Stable Calibration of Raman Lidar Water-Vapor Measurements

A method has been devised to ensure stable, long-term calibration of Raman lidar measurements that are used to determine the altitude-dependent mixing ratio of water vapor in the upper troposphere and lower stratosphere. Because the lidar measurements yield a quantity proportional to the mixing ratio, rather than the mixing ratio itself, calibration is necessary to obtain the factor of proportionality. The present method involves the use of calibration data from two sources: (1) absolute calibration data from in situ radiosonde measurements made during occasional campaigns and (2) partial calibration data obtained by use, on a regular schedule, of a lamp that emits in a known spectrum determined in laboratory calibration measurements. In this method, data from the first radiosonde campaign are used to calculate a campaign-averaged absolute lidar calibration factor (t(sub 1)) and the corresponding campaign-averaged ration (L(sub 1)) between lamp irradiances at the water-vapor and nitrogen wavelengths. Depending on the scenario considered, this ratio can be assumed to be either constant over a long time (L=L(sub 1)) or drifting slowly with time. The absolutely calibrated water-vapor mixing ratio (q) obtained from the ith routine off-campaign lidar measurement is given by q(sub 1)=P(sub 1)/t(sub 1)=LP(sub 1)/P(sup prime)(sub 1) where P(sub 1) is water-vapor/nitrogen measurement signal ration, t(sub 1) is the unknown and unneeded overall efficiency ratio of the lidar receiver during the ith routine off-campaign measurement run, and P(sup prime)(sub 1) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the ith routine off-campaign measurement run. If L is assumed constant, then the lidar calibration is routinely obtained without the need for new radiosonde data. In this case, one uses L=L(sub 1) = P(sup prime)(sub 1)/t(sub 1), where P(sub 1)(sup prime) is the water-vapor/nitrogen signal ratio obtained during the lamp run associated with the first radiosonde campaign. If L is assumed to drift slowly, then it is necessary to postpone calculation of a(sub 1) until after a second radiosonde campaign. In this case, one obtains a new value, L(sub 2), from the second radiosonde campaign, and for the ith routine off-campaign measurement run, one uses an intermediate value of L obtained by simple linear time interpolation between L(sub 1) and L(sub 2)

A Universal Lagrangian for Massive Yang-Mills Theories without Higgs Bosons

A universal Lagrangian that defines various four-dimensional massive Yang-Mills theories without Higgs bosons is presented. Each of the theories is characterized by a constant k contained in the Lagrangian. For k=0, the Lagrangian reduces to one defining the topologically massive Yang-Mills theory, and for k=1, the Lagrangian reduces to one defining the Freedman-Townsend model. New massive Yang-Mills theories are obtained by choosing k to be real numbers other than 0 and 1.Comment: 9 pages, latex, no figure

Validation of the TOLNet lidars: The Southern California Ozone Observation Project (SCOOP)

The North America-based Tropospheric Ozone Lidar Network (TOLNet) was recently established to provide high spatiotemporal vertical profiles of ozone, to better understand physical processes driving tropospheric ozone variability and to validate the tropospheric ozone measurements of upcoming spaceborne missions such as Tropospheric Emissions: Monitoring Pollution (TEMPO). The network currently comprises six tropospheric ozone lidars, four of which are mobile instruments deploying to the field a few times per year, based on campaign and science needs. In August 2016, all four mobile TOLNet lidars were brought to the fixed TOLNet site of JPL Table Mountain Facility for the 1-week-long Southern California Ozone Observation Project (SCOOP). This intercomparison campaign, which included 400¿h of lidar measurements and 18 ozonesonde launches, allowed for the unprecedented simultaneous validation of five of the six TOLNet lidars. For measurements between 3 and 10¿km¿a.s.l., a mean difference of 0.7¿ppbv (1.7¿%), with a root-mean-square deviation of 1.6¿ppbv or 2.4¿%, was found between the lidars and ozonesondes, which is well within the combined uncertainties of the two measurement techniques. The few minor differences identified were typically associated with the known limitations of the lidars at the profile altitude extremes (i.e., first 1¿km above ground and at the instruments' highest retrievable altitude). As part of a large homogenization and quality control effort within the network, many aspects of the TOLNet in-house data processing algorithms were also standardized and validated. This thorough validation of both the measurements and retrievals builds confidence as to the high quality and reliability of the TOLNet ozone lidar profiles for many years to come, making TOLNet a valuable ground-based reference network for tropospheric ozone profiling.Peer ReviewedPostprint (published version

H2O and δD profiles remotely-sensed from ground in different spectral infrared regions

We present ground-based FTIR (Fourier Transform Infrared) water vapour analyses performed in four different spectral regions: 790–880, 1090–1330, 2650–3180, and 4560–4710 cm−1. All four regions allow the retrieval of lower, middle, and upper tropospheric water vapour amounts with a vertical resolution of about 3, 6, and 10 km, respectively. In addition the analyses at 1090–1330 and 2650–3180 cm−1 allow the retrieval of lower and middle/upper tropospheric δD values with vertical resolutions of 3 and 10 km, respectively. A theoretical and empirical error assessment – taking coincident Vaisala RS92 radiosonde measurements as a reference – suggests that the H2O data retrieved at high wavenumbers are slightly more precise than those retrieved at low wavenumbers. We deduce an H2O profile precision and accuracy of generally better than 20% except for the low wavenumber retrieval at 790–880 cm−1, where the assessed upper precision limit of middle/upper tropospheric H2O is 35%. The scatter between the H2O profiles produced by the four different retrievals is generally below 20% and the bias below 10%, except for the boundary layer, where it can reach 24%. These values well confirm the theoretical and empirical error assessment and are rather small compared to the huge tropospheric H2O variability of about one order of magnitude thereby demonstrating the large consistency between the different H2O profile retrievals. By comparing the two δD profile versions we deduce a precision of about 8 and 17‰ for the lower and middle/upper troposphere, respectively. However, at the same time we observe a systematic difference between the two retrievals of up to 40‰ in the middle/upper troposphere which is a large value compared to the typical tropospheric δD variability of only 80‰.M. Schneider has been supported by the Deutsche Forschungsgemeinschaft via the project RISOTO (Geschaftszeichen SCHN 1126/1-1 and 1-2)

Simulation of consumer exposure to deoxynivalenol according to wheat crop management and grain segregation: Case studies and methodological considerations

International audienceWe combined agronomic data and a model simulating exposure based on consumption data to assess the impact of crop management and grain segregation procedures on consumer exposure to deoxynivalenol. We used three scenarios of soil tillage at a regional scale and three scenarios of grain segregation for a supply area. The soil tillage scenarios were applied to a range of mean crop contamination levels, with various coefficients representing the degree of tillage. The grain segregation scenarios were applied to two real datasets of DON content distributions. We found that the increase in consumer exposure in response to increases in "risky" crop management practices such as direct-drilling depends largely on mean contamination and on the value of the tillage coefficient. The results for grain segregation procedures showed that exposure was most strongly affected by contamination distributions as the segregation procedure minimising risk differed for the two datasets

Characterization of Redox Sensitive Brown Algal Mannitol-1-Phosphatases

Macroalgae (seaweeds) are key primary producers in marine coastal habitats and largely contribute to global ocean carbon fluxes. They also represent attractive renewable feedstock for the production of biofuels, food, feed, and bioactive. Brown algae are seaweeds that produce alginates and fucose containing sulfated polysaccharides in their cell wall and laminarin and mannitol for carbon storage. The availability of genomes of the kelp Saccharina japonica and of the filamentous Ectocarpus sp. paved the way for the biochemical characterization of recombinant enzymes involved in their polysaccharide and carbohydrates synthesis, including, notably, mannitol. Brown algal mannitol biosynthesis starts with the conversion of fructose-6-phospate into mannitol-1-phosphate (mannitol-1P), and this intermediate is hydrolysed by a haloacid dehalogenase phosphatase (M1Pase) to produce mannitol. We report here the biochemical characterization of a second M1Pase in Ectocarpus sp. (EsM1Pase1). Both Ectocarpus M1Pases were redox-sensitive enzymes, with EsM1Pase1 active only in presence of the reducing agent. Such catalytic properties have not been observed for any M1Pases yet. EsM1Pases were specific to mannitol-1-P, in contrast to S. japonica M1Pases that could act on other phosphorylated sugars. Finally, brown algal M1Pases formed two well-supported clades, with possible distinct subcellular localization and physiological role(s) under diverse environmental conditions and/or life cycle stages

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products