The potential of elastic and polarization lidars to retrieve extinction profiles

A new method, called ElEx (elastic extinction), is proposed for the estimation of extinction coefficient lidar profiles using only the information provided by the elastic and polarization channels of a lidar system. The method is applicable to lidar measurements both during daytime and nighttime under well-defined aerosol mixtures. ElEx uses the particle backscatter profiles at 532 nm and the vertically resolved particle linear depolarization ratio measurements at the same wavelength. The particle linear depolarization ratio and the lidar ratio values of pure aerosol types are also taken from literature. The total extinction profile is then estimated and compared well with Raman retrievals. In this study, ElEx was applied in an aerosol mixture of marine and dust particles at Finokalia station during the CHARADMExp campaign. Any difference between ElEx and Raman extinction profiles indicates that the nondust component could be probably attributed to polluted marine or polluted continental aerosols. Comparison with sun photometer aerosol optical depth observations is performed as well during daytime. Differences in the total aerosol optical depth are varying between 1.2 % and 72 %, and these differences are attributed to the limited ability of the lidar to correctly represent the aerosol optical properties in the near range due to the overlap problem. © 2020 Author(s)

The development of METAL-WRF Regional Model for the description of dust mineralogy in the atmosphere

The mineralogical composition of airborne dust particles is an important but often neglected parameter for several physiochemical processes, such as atmospheric radiative transfer and ocean biochemistry. We present the development of the METAL-WRF module for the simulation of the composition of desert dust minerals in atmospheric aerosols. The new development is based on the GOCART-AFWA dust module of WRF-Chem. A new wet deposition scheme has been implemented in the dust module alongside the existing dry deposition scheme. The new model includes separate prognostic fields for nine (9) minerals: illite, kaolinite, smectite, calcite, quartz, feldspar, hematite, gypsum, and phosphorus, derived from the GMINER30 database and also iron derived from the FERRUM30 database. Two regional model sensitivity studies are presented for dust events that occurred in August and December 2017, which include a comparison of the model versus elemental dust composition measurements performed in the North Atlantic (at Izaña Observatory, Tenerife Island) and in the eastern Mediterranean (at Agia Marina Xyliatos station, Cyprus Island). The results indicate the important role of dust minerals, as dominant aerosols, for the greater region of North Africa, South Europe, the North Atlantic, and the Middle East, including the dry and wet depositions away from desert sources. Overall, METAL-WRF was found to be capable of reproducing the relative abundances of the different dust minerals in the atmosphere. In particular, the concentration of iron (Fe), which is an important element for ocean biochemistry and solar absorption, was modeled in good agreement with the corresponding measurements at Izaña Observatory (22% overestimation) and at Agia Marina Xyliatos site (4% overestimation). Further model developments, including the implementation of newer surface mineralogical datasets, e.g., from the NASA-EMIT satellite mission, can be implemented in the model to improve its accuracy.This study was supported by the Hellenic Foundation for Research and Innovation project Mineralogy of Dust Emissions and Impacts on Environment and Health (MegDeth - HFRI no. 703). Part of this study was conducted within the framing of the AERO-EXTREME (PID2021-125669NB-I00) project funded by the State Research Agency/Agencia Estatal de Investigación of Spain and the European Regional Development Funds

Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology

Next day wildfire prediction is an open research problem with significant environmental, social, and economic impact since it can produce methods and tools directly exploitable by fire services, assisting, thus, in the prevention of fire occurrences or the mitigation of their effects. It consists in accurately predicting which areas of a territory are at higher risk of fire occurrence each next day, exploiting solely information obtained up until the previous day. The task’s requirements in spatial granularity and scale of predictions, as well as the extreme imbalance of the data distribution render it a rather demanding and difficult to accurately solve the problem. This is reflected in the current literature, where most existing works handle a simplified or limited version of the problem. Taking into account the above problem specificities, in this paper, we present a machine learning methodology that effectively (sensitivity > 90%, specificity > 65%) and efficiently performs next day fire prediction, in rather high spatial granularity and in the scale of a country. The key points of the proposed approach are summarized in: (a) the utilization of an extended set of fire driving factors (features), including topography-related, meteorology-related and Earth Observation (EO)-related features, as well as historical information of areas’ proneness to fire occurrence; (b) the deployment of a set of state-of-the-art classification algorithms that are properly tuned/optimized on the setting; (c) two alternative cross-validation schemes along with custom validation measures that allow the optimal and sound training of classification models, as well as the selection of different models, in relation to the desired trade-off between sensitivity (ratio of correctly identified fire areas) and specificity (ratio of correctly identified non-fire areas). In parallel, we discuss pitfalls, intuitions, best practices, and directions for further investigation derived from our analysis and experimental evaluation

Precipitation Climatology for the Arid Region of the Arabian Peninsula—Variability, Trends and Extremes

The Arabian Peninsula is a region characterized by diverse climatic conditions due to its location and geomorphological characteristics. Its precipitation patterns are characterized by very low annual amounts with great seasonal and spatial variability. Moreover, extreme events often lead to flooding and pose threat to human life and activities. Towards a better understanding of the spatiotemporal features of precipitation in the region, a thirty-year (1986-2015) climatic analysis has been prepared with the aid of the state-of-the-art numerical modeling system RAMS/ICLAMS. Its two-way interactive nesting capabilities, explicit cloud microphysical schemes with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities are significant advantages over an area where dust is a dominant factor. An extended evaluation based on in situ measurements and satellite records revealed a good model behavior. The analysis was performed in three main components; the mean climatic characteristics, the rainfall trends and the extreme cases. The extremes are analyzed under the principles of the extreme value theory, focusing not only on the duration but also on the intensity of the events. The annual and monthly rainfall patterns are investigated and discussed. The spatial distribution of the precipitation trends revealed insignificant percentage differences in the examined period. Furthermore, it was demonstrated that the eastern part and the top half of the western Arabian Peninsula presented the lowest risk associated with extreme events. Apart from the pure scientific interest, the present study provides useful information for different sectors of society and economy, such as civil protection, constructions and reinsurance

Development of a Dust Source Map for WRF-Chem Model Based on MODIS NDVI

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD > 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (>0.0 and >0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance

Precipitation Climatology for the Arid Region of the Arabian Peninsula-Variability, Trends and Extremes

The Arabian Peninsula is a region characterized by diverse climatic conditions due to its location and geomorphological characteristics. Its precipitation patterns are characterized by very low annual amounts with great seasonal and spatial variability. Moreover, extreme events often lead to flooding and pose threat to human life and activities. Towards a better understanding of the spatiotemporal features of precipitation in the region, a thirty-year (1986-2015) climatic analysis has been prepared with the aid of the state-of-the-art numerical modeling system RAMS/ICLAMS. Its two-way interactive nesting capabilities, explicit cloud microphysical schemes with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities are significant advantages over an area where dust is a dominant factor. An extended evaluation based on in situ measurements and satellite records revealed a good model behavior. The analysis was performed in three main components; the mean climatic characteristics, the rainfall trends and the extreme cases. The extremes are analyzed under the principles of the extreme value theory, focusing not only on the duration but also on the intensity of the events. The annual and monthly rainfall patterns are investigated and discussed. The spatial distribution of the precipitation trends revealed insignificant percentage differences in the examined period. Furthermore, it was demonstrated that the eastern part and the top half of the western Arabian Peninsula presented the lowest risk associated with extreme events. Apart from the pure scientific interest, the present study provides useful information for different sectors of society and economy, such as civil protection, constructions and reinsurance

Fuzzy clustering for the within-season estimation of cotton phenology.

Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication