dataesr/scientific-paragraphs-categorization huggingface dataset:
Further work needs to be done on the topic of initialization-related bias to fully address the effects of noise and a priori choice on high-altitude Rayleigh lidar retrievals. However, we are encouraged by our results and cannot discount the possibility that some of the remaining temperature difference is due to errors or bias in the satellite altitudes. When considering the residual temperature differences between the OHP lidars and SABER after the altitude correction based on lidar-derived stratopause height, we can see that much of the seasonally varying bias in the stratosphere and mesosphere has been reduced. We are still left with a general summertime cold bias over most of the atmospheric column, except near 45 km, which now achieves a maximum of -4 K in the June mesosphere. We cannot explain this bias from the perspective of the lidar data as nothing in our range resolution changes, our data acquisition cadence and measurement duration are very similar  (Wing et al.
, 2018) , and we are well into the linear region of lidar count rates and are not influenced by our a priori or saturated count rates. It is possible that there could be a tidal contribution as summer time lidar measurements start a bit later than wintertime measurements due to a shorter astronomical night. However, given that our criteria for coincidence were chosen to minimize the effects of the first few tidal harmonics, this seems unlikely. It is also possible that there is a seasonally dependent bias in the choice of a priori estimates used in the satellite retrieval of the geopotential height, which could influence the satellite altitude vector. The cold bias seen below 30 km is most likely due to possible contamination in the lidar data from aerosols and saturation in the low-gain Rayleigh channel.
Temperature biases between OHP lidar and MLS As with the comparison between the lidar and SABER, the lidar and MLS comparison has a pronounced warm bias above 70 km, which is in keeping with previous studies. However, the magnitude and extent of this warm bias in MLS are much more pronounced than in the SABER comparison plot. Much of this difference is due to the reduced vertical resolution of MLS at these high altitudes. This holds true particularly when comparing lower vertical resolution lidar data to MLS. The lidar MLS comparison has a wintertime stratospheric warm bias which is not much reduced by simply shifting the location of the MLS stratopause (Fig. 14e ). We have reduced the magnitude of the difference by 4 K, but the stratopause altitude correction was markedly less successful than in the case with SABER.
, 2008). The effect is most likely an artefact introduced in some stage of the satellite retrieval. Studies like ours provide a perfect opportunity to incorporate lidar information into the satellite retrieval and improve the satellite data products. Given the confidence we have in the fixed width and amplitude of the vertical kernels in the lidar measurement, a lidar altitude and tempera- ture vector could be used to recalculate the MLS geopotential and temperature profiles to help identify the source of this artefact. It is also important to acknowledge that simply correcting for stratopause height offset was counterproductive for our lidar-MLS comparisons above 50 km, as seen in Fig. 14a. It is likely that any potential lidar-derived correction for MLS will be more complex than a simple scalar offset.
Subsequent to this work,  Hauchecorne et al. (2018)  published a temperature data set derived from GOMOS stellar occultation measurements, which were validated using Rayleigh lidar. In this paper, the tidal contribution to the lidar-satellite temperature bias in the stratosphere is estimated to be less than 2 K, based on tidal characteristics extracted from the Global Scale Wave Model for tides. The same 2-5 K stratopause temperature bias was found by  Dou et al. (2009). This study used 2332 nights of lidar data from six different NDACC lidars and compared them with the zonal mean temperatures from SABER. The bias was attributed to possible tidal aliasing or aerosol contamination. In Part 1 of this paper we note that contamination by aerosols is not significant above 30 or 35 km and that aerosols cannot be responsible for lidar temperature bias in the stratopause.
2. We have seen a layered summer stratospheremesosphere cold bias in lidar-MLS seasonal temperature comparisons with peak differences at 37, 50, and 65 km. There is nothing in the lidar data or retrieval algorithm which could account for this structure. The results of this study will be useful for any future satellite validation studies in the style of  Schwartz et al. (2008)  for which lidar data could be used as a reference data set. In particular, lidar-satellite bias study results are useful for the ongoing NASA project "The Mesospheric and Upper Stratospheric Temperature and Related Datasets" (MUSTARD), which seeks to merge historic and ongoing satellite data sets. 3. The persistent summertime cold bias between the lidar and SABER results from a disagreement in the thermal lapse rate above and below the stratopause, which is independent of the scalar stratopause height offset.
Given that lapse rate is a fundamental geophysical parameter further work, must be done to explore possible errors in vertical resolution and altitude definition. 4. The periods of greatest lidar-satellite temperature disagreement are found during times when the middle atmosphere is highly disturbed. In particular, the amplitude of stratospheric warming events can be underestimated and features like double stratopauses can be missed in the satellite measurements. We have shown that ground-based lidars can provide reliable and consistent temperature measurements over decades. This kind of high vertical resolution temperature database is useful, both as a validation source for other instruments and for fundamental geophysical research.
Acknowledgements. This work is supported by the project  Atmospheric dynamics Research InfraStructure Project  ( ARISE 2 ) funded by the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement no. 653980. French NDACC activities are supported by  Institut National des Sciences de l'Univers/Centre National de la Recherche Scientifique  ( INSU/CNRS ),  Université de Versailles Saint-Quentin-en-Yvelines (UVSQ) , and  Centre National d'Études Spatiales (CNES). The authors would also like to thank the technicians at  La Station Géophysique Gérard Mégie at OHP. Edited by: Markus Rapp Reviewed by: two anonymous referees.
Data availability. The data used in this paper were obtained as part of the Network for the Detection of Atmospheric Composition Change (NDACC; see http://www. ndsc. ncep. noaa. gov/data/, last access: 17 December 2018), the SABER data centre (see http://saber. gats-inc. com/data. php, last access: 17 December 2018), and the MLS data centre (see https://mls. jpl. nasa. gov, last access: 17 December 2018) and are publicly available.
The rest of the manuscript is structured in the following way. Section 2 briefly describes the historical reconstruction of sea ice cover (also used in the next section as one of the initialization datasets for dynamical prediction), the clustering methodology used to extract Arctic sea ice modes of variability and a selection of corresponding results. Section 3 describes the coupled dynamical forecast system used to produce climate predictions and two statistical reference forecast systems. Section 4 assesses the skill of the NH SIT cluster predictions with several widelyused forecast quality metrics for categorical (here cluster or mode) predictions. The final Sect. 5 includes conclusions, discussions and suggestions for future research. To account for the oceanic sources of sea ice uncertainty, the ocean temperature and salinity in historical reconstructions are nudged (restored) towards their monthly values in the five-member ORAS4 ocean reanalysis  (Mogensen et al.
The applied clustering methodology aims to minimize the Euclidean distance between the members of a given cluster and maximizing the distance between the centroids of the different clusters, so the time series of cluster occurrences reveals the unique centroid (mode) to which the system is the closest in a particular month  (Wilks 2011). K-means clustering was chosen to reduce the data dimensionality in a simple manner (using Euclidian distance) that avoids the statistical constraints inherent in other unsupervised learning methods like principal component analysis (PCA), such as orthogonality and linearity. Prior to cluster analysis, the Arctic SIT was first coarse grained into 32 regions to make the method computationally efficient and because there are typically less than 15 effective degrees of freedom of the Arctic SIT fields in a GCM (Blanchard-Wrigglesworth and Bitz 2014).
Hence, our estimate of the transition matrix T varies slightly for each of the 32 forecast years and both start dates in order to ensure that the training and forecast data remain independent, but T has no other dependence. The Arctic SIT modes tend to persist for multiple seasons, including through the changes between sea-ice growing and melting seasons (Fig. 2 ), hence we constructed T without seasonal dependence. Table  1  shows the mean T of CAT, APD and CSD modes for the 1958-2013 period constituted as the average of cross-validated transition matrices for forecast years from 1979 to 2010 (using both start dates). X i = CAT X i = APD X i = CSD P{x i+1 = CAT | X i } 0. 814 0. 105 0. 071 P{x i+1 = APD | X i } 0. 132 0. 852 0. 055 P{x i+1 = CSD | X i } 0. 054 0. 043 0. 874 We evaluate the skill of the first-order Markov chain forecasts with the ranked probability skill score (RPSS) based on the ranked probability score (RPS).
study from the  PICA-ICE  ( CGL2012-31987 ) Project funded by the  Ministry of Economy and Competitiveness of Spain , the  SPECS  ( GA 308378 ) Project funded by the  Seventh Framework Programme  ( FP7 ) and the  PRIMAVERA  ( GA 641727 ) project funded by the  Horizon 2020 framework of the European Commission. NSF was a recipient of the  Juan de la Cierva-incorporación postdoctoral fellowship  from the  Ministry of Economy and Competitiveness of Spain. NCJ was supported by  NOAA's Climate Program Office. The authors acknowledge the computer resources, technical expertise and assistance provided by the  Red Española de Supercomputación  through the  Barcelona Supercomputing Center in Barcelona, Spain , and by the  European Centre for Medium-Range Weather Forecasts in Reading, UK.
Data sets The observed daily cumulated precipitation data are retrieved from the European Climate Assessment & Dataset (ECA & D) and the ENS gridded data set (E-OBS) version 12, which provides daily station-based precipitation and temperature data on regular grids  (Haylock et al. , 2008). While the full E-OBS resolution is 0. 25 • , here data have been upscaled by averaging to 1 • due to the specific focus on large-scale drought with significant socioeconomic impacts. E-OBS data are available from 1950 to the present. Atmospheric predictors are identified by using the geopotential height at 500 hPa. The daily geopotential is derived from the ERA-Interim reanalysis (ERAI,  Dee et al. , 2011)  with a spatial resolution of 1. 125 • covering the period from 1979 to the present. The forecast products (precipitation and 500 hPa geopotential height) are derived from the ENS of the ECMWF  (Molteni et al.
WR classification and MOAWRs calculation The WR classification (step 1 in Fig. S1 ) is detailed previously. The patterns of the geopotential height anomalies for the four seasons are displayed in Fig. S2. The best-known WRs occur in winter, namely the NAO-(Fig. S2a ), the NAO+ (Fig. S2e ), the Atlantic Ridge (Fig. S2i ) and the blocking regime (Fig. S2m ). Once the WR classification is done, the closest WR to the daily geopotential of each ENS member is attributed to both ERAI and ENS. From daily attribution, the climatology and the anomalies of occurrence of each WR can be done (illustrated by the step 2 in Fig. S1 ) using ERAI or ENS depending on the configuration of the forecast experiment (see list in Table  3 ). These two steps allow daily attribution of WRs and then the MOAWRs for both the ERAI and ENS data sets.
Validation of the WR forecasts The first question to address is about the quality of the forecasts of MOAWRs. The purpose here is not to provide a complete evaluation of the WRs forecasts that have been already studied  (Ferranti et al. , 2015; Matsueda and Palmer, 2015) , but to focus on errors that could impact the drought forecasts. To validate the forecast of the WRs, first the comparison of the frequency of occurrence of each daily WR is performed (Fig. 6 ). To do so, the climatology of the total occurrence of each WR among all the members and the entire lead time (5 members × 30-day LT) is calculated to extract the monthly anomalies. The forecasted anomalies are divided by the number of ENS members to create comparable results with the data provided by ERAI. The WR-distributions as given by the forecasts are characterized by a higher degree of similarity than the ones given by ERAI, with a peak of occurrence at around 5-8 days in winter (blue bars, Fig.
Data availability. The correspondence author could provide part of these datasets, but can not provide the forecasts that are provided by ECMWF and are protected by a specific licence. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-18-3297-2018-supplement. Competing interests. The authors declare that they have no conflict of interest. Author contributions. CL analysed the precipitation forecasts and developed the new prediction system. AT provided the WR detection and forecasts. CL, AT and JV interpreted the results. MC and FP provided comments and suggestions to improve the model and the article.
2B ). In particular nannofossil analyses involved counting all specimens within a standard traverse (60 fields of view at •1000 magnification) as well as scanning of the rest of the slide for rare species. Each of the samples is dated using only refined versions of the ''standard'' published biostratigraphic zonation schemes  (Martini, 1971; Sissingh, 1977 Sissingh, , 1978)). Moreover, petrography studies have been conducted to examine the microtextural features of the studied rock interval. For petrographic studies, sixty-five (65) thin sections were prepared and studied with a polarizing microscope at 1250 magnifications, following the Dunham's classification. Also, the fieldwork was supported by hydrogeological, exploration and geotechnical boreholes which are drilled by the Geological Survey Department of Cyprus (GSD) and provide subsurface data on borehole chips or boreholes cores.
6 são o Carbon Bond (CB), o Regional Acid Deposition Model (RADM) e o State Air Pollution Research Center mechanism, version 1999 (SAPRC-99). O Carbon Bond (CB05), versão 5,  Yarwood et al. (2005)  foi utilizado neste trabalho mas há possibilidade de se usar outros mecanismos químicos no SMOKE com pequenas adaptações no arquivo ASSIGNS (SMOKE v2. 7 User's Manual). As especiações de PM2_5 e COV, ou seja, as percentagens de cada componente químico nestes poluentes foram derivadas de referências americanas (SPECIATE V4. 2 database, USEPA, 2009). Na falta de uma informação específica, a relação entre NO/NO x foi considerada igual a 0,62 para todas as categorias que emitem óxidos de Nitrogênio, seguindo o "defaut" de USEPA_2006.
Comparison with in situ measurements Figure  6  shows the time series of ρ sat and the three time series of the in situ surface snow density. We obtain a remarkable agreement even if different daily and weekly variations are observed. The range of observed density is 150-425 kg m -3 for in situ measurements and satellite estimation during the overlap period. From February 2010 to Octowww. the-cryosphere. net/13/1215/2019/ The Cryosphere, 13, 1215-1232, 2019 ber 2011 (when all datasets are available), the mean surface snow density is 339. 8 ± 58. 8, 304. 2 ± 48. 7, 346. 4 ± 43. 9 and 303. 4 ± 57. 6 kg m -3 , respectively, for the satellite, CALVA measurements, PNRA stake, and PNRA pit measurements. The datasets are consistent with one another and the mean values are within the uncertainty range.
Experiment: Accelerometer-Magnetometer Case In this sub-section, we conduct an experiment where the accelerometer-magnetometer combination is adopted. Such a sensor combination is extensively applied nowadays in low-cost attitude estimation schemes. The accelerometer is pre-calibrated using six-direction bias cancelling, while the magnetometer is calibrated using the method proposed by Y. Wu et al. [45]. The hardware is constituted by a battery, a high-end Attitude and Heading Reference System (AHRS) with a high precision internal accelerometer, magnetometer and gyroscope, a transmitter for remote data transmission and a micro-controller for the implementation of the algorithm using the C++ language on the FreeRTOS. With the designed hardware platform shown in Figure  2 , we collect a dataset with 10,000 samples.
In the data history, the magnetometer was distorted by outer unknown electromagnetic and ferromagnetic fields. Furthermore, during the execution process, raw measurements from the gyroscope and accelerometer are also logged with a speed of 250 Hz. The raw data are shown in Figure  7. According to the sensor noise characteristics, the weights of the accelerometer-magnetometer combination are 0. 9 and 0. 1, respectively; while for the accelerometer-magnetometer-GNSS combination, the weights are chosen as 0. 474, 0. 05 and 0. 474, respectively. The reference vector of the magnetometer is determined by the initial GNSS positioning results with the IGRF model  [35]. By making use of algorithms including QUEST, FLAE, OLEQ and SOLEQ, the computation results are summarized in Figures  8  and 9. We specifically add the GNSS velocity norm under the Euler angle results to illustrate the influence of the velocity scalar on the attitude determination results.
3. The computation speeds of OLEQs are tested, revealing that they belong to computationally-efficient algorithms. 4. Moreover, a real vehicular experiment of a GNSS/AM system is designed and conducted showing the effectiveness of the proposed OLEQs in real-world embedded applications. The MATLAB source codes of the proposed algorithms are uploaded as open-source files at https://github. com/zarathustr/OLEQs. The presented approach provides the reader with a brand new viewpoint of attitude evolution and hopefully will benefit related multi-sensor attitude determination applications.
Acknowledgments: This research is supported by the  National Natural Science Foundation of China  under Grant Numbers of 41604025, 41704029 and is also supported by the  State Key Laboratory of Geodesy and Earth's Dynamics (Institute of Geodesy and Geophysics, CAS)  Grant No. SKLGED2018-3-2-E. F. L. Markley  provided constructive suggestions bout the internal theory of this paper. Yunheng Inc. , Chengdu gave us the instrumentation services during the experimental validation. We genuinely thank them for their support. Author Contributions:  Zebo Zhou ,  Jin Wu ,  Jinling Wang  and  Hassen Fourati  all conceived the idea of the paper,  Zebo Zhou  and  Jin Wu  conducted all the equation derivations and performed the experiments. Zebo Zhou  and  Jin Wu  wrote the paper. Jinling Wang  and  Hassen Fourati  improved the paper writing.
Backward trajectories The backward-trajectory (BT) analysis and classification in this study has been adopted from C. Pöhlker et al. (2018) , where an in-depth description of the procedure can be found. Briefly, the BT analysis is based on the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT, NOAA-ARL) with meteorological input data from the Global Data Assimilation System (GDAS1)  (Draxler and Hess, 1998). Three-day BTs have been calculated every 1 h with a starting height of 1000 m above ground level (a. g. l. ) at the ATTO site for the time period of 1 January 2008 until 30 June 2016. A sensitivity test confirmed that starting heights of the BTs at 200 and 1000 m a. g. l. gave similar results. Accordingly, the chosen start height at 1000 m appears to be a good representation of the origin of the boundary layer (BL) air masses at ATTO.
Satellite data and analysis The satellite data products used in this study were obtained from the NASA Giovanni web interface (http://giovanni. gsfc. nasa. gov/; last access: 26 May 2017), developed and maintained by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)  (Acker and Leptoukh, 2007). The following satellite products were used: -Cloud top temperature data (AIRX3STD_v006 product) from the atmospheric infrared sounder (AIRS) instruments on board the satellites Terra and Aqua (data included from 4 July 2002 to 30 June 2016). For the corresponding time series in this study, the Aqua and Terra data were averaged per day for a representative region (i. e. , ROI ATTO ; see below). -Cloud cover data were obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites (included data from 4 July 2002 to 30 June 2016).
The obtained Aqua and Terra time series were averaged for the ROI ATTO. Note that cloud cover strongly depends on the spatial resolution of the instrument as outlined in  King et al. (2013). -Cloud droplet effective radius, r e , data were calculated from MODIS products (included data from 4 July 2002 to 30 June 2016) for the ROI ATTO. Since r e varies with vertical cloud development and total CCN abundance, we filtered the r e data by cloud top temperature  (King et al. , 2013). -Precipitation rate data, P TRMM , are obtained from the Tropical Rainfall Measuring Mission (TRMM) within the ROI ATTO. The TRMM_3B42_Daily_v7 product was used for the time period 1 January 1998 until 30 June 2016. The satellite data were used as time series of area-averaged data products within a region of interest around the ATTO site (ROI ATTO : 3.
Seasonal cycles of remote-sensing and in situ data To provide an overall picture of the seasonal cycle of selected aerosol, meteorological, and cloud microphysical parameters representative for the ATTO region, various multi-year data sets were analyzed and compared. Remote-sensing data products were used in the time frames outlined in Sect. 2. 4. For M BC e , c CO , and the particle concentrations in the accumulation mode range (N acc ), in the Aitken mode range (N Ait ), and in the total particle population (N CN,10 ), 4-6 years of ATTO site measurements were available. Additionally, M BC e data measured at the ZF2 site, located 40 km northwest of Manaus, were used to reflect the conditions back to 2008 (e. g. ,  Martin et al. , 2010a). In terms of sources and conditions, the ATTO and ZF2 sites are comparable  (Saturno et al.
2. 7. For the PR CCN efficiency spectrum, the double-erf fit is the better representation, although the single-erf fit also works as a good approximation. Data for LRT conditions, shown in (c) and (d), represent the LRT3 period as shown in Fig. 3. A single-erf fit describes the experimental data accurately. Data for BB conditions, shown in (c) and (d), represent the time period in August 2014 as shown in Fig. 4. For the BB case, the experimental data have been fitted with a single-erf fit with two modifications: (i) the "default" fit with predefined variable a 1 = 1 as utilized for all other case studies tends to overestimate N CCN (S)/N CN,10 at high S; (ii) a corresponding fit with a free variable a 1 describes the experimental data more accurately. Data for MPOL conditions were separated into a MPOL-LRT case (g), representing sulfate-rich African aerosols, and a MPOL-BB case (h), representing plumes from close-by fires.