dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, a priori-based estimation techniques require the candidate entries to be as congruent as possible to the environmental and surface conditions at the time of the satellite overpass. Being highly variable, the specific sea ice, snow cover, temperature, moisture structure at the time of this GPM overpass may be undersampled in the a priori database. Even though thorough validation is not possible in such remote areas, considering the CPR snowfall as a relatively more accurate reference, the results suggest that the use of a larger dataset of GMI-CPR coincidences may lead to reduced uncertainty in passive MW-based retrieval of high-latitude snowfall.
Exploitation of Cloudsat for Passive MW Snowfall Retrieval Algorithms The multi-year, quasi-global, and complementary DPR and CPR measurements offer a unique and extensive resource to analyze spaceborne microwave radiometer precipitation observational capabilities. This can be particularly useful in remote areas and/or where ground-based observations are sparse or not available (e. g. , high latitudes), and in conditions and regimes where PMW precipitation retrieval is more challenging (e. g. , light precipitation and snowfall). A preliminary analysis to assess and identify a reliable reference for snowfall measurements was carried out by comparing various products from GPM DPR and Cloudsat 2C-SNOW-PROFILE from the CloudSat-GPM coincidence dataset described in this manuscript  [25]. They concluded that the near global CloudSat coverage and higher sensitivity of the W-band CPR (relative to both DPR radars) make it more suitable for snowfall-related research especially at higher latitudes, although it may be affected by significant attenuation in the presence of intense snowfall  [34].
In another study, the potential of the use of the CloudSat-GPM coincidence dataset to improve snowfall detection and retrieval techniques for passive MW radiometers at higher latitudes was described  [35]. The analysis of matched GMI and CloudSat CPR snowfall observations (mainly occurring at latitudes between 55 • and 65 • N) provided insights on microwave multi-frequency signals associated with snowfall. The analysis quantitatively showed the complex interconnection of high-frequency channel response to snowfall with background surface and environmental conditions (e. g. , snow cover over land, sea ice concentration over ocean, water vapor content), and with the presence of supercooled droplets (as inferred from CPR and CALIPSO observations). The study also showed how such a response depends on cloud vertical structure and snowfall intensity.
Based on these findings, SLALOM  [36, 37] , a new algorithm for GMI to retrieve snow water path (SWP) and surface snowfall rate (SSR), has been developed. The machine learning-based SLALOM algorithm exploits the multi-platform (GPM/CloudSat/CALIPSO) snowfall coincidence dataset, and is based on a random forest approach for snowfall and supercooled water detection, and multi-linear regression and gradient boosting for the SWP and SSR retrieval. Results showed very good skills of SLALOM compared to CPR and to ground-based observations, both in terms of surface snowfall occurrence and quantification  [38]. SLALOM complements other studies using the CloudSat-GPM coincidence datasets for passive microwave snowfall retrieval algorithms based on machine learning approaches for AMSU/MHS  [39, 40].
A similar approach to SLALOM has been recently developed and applied to the ATMS onboard Suomi NPP, NOAA-20, and the future Joint Polar Satellite System (JPSS) platforms. The differences between GMI and ATMS (scan geometry, channel frequencies and spatial resolution) have been taken into account in the algorithm design, where a new global Cloudsat-ATMS coincidence dataset was built and used for training. The algorithm prototype (SLALOM-ATMS) is based on four different neural networks modules for the detection of snowfall and supercooled liquid droplets, and for the estimation of surface snowfall rate and snow water path. It is worth noting that during the development of the SLALOM approach for GMI and for ATMS, the impact of the extremely variable frozen surface conditions (snow-covered or sea ice) on the (weak) passive microwave snowfall signal  [41] [42] [43] , including references in  [43] , has been tackled through the exploitation of the low frequency channels (<37 GHz) available in both radiometers.
The Passive microwave Empirical cold Surface Classification Algorithm (PESCA) for GMI and ATMS  [44]  has been recently developed to be able to detect and characterize snow cover (and sea ice) at the time of the satellite overpass. The assessment of the performance of SLALOM-ATMS compared with SLALOM-GMI is currently ongoing and is primarily based on the CloudSat-GPM dataset. Triple coincidences between Cloudsat-GPM-ATMS are used for this purpose. As an example, Figures  7  and 8  show an application of the SLALOM algorithms for GMI and ATMS to a snowfall event that occurred over Canada in the Quebec and Ontario region on 24 November 2014. The top two panels in Figure  7  show the cross-section of CPR and DPR Ku-band reflectivity and the associated total precipitable water vapor (TPW) from ECMWF.
However, because of its narrow spatial coverage and short duration of data availability, many research and operational applications cannot rely on CloudSat data alone. Therefore, there is a need to exploit the radar and radiometer data contained in the collection of CloudSat-GPM coincidences, to further improve and characterize the snowfall estimates from current passive MW sensors such as GMI, ATMS and MHS. The CloudSat-GPM coincidence dataset has been used as a training set in several snowfall retrieval studies. A physically optimized a priori database for a Bayesian GPM GMI snowfall retrieval algorithm over ocean has been developed  [46]. To build the database, CloudSat CPR-derived snow water content profiles are used as initial input to a radiative transfer model. Radiative transfer simulations are then performed to seek the best consis-tency between coincident CPR and GMI observations using a one-dimensional variational (1DVAR) method.
An interesting finding from this study was that the largest remaining biases in the simulated GMI brightness temperatures often occurred for shallow snow cloud cells or very deep convective snow clouds  [47] , which are hypothesized to be due to uncounted rich cloud liquid water in the shallow clouds and uncorrected attenuation to CPR reflectivity in deep clouds. The database is then applied to a Bayesian retrieval algorithm to study the climatology of snow water content profiles over global oceans. A technically less rigorous, but more broadly applicable (e. g. , over any surface types) snowfall detection and retrieval method was proposed by  [48] , in which the radar-derived snowfall rates are used as truth to train passive MW TB observations. In this framework, the primary information contained in the multichannel microwave TB are presented in the three leading principal components (PCs) derived by an empirical orthogonal function analysis.
Using radar and radiometer observations in the coincidence dataset, the probability and the rate of snow precipitation are computed and represented as a three-dimensional lookup table with the three leading PCs as its axes. In generating the lookup table, the method of  [49]  is used to separate snow and rain, and either the CloudSat CPR retrieval  [33]  alone or the combined CloudSat CPR and GPM DPR  [7]  retrieval is considered as the true snowfall rate. Combining the CPR and DPR retrievals can mitigate the shortcomings of the low detectability of the DPR for light precipitation and the attenuation/saturation of the CPR for heavy snow events  [35, 50]. Figure  9  shows the global snowfall distributions derived based on this method from 5 years of GMI data from 2014 to 2019. In the left panel of Figure  9 , snowfall rates are based on the lookup table generated using the CPR snowfall retrieval alone as truth, while on the right, snowfall retrievals are based on the lookup table generated by combined CPR and DPR as truth.
The reason for adapting such a combination is that the CPR is more sensitive to light snow and the DPR is more accurate for retrieving heavy snow. Clearly, the two sets of the retrievals display a similar pattern of global snowfall distribution, such as the snowfall bands along the storm tracks in the northern hemisphere and the circular snowfall pattern in the southern hemisphere. However, the snowfall rates based on the combined CPR and DPR trained lookup table are much larger than those based on the CPR trained lookup table. From these discussions, it is argued that the results based on the combined CPR and DPR trained lookup table (the CloudSat-GPM coincidence dataset described in this study) should more faithfully represent high latitude snowfall amounts and patterns.
The coincident CloudSat-GPM dataset is an ideal tool for further exploring different TB signatures associated with shallow cumuliform and deeper stratiform snowfall regimes, thus improving the ability for radiometer-based retrievals to produce accurate quantitative precipitation estimates for different snowfall categories. In Figure  10 , 2D-histograms of the 166 GHz polarization difference ∆TB (defined as the difference between the 166 GHz vertical (V) TB minus the 166 GHz horizonal (H) TB) vs. the 166V GHz TB are shown for deep stratiform (orange dotted lines) and shallow cumuliform (cyan solid lines) for different surface type. Cloud classification information from the CloudSat 2B-CLDCLASS product that is contained with the 2B-CSATGPM product allows these two snowfall regimes to be easily identified and inflates the scientific value of the ensuing analysis  [27].
Cloud and Precipitation-Sized Ice Microphysics The discussion in Section 3 focused on the use of the CPR to account for the limited sensitivity of the DPR Ku-and Ka-band radars to near-surface cold-season precipitation. Similarly, the limited sensitivity of the DPR also masks the detection of upper-level ice (see top panels of Figure  4 ). In this section, the use of the CloudSat-GPM coincidence dataset is investigated as a means to study the characteristics of ice crystals that are commonly noted above convective cloud systems.
Ice Crystal Habit and Orientation The CloudSat-GPM collocated dataset provides a unique opportunity to (1) validate space capability of applying multi-frequency radar theorem and algorithms that were previously developed based on ground or airborne measurements, and (2) explore the hidden ice microphysical information in high-frequency passive radiometer measurements that were previously unavailable or overlooked. Both practices are illuminating for future satellite mission development and selection. Cloud ice and snow microphysical characteristics, mainly particle size distribution (PSD), shape and density, are complex and vary widely in different regions and weather systems  [55]. It is critical to measure and simulate these properties accurately for better constraint of the cloud radiative effect (CRE) as well as for better the prediction of surface precipitation details such as phase, intensity and onset time.
As different frequency radars are sensitive to different parts of the size spectrum, multi-frequency radar joint observations were proposed for ground instrumentation as early as 2004  [56]. Theories and retrieval algorithms have been developed ever since to retrieve ice microphysical characteristics from the "triple-frequency diagram", which is essentially mapping the dual-frequency ratio (DFR, radar reflectivity difference) observations on the pre-computed look-up-table  [57] [58] [59]. This approach has been demonstrated to be powerful through comparison against in situ measurements [60,61], where collocation amongst the different radar beams is more readily achieved compared to the collocation obtained from the separate DPR and CloudSat beams. The CloudSat and GPM-DPR (Ku and Ka) collocated measurements provide the first "pseudo-triple frequency" radar dataset from space that has been used to study cloud ice microphysics.
This dataset was used to differentiate distinct ice microphysical regimes for stratiform and convective clouds, respectively  [62]. In  [63] , it was used in conjunction with collocated GMI 166 GHz polarimetric observations to uncover ice crystal orientation information. The positive polarization difference (PD) signals between GMI 166 GHz vertically and horizontally polarized radiance observations were attributed to the dominantly horizontal orientated large cloud ice or snow particles, which are found to frequently occur in anvil and stratiform cloud layers  [64]. PD measurements are further separated into "high-PD" and "low-PD" groups  [63] , and collocated CloudSat and DPR DFRs are composited on the triple-frequency diagram, as shown in Figure  11a ,b (colored shades for "low-PD" and colored contours for "high-PD").
With the aid from ECMWF-AUX data that are provided in the CloudSat-GPM collocation dataset (Appendix A), the former scenario was likely associated with the decaying stage of the mesoscale convective system, and a positive PD-surface precipitation intensity relationship was found for the "high-PD" scenes  [63]. This paper pointed out that an alternative costefficient way to replace the multi-frequency spaceborne radar is to use multi-frequency polarized high-frequency passive microwave and sub-millimeter radiometer observations. Based on the observational evidence provided in  [63] , an ice crystal orientation induced longwave radiative mechanism that facilitates (prohibits) the water vapor depositional growth of horizontally (vertically) aligned ice crystals was proposed  [65]. Collocated GMI and CloudSat observations are found to support this mechanism.
The PRO technique augments the traditional thermodynamic parameters (temperature, pressure and water vapor) available from RO, with an indication of precipitation along the GNSS propagation path  [66]. To validate these observations, the CloudSat-GPM coincidence dataset was used to build a look-up-table of vertical profiles of CloudSat-retrieved ice water content (IWC), based on the coincident information from GMI. In this way, IWC 3-dimensional fields can be used to simulate ∆φ based only on collocations between PAZ and GPM, and compare them with actual ∆φ observations  [67]. Figure  12  presents one example from a ROHP observation on 16 March 2019, where a good agreement is found above the freezing level when the CloudSat-retrieved IWC is used, whereas the lower layers are better reproduced when using the Ku-band (NS) GPM combined algorithm (CORRA)  [8]  water content to perform the ∆φ simulations.
An upcoming version will incorporate a non-raining optimal estimation (OE) retrieval (described in Section 3 of  [42] ), making the product more easily adaptable to GPROF database construction and the two products generally more consistent. Screening for the occurrence of light precipitation can therefore be implemented within this framework. The cutoff value of chi-squared (or normalized cost function) will be determined using CloudSat-GPM coincident overpasses from the coincidence dataset described in Section 2, which contains all radar and radiometer data for CloudSat-GPM orbit coincidences. Light precipitation will be assumed in areas where there is no radar return, but the OE is unable to converge. The second piece of 2B-CMB implementation involves determining the microphysical characteristics of the retrieved light precipitation profiles.
This will be carried out offline from the 2B-CMB code and will test two approaches, with the most successful to be presented for implementation in the main retrieval. Both start with the database of CloudSat coincidences. Based upon the chi-squared cutoffs determined in the previous step, reflectivity profiles from CloudSat will be used along with associated hydrometeor profile retrievals. These profiles will be analyzed to determine whether generalizations of hydrometeor amount and profile shape (with phase determined by temperature) can be applied in a bulk sense, under atmospheric constraints, or are sufficiently unique to require a Bayesian retrieval within the light precipitation areas using a database derived from the CloudSat profiles with the goal of accurately reproducing the multi-spectral GMI signal from the light precipitation.
Cloud Effects on the Estimation of Surface Emissivity Variability The discussion in the previous sections has largely been directed at the use of the CloudSat-GPM coincidence data for the detection of precipitation that is missed by the GPM radars. Conversely, CloudSat's superior cloud detection capability (relative to DPR) is also important for identifying "cloud-free" scenes. A large collection of cloud-free GMI (and other passive MW) observations is required to examine the underlying surface emissivity. The DPR is largely transparent to the presence of cloud liquid water, whose absorption properties influence the net upwelling TB, especially for channels above 90 GHz. With cloud-free scenes, the remaining contribution to the upwelling TB is the intervening absorption from constituent gases, which is well-known from microwave transmission models  [69]  and whose inputs can be obtained from nearby-time weather forecast models or climate model reanalyses.
An example name for a CloudSat-GPM file is (similar conventions for CloudSat-TRMM): 2B. CSATGPM. COIN. 51S_113W_07487_000_279_561. 20150124-S215831-E220121. 005154. V04. The fourth field provides specific details on the coincidence (51S_113W_07487_000_279_561) using an underscore delimiter. The fifth field carries the start and end times of the coincidence segment (20150124-S215831-E220121), the sixth field is the GPM orbit revolution number (005154). The interpretation of the six values within this fourth field is listed in Table  A2. The seventh field identifies the version of the coincidence dataset (V04 as of April 2021). Therefore, by only parsing the filename, coincidences meeting specific requirements can easily be located without having to open and read the file. For example, to isolate over-land snowfall cases, a user would look for files with the 2-m air temperature near or below 273K, 100 percent over land, and with a sufficiently large number of CloudSat bins (e.
g. , more than 1000) that identified cloud-affected CloudSat bins. Running the "ncdump-h" command on a file will list all global attributes, followed by a number of groups. For CloudSat, which does not scan, the along-track dimension is referred to as a ray. For PR and DPR, the along-track and across-track dimensions are referred to as a scan and ray, respectively. For TMI and GMI, the along-track and acrosstrack dimensions are referred to as a scan and pixel, respectively. The radar vertical bin dimension is referred to as a level. This maintains the native dataset nomenclature. For example, the first few global attributes for the file example above are listed below. The file contains 1067 CloudSat beams, and each beam has 125 vertical levels. Various attributes carry the filename of the individual Level-1 or Level-2 datasets, start and stop times of the coincidence segment, orbit numbers, etc.
(remainder of global attributes) Each group is identified by its associated dataset name (the first column of Table  A1 ). Inside of each group are the associated variables for that group, copied from the file listed in the global attributes. For example, for the 2B-GEOPROF group, there are a selected number of variables that are copied over from the 2B-GEOPROF file associated with the dimensions: nscan_MS = 137; nray_MS = 25; nlev_MS = 176; variables: short zFactorMeasured(nscan_MS, nray_MS, nlev_MS); zFactorMeasured:scale_factor = 0. 01f; zFactorMeasured:units = "dB"; zFactorMeasured:description = "zFactorMeasured for MS Ku-band full swath, copied from 2A. GPM. DPR"; (remainder of variables for the SWATH subgroup) }//group SWATH }//group MS }//group \2A. GPM. DPR Other groups follow (e. g.
, 1C. GPM. GMI, etc. ), with each group containing selected variables from each dataset listed in Table  A1 , copied from its corresponding GPM or Cloud-Sat file name listed in the global attributes. For 2A. GPM. GMI. GPROF and 2A. TRMM. TMI. GPROF, the value of the surface type index is listed in Table  A3. For group MODIS-AUX, the eleven MODIS channel bands are listed in Table  A4.
All samples were incubated with LIVE/DEAD Fixable Aqua or LIVE/DEAD Fixable Blue (Invitrogen) to exclude dead cells from the analysis. Cells were acquired on BD FACSCanto II or BD LSR II (BD Biosciences). Expression of each molecules was assessed with FlowJo software (TreeStar) in CD4 + T cell subset or for cultured cells among activated (FSC hi SSC hi ) helper T cells. To detect the expression of Tox2 in human tonsils, PrimeFlow RNA assay (Invitrogen) was performed following the manufacturer's instructions.
Acknowledgments: We thank donors of tonsil and LN tissue samples. We thank  A. Witzl  for IRB paperwork and handling of the human samples. Some tonsil samples were provided by the  NCI Cooperative Human Tissue Network (CHTN). We thank  F. Krammer  and  S. Strohmeier  for the recombinant H3N2-HA protein. We thank  Flow Cytometry Core  and  Microscopy Core in Mount Sinai and Flow Cytometry Core in Baylor Institute for Immunology Research. Funding: This study was supported by  R03-AI142046-02 (R. A. A. ) ;  Centers of Excellence for Influenza Virus Research and Surveillance (CEIRS)  contract  HHSN272201400008C  (W. -C. L. and R. A. A. );  NIH  grants  U19-AI057234 ,  U19-AI082715 ,  U19-AI089987 , and  R21-AI53673 ;  Baylor Health Care System; Icahn School of Medicine at Mount Sinai ; and the Advanced Research and Downloaded from https://www.
For simplicity, universal markers will throughout the paper be referred to as the target diet categories i. e. plants, invertebrates and vertebrates. Blocking oligonucleotides were used to minimize amplification of badger, fox and human sequences with the vertebrate marker, and of mammalian and human sequences with the invertebrate marker (Supplementary Note 1 and Table  1 ). For taxonomic classification, a sequence reference database was built for each eDNA metabarcoding marker by extracting the relevant DNA region for plants, vertebrates, and invertebrates (Arthropoda and Mollusca) from EMBL nucleotide library (release 128) using the ecoPCR program  41. Sequence reads were filtered and taxonomically identified following the procedure described in  39  using the OBITools 42 and R v. 3. 3.
The extent of dietary overlap between the two predators was evaluated using non-metric multidimensional scaling (NMDS) by computing the Jaccard distance on the presence-absence matrix of faecal samples versus diet components assigned to taxonomic order level, using the vegan package v. 2. 5-8  44. The dietary uptake of the two predators was statistically compared by a permutational multivariate analysis of variance on the distance matrices using the adonis function in vegan. To assess the effect of faecal age (estimated time of exposure to environmental DNA degradation), we used only badger faecal samples, since the number of fox samples was too low for this analysis. We tested the effect of faecal age on the total number of taxa and reads per sample, using a two-way ANOVA with primer, DNA quality and their interaction as fixed effects.