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.

/pdf/the-hitran-2008-molecular-spectroscopic-database-1ud8clpej3.pdf

The HITRAN 2008 molecular spectroscopic database

The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems (IS). DS is of importance in a discipline oriented to the creation of successful artifacts. Several researchers have pioneered DS research in IS, yet over the past 15 years, little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.

A Design Science Research Methodology for Information Systems Research

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-/spl mu/m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements.

http://www.atmos.berkeley.edu/~inez/MSRI-NCAR_CarbonDA/papers/barnet_refs/Aumann_IEEE8big15aug02.pdf

AIRS/AMSU/HSB on the Aqua mission: design, science objectives, data products, and processing systems

. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980–2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003–2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011–2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land–atmosphere feedbacks.

/pdf/gleam-v3-satellite-based-land-evaporation-and-root-zone-soil-rk15enx2ox.pdf

GLEAM v3: satellite-based land evaporation and root-zone soil moisture

[1] Objective measures of climate model performance are proposed and used to assess simulations of the 20th century, which are available from the Coupled Model Intercomparison Project (CMIP3) archive. The primary focus of this analysis is on the climatology of atmospheric fields. For each variable considered, the models are ranked according to a measure of relative error. Based on an average of the relative errors over all fields considered, some models appear to perform substantially better than others. Forming a single index of model performance, however, can be misleading in that it hides a more complex picture of the relative merits of different models. This is demonstrated by examining individual variables and showing that the relative ranking of models varies considerably from one variable to the next. A remarkable exception to this finding is that the so-called “mean model” consistently outperforms all other models in nearly every respect. The usefulness, limitations and robustness of the metrics defined here are evaluated 1) by examining whether the information provided by each metric is correlated in any way with the others, and 2) by determining how sensitive the metrics are to such factors as observational uncertainty, spatial scale, and the domain considered (e.g., tropics versus extra-tropics). An index that gauges the fidelity of model variability on interannual time-scales is found to be only weakly correlated with an index of the mean climate performance. This illustrates the importance of evaluating a broad spectrum of climate processes and phenomena since accurate simulation of one aspect of climate does not guarantee accurate representation of other aspects. Once a broad suite of metrics has been developed to characterize model performance it may become possible to identify optimal subsets for various applications.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007JD008972

Performance metrics for climate models

Data were collected by the scanning microwave spectrometer onboard Nimbus 6 during the June 1975 typhoon in the Philippine Sea. The spectrometer was equipped with channels centered on 22.23 GHz (a water vapor band), 31.65 GHz (a transmittance window), and 52.85, 53.85, and 55.45 GHz (an oxygen band). Temperature maps, derived from oxygen band measurements, showed that the typhoon eye had a single peak varying in amplitude with time. Water line and window measurements were used to develop a coordinate system having mutually orthogonal atmospheric variables of column water-vapor content and cloud liquid-water content. Vapor measurements showed a maximum around the intensifying typhoon with a more developed structure during typhoon development. Values were extrapolated for surface wind speed and cloud liquid water vapor content by assuming the troposphere to be saturated with respect to the water vapor in the typhoon. Comparisons with infrared cloud imagery and aircraft flight data show different time variations, attributed to poor typhoon-eye resolution in the microwave images.

Typhoon June (1975) viewed by a scanning microwave spectrometer

Estimation of atmospheric relative humidity profiles using passive remote sensing techniques is difficult when the temperature profile is not well known, and such retrievals approach singularity when the atmosphere is nearly isothermal. A retrieval method that is more robust near isothermal regions and temperature inversions is described. Its robust character results from an iterative combination of statistical methods based on a priori data, which stabilize the effects of any singularities, and physical methods that reflect the nonlinear character of the equation of radiative transfer and the dependence of measurements on uncertain surface reflectivities and temperature profiles. This method can be used to interpret data from meteorological satellites. It was tested extensively using simulated clear-sky microwave observations from space at 89 GHz, 166 GHz, and three frequencies near the 183-GHz water vapor resonance and the 60-GHz oxygen band, which is sensitive to the atmospheric temperature profile. Humidity profiles from the tropical, midlatitude, and arctic regions were retrieved. Relative humidity profiles retrieved using the statistical iterative method typically had errors between 5 and 10% in the 300-1000 mbar pressure region. These errors were somewhat less in the tropics and greater in the polar regions, and represented significantly better performances than a linear statistical retrieval method. >

Statistical iterative scheme for estimating atmospheric relative humidity profiles

The remote sensing inverse problem is considered in which the sensor does not necessarily view the same area on the earth at each wavelength, and spatial correlations of the geophysical parameters may be present. Under the conditions of linearity and stationary statistics, the minimum mean-square error solution to the problem of inverting such data is a spatial filter of the Wiener-Kolmogorov class. The resulting remote-sensing system can be characterized by an impulse response matrix in ordinary space or by a transfer matrix in frequency space. A signal-to-noise matrix for the geophysical parameters to be sensed is also defined; this matrix depends on the postulated a priori statistics and on the characteristics of the remote-sensing instrument. The system transfer matrix and the signal-to-noise matrix are simultaneously diagonalizable. The optimum transfer matrix filters out of the estimate vector those eigenparameters for which eigenvalues of the signal-to-noise matrix are less than unity.

Inversion of data from diffraction‐limited multiwavelength remote sensors: 1, Linear case

The 60-GHz band of atmospheric oxygen was studied in the temperature range of −28° to +60 °C at atmospheric pressure by means of a resonator spectrometer with absorption-variation sensitivity of 0002 dB/km The experimental data obtained have sufficient signal-to-noise ratio to take second-order mixing into account, increasing the accuracy of the millimeter-wave propagation model (MPM) A refined set of mixing coefficients for the model is derived from the new data and presented The fidelity of the new model to the spectrometer data is generally better than 2% between 54 and 65 GHz

http://radiometrics.com/data/uploads/2012/11/Makarov_JQSRT_2011.pdf

60-GHz oxygen band: Precise experimental profiles and extended absorption modeling in a wide temperature range

We quantify the level of polarization of the atmosphere due to Zeeman splitting of oxygen in the Earth's magnetic field and compare it to the level of polarization expected from the polarization of the cosmic microwave background radiation. The analysis focuses on the effect at mid-latitudes and at large angular scales. We find that from stratospheric balloon borne platforms and for observations near 100 GHz the atmospheric linear and circular polarized intensities is about 10^{-12} and 100 x 10^{-9} K, respectively, making the atmosphere a negligible source of foreground. From the ground the linear and circular polarized intensities are about 10^{-9} and 100 x 10^{-6} K, making the atmosphere a potential source of foreground for the CMB E (B) mode signal if there is even a 1% (0.01%) conversion of circular to linear polarization in the instrument.

https://arxiv.org/pdf/astro-ph/0307052.pdf

Philip W. Rosenkranz

Papers

Typhoon June (1975) viewed by a scanning microwave spectrometer

Statistical iterative scheme for estimating atmospheric relative humidity profiles

Inversion of data from diffraction‐limited multiwavelength remote sensors: 1, Linear case

60-GHz oxygen band: Precise experimental profiles and extended absorption modeling in a wide temperature range

Polarization of the Atmosphere as a Foreground for Cosmic Microwave Background Polarization Experiments