This paper describes the contents of the 2016 edition of the HITRAN molecular spectroscopic compilation. The new edition replaces the previous HITRAN edition of 2012 and its updates during the intervening years. The HITRAN molecular absorption compilation is composed of five major components: the traditional line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, infrared absorption cross-sections for molecules not yet amenable to representation in a line-by-line form, collision-induced absorption data, aerosol indices of refraction, and general tables such as partition sums that apply globally to the data. The new HITRAN is greatly extended in terms of accuracy, spectral coverage, additional absorption phenomena, added line-shape formalisms, and validity. Moreover, molecules, isotopologues, and perturbing gases have been added that address the issues of atmospheres beyond the Earth. Of considerable note, experimental IR cross-sections for almost 300 additional molecules important in different areas of atmospheric science have been added to the database. The compilation can be accessed through www.hitran.org. Most of the HITRAN data have now been cast into an underlying relational database structure that offers many advantages over the long-standing sequential text-based structure. The new structure empowers the user in many ways. It enables the incorporation of an extended set of fundamental parameters per transition, sophisticated line-shape formalisms, easy user-defined output formats, and very convenient searching, filtering, and plotting of data. A powerful application programming interface making use of structured query language (SQL) features for higher-level applications of HITRAN is also provided.

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The HITRAN 2008 molecular spectroscopic database

Research into terahertz technology is now receiving increasing attention around the world, and devices exploiting this waveband are set to become increasingly important in a very diverse range of applications. Here, an overview of the status of the technology, its uses and its future prospects are presented.

Cutting-edge terahertz technology

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA’s Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA’s Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF Re-Analysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses i...

MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of M...

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The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Abstract. A new algorithm to derive near-real-time (NRT) data products for the Aura Microwave Limb Sounder (MLS) is presented. The old approach was based on a simplified optimal estimation retrieval algorithm (OE-NRT) to reduce computational demands and latency. This paper describes the setup, training, and evaluation of a redesigned approach based on artificial neural networks (ANN-NRT), which is trained on >17 years of MLS radiance observations and composition profile retrievals. Comparisons of joint histograms and performance metrics derived between the two NRT results and the operational MLS products demonstrate a noticeable statistical improvement from ANN-NRT. This new approach results in higher correlation coefficients, in addition to lower root-mean-square deviations and biases at almost all retrieval levels compared to OE-NRT. The exceptions are pressure levels with concentrations close to 0 ppbv (parts per billion by volume), where the ANN models fail to establish a functional relationship and tend to predict 0. Depending on the application, this behavior might be advantageous. While the developed models can take advantage of the extended MLS data record, this study demonstrates that training ANN-NRT on just a single year of MLS observations is sufficient to improve upon OE-NRT. This confirms the potential of applying machine learning to the NRT efforts of other current and future mission concepts.


Applying machine learning to improve the near-real-time products of the Aura Microwave Limb Sounder

. Until now our understanding of the 11-year solar cycle signal (SCS) in stratospheric ozone has been largely based on high quality but sparse ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE) II or coarsely resolved ozone profiles from the nadir-viewing Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments. Here, we analyse 16 years (2005–2020) of ozone profile measurements from the Microwave Limb Sounder (MLS) instrument on the Aura satellite to estimate the 11-year SCS in stratospheric ozone. Our analysis of Aura-MLS data suggests a single-peak-structured SCS profile (about 3 % near 4 hPa or 40 km) in tropical stratospheric ozone, which is significantly different to the SAGE II and SBUV-based double-peak-structured SCS. We also find that MLS-observed ozone variations are more consistent with ozone from our control model simulation that uses Naval Research Laboratory (NRL) v2 solar fluxes. However, in the lowermost stratosphere where modelled ozone shows a negligible SCS compared to about 1 % in Aura-MLS data. An ensemble of Ordinary Least Square (OLS) and three regularised (Lasso, Ridge and ElasticNet) linear regression models confirms the robustness of the estimated SCS. Our analysis of MLS and model simulations also shows a large SCS in the Antarctic lower stratosphere that was not seen in earlier studies. We also analyse chemical transport model simulations with alternative solar flux data. We find that in the upper (and middle) stratosphere the model simulation with Solar Radiation and Climate Experiment (SORCE) satellite solar fluxes are also consistent with the MLS-derived SCS and agree well with the control simulation and one which uses Spectral and Total Irradiance Reconstructions (SATIRE) solar ﬂuxes. Hence, our model simulation suggests that with recent adjustments and corrections, SORCE solar ﬂuxes can be used to analyse effects of solar ﬂux variations. Finally, we argue that the overall signiﬁcantly different SCS compared to earlier estimates might be due to a combination of different factors such as much denser MLS measurements, almost linear stratospheric chlorine loading changes over the analysis period, as well as a stratospheric aerosol layer relatively unperturbed by major volcanic eruptions.

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A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations

1Bureau of Meteorology, Melbourne, Australia, 2National Center for Atmospheric Research, Boulder, USA, 3Deutsches Zentrum fur Luftund Raumfahrt (DLR), Institut fur Physik der Atmosphare, Oberpfaffenhofen, Germany, 4NOAA Earth Science Research Laboratory, Boulder, CO, USA, 5Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA, 6Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, 7Department of Earth and Planetary Science, University of Tokyo, Tokyo, Japan, 8Met Office Hadley Centre, Exeter, UK, 9Lancaster Environment Centre, Lancaster University, Lancaster, UK, 10Australian Antarctic Division, Kingston, Tasmania, Australia, 11Institute of Earth Sciences, Hebrew University, Israel, 12School of Marine and Atmospheric Sciences, Stony Brook University, New York, USA, 13Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand, 14National Centre for Atmospheric Science, Department of Physics, University of Oxford, Oxford, UK, 15GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany

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Michelle L. Santee

Papers

Applying machine learning to improve the near-real-time products of the Aura Microwave Limb Sounder

A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations

Report on the 5 th SPARC General Assembly 12-17 January 2014, Queenstown, New Zealand