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
Numerical simulation applied to agriculture or wastewater treatment (WWT) is a complementary tool to understand, a priori, the impact of meteorologica…Numerical simulation applied to agriculture or wastewater treatment (WWT) is a complementary tool to understand, a priori, the impact of meteorological parameters on productivity under limiting environmental conditions or even to guide investments towards other more relevant circular economic objectives. This work proposes a new methodology to calculate Typical Meteorological Sequences (TMS) that could be used as input data to simulate the growth and productivity of photosynthetic organisms in different biological systems, such as a High-Rate Algae Pond (HRAP) for WWT or in agriculture for crops. The TMS was established by applying Finkelstein-Schafer statistics and represents the most likely meteorological sequence in the long term for each meteorological season. In our case study, 18 locations in the Madrid (Spain) region are estimated depending on climate conditions represented by solar irradiance and temperature. The parameters selected for generating TMS were photosynthetically active radiation, solar day length, maximum, minimum, mean, and temperature range. The selection of potential sequences according to the growth period of the organism is performed by resampling the available meteorological data, which, in this case study, increases the number of candidate sequences by 700%.more
This article presents a comprehensive evaluation of the 2000–2018 Clouds and Earth's Radiant Energy System Synoptic 1° Ed4A (CERES SYN1deg Edition 4A)…This article presents a comprehensive evaluation of the 2000–2018 Clouds and Earth's Radiant Energy System Synoptic 1° Ed4A (CERES SYN1deg Edition 4A) surface solar radiation (SSR) product. In particular, the global assessment is conducted over different temporal scales (i.e., hourly, daily, and monthly-average) with special attention given to the impact of clouds, and a regional evaluation is further implemented over the Mainland of China (MC) using SSR measurements from a denser observational network provided by the China Meteorological Administration. Evaluation across all valid station-grid pairs yields mixed performance with |MBE|≤2.8 (6.2) W m−2, RMSE≤89.5 (31.6) W m−2, and R≥0.95 (0.93) over the globe (MC) for different temporal scales, and the monthly CERES SSR, with RMSE≤20 W m−2, is found to hold promise for global numerical weather prediction and climate monitoring. In addition, CERES is found to generally underestimate and overestimate SSR over land and ocean, respectively. Comparison between year-round and cloudy-season suggests that the presence of clouds may potentially impact the SSR retrievals, especially at the hourly temporal scales, with an increase in RMSE values larger than 10 W m−2 for most stations. Further investigation of subgrid heterogeneity suggests that most in situ SSR measurements can reasonably represent the 1° grid average except for some stations with specific geographic deployments, which may raise significant spatial representativeness issues and, therefore, need to be used with great caution.more