Maximum covariance analysis of the sea surface backscatter signal models

In this work we study the applicability of the maximum covariance analysis (MCA) for the analysis of matrices characterizing the spatiotemporal models of sea surface backscatter signals for different types of sea waves. The method is based on the singular value decomposition of the covariance matrix describing the relationship between two spatiotemporal matrices. The dependence of the obtained correlation coefficients on the degree of sea roughness, as well as on the ratio of the heights of wind waves and rogue waves are determined. The statistical characteristics of the obtained correlation coefficients of the sea surface backscatter signals are analysed. Our results indicate that the MCA method, at least from the modelling perspective, could be applicable to the classification of the sea surface from its backscatter signal characteristics, including an early detection and analysis of the rogue waves onset and development.

Lagged teleconnections of climate variables identified via complex rotated Maximum Covariance Analysis

A proper description of ocean-atmosphere interactions is key for a correct understanding of climate evolution. The interplay among the different variables acting over the climate is complex, often leading to correlations across long spatial distances (teleconnections). In some occasions, those teleconnections occur with quite significant temporal shifts that are fundamental for the understanding of the underlying phenomena but which are poorly captured by standard methods. Applying orthogonal decomposition such as Maximum Covariance Analysis (MCA) to geophysical data sets allows to extract common dominant patterns between two different variables, but generally suffers from (i) the non-physical orthogonal constraint as well as (ii) the consideration of simple correlations, whereby temporally offset signals are not detected. Here we propose an extension, complex rotated MCA, to address both limitations. We transform our signals using the Hilbert transform and perform the orthogonal decomposition in complex space, allowing us to correctly correlate out-of-phase signals. Subsequent Varimax rotation removes the orthogonal constraints, leading to more physically meaningful modes of geophysical variability. As an example of application, we have employed this method on sea surface temperature and continental precipitation; our method successfully captures the temporal and spatial interactions between these two variables, namely for (i) the seasonal cycle, (ii) canonical ENSO, (iii) the global warming trend, (iv) the Pacific Decadal Oscillation, (v) ENSO Modoki and finally (vi) the Atlantic Meridional Mode. The complex rotated modes of MCA provide information on the regional amplitude, and under certain conditions, the regional time lag between changes on ocean temperature and land precipitation.

Signature Matching For Seismic Signal Identification

Seismic signals can be classified as natural or manmade by matching signature of similar events that have occurred in the past. Waveform matching techniques can be effectively used for discrimination since the events with similar location and focal mechanism have similar waveform irrespective of magnitude. The seismic signals are inherently non-stationary in nature. The analysis of such signals can be best achieved in multiresolution framework by resolving the signal using continuous wavelet transform (CWT) in time-frequency plane. In this paper similarity testing and classification of nuclear explosion and earthquake are exploited with correlation, continuous wavelet transform, cross-wavelet transform and wavelet coherence (WC) of P phase of seismogram. Clustering of seismic signals continuous wavelet spectra is performed using maximum covariance analysis. The proposed classifier has an average classification accuracy of 94%.

Spatiotemporal Associations between PM2.5 and SO2 as well as NO2 in China from 2015 to 2018

Given the critical roles of nitrates and sulfates in fine particulate matter (PM2.5) formation, we examined spatiotemporal associations between PM2.5 and sulfur dioxide (SO2) as well as nitrogen dioxide (NO2) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO2 and NO2 to PM2.5 was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM2.5 and SO2 decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM2.5 and SO2 loadings therein. The statistically significant coupled MCA mode between PM2.5 and SO2 indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing–Tianjin–Hebei region. Further statistical modeling practices revealed that the observed PM2.5 variations in northern China could be explained largely by SO2 rather than NO2 therein, given the estimated relatively high importance of SO2. In general, the evidence-based results in this study indicate a strong linkage between PM2.5 and SO2 in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.

Cloud responses to climate variability over the extratropical oceans as observed by MISR and MODIS

Linear temporal trends in cloud fraction over the extratropical oceans, observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR) during the period from 2000 to 2013, are examined in the context of coincident European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data using a maximum covariance analysis. Changes in specific cloud types defined with respect to cloud-top height and cloud optical depth are related to trends in reanalysis variables. A pattern of reduced high-altitude optically thick cloud and increased low-altitude cloud of moderate optical depth is found to be associated with increased temperatures, geopotential heights, and anti-cyclonic flow over the extratropical oceans. These and other trends in cloud occurrence are shown to be correlated with changes in the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the North Pacific index (NPI), and the Southern Annular Mode (SAM).

Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

Cloud responses to climate variability over the extratropical oceans as observed by MISR and MODIS

Linear temporal trends in cloud fraction over the extratropical oceans, observed by NASA's Multiangle Imaging Spectro-Radiometer (MISR) during the period 2000–2013, are examined in the context of coincident ECMWF reanalysis data using a maximum covariance analysis. Changes in specific cloud types defined with respect to cloud top height and cloud optical depth are related to trends in reanalysis variables. A pattern of reduced high altitude optically thick cloud and increased low altitude cloud of moderate optical depth is found to be associated with increased temperatures, geopotential heights, and anticyclonicity over the extratropical oceans. These and other trends in cloud occurrence are shown to be correlated with changes in the El Niño Southern Oscillation, the Pacific Decadal Oscillation, the North Pacific Index, and the Southern Annular Mode.