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Register a new userEstimating Atmospheric Mass Using Air Density
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Since the late 19th century, several investigators have estimated the mass of the atmosphere. Unlike previous studies, which focus on the average pressures on the earths surface, this analysis uses the density of air above the earths surface to predict the mass of the atmosphere. Results are consistent with recent pressure-based estimates. They indicate that changes in the latest estimates can be attributed to improved land elevation measurements between 1 km and 3 km. This work also provides estimates of atmospheric mass by layer and mean and median land elevations.
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One the major factors determining the development and evolution of atmospheric convection is the sea surface temperature and its variability. Results of this thesis show that state of atmospheric convection impacts the diurnal distribution of thermal energy in the upper ocean. Under calm and clear sky conditions a shallow warm layer of several meters depth develops on the surface of the ocean. This warm layer drives an anomalous flux from the ocean to the atmosphere. A novel Kelvin wave trajectory database based on satellite data is introduced in this study. The investigation of its data shows that substantial fraction of Kelvin waves is initiated as a result of interaction with another Kelvin wave. Two distinct categories are defined and analyzed: the two- and multiple Kelvin wave initiations, and a spin off initiation. Results show that primary forcing of such waves are high diurnal cycle and/or increased wind speed and latent heat flux at the ocean surface. Variability of the ocean surface and subsurface along Kelvin wave trajectories over Indian Ocean is investigated: wind speed and latent heat flux increase and a sea surface temperature anomaly decreases during a wave passage. It is also shown that Kelvin waves are longitude-diurnal cycle phase locked over the Maritime Continent. This cycle phase locking is such that it agrees with mean, local diurnal cycle of convection in the atmosphere. The strength of the longitude-diurnal cycle phase locking differs between non-blocked Kelvin waves, which make successful transition over the Maritime Continent, and blocked waves that terminate within it. The distance between the islands of Sumatra and Borneo agrees with the distance travelled by an average Kelvin wave in one day. This suggests that the Maritime Continent may act as a filter, favoring successful propagation waves, which are in phase with the local diurnal cycle of convection.
In the analysis of empirical signals, detecting correlations that capture genuine interactions between the elements of a complex system is a challenging task with applications across disciplines. Here we analyze a global data set of surface air temperature (SAT) with daily resolution. Hilbert analysis is used to obtain phase, instantaneous frequency and amplitude information of SAT seasonal cycles in different geographical zones. The analysis of the phase dynamics reveals large regions with coherent seasonality. The analysis of the instantaneous frequencies uncovers clean wave patterns formed by alternating regions of negative and positive correlations. In contrast, the analysis of the amplitude dynamics uncovers wave patterns with additional large-scale structures. These structures are interpreted as due to the fact that the amplitude dynamics is affected by processes that act in long and short time scales, while the dynamics of the instantaneous frequency is mainly governed by fast processes. Therefore, Hilbert analysis allows to disentangle climatic processes and to track planetary atmospheric waves. Our results are relevant for the analysis of complex oscillatory signals because they offer a general strategy for uncovering interactions that act at different time scales.
On the basis of experience acquired at creation of the Pulkovo Spectrophotometric Catalog the method of investigation of a terrestrial atmospheric components (aerosols and water vapor) in night time are designed. For these purposes the small-sized photometers were created. Carried out in 1995-1999{Gamma}.{Gamma}. series of night and daily monitoring of the atmospheric condition in Pulkovo, in MGO by A.I.Voejkov., in Germany (complex experiments LITFASS 98 and LACE 98) confirmed suitability of devices, techniques of observations and their reduction designed in Pulkovo Observatory for the solution of geophysical and ecological problems. A final aim of this work - creation of small-sized automatic complexes (telescope + photometer), which would be rightful component of meteorological observatories. Such complexes will work without the help of the observer and would provide the daily monitoring of a terrestrial atmosphere.
Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground-level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work are achieved based on a machine learning model. Specifically, we establish the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref-PM modeling). Taking the Wuhan Urban Agglomeration (WUA) as a case study, the results demonstrate that compared with the AOD-PM modeling, the Ref-PM modeling obtains a competitive performance, with out-of-sample cross-validated R2 and RMSE values of 0.87 and 9.89 ug/m3 respectively. Also, the TOA-reflectance-derived PM2.5 have a finer resolution and larger spatial coverage than the AOD-derived PM2.5. This work updates the traditional cognition of remote sensing PM2.5 estimation and has the potential to promote the application in atmospheric environmental monitoring.
Global lockdowns in response to the COVID-19 pandemic have led to changes in the anthropogenic activities resulting in perceivable air quality improvements. Although several recent studies have analyzed these changes over different regions of the globe, these analyses have been constrained due to the usage of station-based data which is mostly limited upto the metropolitan cities. Also, the quantifiable changes have been reported only for the developed and developing regions leaving the poor economies (e.g. Africa) due to the shortage of in-situ data. Using a comprehensive set of high spatiotemporal resolution satellites and merged products of air pollutants, we analyze the air quality across the globe and quantify the improvement resulting from the suppressed anthropogenic activity during the lockdowns. In particular, we focus on megacities, capitals and cities with high standards of living to make the quantitative assessment. Our results offer valuable insights into the spatial distribution of changes in the air pollutants due to COVID-19 enforced lockdowns. Statistically significant reductions are observed over megacities with mean reduction by 19.74%, 7.38% and 49.9% in nitrogen dioxide (NO2), aerosol optical depth (AOD) and PM 2.5 concentrations. Google Earth Engine empowered cloud computing based remote sensing is used and the results provide a testbed for climate sensitivity experiments and validation of chemistry-climate models. Additionally, Google Earth Engine based apps have been developed to visualize the changes in a real-time fashion.
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