Irina Gladkova

Profiling risk and sustainability in coastal deltas of the world

Deltas are growing centers of risk Population growth, urbanization, and rising sea levels are placing populations living in delta regions under increased risk. The future resiliency and potential for adaptation by these populations depend on a number of socioeconomic and geophysical factors. Tessler et al. examined 48 deltas from around the globe to assess changes in regional vulnerability (see the Perspective by Temmerman). Some deltas in countries with a high gross domestic product will be initially more resilient to these changes, because they can perform expensive maintenance on infrastructure. However, short-term policies will become unsustainable if unaccompanied by long-term investments in all delta regions. Science, this issue p. 638; see also p. 588 Present-day strategies for risk management may impede long-term sustainability of river deltas. [Also see Perspective by Temmerman and Kirwan] Deltas are highly sensitive to increasing risks arising from local human activities, land subsidence, regional water management, global sea-level rise, and climate extremes. We quantified changing flood risk due to extreme events using an integrated set of global environmental, geophysical, and social indicators. Although risks are distributed across all levels of economic development, wealthy countries effectively limit their present-day threat by gross domestic product–enabled infrastructure and coastal defense investments. In an energy-constrained future, such protections will probably prove to be unsustainable, raising relative risks by four to eight times in the Mississippi and Rhine deltas and by one-and-a-half to four times in the Chao Phraya and Yangtze deltas. The current emphasis on short-term solutions for the world’s deltas will greatly constrain options for designing sustainable solutions in the long term.

LOSSLESS COMPRESSION ALGORITHM FOR HYPERSPECTRAL DATA

In this paper, which is an expository account of a lossless compression techniques that have been developed over the course of a sequence of papers and talks, we have sought to identify and bring out the key features of our approach to the efficient compression of hyperspectral satellite data. In particular we provide the motivation for using our approach, which combines the advantages of a clustering with linear modeling. We will also present a number of visualizations which help clarify why our approach is particularly effective on this dataset. At each stage, our algorithm achieves an efficient grouping of the data points around a relatively small number of lines in a very large dimensional data space. The parametrization of these lines is very efficient, which leads to efficient descriptions of data points. Our method, which we are continuing to refine and tune, has to date yielded compression ratios that compare favorably with what is currently achievable by other approaches. A data sample consisting of an entire days worth of global AQUA-EOS AIRS Level 1A counts (mean 12.9 bit-depth) data was used to evaluate the compression algorithm. The algorithm was able to achieve a lossless compression ratio on the order of 3.7 to 1.

Design of frequency modulated waveforms via the Zak transform

This paper introduces a new technique for designing frequency modulated waveforms that have ambiguity functions with desirable properties, such as strong peaking at the origin and low sidelobes. The methods employed involve signal design in the Zak transform space, as well as the use of stationary phase arguments in the analysis of ambiguity functions.

Information Content of Seasonal Forecasts in a Changing Climate

We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

Superradiant amplification in an optically dense three-level cascade system

We study superradiant amplification in a pressure-broadened three-level cascade system. The effects of the system initial coherence, degree of atomic inversion, and the length of the sample on the spatio-temporal distributions of the emitted fields from the upper and lower transitions are computed for a slab geometry.

Modifications due to local field corrections of the electromagnetically induced transparency propagation parameters in a driven optically dense three-level cascade system

Abstract We compute, under conditions of electromagnetically induced transparency, the frequency dependence of the transmission coefficient and the group velocity of a probe pulse resonant with the lower transition of a three-level cascade system in the presence of a cw pump field resonant with the system’s upper transition. We show that the presence of local field corrections can substantially modify the profile of both these transmission-related quantities.

Increasing the Accuracy of MODIS/Aqua Snow Product Using Quantitative Image Restoration Technique

The National Aeronautics and Space Administrations Moderate Resolution Imaging Spectroradiometer (MODIS)-based snow mask product critically uses 1.6 μm band 6. The snow mask algorithm for MODIS on Aqua has been adapted to use the 2.1 μm band 7, since some of Aquas MODIS detectors are nonfunctional. We have previously introduced an algorithm for quantitative image restoration (QIR) that can restore missing pixels or scan lines, using multilinear regression with input from a spatial-spectral window in other bands. In this letter, we argue that the use of MODIS Aqua band 6 data restored with the QIR technique in the snow algorithm results in a higher accuracy snow product as compared to the current MODIS Aqua snow product based on band 7 data. We show this by comparing a QIR-restored band 6 based product to the band 7 based product, applied to MODIS Terra, where we have simulated the Aqua-like damage to band 6. We demonstrate improved performance on representative granules covering different surface land-type conditions.

Spatial periodicity enhanced superradiance from an optically dense two-level medium

Abstract We show that, in an optically dense medium having an inverted active atom number density periodically distributed in space, it is possible, in a slab configuration, to enhance the forward–backward symmetric superradiance emission efficiency if the periodicity reciprocal lattice vector obeys a Bragg’s-like condition for coupling the forward and backward components of the active atoms polarization. We also show that the burst of superradiant light is emitted, in this case, at an earlier time than that for a homogeneously distributed medium having the same average atomic number density. Furthermore, this burst’s temporal duration is shown to be substantially shorter than that for the homogeneous active atoms distribution.

A global empirical typology of anthropogenic drivers of environmental change in deltas

It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results from case studies in specific deltas. Here, we present a methodology for quantitatively constructing an empirical typology of anthropogenic change in global deltas. Utilizing a database of environmental change indicators, each associated with increased relative sea-level rise and coastal wetland loss, a clustering analysis of 48 global deltas provides a quantitative assessment of systems experiencing similar or dissimilar sources and degrees of anthropogenic stress. By identifying quantitatively similar systems, we hope to improve the transferability of scientific results across systems, and increase the effectiveness of delta management best practices. Both K-Means and Affinity Propagation clustering algorithms find similar clusters, with relative stability across small changes in K-Means cluster number. High-latitude deltas appear similar, in terms of anthropogenic environmental stress, to several low-population, low-latitude systems, including the Amazon delta, despite substantially different climatic regimes. Highly urbanized deltas in Southeast Asia form a distinct cluster. By providing a quantitative boundary between groups of delta systems, this approach may also be useful for assessing future delta change and sustainability given projected population growth, urbanization, and economic development trends.

Impact of the Aqua MODIS Band 6 Restoration on Cloud/Snow Discrimination

AbstractDistinguishing between clouds and snow is an intrinsically challenging problem because both have similar high albedo across many bands. The 1.6-μm channel (band 6) on the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides an essential tool for distinguishing clouds from snow, since snow typically has a much lower albedo in this band. Unfortunately, this band is severely damaged on the MODIS/Aqua platform and is typically not used in either snow or cloud products. An algorithm was previously introduced for quantitative image restoration (QIR) that can restore missing pixels of band 6 using multilinear regression with input from a spatial-spectral window in other bands. Also previously demonstrated was the effectiveness of this restoration for snow products over cloud-free pixels only. The focus of the authors’ previous work was to evaluate the impact of this restoration on the snow product, and they had relied on the current cloud mask, which does not use any information from...