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The lightning clustering algorithm for spaceborne lightning observations clusters events into flash products and plays a crucial role in lightning-related research. This paper modified the lightning clustering algorithm based on the routine algorithm of Lightning Mapping Imager (LMI) and applied it to lightning events recorded by the LMI in 2019 to study the flash duration and flash area and specially to reveal the extreme lightning. To reveal the temporal and spatial features of lightning, the MCA (modified cluster algorithm) flash products defined flash duration and flash area. The results showed that continental regions have higher lightning frequencies but smaller events per flash, flash durations and flash areas than the ocean. The Tibetan Plateau has a low lightning frequency and smaller flash duration and area than eastern China. The flash count, events per flash, flash duration and flash area are higher during nighttime than during daytime. The relationship between flash duration and flash area showed distinct regional differences. In the MCA flash product, about 0.6% (0.1%) flashes have duration (area) that exceeds 1.0 s (5,000 km2). Finally, we analyzed two extreme lightning cases in eastern China, where the longest flash duration was 4.748 s and the largest flash area was 17,100 km2. This research contributes to advancing our understanding in the features of space-based lightning in the Eastern Hemisphere and applications to convection study. • Clustering algorithm was modified to study lightning with extreme duration and area. • Lightning duration and area both implied regional differences. • Quantitative relationship of duration and area was established in different regions. • The longest flash duration/largest flash area was 4.75 s/17100 km2 in eastern China. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for cloud detection based on high dynamic range images from a sky camera and ceilometer measurements. The algorithm is also able to detect the obstruction of the sun. This algorithm, called CPC (Camera Plus Ceilometer), is based on the assumption that under cloud-free conditions the sky field must show symmetry. The symmetry criteria are applied depending on ceilometer measurements of the cloud base height. CPC algorithm is applied in two Spanish locations (Granada and Valladolid). The performance of CPC retrieving the sun conditions (obstructed or unobstructed) is analyzed in detail using as reference pyranometer measurements at Granada. CPC retrievals are in agreement with those derived from the reference pyranometer in 85% of the cases (it seems that this agreement does not depend on aerosol size or optical depth). The agreement percentage goes down to only 48% when another algorithm, based on Red-Blue Ratio (RBR), is applied to the sky camera images. The retrieved cloud cover at Granada and Valladolid is compared with that registered by trained meteorological observers. CPC cloud cover is in agreement with the reference showing a slight overestimation and a mean absolute error around 1 okta. A major advantage of the CPC algorithm with respect to the RBR method is that the determined cloud cover is independent of aerosol properties. The RBR algorithm overestimates cloud cover for coarse aerosols and high loads. Cloud cover obtained only from ceilometer shows similar results than CPC algorithm; but the horizontal distribution cannot be obtained. In addition, it has been observed that under quick and strong changes on cloud cover ceilometers retrieve a cloud cover fitting worse with the real cloud cover. [ABSTRACT FROM AUTHOR]

Abstract: The estimation of the recurrence rate and the distribution in severity of extreme ice events, which is required in order to determine design criteria for structures such as electric transmission lines, suffers from the fact that there appears to be a great variability in freezing rain events, in terms of duration, quantity of precipitation, and wind speed. Rauber et al. (2001), developed 7 archetypical patterns for freezing rain storms that indeed articulated differences in duration, spatial extent and intensity of storms. This finding suggests that it would not be correct to consider all freezing rain storms as a single homogeneous statistical population, which is the current practice in the extreme value analysis of ice storms. This paper presents an automated and objective methodology for classifying freezing rain storms that can be used in a de-aggregated extreme value analysis. This paper presents the theory, methodology, and results for clustering freezing rain storms. The procedure is tested using a base data set of previously classified storms: the storms identified and classified by Rauber et al. (2001) that occurred over North Eastern United States. The storms are described by making average anomaly maps based on sea level pressure (SLP), and 1000–500hPa geopotential heights using NCEP reanalysis data. Synoptic categories are then formed using clustering algorithms and principal component analysis. The k-means clustering algorithm is compared with a number of hierarchical algorithms. The resulting clusters using the k-means algorithm are compared amongst themselves as well as with the archetypical patterns of Rauber et al. For each event, statistics on wind speeds and precipitation are calculated using NCEP reanalysis data and NCEP hourly precipitation data respectively to verify the significance of the clusters. The three cluster solutions based on 1000–500hPa anomaly maps using the typing method developed here are found to be suitable for grouping storms in homogeneous populations. [Copyright &y& Elsevier]

Abstract: A foundation of forecasting Tropical Cyclones (TCs) is to locate their centers. There are still many difficulties in TC center locating, especially when there is an unclear eye or when spiral bands are difficult to be recognized. It is usually done by hand operationally, and multiple forecasters'' results may be inconsistent. In this paper, we treat the locating problem as an optimization one. An objective locating scheme is developed, using infrared (IR) satellite cloud images. A novel Spiral Band Model (SBM) is designed to extract and describe the spiral pattern of a spiral band which spirals out from a TC''s center. SBM is based on a spiral band region, different from existing curvilinear models which are based on curves. A simple criterion is also defined to score the matching degree between a template derived by SBM and the spiral band of a TC. In this paper the TC center locating problem is transformed to an optimization problem, and the Chaos Immune Evolutionary Algorithm (CIEA) is employed to solve this optimization problem. The spiral center of the template found at the end of the optimization process is the estimated location of the true TC''s center. The experimental result demonstrates that the proposed scheme obtains the best average error of 0.0930° in latitude/longitude in comparison with the best track data. It is well within the relative error of about 0.3° from the results of different TC warning centers. It provides objective information for forecasters to estimate TCs'' centers. [Copyright &y& Elsevier]

Systematic high quality oceanic in-situ precipitation measurements are requested on an international science level and are essential for improved understanding and validation of hydrological processes in satellite, re-analysis and model data. OceanRAIN, the shipboard “Ocean Rainfall And Ice-phase precipitation measurement Network” for surface validation is, to date, the only systematic long-term disdrometer-based oceanic shipboard precipitation data collection effort to establish a comprehensive statistical basis of precipitation for all climate related hotspots over the global oceans. OceanRAIN utilizes automated disdrometer systems (ODM470) capable of measuring precipitation occurrence, intensity and accumulation and discriminates for rain, snow and mixed-phase precipitation through minute-based particle size distributions. The ODM470 was especially designed for shipboard operation under high and frequently varying wind speeds and rough sea states. This paper provides an overview on the OceanRAIN project, the instrumentation, algorithms, methodology, and data products. The procedure of the data processing chain is outlined, including calibration, shipboard operation, data ingest and quality control. The selected research ships do not circumvent high impact weather, allowing for a collection of the full precipitation spectrum including extremes. By October 2014 the fast growing OceanRAIN database comprised more than 3.7 million minutes of precipitation measurements (including true zeros) since its start in 2010. OceanRAIN aims at increasing knowledge about oceanic precipitation, improving error characterization of GPM (Global Precipitation Measurement) era precipitation retrievals, adding to the continual improvement of the satellite retrieval algorithms, as well as benchmarking existing satellite-based climatologies, re-analysis and model data. The accumulating data volume can be utilized for statistical and process study applications on different temporal and spatial scales, microphysical studies of rain and snow formation, and yields insight to the point-to-area problem of precipitation. Information on OceanRAIN including data products is available via http://www.oceanrain.org . [ABSTRACT FROM AUTHOR]

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957–2008 and the monthly EDI predicted over the period 2009–2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg–Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties. [ABSTRACT FROM AUTHOR]

Aerosol retrieval using MODerate resolution Imaging Spectroradiometer (MODIS) has been well researched over the past decade. However, the application is limited to global- and regional-scale studies, which may not be applicable for urban areas due to its low spatial resolution. To overcome the limitation, this paper proposed an improved aerosol retrieval algorithm for Landsat images (ImAero-Landsat) at spatial resolution of 30 m. This ImAero-Landsat algorithm has been improved in the following two aspects: (i) it does not require a comprehensive look up table and thus it is more efficient in AOT retrieval; and (ii) it can be operated in both bright and dark surfaces. The derived aerosol optical thickness (AOT) images were validated with AErosol RObotic NETwork (AERONET) measurements as well as MODIS MOD04 AOT products. Small root mean square errors (RMSEs) of 0.11 and 0.14 and mean absolute difference (MAD) of 0.07 and 0.11 between ImAero-Landsat AOT, with MODIS MOD04 and AERONET products were observed. By correlating with ground based PM 10 concentrations, the ImAero-Landsat method outperforms (r 2 = 0.32) than MOD04 AOT products (r 2 = 0.23). In addition, the accuracy of estimating PM 10 can be improved to r 2 = 0.55 when the derived AOT was integrated with meteorological parameters. The accuracy is similar to the results derived from AERONET AOT (r 2 = 0.62). This study offers a simple and accurate method to investigate aerosol optical thickness at detailed city-scale. Environmental authorities may use the derived methods for deriving aerosol distribution maps and pinpointing the sources of pollutants in urban areas. [ABSTRACT FROM AUTHOR]

Abstract: The Water vapor Strong Lines at 183GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convective/stratiform). It exploits the water vapor absorption line observations centered at 183.31GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15/-17 and NOAA-18-19/MetOp-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The “ground truth” dataset consists of 2years and 7months of rainfall intensity fields from the NIMROD radar network, which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds and 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates. The results reveal that the 183-WSL technique shows good skills in the detection of rain/no-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the Probability Of Detection (POD), False Alarm Ratio (FAR) and Hanssen–Kuiper discriminant (HK) indices varying in the range 0.80–0.82, 0.33–0.36 and 0.39–0.46, respectively. The RMSE value is 2.8mmh−1 for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer during winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This is verified by the discrepancy distribution diagrams where the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tend to maximum values. The stable behavior of the 183-WSL algorithm is demonstrated over the whole study period by an overall overestimation for rain rate intensities less than 1mmh−1. This threshold is especially crucial in wintertime when the classification of low-intensity precipitation regimes is difficult. [Copyright &y& Elsevier]

Abstract: The paper addresses anomalies in brightness temperature difference (BTD) between WV and IR window bands using MSG rapid scan data. One of our aims is to eliminate subjectivity in their detection. The important finding is that it is not possible to study the anomalous behavior when taking into account all pixels related to a storm cloud top. It is necessary to identify and study anomalous regions separately, otherwise their behavior is masked by the majority of the other pixels. We propose a detection algorithm based on the thresholding method with the threshold depending on the dispersion of the BTD data. Outputs of the algorithm and first analyses for one chosen case are shown. According to the first results, the typical relationship between BT and BTD (most of the pixels with growing BTD exhibit a decrease in BT) is retained during the time evolution of the storm also for anomalous pixels, even though their BTDs reach higher values than for non-anomalous pixels at a similar BT level. [Copyright &y& Elsevier]

Abstract: Recently, we proposed a novel remote sensing method for rainfall rate estimation in real time by means of tomographic processing applied to power attenuation measurements made across the microwave links defined by radiobase station networks for mobile communication systems. In this paper we analyze the results of a new tomographic algorithm that has been specifically developed for the typical topology of the urban radiobase station networks. Such algorithm has been tested on simulated specific attenuation (K) maps based on true polarimetric weather radar data. We considered 18, 23, and 38GHz for the carrier frequencies of the radiobase network and the weather data gathered by the POLAR 55C polarimetric radar located in Rome. [Copyright &y& Elsevier]

Abstract: A drop counter catching-type gauge and a tipping-bucket rain intensity gauge are compared in this paper in order to investigate the performance of suitable correction algorithms. Comparison in the field is performed at the resolution of 1min by assuming the drop counter as a working reference at low to medium rainfall rates. The observed deviations are mainly the result of sampling errors due to the low resolution (0.5mm) of the tipping-bucket. Two correction algorithms for this gauge are compared based on the knowledge of the time of tipping: since this is not actually available for the instrument in hand, numerical simulation is performed using information from the drop counter at the resolution of 10s. The first correction is obtained by distributing the tip volume over the inter-tip periods. The second algorithm further applies a “smoothing” procedure that redistributes the volume of initial, isolated and final types of tips according to climatology. Relative deviations of the one-minute rainfall intensity figures are used to assess improvements in the observed measurement performance. Sensitivity of the results upon varying the sampling resolution of a virtual tipping-bucket rain gauge is also investigated using numerical simulation. It is concluded that the knowledge of the time of tipping is essential to improve the performance of the tipping-bucket rain gauge. Distributing the tip volume over the inter-tip period is indeed sufficient to considerably improve the measurement accuracy, especially at low to medium intensities and a fine sampling resolution. The influence of the correction at high rainfall intensity increases with decreasing sampling resolution. Although the proposed smoothing algorithm provides further improvements at low intensities for gauges characterized by coarse sampling resolution, where the first algorithm is less effective, the results are still affected by a significant spread of deviation. [Copyright &y& Elsevier]

Abstract: Classification of weather maps at various isobaric levels as a methodological tool is used in several problems related to meteorology, climatology, atmospheric pollution and to other fields for many years. Initially the classification was performed manually. The criteria used by the person performing the classification are features of isobars or isopleths of geopotential height, depending on the type of maps to be classified. Although manual classifications integrate the perceptual experience and other unquantifiable qualities of the meteorology specialists involved, these are typically subjective and time consuming. Furthermore, during the last years different approaches of automated methods for atmospheric circulation classification have been proposed, which present automated and so-called objective classifications. In this paper a new method of atmospheric circulation classification of isobaric maps is presented. The method is based on graph theory. It starts with an intelligent prototype selection using an over-partitioning mode of fuzzy c-means (FCM) algorithm, proceeds to a graph formulation for the entire dataset and produces the clusters based on the contemporary dominant sets clustering method. Graph theory is a novel mathematical approach, allowing a more efficient representation of spatially correlated data, compared to the classical Euclidian space representation approaches, used in conventional classification methods. The method has been applied to the classification of 850hPa atmospheric circulation over the Eastern Mediterranean. The evaluation of the automated methods is performed by statistical indexes; results indicate that the classification is adequately comparable with other state-of-the-art automated map classification methods, for a variable number of clusters. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new method for locating lightning discharges. The proposed method is based on the time reversal technique introduced for acoustics in the early 1990s. By analogy to the derivations in acoustics, equations for the focusing of electromagnetic fields back to the source by time reversal are derived. We show that the wavefronts generated by back-propagating the time-reversed fields will add up in phase at the lightning strike location. Based on this observation, we present an algorithm to locate lightning discharges which requires at least three field sensors. The algorithm is illustrated and shown to be very efficient using numerically-generated fields for different cases and considering different numbers of sensors. [Copyright &y& Elsevier]

Abstract: Current satellite aerosol retrieval products could be complemented by contrast reduction methods to overcome limitations regarding highly reflective or heterogeneous surfaces such as urban, desert or snow covered areas. Algorithms based on the contrast reduction principle, define contrast loss in an image, inside a pre-determined window size, as an exponential function of the Aerosol Optical Thickness (AOT) difference between two images (a reference and a polluted) acquired under similar observation geometry conditions. This paper presents a contrast reduction algorithm designed for the MODIS sensor, based on the Differential Texture Analysis (DTA) approach. It focuses on algorithm optimization by: a) determining an optimal AOT spatial resolution; b) constraining the relative observation geometry differences between polluted and reference images; and c) assessing the influence of several land cover classes on the accuracy of the retrievals. A comparison of the results obtained for 192 images acquired for the year 2005 with data from five European AERONET stations is performed to assess overall algorithm accuracy as well as the impact of the proposed improvements. Comparative analysis of the results for the various sites showed an optimal algorithm performance for MODIS images using a 39pixel distance window, composed of only forest and urban pixels. Comparison with AERONET AOT data showed a good agreement with a correlation coefficient of 0.78. A similar correlation is found when comparing AERONET measurements and MODIS aerosol standard product. This research supports the establishment of contrast reduction methods as a potential complement to other aerosol retrieval methodologies. Future work will aim at removing the residual aerosol influence from reference images, including BRDFs to better reproduce surface heterogeneity and observation geometry influences and expanding the scope of this study to other AERONET sites so as to further test the algorithm at a global scale. [Copyright &y& Elsevier]

Abstract: Aerosol remote sensing over land is still a great challenge. The retrieval of optical and micro-physical aerosol properties usually requires comprehensive and computationally extensive retrieval algorithms. An efficient approach for the retrieval of the aerosol optical depth (AOD) is proposed in this paper. It is based on a fast and simple atmospheric radiative transfer model using approximations and analytical equations. The proposed algorithm is validated on both synthetic data from the benchmark radiative transfer model SCIATRAN and on real data from the airborne imaging spectrometer APEX. The promising results confirm the feasibility of retrievals using single wavelength nadir observations under constrained conditions. The AOD retrieval is found to be very sensitive to uncertainties in the surface albedo. In the current experimental setup, 0.01 surface albedo uncertainty leads to approximately 0.2 AOD retrieval uncertainty. Generally, larger AOD leads to the increase of reflectance R at sensor level for underlying “dark” surfaces and to a decrease of R for “bright” surfaces. R does not depend strongly on AOD for a surface albedo in the range of approximately 0.2–0.4. Third and higher orders of aerosol scattering, polarization and three-dimensional effects at the surface–atmosphere interface are not taken into account by this study. Nevertheless, a brief validation shows the feasibility with promising results of the proposed simple and fast AOD retrieval algorithm from remote sensing data. [Copyright &y& Elsevier]

Abstract: Currently, many satellite-based aerosol retrievals make use of lookup tables (LUTs) containing precomputed solutions to the radiative transfer (RT) equation. The benefit of this strategy is the avoidance of expensive runtime calculations, but its main drawback is that the LUTs discretize what is inherently a continuous, multivariate solution space. The operational retrieval algorithm for the Multi-angle Imaging SpectroRadiometer (MISR), for example, compares the observations to a set of 74 aerosol mixtures, each composed of particle models having prescribed optical properties and size distributions. In a recent “blind” study comparing the performance of several satellite retrieval algorithms on simulated data over a black surface, the MISR algorithm performed reasonably well in recovering the “true” spectral aerosol optical depths (AODs), but because the correct aerosol model was not contained within the MISR LUT, the retrieved AODs were biased low by ~14%. This motivated an investigation of whether an optimization approach, in which the aerosols are modeled by a set of continuously variable parameters recovered using nonlinear least-squares, could improve the results. In this paper, we demonstrate that such an approach using Levenberg–Marquardt optimization yields superior accuracy. Advances in computer speed, development of more efficient RT codes, and algorithm innovations will be necessary for this approach to satisfy the demands of a global, production-level satellite aerosol retrieval process, especially when used in conjunction with future instruments having enhanced sensitivity to diverse aerosol properties. [Copyright &y& Elsevier]

Abstract: The main objective of this study is the characterization of meteorological conditions in the region of Annaba (Algeria) using clustering tools. The proposed two stages clustering approach is based on using the Self-Organizing Maps (SOMs) and the well known K-means clustering algorithm. Quantitative (using two categories of validity indices) and qualitative criteria were introduced to compare and verify the correctness of the results. The different experiments developed, extracted five classes, which were related to typical meteorological conditions in the area. The obtained meteorological clusters are then used to better elucidate the dependency of meteorology on air quality in the presence of seven measured pollutants. In the current paper, Artificial Neural Networks (ANNs), and more precisely, Multi-Layered Perceptron (MLP) is used for modeling air pollutants, as well as, simulating their behaviour in relation to the meteorological parameters of interest. This behaviour is also investigated with the aid of correlation coefficient, where only results are shown for comparison, several relations and conclusions have been drawn. [Copyright &y& Elsevier]

Abstract: The purpose of this work is to obtain daily mean wind speed and wind gust estimations by means of the analog model. To do this, three data sets have been used: daily 1000hPa geopotential height field over the North Atlantic, observational daily mean wind speed and observational daily gust wind speeds in Spain. Previously to the analog method, a Principal Component Analysis is used to reduce the dimensionality of the large-scale atmospheric database. Here, the analog method is based on finding in the historic geopotential height data base, a PC subset of large-scale atmospheric patterns that are the most similar to a large-scale atmospheric pattern used as input. In the algorithm process of the analog model, two Euclidean metrics in two different distances used as weighting functions are defined and analyzed for finding the best analogs. Thus, similar atmospheric situations to any particular atmospheric situation to be modeled have been determined and from them, several and different wind fields have been estimated, to characterize the wind fields in Spain. Several deterministic and probabilistic results are shown. Results of bias, spatial correlations and root mean squared errors between the estimated and observational wind fields are displayed, evaluating the model skilful for predicting mean wind and gusts. The probabilistic results are shown by means of Brier Skill Scores, rank histograms and reliability diagrams. The procedure used in this paper to find analogs, reducing previously the dimensionality of the large-scale atmospheric data and obtaining PCs, has been revealed as a good technique to find similar patterns to any particular one. [Copyright &y& Elsevier]

Abstract: This purposes of this paper are to: 1) demonstrate an objective overshooting top (OT) detection method using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared data, 2) produce an OT database for all operational SEVIRI data over Europe and north Africa for the six Northern Hemisphere warm seasons observed by SEVIRI to date, and 3) determine the frequency of OT detections in the vicinity of confirmed severe weather reports recorded within the European Severe Weather Database (ESWD). Algorithm performance is demonstrated for a case where numerous severe storms were present. Qualitative comparisons indicate that most OT detections correspond with the characteristic OT signature in SEVIRI 1km resolution visible channel imagery, but some OTs were left undetected due in part to relatively coarse SEVIRI spatial resolution over the European domain. The SEVIRI OT database shows a strong relationship between OT maxima and regions with high terrain. OTs are found to occur more frequently during the day over land and during the night over water. Inter- and intra-seasonal variability in OT frequency and location are also shown. An OT was found near 47% of the confirmed ESWD events. The OT–severe weather relationship is strong for large hail (53%) and severe wind (52%) events but relatively weak for tornado events (14%). The weak OT–tornado relationship may be related two factors: 1) low-level wind shear is found to be of greater importance than large CAPE and strong updrafts (i.e. OTs) in tornadic storm environments across Europe and 2) a weakening of the storm updraft and collapse of the OT region has been documented prior to tornado formation. The relatively strong overall OT–severe weather relationship suggests that OT detections can be used to increase forecaster confidence that a given storm is severe, especially in regions where frequent, ground based Doppler weather radar data is unavailable. [ABSTRACT FROM AUTHOR]

Abstract: Air Pollution control is of major concern for the Greater Athens Area (GAA) of Greece. High concentrations of Particulate Matter with diameter less than 10μm (PM10) have been observed and often reported to exceed the currently EU legislated 24-hour limit of 50μg/m3. Efforts therefore have been placed on understanding the PM10 concentration pattern in the area so that mitigation measures can be taken accordingly. The present paper presents a statistical methodology to discover causal relationships between daily PM10 exceedances at a monitoring site, with PM10 concentrations from existing stations from the monitoring network and associated weather patterns. The proposed approach utilised a dimension reduction algorithm, Positive Matrix Factorisation (PMF) algorithm, coupled with the k-means clustering algorithm to identify distinct groups of data. Then for each resulted cluster, the Granger Causality method aided by the Pearson correlation is applied to establish the causal relationships between the meteorological patterns and the observed PM10 exceedances. The study was conducted using 6-years of daily PM10 concentration data from the monitoring network in the GAA, complemented with meteorological data available from the National Centres for Environmental Prediction (NCEP) Global Forecasting System (GFS). The analysis yielded that the PM10 exceedances in the Athens area can be classified into 6 distinct types identified with varying spatial distribution characteristics and air pollution contributors. [Copyright &y& Elsevier]

Abstract: Most of the existing satellite-based evapotranspiration algorithms have been developed using fine-resolution Landsat TM and ASTER data. However, these algorithms are often applied to coarse-resolution MODIS data. In this paper, we investigate the feasibility of this approach using data from ASTER overpasses of the Las Cruces region in New Mexico, which consisted of irrigated and bare land surface conditions in a desert climate, and a satellite-based evapotranspiration algorithm which has been developed for the Las Cruces region using fine-resolution ASTER data. The evaluation technique consists of comparing evapotranspiration calculated in two ways: first, calculate at the ASTER resolution and average to the MODIS resolution; and second, calculate directly at the MODIS resolution by aggregating the ASTER data to the MODIS resolution. Results show that applying the satellite-based algorithms, which are developed at ASTER resolution, to MODIS resolution leads to ET estimates that (1) preserve the overall spatial pattern (spatial correlation in excess of 0.90), (2) increase the spatial standard deviation and maximum value, and (3) have modest conditional bias: underestimate low ET rates (<1mm/day) and overestimate high ET rates; the overestimation is within 20%. The results emphasize the need for exploring alternatives for estimation of ET from MODIS. [Copyright &y& Elsevier]

Abstract: Since climate trends are getting considerable attention in recent years, we aimed in this study to compare trends and rhythms of complexity (fractal dimension, FD) of rainfall data series between two continents: Latin America and Europe. Two parallel nonlinear methods for calculating FD of a temporal data series, Higuchi''s and consecutive differences, were combined with Fast Fourier Transform (FFT) to obtain FD oscillations of monthly accumulated rainfall. The data were collected for the last thirty years in Pastaza province, Ecuador and Veneto province, Italy. In order to calculate their FD time dependence, FD(t), moving windows of different lengths (short, 10–20 and long 21–350 samples), were applied. Both methods, each combined with FFT, detected identical (or very similar) rhythms of detrended FD(t) in the two data series, but frequencies with dominant amplitudes differed (4.4 years in Ecuador, 10.3 years in Italy). Long-term FD(t) trends were also studied using optimized long window lengths (~200 samples). A linear positive trend was obtained for the Ecuadorian rainfall data over the whole recorded period. Italian fractal trend profile was, however, characterized by two periods: a constant high value for years 1974–1993, followed by a linear decrease for 1993–2005. Trend results, obtained with two different methods, were also similar. Accordance of the results, reported in the present paper by applying these two methods, validates their use as a tool in future fractal meteorological measurements. As well, these results indicate that positive FD trend obtained for Pastaza (Ecuador) and negative trend computed for Veneto (Italy) account for a local or regional phenomenon, most probably caused by extensive deforestation and land use change (Ecuador) and continental or global atmospheric pattern variability (Italy). [Copyright &y& Elsevier]

Satellite remote sensing has been extensively utilized for monitoring dust storms in space and time. Dust storm detection using satellite observations is important to analyze the dust storm trajectories and sources. This paper reviews the algorithms for dust storm detection used in multispectral satellite sensors, spanning visible to thermal wavelengths. Four categories of dust detection algorithms are summarized, namely, dust spectral index algorithms, temporal anomalous detection algorithms, spatial coherence tested algorithms (physical-based algorithms) and machine learning-based algorithms. Following discussions of dust storm detection algorithms, the dust presence validation methods are also reviewed. Future developments for dust storm detection are focused upon three aspects: detection of dust storms at nighttime; development of more efficient machine learning methods for retrieval; and integrating physical and machine learning methods for satellite images. • Reviewing of algorithms for dust storm detection for multispectral sensors • Physical-based algorithms and Machine-learning algorithms for dust storm detection are summarized. • Commonly used dust detection validation methods are discussed. • Limitations of current algorithms are pointed out and future developments of dust detection algorithms are suggested. [ABSTRACT FROM AUTHOR]

The selection of global climate models (GCMs) for a region remained a difficult step in climate change studies. A state-of-the-art Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm is proposed in this paper for GCM selection. The ranking of GCMs obtained using SVM-RFE was compared to that obtained using entropy-based similarity assessment index known as Symmetrical Uncertainty (SU). The study was conducted in the sub-Himalayan region of Pakistan where a reliable projection of climate is highly significant for water resources management in the entire western part of South Asia. The RF-based regression model was employed to generate a multi-model ensemble (MME) mean of the top-ranked GCMs. The MME mean projection was utilized to estimate the spatiotemporal changes in annual precipitation in comparison with precipitation of 1961‐–2000 for various representative concentration pathway (RCP) scenarios. The SVM-RF selected five GCMs (MIROC5, EC-EARTH, CNRM-CM5, BCC-CSM1.1(m) and BCC-CSM1.1) as most suitable for climate change projections in the study area. Obtained results were found to collaborate well with the results of multiple conventional statistical metrics. The MME mean projections revealed precipitation alteration between −1% and 18% during 2020‐–2059, and 0 and 24% during 2060–2099 for different RCPs. Precipitation was projected to increase up to 20% in the north whereas a decrease up-to −16% in the south. • SVM-RFE algorithm is used for selection of GCMs for Upper Indus Basin (IUB). • Performance of SVM-RF was compared with symmetrical uncertainty (SU) and conventional statistics based methods. • SVM-RFE was found to select the same GCMs as selected by SU and conventional statistics. • Precipitation projections in IUB revealed high heterogeneity in future changes in precipitation. [ABSTRACT FROM AUTHOR]