longueur d’onde. L’utilisation des produits de télédétection spatiale sur les surfaces continentales pour des applications de recherche et pré-opérationnelles est développée à Météo-France dans le cadre de la plate-forme de modélisation SURFEX, incluant une représentation du système sol-plante (ISBA), des zones urbaines (TEB) et des lacs (FLAKE). La spatialisation des paramètres du modèles est réalisée dans SURFEX grâce à la base de données ECOCLIMAP, et sur l’Europe et l’Afrique, une version plus récente (ECOCLIMAP2). Le rayonnement incident et l’albédo (produits par le SAF-Land d’EUMETSAT en particulier) permettent de contraindre le modèle. Humidité superficielle du sol et LAI peuvent être assimilés dans le modèle, pour corriger sa « trajectoire ». Température de surface, et une indication du gel/dégel du sol permettent de vérifier les simulations. Enfin, étant donné la disponibilité de séries satellitaires de plus en plus longues (AVHRR, micro-ondes actives ou passives), la télédétection est susceptible d’être utilisée dans la production ou la vérification de réanalyses climatiques. Un développements important de l’utilisation de données de télédétection pour les surfaces continentales est à attendre dans le cadre de GMES (Global Monitoring for Environment and Security) et des SAF d’EUMETSAT. En parallèle, il est important de développer des sites de mesure pour la validation croisée satellite / modèle / in-situ

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

International audienceLDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states

The influence of vegetation drought stress on formaldehyde and ozone distributions over a central European city

International audienc