dataesr/scientific-paragraphs-categorization huggingface dataset:
Other studies have shown that MR measurements at low-frequency L-band from ESA's SMOS mission can be used to retrieve SIT of up to 0. 5-1. 0 m  [24] [25] [26] , where the maximum retrievable SIT depends on ice temperature and salinity. Since 2015, NASA's SMAP mission has been providing T B data at the same frequency. Within the framework of SPICES, combining T B data from SMOS and SMAP for SIT retrieval was investigated  [27]. The SIT retrieval follows  [25]  and is based on the brigthness temperature intensity which is the average of the horizontally and vertically polarized brightness temperatures, equal to 0. 5(T BH + T BV ). For the combined use of SMOS and SMAP T B data, the multi-angular SMOS T B measurements were fit to a SMAP incidence angle of 40 •. We found that a two-step regression fitting method by Zhao et al.
Generally, the two 's agree very well with correlations exceeding 0. 99 over sea ice, but they show an intensity bias of about 2. 7 K over both ocean and seaice regions. When this bias was compensated using a linear fit, a very good agreement between the retrieved SITs was attained, see Figure  4. The main advantages of a combined SIT product are the increased number of daily overpasses leading to an improved data coverage, also towards lower latitudes, as well as a continuation of retrieved time series if one of the sensors stops delivering data. Within the SPICES project, a combined daily SIT dataset from SMOS and SMAP has been produced from 1 April 2015 (first available SMAP data) to 15 April 2015 and for the winter seasons (15 October-15 April) 2015/16 to 2017/18  [29]. Typical SIT uncertainties caused by uncertainties have been estimated to be around 1 cm (or 5%) for 20 cm-thick ice and 4 cm (or 7%) for 65 cm-thick ice  [30].
The median maximum retrievable SIT ranges from about 95 cm for cold, low salinity sea ice to about 70 cm for sea ice closer to the melting point. Within the SPICES project, a combined daily SIT dataset from SMOS and SMAP has been produced from 1 April 2015 (first available SMAP data) to 15 April 2015 and for the winter seasons (15 October-15 April) 2015/16 to 2017/18  [29]. Typical SIT uncertainties caused by T B uncertainties have been estimated to be around 1 cm (or 5%) for 20 cm-thick ice and 4 cm (or 7%) for 65 cm-thick ice  [30]. The median maximum retrievable SIT ranges from about 95 cm for cold, low salinity sea ice to about 70 cm for sea ice closer to the melting point.
Sea-Ice Drift, Thickness and Volume Fluxes Estimation Using Multi-Sensor Satellite Data Since the 1990s, sea-ice radar backscatter maps have been produced using different scatterometers; recently the Advanced Scatterometer (ASCAT) in the MetOp satellites. Daily data can be found at the CERSAT centre at Ifremer which provides backscatter data and maps over sea ice since 1992 until present using ERS-1/2 (European Remote Sensing satellite), QuikSCAT, NSCAT (NASA Scatterometer), and the ASCATs sensor series  [31]. Backscatter intensity over sea ice depends on scatterometer incidence angle and frequency, and sea-ice properties like surface roughness, salinity, temperature, water at the surface. As data from ASCATs are at several incidence angles, backscatter maps are adjusted at a constant incidence angle to ease their interpretation  [32].
These daily maps are available at a 12. 5 km × 12. 5 km grid at  [31]. In SPICES, the backscatter maps have been reprocessed over the entire ASCAT/MetOp-A time series (Jan 2007 onwards), and by merging the MetOp-B data (Oct 2012 onwards). The backscatter parameter is useful for detecting the sea-ice edge, in particular at the onset of freeze-up. It is also a proxy of sea-ice type, and can be used to estimate sea-ice displacement at coarser grid resolution (62. 5 km)  [33, 34]. Sea-ice motion and type inferred from scatterometer data have been used to estimate FYI and MYI sea-ice volume and export when combined with SIC from MR data and SIT from the merging of the CryoSat-2 SIRAL and SMOS MR data  [35]. The 2015-2016 season, with its record low winter ice extent, highlights how the Arctic sea ice becomes more sensitive to climate anomalies due to the loss of MYI.
The interannual variability of the relative volume of FYI and MYI as estimated by Ricker et al. [36]  highlighted the importance of winter sea-ice growth as a key component. In March 2016 the average Arctic-wide thinning was found to be 10 cm in comparison to the last six-year average, while maximal regional values of 33 cm and 24 cm occurred in the Beaufort Sea and Barents Sea, respectively, see Figure  5. These regional SIT anomalies result from an interplay between ice dynamics and thermodynamics. The Barents Sea SIT reduction seems to be a result of the air temperature increase according to 1948-2016 reanalysis data above 70 • N (U. S. National Centers for Environmental Prediction (NCEP) data), whereas the Beaufort Sea reduction seems to be associated with ice volume flux divergence. This data has also been used to estimate sea-ice volume export through the Fram Strait for the years 2010 to 2017 together with a new CryoSat-2 SIT dataset created by the Alfred Wegener Institute.
[39]  that were applied on optical spectrometer data from MERIS (Medium-Resolution Imaging Spectrometer) on ENVISAT (2002-2012) and OLCI (Ocean and Land Color Instrument) on Sentinel-3 (2016 onwards). MPD2 steps away from the pixel-wise approach of conventional algorithms but uses additional cumulative melting degree day index (CMDD) data inferred from reanalysis air temperature ( ) to determine the sea-ice albedo. As a result, the MPF, sea-ice albedo and SIC can be determined simultaneously. The CMDD is also used in a second, new retrieval of MPF and SIC based on the L-band SMOS anomalies and uncertainties for each year with respect to the marine domains defined in (a). Figure from  [36]. Used with permission from AGU.
[39]  that were applied on optical spectrometer data from MERIS (Medium-Resolution Imaging Spectrometer) on ENVISAT (2002-2012) and OLCI (Ocean and Land Color Instrument) on Sentinel-3 (2016 onwards). MPD2 steps away from the pixel-wise approach of conventional algorithms but uses additional cumulative melting degree day index (CMDD) data inferred from reanalysis air temperature (T a ) to determine the sea-ice albedo. As a result, the MPF, sea-ice albedo and SIC can be determined simultaneously. The CMDD is also used in a second, new retrieval of MPF and SIC based on the L-band SMOS radiometer data  [40]. The aim of this retrieval is to improve the spatial and temporal coverage of the MPF retrievals beyond that of optical data. Here we focus on the SMOS retrievals. Due to high water absorption at microwave frequencies, these cannot be used to distinguish melt ponds and open water.
Therefore, a direct single pixel MPF retrieval from SMOS is impossible when SIC is below 100%. However, using additional T a and ice drift data, and tracking a SMOS pixel throughout the whole summer accounting for its T a history can give the necessary boundary conditions and help separating melt ponds and open water in that pixel throughout the given summer. We use U. S. National Snow and Ice Data Center (NSIDC) v3 drift data to correct for sea-ice drift, NCEP T a at the surface to calculate the CMDD for each day along the drift track. CMDD is the sum of daily NCEP T a 's during all previous days of the year along the drift track since 1 May of the given year. CMDD is set to zero when the T a was negative for more than 1. 5 days. SMOS T B 's and where available optical MPF have been extracted for each day of the drift track as well.
Improvements in Predicting Large-Scale Seasonal Sea-Ice Anomalies At the European Centre of Medium-Range Weather Forecast (ECMWF), global sea-ice forecasts days to seasons ahead are routinely initialized using satellite observations of SIC. This is not yet the case for SIT, although it is arguably at least as important for forecast reliability. The new satellite products of large-scale SIT becoming available from the SPICES project and elsewhere provide exciting opportunities to improve global sea-ice forecasts. Here, the weekly-mean SIT data set CS2SMOS  [35]  is used to improve the initial conditions of the ECMWF seasonal forecasts  [52]. CS2SMOS is derived from CryoSat-2 SIRAL and SMOS L-band MR observations; therefore, it combines the strengths of the two datasets. A series of data assimilation and re-forecasting experiments have been carried out to demonstrate how seasonal forecasts can benefit from initializing SIT using the C2SMOS product  [7].
Discussion The detection, monitoring, and forecasting (both in short and seasonal time scales) of sea-ice conditions and their extremes is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. For ship navigation and offshore operations, sea-ice information has been available mainly in the form of ice charts, which are based on manual interpretation of available EO-data, mainly SAR imagery, and supporting data like in situ observations and sea-ice models, by ice analysts at national ice services. The charts divide the ice cover into rather large polygons (~10-100 km) to which ice types and properties are assigned. They do not explicitly show in fine resolution the location of hazardous sea-ice conditions (e. g. , MYI floes). In addition, the amount of EO-data for sea-ice monitoring is now large, and will increase in the near future.
The sea-ice export through the Fram Strait was found to have large yearly variation, and around half of the changes in Arctic MYI volume in wintertime could be explained by the ice export  [37]. Previously, thin ice thickness has been estimated using L-band SMOS MR data  [24] [25] [26] , and daily products are delivered by the Universities of Bremen and Hamburg, but they do not yet have the same level of operational maturity as other sea-ice products (e. g. , SIC, ice drift) from the coarse resolution (~10-25 km) satellite data. Here L-band data from the SMOS and SMAP sensors were.
The sea-ice export through the Fram Strait was found to have large yearly variation, and around half of the changes in Arctic MYI volume in wintertime could be explained by the ice export  [37]. Previously, thin ice thickness has been estimated using L-band SMOS MR data  [24] [25] [26] , and daily products are delivered by the Universities of Bremen and Hamburg, but they do not yet have the same level of operational maturity as other sea-ice products (e. g. , SIC, ice drift) from the coarse resolution (~10-25 km) satellite data. Here L-band T B data from the SMOS and SMAP sensors were fitted together  [27]  and used for a new SIT product  [29, 30]. This SIT product is based on an increased number of daily sensor overpasses compared to a SIT product from a single sensor, which allows better data coverage at lower latitudes, and less data loss due to radio frequency interference in the SMOS data.
Snow depth on sea ice can be retrieved operationally with the MR data [45], but the results are often unreliable, especially for MYI  [46]. In SPICES, a new method for snow depth retrieval with the MR data was developed and tested. It is based on Bayesian inversion of an empirical forward model of sea-ice surface emission  [50]. Using the AMSR2 data, the average pan-Arctic snow depth in April was retrieved. This is believed to be an important predictor for the start of the sea-ice melt season, and hence for the sea-ice summer extent. The spring snow depths were significantly less than the Warren climatological mean  [51] , which is partly due to the decreasing area of MYI in the Arctic. A large spatial and temporal snow depth variability was observed and, thus, continuous snow depth mapping is needed for the satellite altimetry SIT retrievals.
During the sea-ice melting season, surface wetness and melt ponds have a significant effect on the MR T B data, decreasing the accuracy of the SIC retrievals. Using optical spectrometer data, MPF can be estimated  [39] , but the temporal and spatial coverage is heavily restricted by cloud cover. In SPICES we developed simultaneous retrieval of MPF and SIC from L-band SMOS data together with CMDD, ice drift tracking, and a seven-category Melt Stage Index (MSI)  [40]. The SMOS-based retrievals have full daily Arctic coverage. For safe ship navigation in the Arctic and Antarctic, IMO has adopted the Polar Code and related amendments  [17, 18]. It includes the RIO value, which is a number between -30 and 30 representing the risk of a ship getting damaged by sea ice. The RIO value for a ship can be further classified into "go," "no-go," and "go with restrictions" cases.
It is envisaged that RIO-based decision-making will become a common practice in ship navigation in ice-covered waters. This requires integrating all available sea-ice information to an operational product which summarizes ice conditions for ship navigation. In SPICES we demonstrated RIO mapping for the Barents and Kara Seas based on the CryoSat-2 SIRAL data. The next step is to add data from other sensors, like SAR and MR, and sea-ice models for RIO maps, which are updated daily over an area of interest. Currently at ECMWF, global sea-ice forecasts days to seasons ahead are routinely initialized using MR-based SIC. In SPICES, a series of data assimilation and re-forecasting experiments were carried out to demonstrate how seasonal forecasts could benefit from also using the weekly CS2SMOS SIT product in the initialization  [7, 35].
[ Critical Care Research Letter ] inclusion criteria had complete data and were analyzed (age 63 [54-67] years, sex ratio 2. 8, BMI 28 [26-32]). Median time from ICU admission was 4 (2-6) days. At the time of almitrine infusion, 15 of 19 (79%) were under MV with neuromuscular blockade. PEEP was set at 10 (10-12) cm H 2 O, and static compliance was 32 (28-35) mL/cm H 2 O. Eighteen patients (95%) had at least one session of prone positioning before almitrine. chestjournal. org.
By adopting this approach, some countries actually exceeded the expected number of participating centers per country. For calculation of the survey response rate, the number of EDs exceeding the predetermined expected number of participating EDs per country was not considered. ED directors or their delegates, staff members appointed by the ED director as most suitable to complete the survey, completed one survey for each participating center. The survey was open on March 20 and 21, 2020. Survey responses were collected in Research Electronic Data Capture (version 9. 10. 0; Vanderbilt University, Nashville, TN) a validated online data collection system. 16 espondents were asked to state their country of residency, but it was not mandatory to give the name of their hospital. Each country lead recorded the name of the invited and participating hospitals.
The country lead survey included questions on range of COVID-19 cases (total and pediatric) per country, the date of identification of the first COVID-19 cases in the country, and the type of containment measures enforced in their country. Data on range of confirmed cases per country, as well as deaths, were cross-checked with the European Center for Disease Control and Worldometer Web sites on March 21 to ensure complete update of data up to March 20, 2020. 17, 18 There were no reported pediatric COVID-19 confirmed deaths in participating countries at the survey. The ED survey completed by each participating center focused on organizational and operational aspects of preparedness and response, as reported in the abovementioned definition. We followed Strengthening the Reporting of Observational Studies in Epidemiology guidelines for reporting of observational studies.
Acknowledgements. This research has been conducted during the G. Thomas' PhD, funded by  Météo France. The authors would like to thank  Sylvain CHAUMONT  for the providing of polarimetric radar data, and  Béatrice FRADON  for her advices and her carefulness about those data. The authors would also thank  Eric WATTRELOT  for numerous advices given the first year of this PhD. A special aknowledgement also has to be done to Maud MARTET who helped with the polarimetric data decoding, provided advices on weather radar technology and who.
Code availability. The polarimetric observation operator is available in the MESO-NH NWP model. It is an non-hydrostatic research model under the CeCILL-C free licence. For more information, please consult MESO-NH website (http://mesonh. aero. obs-mip. fr/mesonh54). Data availability. The Météo France polarimetric radar data are accessible against licence-fee only. For more information, please consult the following webpage: https://donneespubliques. meteofrance. fr/?fond=produit&id_produit=105&id_rubrique=35.
Introduction Knowledge of accurate molecular spectroscopic parameters is essential for interpreting and modeling spectra of terrestrial and planetary atmospheres, including those of exoplanets or even those of brown dwarfs. These parameters are provided in the HITRAN  [1]  and HITEMP  [2]  molecular spectroscopic databases. Broadening and shift of spectral lines due to the collisions of target molecules with those of ambient gases are among important parameters that need to be used in the opacity calculations. Tra-ditionally, the HITRAN database provided the single temperature exponent to the power law, in order to extrapolate the value of the collisional half-width at half maximum from the reference value given at 296 K to any other temperature. This approach works fairly well within the temperature ranges encountered in the terrestrial atmosphere.
[4]. One way of improving the single power-law approach for pressure broadening was to split it into four temperature ranges (4TR representation)  [3]  ; in the case of the shift parameter, a linear function was split into the four ranges  [3]. The 4TR approach was adopted in the 2016 edition of the HITRAN database  [1]. Following previous works  [11] [12] [13] [14]  , a double-power-law (DPL) was recently proposed to approximate the temperature dependences of both the broadening and shift  [4]  , see also Ref  [15]  for the review of the modern line-shape theory. It was shown that the DPL reproduces the temperature dependences much better than the single power-law function for broadening and linear function for shift  [4]. In this paper, we directly compare the 4TR and DPL approaches for several molecular systems.
We recommend the usage of DPL representation in HITRAN, and present DPL parametrization for Voigt and beyond-Voigt line profiles that will be adopted in the HITRAN database. In Sections 2 and 3 , we discuss the methodology and details of the fully quantum and semiclassical calculations, respectively. In Sections 4-6 , we show the comparison of the two approaches for the ( γ 0 and δ 0 ), ( γ 2 and δ 2 ) and ( ν r opt and ν i opt ) parameters. In Section 7 , we demonstrate the definition of the names of the DPL parameter that will be adopted in HITRAN.
The γ 0 and δ 0 parameters are the speed-averaged values of pressure broadening and shift parameters. Even though the exact, active molecule, speed dependence, γ ( v ) and δ( v ), can be determined numerically with the ab initio calculations, it is difficult to represent them in the spectroscopic databases. To address this problem, the speed dependence is expressed in terms of the quadratic approximation  [25] [26] [27]  : γ (v ) + iδ(v ) ≈ γ 0 + iδ 0 + ( γ 2 + iδ 2 ) v 2 v 2 m - 3 2 , ( 2 ) with v m being the most probable (absolute) speed. The parameters γ 2 and δ 2 can be expressed as a function of temperature  [28]  : γ 2 (T ) + iδ 2 (T ) = v m 2 d dv j 2 p j 2 (T ) γ ( v ) + iδ( v ) v = v m. ( 3 ) We provide the exact formulas for γ ( v ) and δ( v ) in Appendix C. Similarly to γ 0 and δ 0 , the Dicke parameter, ν opt , can also be obtained from purely ab initio calculations, based on the generalized spectroscopic cross sections  [28, 29]  : ν opt (T ) = ν opt (T ) p = 1 2 π c n v r M 2 j 2 p j 2 (T ) × ∞ 0 dx xe -x 2 3 xσ q 1 v i , j i , v f , j f , j 2 ; E kin = xk B T -σ q 0 v i , j i , v f , j f , j 2 ; E kin = xk B T , ( 4 ) where M 2 = m p m + m p , with m and m p being the masses of the active and perturbing molecules, respectively.
CO-He The 12 C 16 O rovibrational energy levels, used in the basis for the CO-He and CO-Ar dynamical calculations, are those provided by the HITRAN2016 database. The energy levels of CO in HI-TRAN2016 are based on Li et al. [45]  with some corrections associated with the implementation of the Coxon and Hajigeorgiou potential  [46]. The dynamical calculations were performed on the 3D CO-He PES of Heijmen et al. [47]. A Fortran code provided by van der Avoird  [47]  contains the vibrationally averaged PES in the fundamental and first excited vibrational states of 12 C 16 O. The resulting PESs have been expanded over 14 Legendre polynomials (up to l = 13). The close-coupling calculations are very similar to the ones performed on these PESs, in Ref. [48]  and, thus, will not be described. In order to deduce the pressure broadening and shift coefficients for the purely rotational and rovibrational R(0) 0-0, R(0) 1-0, R(20) 0-0 and R(20) 1-0 lines, necessary  [48]  S-matrix elements were computed for about 180 and 140 relative kinetic energies, ranging from 0.
(  14 )-  (17). The same parametrization as in the HT profile was adopted in HITRAN2016  [1, 3]  to represent the non-Voigt line profiles. In this paper, we argue that the more straightforward notation, i. e. ν opt = ν r opt + i ν i opt , allows one to avoid some additional difficulties with the temperature dependences that may arise when the ν vc and η parametrization is used, see Appendix A. We recommend the use of the explicit notation of the real and imaginary parts of the Dicke parameter, i. e. ν opt = ν r opt + i ν i opt , and to neglect its speed dependence. For details of the DPL representation for the HITRAN database see Section 7.
The DPL representation for the HITRAN database The full set of the coefficients that is needed to represent the DPL temperature dependencies of all the six line-shape parameters (i. e. : γ 0 , δ 0 , γ 2 , δ 2 , ν r opt and ν r opt ) described in Sections 4 -6 consists of 24 coefficients per one molecular transition (4 coefficients per each line-shape parameter), see Table  1. The structure of this parametrization is simpler and requires three parameters fewer than the 4TR parametrization adopted in HITRAN2016  [1, 3]  (cf. Tab. 7 in Ref. [3]  ).
Conclusion In this article, we analyzed the problem of seeking an optimal simple analytical representation of temperature dependences of collisional line-shape parameters that can be adopted in the HI-TRAN database. We used our ab initio fully quantum and semiclassical collisional line-shape calculations to compare the 4TR representation, adopted in the HITRAN database in 2016  [1]  , with the DPL representation. We considered two basic collisional lineshape parameters ( γ 0 and δ 0 ) that enter into the Voigt profile and four beyond-Voigt line-shape parameters ( γ 2 , δ 2 , ν r opt , ν i opt ). We demonstrated that DPL gives better overall approximation of the temperature dependences than 4TR. It should be emphasized that DPL requires fewer parameters and its structure is much simpler and more self-consistent than the structure of 4TR.
The DPL datasets can be easily implemented into the HITRAN relational database structure due to its flexibility  [57]. We present a DPL parametrization for Voigt and beyond-Voigt line profiles that will be adopted in the HITRAN database. We also discussed the problem of the Hartmann-Tran profile parametrization in which the correlation parameter, η, and frequency of the velocity-changing collisions parameter, ν vc , diverge to infinity when the collisional shift crosses zero. We demonstrate that this issue can be resolved by using a direct parametrization with a complex Dicke parameter, ν opt , instead of η and ν vc that were adopted in the HT profile. One should be aware that when modeling spectra of hot environments, the usage of the HITEMP  [2]  database rather than HI-TRAN is strongly advised.