This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to …This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products.
The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8–13 W/m2, whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.more
Les vecteurs vents extraits à partir des images satellite sont utilisés quotidiennement dans les modèles de prévision numérique du temps afin d'amélio…Les vecteurs vents extraits à partir des images satellite sont utilisés quotidiennement dans les modèles de prévision numérique du temps afin d'améliorer la qualité des prévisions météorologiques. Ils constituent en fait la seule observation du déplacement des masses d'air disponible aux hautes latitudes et au-dessus des océans. Cet article présente une vue d'ensemble de l'extraction des vecteurs vents à partir des images satellite à Eumetsat. La première partie décrit l'algorithme utilisé pour extraire des vents à partir des satellites géostationnaires Météosat, la seconde partie décrit l'extraction de ces vecteurs vents à partir des satellites polaires en orbite basse.more
Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preco…Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preconditioning, local feedback mechanisms, oceanic heat transport and the synoptic- and large-scale atmospheric circulations. However, a robust quantitative statistical analysis of extremes of sea ice reduction is hindered by the small number of events that can be sampled in observations and numerical simulations with computationally expensive climate models. Recent studies tackled the problem of sampling climate extremes by using rare event algorithms, i.e., computational techniques developed in statistical physics to reduce the computational cost required to sample rare events in numerical simulations. Here we apply a rare event algorithm to ensemble simulations with the intermediate complexity coupled climate model PlaSim-LSG to investigate extreme negative summer pan-Arctic sea ice area anomalies under pre-industrial greenhouse gas conditions. Owing to the algorithm, we estimate return times of extremes orders of magnitude larger than feasible with direct sampling, and we compute statistically significant composite maps of dynamical quantities conditional on the occurrence of these extremes. We find that extremely low sea ice summers in PlaSim-LSG are associated with preconditioning through the winter sea ice-ocean state, with enhanced downward longwave radiation due to an anomalously moist and warm spring Arctic atmosphere and with enhanced downward sensible heat fluxes during the spring-summer transition. As a consequence of these three processes, the sea ice-albedo feedback becomes active in spring and leads to an amplification of pre-existing sea ice area anomalies during summer.more
Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and h…Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and high solar zenith angle cause missing values to the optical satellite products, especially around the polar areas. Therefore, our motivation is to develop gap filling models. For that purpose, we will apply monthly gradient boosting (GB) based models to the Arctic sea ice area of the 34 years long albedo time series of the Satellite Application Facility on Climate Monitoring (CM SAF) project. We demonstrate the ability of the GB models to accurately fill missing data using albedo monthly mean, brightness temperature, and sea ice concentration as model inputs. Monthly GB models produce the most unbiased, precise, and robust estimates when compared to alternative estimates presented, such as monthly mean albedo values or estimates from monthly linear regression (LR) models. The mean relative differences between GB based estimates and original pentad values vary from-20% to 20% with RMSE being 0.048, compared to relative differences varying from-20% to over 60% with RMSE varying from 0.054 to 0.074 between other estimates and original pentad values. Pixelwise mean differences and standard deviations (std) over the whole Arctic sea ice area show that GB based estimates are more accurate (mean differences from-0.02 to 0.02) and more precise (std from 0.02 to 0.08) than other estimates (mean differences varying between-0.05 to over 0.05, and std varying from around 0.03 to over 0.1). Also, albedo of the melting sea ice is predicted better by the GB model, with negligible mean differences, compared to the LR model. Based on these results, we show that GB method is a useful technique to fill missing data, and the brightness temperature and sea ice concentration are useful additional model input data sources.more
The aim of this study is to investigate the potential of the
Global Ozone Monitoring Experiment-2 (GOME-2) instruments, aboard the
Meteorological Oper…The aim of this study is to investigate the potential of the
Global Ozone Monitoring Experiment-2 (GOME-2) instruments, aboard the
Meteorological Operational (MetOp)-A, MetOp-B and MetOp-C satellite programme platforms, to
deliver accurate geometrical features of lofted aerosol layers. For this
purpose, we use archived ground-based lidar data from stations available
from the European Aerosol Research Lidar Network (EARLINET) database. The
data are post-processed using the wavelet covariance transform (WCT) method
in order to extract geometrical features such as the planetary boundary
layer (PBL) height and the cloud boundaries. To obtain a significant number
of collocated and coincident GOME-2 – EARLINET cases for the period between
January 2007 and September 2019, 13 lidar stations, distributed over
different European latitudes, contributed to this validation. For the 172
carefully screened collocations, the mean bias was found to be −0.18 ± 1.68 km,
with a near-Gaussian distribution. On a station basis, and with a
couple of exceptions where very few collocations were found, their mean
biases fall in the ± 1 km range with an associated standard deviation
between 0.5 and 1.5 km. Considering the differences, mainly due to the
temporal collocation and the difference, between the satellite pixel size
and the point view of the ground-based observations, these results can be
quite promising and demonstrate that stable and extended aerosol layers as
captured by the satellite sensors are verified by the ground-based data. We
further present an in-depth analysis of a strong and long-lasting Saharan
dust intrusion over the Iberian Peninsula. We show that, for this
well-developed and spatially well-spread aerosol layer, most GOME-2
retrievals fall within 1 km of the exact temporally collocated lidar
observation for the entire range of 0 to 150 km radii. This finding further
testifies for the capabilities of the MetOp-borne instruments to sense the
atmospheric aerosol layer heights.more