Clouds of Suspicion: Airspace Arrangements, Escalation, and Discord in U.S./NATO-Russian Relations

How have cooperative airspace arrangements contributed to cooperation and discord in the Euro-Atlantic region? This study analyzes the role of three sets of airspace arrangements developed by Euro-Atlantic states since the end of the Cold War—(1) cooperative aerial surveillance of military activity, (2) exchange of air situational data, and (3) joint engagement of theater air and missile threats—in political-military relations among neighbors and within the region. These arrangements provide insights into the integration of Central and Eastern European states into Western security institutions, and the current discord that centers on the conflict in Ukraine and Russia’s place in regional security. The study highlights the role of airspace incidents as contributors to conflict escalation and identifies opportunities for transparency- and confidence-building measures to improve U.S./NATO-Russian relations. The study recommends strengthening the Open Skies Treaty in order to facilitate the resolution of conflicts and improve region-wide military transparency. It notes that political-military arrangements for engaging theater air and missile threats created by NATO and Russia over the last twenty years are currently postured in a way that divides the region and inhibits mutual security. In turn, the U.S.-led Regional Airspace Initiatives that facilitated the exchange of air situational data between NATO and then-NATO-aspirants such as Poland and the Baltic states, offer a useful precedent for improving air sovereignty and promoting information sharing to reduce the fear of war among participating states. Thus, projects like NATO’s Air Situational Data Exchange and the NATO-Russia Council Cooperative Airspace Initiative—if extended to the exchange of data about military aircraft—have the potential to buttress deterrence and contribute to conflict prevention. The study concludes that documenting the evolution of airspace arrangements since the end of the Cold War contributes to understanding of the conflicting narratives put forward by Russia, the West, and the states “in-between” with respect to reasons for the current state of regional security. The long-term project of developing a zone of stable peace in the Euro-Atlantic must begin with the difficult task of building inclusive security institutions to accommodate the concerns of all regional actors.

Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Executing a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements.