New 3-D gas density maps of NaI and CaII interstellar absorption within 300pc

We present new high resolution (R>50,000) absorption measurements of the NaI doublet (5889 - 5895A) along 482 nearby sight-lines, in addition to 807 new measurements of the CaII K (3933A) absorption line. We have combined these new data with previously reported measurements to produce a catalog of absorptions towards a total of 1857 early-type stars located within 800pc of the Sun. Using these data we have determined the approximate 3-dimensional spatial distribution of neutral and partly ionized interstellar gasdensity within a distance-cube of 300pc from the Sun. All newly recorded spectra were analyzed by means of a multi-component line profile-fitting program, in most cases using simultaneous fits to the line doublets. Normalized absorption profiles were fitted by varying the velocity, doppler width and column density for all intervening interstellar clouds. The resulting total column densities were then used in conjunction with the Hipparcos distances of the target stars to construct inversion maps of the 3-D spatial density distribution of the NaI and CaII bearing gas. A plot of the equivalent width of NaI versus distance reveals a wall of neutral gas at ~80pc that can be associated with the boundary wall to the central rarefied Local Cavity region. In contrast, a similar plot for the equivalent width of CaII shows no sharply increasing absorption at 80pc, but instead we observe a slowly increasing value of CaII equivalent width with increasing sight-line distance sampled.Comment: A&A accepte

Comparison of two different extinction laws with Hipparcos observations

Interstellar absorption in the galactic plane is highly variable from one direction to another. In this paper colour excesses and distances from a new open cluster sample are used to investigate the spatial distribution of the interstellar extinction. An inverse method (Tarantola & Valette, 1982) is used to construct the extinction map in the galactic plane below $|b| < 10^{o}$. The $A_{v} (r,l)$ diagrams are compared with those derived from individual stars (Arenou et al. 1992, Neckel & Klare 1980). An analytic expression for the interstellar extinction as a function of galactic longitude and distance in the solar neighborhood is given. The comparison of the model predictions with Hipparcos observations in the 4-dimensional space of ($V$, $B-V$, $H_v$, $r$) shows that our extinction model provides a better fit to the data. However, a new and more detailed extinction model is still lacking.Comment: 16 pages, 16 figures, latex, accepted for pubblication in Astronomy and Astrophysics Main Journa

233: Symmetric dimethylarginine serum level as a new marker of left ventricular ejection fraction in patients with acute myocardial infarction

Asymmetric dimethylarginine (ADMA) is a by-product of protein methylation implicated in the prognosis after acute myocardial infarction (MI) and heart failure through Nitric Oxide Synthase (NOS) inhibition. We aimed to investigate whether SDMA - the endogenous symmetrical stereoisomer of ADMA - that has insignificant inhibitory effects on NOS might be a marker of left ventricular function in acute MI.MethodsBlood samples from 468 consecutive patients hospitalized <24 hours after acute MI were taken on admission. Serum levels of ADMA and SDMA were determined using high-performance liquid chromatography. Left ventricular ejection fraction (LVEF) was assessed by echocardiography at 2±1 d after admission.ResultsAmong the study population, mean age was 66±14 y, most were male (77%), hypertensive (54%), with prior CAD (20%) or diabetes (20%). On admission, half had ST segment elevation MI (STEMI) (55%), and ¼ suffered from heart failure, as assessed by killip>1 (23%). Mean LVEF was 52±13%. Mean ADMA and SDMA levels were at 0.81±0.42 and 0.61±0.44, respectively. Spearman analysis showed that LVEF was correlated negatively with SDMA (r=-0.135, p=0.006), but neither with ADMA (r=-0.001, p=0.99). SDMA was strongly associated with age (r=+0.354, p<0.001), creatinine clearance (r=-0.416, p<0.001), CRP (r=+0.134, p=0.004) and homocysteine (r=+0.413, p<0.001). By univariate linear regression analysis, age, homocysteine, hypertension, diabetes, prior CAD, admission heart rate, creatinine clearance, anterior wall location, STEMI, CK peak, and acute statin treatment, in addition to SDMA, were significantly associated with LVEF (p<0.05). Backward multivariate analysis including these covariates showed that SDMA remains an independent predictor of LVEF (B=-3.422; SE=1.687, p=0.043), beyond classical determinants of LVEF including age, homocysteine and renal function.ConclusionOur large prospective study showed for the first time that SDMA, but ADMA, may be linked to left ventricular function in patients with acute MI, and suggests that such dimethylarginines may probably exert biological activity by other pathways than NOS activity inhibition and beyond renal function

Satellite and in situ sampling mismatches: Consequences for the estimation of satellite sea surface salinity uncertainties

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters’ depth, which are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016–2018 period, the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using SSS from a high-resolution model reanalysis, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity is observed in regions with large estimated sampling mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly taken into account. We find that (1) the observed differences between Argo and CCI data in dynamical regions (river plumes, fronts) are mainly due to the sampling mismatch; (2) overall, the uncertainties are well estimated in CCI version 3, much improved compared to CCI version 2. There are a few dynamical regions where discrepancies remain and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated

SMOS OS Level 3 and Level 4. Algorithm Theoretical Basis Document

The purpose of this Algorithm Theoretical Basis Document is to describe the procedure that is used in the SMOS CATDS Production Data Center (CPDC) to generate operational sea surface salinity (SSS) maps (also called L3OS/L4OS product, Level 3/4 Ocean Salinity product). Moreover, this document gives an overview of the different steps of the global processing, from Level 1b to Level 3 and Level 4 products knowing that only L3OS/L4OS products are provided to users by the CATDS (intermediate products are not officially distributed)

3D tomography of local interstellar gas and dust

http://sf2a.cesr.fr/2010/2010sf2a.conf..0045R.pdfInternational audienceInterstellar absorption data and Strömgren photometric data for target stars possessing a Hipparcos parallax have been combined to build a 3D tomography of local gas and dust. We show the latest inverted 3D distributions within 250 pc, compare gas and dust maps and discuss the present limitations and work in progress. Gaia extinction data and follow-up ground-based stellar spectra (e.g. with GYES at the CFHT) will provide a far larger database that should allow a 3D tomography of much higher quality and extended to much larger distances

A cardioid model for multi-angular radiometric observations

International audienceL-band passive microwave remote sensing sensors are able to provide estimates of surface soil moisture, on both spatial and temporal scales compatible with applications in the fields of meteorology and hydrology. A radiometric system using a 2-D interferometric design with multi-angular viewing capabilities will be borne by the Soil Moisture and Ocean Salinity (SMOS) space mission. The basic rationale for retrieving soil moisture from radiometric measurements is the assumption that the surface layer can be modeled as a dielectric medium. Its dielectric constant then depends on several physical parameters, including soil moisture; emissivities for various incidence angles are computed using Fresnel's formulas. Many controlled field experiments have demonstrated the validity of this approach. Scenes exist however (e.g. ice covered or frozen surfaces, complete desert areas) where surface soil moisture is not a relevant concept. For such scenes, information should however be available on the complex dielectric constant itself. This communication describes a methodology which aims at retrieving in an optimized way the dielectric constant information available from multiangular radiometric data

SMOS SSS L3 maps generated by CATDS CEC LOCEAN. debias V8.0

This eighth version of Level 3 SMOS SSS covers the period January 2010-December 2022. A correction of SMOS SSS from systematic biases uses an improved ‘de-biasing’ technique: with respect to earlier versions (see a full description of the version 2 method in Boutin et al. RSE 2018), the algorithm for computing the relative biases is unchanged, but the adjustment of the seasonal latitudinal biases, of the dielectric constant correction, of the rain correction and of the wind correction have been updated leading to local improvements in particular in high latitudes areas. Validation reports of the product compared to various sources of in situ measurements are available at PIMEP (https://www.salinity-pimep.org). At global scale, without any filtering, r2 between CEC v8 SSS and Argo delayed time SSS are 0.949 (9-day products; 0.949 with CEC V7) and 0.970 (18-day products; it was 0.971 with CEC V7), robust std of the difference is 0.26 (9-day products; 0.27 with CEC V7) and 0.18 (18-day products; it was 0.19 with CEC V7). The successive evolutions of the corrections are recalled below: V8 Main algorithm evolutions : V7+seasonal-latitudinal bias update + BVZ diel. Cst. + wind correction + wind dependent rain correction Main SSS improvements : Reduced seasonal biases and noise in high latitudes V7 Main algorithm evolutions : New L1 processing (v7, gibbs 2 algorithm); New L2 processing (v7, BV dielectric constant model and specific RFI filtering), correction for rain instantaneous effect ; debiasing method similar to V5 but with biases estimated over different period/regions Main SSS improvements : Better stability. Reduced latitudinal seasonal biases and RFI contamination. Corresponding CATDS Near Real time products : L3G products (RE07 and real time CPDC processings since end May 2021) V5 Main evolutions : V4 + refined absolute correction Main improvements : Decrease of biases in very variable and noisy regions (high latitudes, RFI contaminated areas) V4 Main evolutions : V3 + wind speed limited to 16m/s, Acard filtering, update of SST correction in cold waters, refined absolute correction Main improvements : Decrease of mean bias over the open ocean, improved ice filtering, improved SSS at high latitudes (especially in the Southern Ocean) V3 Main evolutions: V2 + SSS natural variability varying seasonally; latitudinal bias correction applied everywhere; SSS correction at low SST; improved absolute correction Main improvements: V2 + improved adjustment of land-sea biases close to coast; adjustment of high latitudinal biases Corresponding CATDS Near Real time products: In development (to be released soon; global ocean coverage) V2 Main evolutions: V1 + SSS natural variability varying spatially; no latitudinal bias correction outside 47S-47N Main improvements: V1 + improved land-sea contamination in very dynamic areas Reference: Boutin et al., 2018, RSE Corresponding CATDS Near Real time products: L3Q products (RE05 and real time CPDC processings; limited to 47°N-47°S) V1 Main evolutions: V0 + seasonal latitudinal correction (same SSS natural variability everywhere) Main improvements: V0+ Reduced latitudinal biases Corresponding CATDS Near Real time products: No V0 Main improvements: Reduced land-sea contamination Reference: Kolodziejczyk et al., 2016 Corresponding CATDS Near Real time products: No Introduction to the ‘De-biasing’ corrections: When considering monthly SSS anomalies, with respect to a SMOS monthly climatology, the precision of SMOS SSS monthly anomalies is on the order of 0.2 pss (Boutin et al. 2016); working in terms of monthly anomalies, removes most of the biases occurring around continents and varying latitudinally. In view of these good results, we have developed a method that corrects SMOS SSS systematic biases by preserving the temporal SMOS SSS dynamic. We recall at the end of this note the principle of the method. In version 8, the algorithm for computing the relative biases is as in version 7, but the reference climatological latitudinal profiles used for adjusting the seasonal latitudinal biases has been updated (it now considers mean longitudinal averages instead of median longitudinal averages; the monthly SSS climatology is derived from ISAS SSS), a wind speed related effect and a correction for the dielectric constant model (mainly a SST related effect) have been added. Moreover, the correction for rain instantaneous effect (estimated in 1mm hr-1 IMERG rain rate classes, see Fig5 of Supply et al., 2020) introduced in v7, has been updated in v8, by introducing a dependency with wind speed. Hence, in rainy areas, CEC v8 products are close to a bulk salinity. Main improvements of CEC V8 products with respect to CEC V7 are a better stability and reduced noise of the SSS at high latitudes. The V8 maps are provided every 4 days from 01/2010 to 12/2022 and are derived from a combination of ascending and descending orbits. Debiased SSS are temporally averaged using a slipping Gaussian kernel with a full width at half maximum of 9 days (9 day product) and of 18 days (18 days product). Maps are at a spatial resolution 25x25km2; a mean over neighbor pixels at less than 30km is applied. They also contain a raw estimation of the mean error of the salinities (field eSSS) obtained from the spatial standard deviation of the SSS in the 50km radius around each grid node. This error estimate also contains spatial natural variability and should only be considered as a qualitative indicator (e.g. larger error expected in areas contaminated by RFI); this raw estimate leads to unrealistically small errors in continents vicinity. Summary of the methodology: The SMOS sea surface salinities (SSS) are affected by biases coming from various unphysical contaminations such as the so-called land-sea contamination and latitudinal biases likely due to the thermal drift of the instrument. These biases are relatively weak and have almost no impact on soil moisture retrieval. On the contrary, for salinity estimation, the impact is non-negligible and can reach more than 1 salinity unit in some regions close to the coasts. These biases are not easy to characterize because they exhibit very strong spatial gradients and they depend on the coast orientation in the Field Of View (FOV). Moreover, these biases are dependent on the position on the swath. The zero order bias is the so-called Ocean Target Transformation (OTT) which is a correction applied at brightness temperature level. Here, we consider remaining biases on the SSS retrieved from brightness temperatures corrected with an OTT. SSS maps are obtained from a correction applied at salinity level. This correction is determined using the 2013- 2021 period of SMOS observations. Indeed, it is possible to build salinity time series for each grid point obtained in various observation conditions (depending on the orbit direction and at various distance from the center of the track) and check, from a statistical point of view, the consistency of the salinities. The first step of this empirical approach is to characterize as accurately as possible these biases as a function of the dwell line position. We first characterize the seasonal variation of the latitudinal biases using SSS far from the coast and from RFI contaminated areas after having empirically corrected a SST-dependent bias related to dielectric constant model issue based on Zhou et al (IEEE TGRS, 2017) in v3, on Dinnat et al. (Remote sensing, 2019) in v4 and v5, on BV (Boutin et al. 2021) in v7, and on BVZ (Boutin et al. 2023) in v8. Up to v7, we looked for the dwell line (i.e. across track position) the least affected by latitudinal biases (at the center of the swath, ascending orbits) and we adjust all the SSS for a latitude and time varying bias estimated from median averages of the biases with respect to the reference dwell line. In v8, the seasonal latitudinal biases are estimated using longitudinal mean (instead of median) averages and taking as reference a monthly SSS climatology derived from ISAS SSS (ISAS17 (Kolodziejczyk et al., 2021) and ISAS delayed time (or NRT when delayed time is not available) SSS up to 2022 (https://data.marine.copernicus.eu/product/INSITU_GLO_PHY_TS_OA_MY_013_052/description) instead of a climatology derived from a SMOS reference dwell line. The second step is to correct for biases in the vicinity of land. We have found that these biases vary little in time, and can be characterized according to the grid point geographical location (latitude, longitude) and to its location across track. If we assume that the salinity at a given grid point varies within a given range (defined by the SSS natural variability plus the SMOS SSS noise) during a given period, then, the different satellite passes crossing the same pixel during the given period should give consistent salinities. Additionally, assuming that the bias does not vary temporally for a given grid point implies that the relative salinity variation over the whole period should be the same whatever the distance to the center of the track. It is then possible to estimate the relative biases between the various distances across track and to obtain, with a least squares approach, a time series of relative salinity variations obtained from all the satellite passes. In the CATDS CEC LOCEAN debiased products version 0 (delivered in March 2015) only systematic biases near continents were removed. Version 1 (delivered in July 2016), has been updated to remove a latitudinal bias. The main difference between the debias_v1 version and the debias_v2 version (delivered in May 2017), is the SSS natural variability between the various SMOS SSS measured within 18 days at the same latitude, longitude: in debias_v2 version, we take into account an estimate of the natural variability expected from SMOS observed SSS while in debias_v1 version only a geographical constant noise on SMOS SSS was considered. In version 3 to 8, the natural SSS variability varies spatially and seasonally. Hence the v3 to v8 versions better preserves SSS natural variability especially close to river plumes. Note that the across track relative bias estimate does not use any external climatology. It allows minimizing relative biases between SMOS SSS retrieved at various distances across track and on ascending or descending orbits. These relative salinity variations are then converted, in a last step, to salinities by adding a single constant determined, in each pixel, from SSS statistical distribution over the whole period (SMOS SSS distribution compared to ISAS SSS (see a description of ISAS methodology on http://www.umr-lops.fr/SNO-Argo/Products/ISAS-T-S-fields). This last step only determines the absolute SSS calibration in each grid point; the SMOS SSS temporal variation is independent of this adjustment. Up to version 2, the median of SMOS SSS over the whole study period was adjusted to the median of ISAS SSS. In version 3 and 4, in order to avoid incorrect adjustments in very dynamical river plumes not well captured by Argo floats and hence by ISAS optimal interpolation, the adjustment is made using upper quantiles (80% in version 3, 70 to 90% in version 4, 50% to 80% depending on SSS variability in versions 5 to 8) of ISAS and SMOS SSS distributions over the considered bias calculation period (2011-2017 in v3, 2012-2018 in v4 and v5, 2013-2020 in v7, 2013-2021 in v8)