A Trend Analysis of Aerosol Related Parameters and their Relation to Precipitation Variability in Arizona

The Objective of our research was to investigate if there is a correlation between haboob outbreaks, resulting in large dust storms over Arizona, and the precipitation patterns over the region. We examined the extent of this correlation over the last ten years using satellite daily observations to highlight the possibility of better forecasts for precipitation events, such as monsoon thunderstorms. Our research indicates that haboobs increase precipitation in the Sonoran desert of Arizona because the dust particles are large enough to act as cloud condensation nuclei (CCN). Data was collected from five locations spread out over the state of Arizona for the years of 2002-2012. The method we utilized was data oriented and required a quantitative analytical approach, where aerosol optical depth (AOD) data from NASA\u27s Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was collected and analyzed. Rainfall data from NASA\u27s Tropical Rainfall Measuring Mission (TRMM) satellite was collected and analyzed in coherence with the aerosol data. By manipulating this data into a time-series form, we determined the direct correlation between dust and precipitation events. It was found that increased dusty events increased precipitation with an average of two months lag time. Each of the five locations indicated that a strong correlation does exist between the AOD, Angstrom exponent, and precipitation data, indicating that the there are complex interactions occurring between dust and precipitation in Arizona at a microphysical level

How the Presence of Plastic in the North Pacific Gyre Affects the Growth of Thalassiosira through Remote Sensing and Laboratory Replication

Through the use of remote sensing, we are able to determine the approximate location of the garbage patch in the North Pacific Gyre. Though remote sensing does not penetrate the surface of the ocean, monthly satellite images can be analyzed to determine the rate of growth or rate of decrease of certain parameters, such as atmospheric gases, phytoplankton, and dissolved organic matter. Over the past decade, data from the Goddard Earth Sciences Data and Information Services Center (Giovanni program) has shown a significant increase in dissolved organic matter and chlorophyll a content in the area of the North Pacific Garbage Patch (180-110° W, 40-45°N) (Bograd, DiLorenzo). The areas with increased chlorophyll are likely to show the regional location of the subject area (Villareal). By using laboratory techniques, we will be able to determine whether the presence of plastic effects the growth of phytoplankton and diatoms in the area. The research conducted will study the effects of plastic on algae growth, focusing on the diatoms, Thalassiosira, which are found in the North Pacific Gyre. The question is whether algae increase is due to plastic, or the visibility through remote sensing is increased due to the algae using plastic as a substrate. The focus on the effects of plastic on algae will be directed under normal Pacific Ocean conditions; however, without the upwelling and currents seen in the area

Aerosols, Hurricanes, and their Interactions : A Case Study of Hurricane Sandy

While the effects of aerosols on precipitation have been studied, their effects on more extreme precipitation events like Tropical Cyclones have only been questioned relatively recently. Because of the rarity of the intersection of significant quantities of aerosols and forming/formed tropical cyclones, as well as the possible destruction caused, most experiments about their effects take place in computer models that may not fully simulate the effects of the aerosols. Limitations in satellite sensing make it difficult to track processes and material distributions in hurricanes from afar as well. Hurricane Sandy, a devastating hurricane that formed in October of 2012, may have formed while influenced by relatively smaller but still significant amounts of dust from an African dust event. Since this quantity is different from most heavy polluting scenarios that are simulated, evaluating the extent of the presence, position, and activation of aerosols within this hurricane may give us insight into the potential influences of aerosols in tropical cyclones. MODIS, MISR, AIRS, and CALIPSO data, while having limitations, is analyzed. The extent and possible effects of additional Saharan dust aerosol loading is discussed

Aerosols Size Distribution Characteristics and Role of Precipitation During Dust Storm Formation over Saudi Arabia

Kingdom of Saudi Arabia and the Gulf region are frequently exposed to major dust storms and anthropogenic emissions from rapidly growing industrial activities that affect aerosols optical and physical characteristics. This paper integrates observations from space-borne sensors namely MODIS and CALIPSO, together with AERONET ground observations to examine eight years aerosols characteristics during the (March–May) season of 2003 to 2010 over Saudi Arabia. Aerosol analysis from the interdependent data assessment show comparable aerosols characteristics over the eight year period with higher aerosols mean optical depths over enhanced dust load region, (46–50°E, 25–29°N), during March–May of 2009 and 2010. The mean angstrom exponent during March–May 2003 to 2008 was found ~17% higher than the same period during 2009. The major dust storm on March 9 and 10, 2009 could have an effect on the coarse mode particles increment during 2009. Over the eight years the highest angstrom exponent was observed on 2004 suggesting dominance of fine-mode particles, whereas a declination in the angstrom exponent values is observed during 2005, 2006, 2007, and 2008. The aerosols size distribution measured by sunphotometer indicates a maximum value of ~ 47% higher in 2009 compared to 2010 suggesting the domination of coarse mode particles in 2009. Using the CALIPSO volume depolarization ratio, a possible mixing of anthropogenic aerosols with dust was observed during March–May of 2009 and 2010 featured by coarse particles domination and high percentage of fine particles during 2009. The effect of precipitation prior to dust storms on dust loading was investigated. Our observation suggests a possible impact of the varying precipitation rate prior to dust storms outbreak and the actual dust loading during dust events

Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R2\u3e 0.95) in the testing and (R2\u3e 0.93) in the evaluation process

Transport of Dust and Anthropogenic Aerosols Across Alexandria, Egypt

The flow of pollutants from Europe and desert dust to Europe from the Sahara desert both affects the air quality of the coastal regions of Egypt. As such, measurements from both ground and satellite observations assume great importance to ascertain the conditions and flow affecting the Nile Delta and the large city of Alexandria. We note that special weather conditions prevailing in the Mediterranean Sea result in a westerly wind flow pattern during spring and from North to South during the summer. Such flow patterns transport dust-loaded and polluted air masses from the Sahara desert and Europe, respectively, through Alexandria, and the Nile Delta in Egypt. We have carried out measurements acquired with a ground- based portable sun photometer (Microtops II) and the satellite-borne TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) sensor during the periods of October 1999–August 2001 and July 2002–September 2003. These measurements show a seasonal variability in aerosol optical depth (AOD) following these flow patterns. Maximum aerosol loadings accompanied by total precipitable water vapor (W) enhancements are observed during the spring and summer seasons. Pronounced changes have been observed in the Angstrom exponent (α) derived from groundbased measurements over Alexandria (31.14 N, 29.59 E) during both dust and pollution periods. We have followed up the observations with a 3-day back-trajectories model to trace the probable sources and pathways of the air masses causing the observed aerosol loadings. We have also used other NASA model outputs to estimate the sea salt, dust, sulfates and black carbon AOD spatial distributions during different seasons. Our results reveal the probable source regions of these aerosol types, showing agreement with the trajectory and Angstrom exponent analysis results. It is confirmed that Alexandria is subjected to different atmospheric conditions involving dust, pollution, mixed aerosols and clean sky

Inter-continental Transport of Dust and Pollution Aerosols across Alexandria, Egypt

The flow of pollutants from Europe and desert dust to Europe from the Sahara desert both affects the air quality of the coastal regions of Egypt. As such, measurements from both ground and satellite observations assume great importance to ascertain the conditions and flow affecting the Nile Delta and the large city of Alexandria. We note that special weather conditions prevailing in the Mediterranean Sea result in a westerly wind flow pattern during spring and from North to South during the summer. Such flow patterns transport dust-loaded and polluted air masses from the Sahara desert and Europe, respectively, through Alexandria, and the Nile Delta in Egypt. We have carried out measurements acquired with a ground- based portable sun photometer (Microtops II) and the satellite-borne TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) sensor during the periods of October 1999–August 2001 and July 2002–September 2003. These measurements show a seasonal variability in aerosol optical depth (AOD) following these flow patterns. Maximum aerosol loadings accompanied by total precipitable water vapor (W) enhancements are observed during the spring and summer seasons. Pronounced changes have been observed in the Angstrom exponent (_) derived from groundbased measurements over Alexandria (31.14_ N, 29.59_ E) during both dust and pollution periods. We have followed up the observations with a 3-day back-trajectories model to trace the probable sources and pathways of the air masses causing the observed aerosol loadings. We have also used other NASA model outputs to estimate the sea salt, dust, sulfates and black carbon AOD spatial distributions during different seasons. Our results reveal the probable source regions of these aerosol types, showing agreement with the trajectory and Angstrom exponent analysis results. It is confirmed that Alexandria is subjected to different atmospheric conditions involving dust, pollution, mixed aerosols and clean sky

Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image dierencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four dierent reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe

Long Term Ground Based Precipitation Data Analysis: Spatial and Temporal Variability

California is an area of diverse topography and has what many scientists call a Mediterranean climate. Various precipitation patterns exist due to El Niño Southern Oscillation (ENSO) which can cause abnormal precipitation or droughts. As temperature increases mainly due to the increase of CO2 in the atmosphere, it is rapidly changing the climate of not only California but the world. An increase in temperature is leading to droughts in certain areas as other areas are experiencing heavy rainfall/flooding. Droughts in return are providing a foundation for fires harming the ecosystem and nearby population. Various natural hazards can be induced due to the coupling effects from inconsistent precipitation patterns and vice versa. Using wavelets, we were able to identify anomalies of high precipitation and droughts within California\u27s 7 climate divisions using NOAA\u27s hourly precipitation data from rain gauges and compared the results with modeled data, SOI, and PDO. The identification of anomalies can be used to compare and correct remote sensing measurements of precipitation and droughts. Promising results show a possible connection with increasing tropical moisture activity

Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean

This study was an evaluation of the spectral signature generalization properties of coral across four remote Pacific Ocean reefs. The sites under consideration have not been the subject of previous studies for coral classification using remote sensing data. Previous research regarding using remote sensing to identify reefs has been limited to in-situ assessment, with some researchers also performing temporal analysis of a selected area of interest. This study expanded the previous in-situ analyses by evaluating the ability of a basic predictor, Linear Discriminant Analysis (LDA), trained on Depth Invariant Indices calculated from the spectral signature of coral in one location to generalize to other locations, both within the same scene and in other scenes. Three Landsat 8 scenes were selected and masked for null, land, and obstructed pixels, and corrections for sun glint and atmospheric interference were applied. Depth Invariant Indices (DII) were then calculated according to the method of Lyzenga and an LDA classifier trained on ground truth data from a single scene. The resulting LDA classifier was then applied to other locations and the coral classification accuracy evaluated. When applied to ground truth data from the Palmyra Atoll location in scene path/row 065/056, the initial model achieved an accuracy of 80.3%. However, when applied to ground truth observations from another location within the scene, namely, Kingman Reef, it achieved an accuracy of 78.6%. The model was then applied to two additional scenes (Howland Island and Baker Island Atoll), which yielded an accuracy of 69.2% and 71.4%, respectively. Finally, the algorithm was retrained using data gathered from all four sites, which produced an overall accuracy of 74.1%