dataesr/scientific-paragraphs-categorization huggingface dataset:
As the incorporation of MWIR channels in daytime SST retrieval is a first of its kind, a thorough investigation is presented here. Physical Deterministic Sea Surface Temperature scheme has already been applied to the MODIS-AQUA radiance data for retrieving the SST and is reported in earlier publications  (Koner and Harris, 2016a,b) , where the validation has been made using millions of global matchups and nighttime scenarios. The PDSST suite has also applied to daytime SST retrieval from MODIS-AQUA using global matchups in  Koner, 2020b. This study mainly focusses on the detailed performances of PDSST for coastal areas using swath processed data. The enhanced information content of daytime SSTs in PDSST retrieval is discussed in terms of both the quality and Data Coverage (DC) of the swath-processed daytime SSTs from MODIS-AQUA.
The MODIS instrument is considered here for the case study to evaluate the performances of the daytime SST retrievals using MWIR channels in the PDSST suite because MODIS has been providing unique highquality radiance data for almost two decades and geophysical parameter values from these measurements are greatly important for many geoscience models. Also, only the MODIS instrument has three MWIR channels which can help to understand the importance of MWIR in daytime SST retrieval. The quality of SSTs is measured by Root Mean Squared Differences (RMSD) from the values of in situ matches. The DC is defined by the number of cloud-free pixels for swath-processed SSTs as compared to the total number of ocean pixels in a granule. A detailed comparison between the daytime SST retrieval using the Physical Deterministic Sea Surface Temperature suite, including MWIR channels, and operational Regressionbased daytime MODIS SSTs (RGSSTs), obtained from Physical Oceanography Distributed Active Archive Center (PO.
DAAC), is conducted to address the information gain by PDSST scheme. Elaborately, Section "Description of PDSST Suite" provides a short description of the PDSST-suite. Section "Verification of Fast Forward Model" demonstrates the usability of the publicly available Community Radiative Transfer Model (CRTM v2. 3), NOAA for daytime SST retrieval. Section "Comparison and Validation of PDSSTs" addresses the comparison and quantitative validation of swath processed SSTs using PDSST and RGSST from MODIS-AQUA, for coastal and near-coastal regions, using in situ matches obtained from NOAA website  (Xu and Ignatov, 2014). Section "Cloud Detection and Quality Flag" discusses the cloud detection issue, using the Cloud and Error Masking (CEM) in PDSST-suite, and Quality Flag (QF) obtained from PO. DAAC database.
The three parameters in the state vector (x ttls ) are [s log(w) log(a)], where w is Total Column Water Vapor (TCWV), s is SST, and a is the sum of total columns of all aerosols. The unprecedented capability of the PDSST algorithm is that aerosol is used in a forward model calculation as well as in the retrieved vector. Global Forecast Simulation (GFS) data are used as input for CRTM. Two out of six channels  (3. 9, 4, 4. 5, 11, 13. 4, and 13. 6 µm)  in the PDSST algorithm are considered from the MWIR band. On the other hand, both operational products of RGSST from MODIS and Microwave SST (MWSST) from Advanced Microwave Scanning Radiometer (AMSR2), obtained from the Remote Sensing Systems (RSS) website, are based on the regression method. RGSST from the MODIS retrieval algorithm uses only the two channels of 11 and 12 µm for daytime.
The spatial test for CEM is highly flexible. The targeted pixel is discarded if the difference in the value of maximum and minimum of the surrounding 3 × 3 pixels of the measured 3. 9 µm channel is more than 2. 5 K. Also, the difference of the measured BT value of the targeted 11 µm channel and the maximum value in the neighborhood of 3 × 3 pixels should be less than 0. 75 K for being cloudfree. As there is an absence of the cloud flag field in the RGSST from PO. DAAC database, the highest value of QF = 5 (RGSST5) is considered as the cloud-free subset. The values of QF are determined by some threshold-based criteria.
DAAC, as shown in Figure  2. The retrieved SST map of RGSST5, for QF = 5, are shown in Figure  2A  using the same granule as shown in Figure  1C. The value of RMSD using 4 matched in situ is 0. 44 K, which is ∼40% more compared to the same for PDSST (0. 32 K). The DC for RGSST5 is 21. 05%, which is less than half of the DC from PDSST (50. 24%). It is observed from Figure  2A  that most of the regions where θ spec < 30 • are masked by a low value of QF in the PO. DAAC database and are shown as empty (deep blue color in Figure  2A ). The quality of PDSST for the region of θ spec < 30 • cannot be verified by comparing it with the values of RGSST5, thus, the flagging threshold is decreased up to QF ≥ 3 (RGSST3), and is plotted in Figure  2B. The DC for RGSST3 increases drastically to 44. 12% as compared to the same for RGSST5, but still is lower than PDSST (50.
Stoffelen, 1998)  on the point matchups to assess the regional true noise associated with PDSST retrievals in the context of skin temperatures. It is assumed in TCM that any dataset consists of a hypothetical truth corrupted by a noise: R i = T + n i , where R is retrieved data, T is hypothetical truth, i represents a dataset (1, 2, 3) and n is noise. Noise (n) has two components: the systematic part, which is referred to as bias (b), and the random part (σ). Using three collocated datasets representing the same physical quantity ("alike"), individual σ i values can be separated out if they are uncorrelated: (R 1 -R 2 ) = b 1 -b 2 + σ 1 -σ 2 , where b 1 -b 2 = (R 1 -R 2 ) (8) Using Eqn. 8 and further expanding: DC for PDSST is DC (pd) and for RGSST5 is DC (rg), number of buoys in the cloudfree set for PDSST is NB (pd) and for RGSST5 is NB (rg), and RMSD of PDSST is ε (pd) and that of RGSST5 is ε (rg).
N stands for granule number and T denotes total or average. OC is percentage of ocean cover and NB is the total number of buoys for a particular granule. (R 1 -R 2 ) -(R 1 -R 2 ) (R 1 -R 3 ) -(R 1 -R 3 ) = σ 1 σ 1 -σ 1 σ 2 -σ 1 σ 3 + σ 2 σ 3 (9) If the random errors of the three "alike" datasets (σ 1 , σ 2 , σ 3 ) are uncorrelated, then: σ 1 σ 2 = σ 1 σ 3 = σ 2 σ 3 = 0. Following this, one can find the true random error in data set R 1. Here, R 1 , R 2 and R 3 correspond to the SSTs from PDSST and RGSST5, and the collocated buoy measurements, respectively. To explain further in the context of SSTs, one may raise the question that the errors are correlated if any two of the above-mentioned retrievals are from the same sensor. It is expected that correlated error would be negligibly small due to the fact that the applied inverse methods are in different paradigms and the errors in measured BT are significantly smaller than the errors in SSTs.
The DC of RGSST from MODIS-AQUA increases to 5. 74% (figure not shown) when QF is set to be ≥3, but is still very low as compared to the really cloud-free measurement of this granule (see Figure  3A ). Reducing the value of QF even further (to QF ≥ 2) still does not show any increase in DC for this granule. Thus, all RGSSTs from the PO. DAAC database, with a constraint of the value of SST > 278 K, have been plotted in Figure  3C. This set of RGSST SSTs matched with 36 buoy measurements and the RMSD value is 1. 25 K. The value of QF for PO. DAAC is 1 for all (36) matched locations. However, Figure  3C  shows that most of the SSTs are comparable qualitatively (using the color map) with the same using PDSST. This implies that there is no relation between the value of QF and cloud-covered pixel.
Figures  7B, C  are qualitatively comparable, which implies that the quality of PDSST, including the MWIR channels, is as good as RGSST from MODIS-AQUA, without MWIR channels, under PDSST cloudfree subset for this region. However, PDSST cloud-free subset can extract 3 times more SST information as compared to the same for RGSST5. Such a comparative study may help to evaluate product quality when in situ measurements are hard to get for quantitative validation. As can be seen from    MWSST database. Thus, the differences between RGSSTP and PDSST are plotted in Figure  7D. A large number of retrievals from RGSSTP and PDSST are compared in Figure  7D. It is observed that the QF values of 48% pixels of the selected RGSSTP are 1, where the QF values are 5 for 38% of total matched pixels. On the other hand, all pixels for the said subset are cloud-free according to PDSST-suite.
As alternative in situ databases for skin SST are not readily available, firstorder validation of satellite-derived SSTs is accepted using buoy measurements. Moreover, RGSSTs are conventionally bulk SSTs due to the fact that regression coefficients are generated using bulk buoy measurements, whereas the PDSSTs are skin SSTs by definition (see details in  Koner, 2020b). Physical Deterministic Sea Surface Temperature is capable of fetching an enormous amount of valuable SST information as compared to the currently operational regression-based SST scheme for the dynamic oceanographic area of CC as per Figures  3A, B, 4A, B, 6A, B. One of the major reasons this has achieved such high information gain (∼20 times for a specific granule of Figures  4A, B ) as compared to RGSST5 is that the PDSST suite uses more channels including MWIR channels and local atmospheric information from GFS data in daytime SST retrieval as well as for cloud detection scheme.
Parametrizations of sea surface turbulent fluxes used in general circulation models (GCMs) assume horizontal homogeneity of atmospheric properties at the grid-cell scale. The present study assesses the contribution of the meso-scale (i. e. , subgrid) to the grid-scale surface fluxes, for GCM resolution ranging from 20 to 200 km, and thus quantifies the associated GCM surface flux error. A coarse-graining method allows for an a priori analysis of the subgrid information. It is based on an atmospheric reference dataset produced by the convection-permitting operational model AROME. The meso-scale relative contribution to GCM-scale fluxes exceeding 10% is shown to have large regional patterns, with large values (up to 90%) and high frequency of occurrence (up to 76% of the time). These meso-scale motions are not necessarily due to convective activity but also occur frequently under dynamical perturbation conditions.
Here, the operational and convection-permitting NWP model AROME  (Seity et al. , 2011; Brousseau et al. , 2016)  is used to quantify the impact of meso-scale atmospheric heterogeneities on the surface fluxes at the scale of a general circulation model (GCM) grid cell. This meso-scale contribution, if neglected in GCMs, corresponds to the error made when applying directly common surface flux parametrizations in GCMs. The paper is organised as follow. Section 2 provides some background about surface flux parametrizations. It also defines the total contribution of meso-scale heterogeneities to larger-scale surface fluxes, typically at the scale of a GCM grid cell. Section 3 then introduces the numerical framework used to build a large dataset (one month of hourly data at 2. 5 km resolution covering two large domains over the tropical Indian Ocean and the Caribbean Sea) which serves as a basis to quantify the meso-scale contribution in Section 4.
Atmospheric forcing The atmospheric forcing dataset is provided by the convection-permitting model (CPM) AROME  (Seity et al. , 2011; Brousseau et al. , 2016) , which has been operating for NWP activities for the last decade over several domains covering France and some of the French overseas territories  (Faure et al. , 2020). In the present work, we focus on two oceanic domains covering La Réunion island in the tropical Indian Ocean (Figure  1 ) and the West Indies in the Caribbean Sea and a fraction of the tropical Atlantic (Figure  13  below). These AROME configurations have a horizontal resolution of 2. 5 km and a 90-level vertical grid  (Brousseau et al. , 2016) , with 33 levels below 2000 m height. Only the atmospheric fields of the first model level (5 m above the surface) are used.
Mean contribution The monthly mean meso-scale contribution ⟨ F MS ⟩ to the GCM-scale (dx-100) surface fluxes (Figures  3a, c e ), as defined by Equation (  10 ), are approximately correlated to mean rainfall patterns (Figure  1a ). They are also consistent among the three fluxes, although they cover wider regions for momentum. Regions of larger mean contribution are also those where the contribution is most frequently 10% higher than the GCM-scale flux (contours in Figure  3a , c e). The northeastern quarter of the domain is characterized by a large and frequent meso-scale contribution to the momentum flux: it can reach -0. 011 N⋅m 2 and occurs up to 60% of time. The meso-scale relative contribution reaches 20% of the average local SM-flux (Figure  S1a  in the F I G U R E 2 Histogram (left axis) of the mean wind speed U for the entire dataset (light blue), for the  MS ∕ ≥ 10% subdataset (turquoise), and for the H MS ∕H ≥ 10% subdataset (blue).
It is particularly high and frequent west of La Réunion for the momentum flux in the lee side of the island. It reaches -0. 0127 N⋅m 2 , which is about 20% of the average local GCM-scale flux (Figure  S1a ), 76% of time. Regarding sensible and latent heat fluxes, local maxima of the meso-scale contribution are mostly northwest of Madagascar. There, they reach 2. 2 W⋅m 2 (∼ 15% of the local average SM-flux; Figure  S1b ), with an occurrence of 46% for the sensible heat flux, and 16 W⋅m 2 (∼ 10% of the local average SM-flux; Figure  S1c ), with an occurrence of 44% for the latent heat flux. The processes associated with these large contributions are analysed in detail in Section 4. 2. A comparison between GCM-computed fluxes (from SFX-GCM simulation) and reference SM-fluxes (or GCM-scale fluxes) is further presented for the entire dataset in Figures  3b, d, f.
GCM-computed fluxes are systematically lower in amplitude than SM-fluxes, which indicates that the meso-scale contribution systematically enhances the fluxes. It is the most frequent in the low-to-moderate range of the fluxes, but note that large flux events are less sampled in the present dataset. The error can be large even for weak fluxes, thus the meso-scale contribution can be important even under weak wind regimes. For the three fluxes, the regional mean meso-scale relative contribution to fluxes remains weak: 5. 5, 3. 8 and 2. 2% for momentum, sensible heat and latent heat fluxes, respectively.
Jorgensen et al. , 1997). A systematic analysis of the present dataset (not shown) also indicates a significant role of isolated or less organized convective cells (e. g. , in the southern Mozambique Channel) as well as of tropical depressions (e. g. , northeast quarter of the domain and open ocean east of Madagascar). An example of each these two mechanisms is provided in the Supporting Information. A more objective classification of the processes that lead to a meso-scale enhancement of GCM-scale surface fluxes would probably help in establishing a process-based parametrization for large-scale models. Although it is beyond the scope of the present work, we think such a classification is not straightforward as we have often observed a superimposition of processes in our dataset (e. g. , dynamical and convective features).
Governing terms of the meso-scale contribution Even though the meso-scale contribution can be large when it occurs, and frequent over some regions (Section 4), it remains rather frequently weak (Figures  3b, d, f ). In order to better understand the meso-scale contribution to a given flux, the following analysis focuses on subdatasets for which the meso-scale relative contribution to the flux is larger than 10% of its value (F MS ∕F  ≥ 10%). This threshold is rather arbitrary, but the main conclusions of this study are not sensitive to this threshold. It leads to a subdataset of 26, 9 and 8% of the entire dataset for momentum, sensible heat and latent heat fluxes, respectively (around 9. 3×10 4 , 3. 2×10 4 and 2. 9×10 4 points). The meso-scale contributions to the momentum flux meeting the previous condition occur mainly at moderate wind speed, around 4.
2 m⋅s -1 on average (Figure  2 ). The meso-scale contributions to both the sensible and latent heat fluxes occur at comparable wind speed, around 3. 6 and 3. 4 m⋅s -1 on average, respectively, with a shorter distribution tail toward high wind speed. Based on these subdatasets, the difference between SAM and VAM wind speed calculations within the bulk formula (T 1b term) is found to be the major contribution to the meso-scale variability (Figure  9 ). It reaches 20, 22 and 24% of the total momentum, sensible heat and latent heat fluxes, respectively. For the momentum flux, the term associated with wind velocity variance (T 2a ) has also a significant contribution of 9%. Then, the sum of T 1c , T 2b , T 3 , T 4 and T 5 leads, on average, to a residual contribution of 1%. For the sensible heat flux, the contribution of the exchange coefficient C H variability (T 1c ) reaches 4%.
A priori reconstruction of GCM-scale flux The flux reconstruction of Equations (  25 ) to (  27 ) is further assessed by comparing the true GCM-scale fluxes (or the SM fluxes F  ) to the proposed approximations, in the context of the present dataset (a priori flux reconstruction from the CPM data). If we were able to parametrize these terms in a GCM, this a priori reconstruction represents the maximum improvement that could be achieved. Various intermediate flux reconstructions are compared to the GCM-scale (SM) fluxes for both the F MS ∕F  ≥ 10% subdatasets and the entire dataset (Figure  10 ). The addition to the GCM-computed flux of the difference between the SAM and VAM wind magnitudes in the complete bulk formulae (T 1a + T 1b , red) significantly reduces the initial GCM-computed flux error.
For the 10% meso-scale contributions datasets, the residual error in the momentum, sensible heat and latent heat fluxes is reduced to -9. 9, -1. 6 and 0. 9%, respectively (NRMSEs to 14. 4, 7. 0 and 3. 4%, respectively). If the reconstruction T 1a + T 1b (red points) already significantly reduces the momentum flux initial error, the addition of the term T 2a (black points) clearly further improves the flux reconstruction: the mean relative residual is reduced to -2. 9%, and the NRMSE to.
Wind range dependency The quality of the flux reconstruction of Equations (  25 )-(  27 ) is further analysed as a function of the wind intensity (Figure  11 ). The largest meso-scale relative contributions are found for the lowest wind subrange (entire dataset; Figure  11b ). Based in the 10% meso-scale relative contributions datasets (Figure  11a ), the wind magnitude difference between SAM and VAM in the bulk formula (T 1b term) is the main meso-scale contribution for the three fluxes over the entire wind range. For the heat fluxes, this term is mainly due to the U -Ũ wind difference, which decreases as the wind speed increases (Figure  2 ). For the momentum, the addition of the term T 2a yields an overestimate of the flux at low wind speed (≤ 2. 5 m⋅s -1 ), but it remains relevant for higher wind ranges.
Geographical dependence The regional dependence of the meso-scale contributions to fluxes is analysed using another domain, located in the Caribbean Sea, on which the AROME model is run operationally. The AROME configuration is identical to the one used over the Indian Ocean and the same numerical framework (Section 3) has been set up. The month of August 2017 is chosen for the occurrence of a large convective activity over the southern part of the domain (Figure  13a ). Easterly trade winds dominate over the domain (Figure  13b ), with a mean wind speed of 7. 12 m⋅s -1 (Figure  S7 ), higher than for the Indian Ocean dataset. The subdataset corresponding to meso-scale relative contributions to the momentum flux greater than 10% has also a lower mean wind speed (5. 6 m⋅s -1 ) than the entire dataset.
The proportion of the dataset that presents meso-scale relative contributions greater than 10% (9. 5, 1. 3 and 1. 0% of the total of 108720 GCM points for the momentum, sensible heat and latent heat fluxes, respectively) is lower than that for the Indian Ocean configuration. However, as in the previous configuration, the meso-scale contribution has strong regional features and can reach locally large values and frequencies of occurrence (up to 30% of time for the momentum flux; Figure  13c ). Once again, the sources of the meso-scale motions are multiple. The main cases that can be identified correspond to isolated convective cells, organized convection, orographic perturbation on the lee side of the Caribbean archipelago and a few tropical depressions. The analysis of the flux decompositions of Equations (  22 )-(  24 ) leads to the selection of the same governing terms as for the previous case-study (Equations (  25 )-(  27 ); Figure  S8 ).
Subdataset selection dependence As mentioned in Section 4. 2. 3, it is not possible to classify the different situations that lead to meso-scale contributions in a category of identified processes, as they often result from a combination of mechanisms. It is thus hardly possible to assess the sensitivity of the flux reconstruction to the process type. However, the analysis has been conducted on several locations that contain a strong redundancy of an identified process (the ones detailed in Section 4. 2) as well as on other locations and specific time periods that contain isolated convective cells (Figure  S2 ) or tropical depressions (Figure  S3 ) and no variation on the governing terms has been shown significant between those examples. Meso-scale contributions to surface flux are often solely associated with convective activity  (Redelsperger et al.
However, at low wind speed (<2 m⋅s -1 ), the term T 2a tends to over-correct the momentum flux, suggesting that a wind dependency may be relevant. The boundary-layer convection scale is not addressed in the present study. The scale separation proposed by  Redelsperger et al. (2000)  suggests that this would not influence our present results. The relevance of this hypothesis is beyond the scope of the present study, but we expect that datasets similar to the present one, but resolving explicitly the large eddies of the boundary layer, are becoming available and will enable us to assess it. The work of  Ledvina et al. (1993)  was the first to analyse the flux decomposition for these scales. In fact, by varying the averaging time from 2 to 72 hr of their time series, they considered scales from the boundary-layer free convection to the deep convection.
This wind correction corresponds to the difference between the average of the meso-scale wind magnitude (scalar average method) and the wind magnitude calculated from the average wind components (vector average method). However, no consensus on the relevance of this approach exists in the literature. Using a Reynolds decomposition of the different variables in the bulk formulae, all the contributions, both of the average (large-scale) and fluctuating (subgrid) terms, are quantified. One-month-long simulations are run for two numerical domains, providing around 5 × 10 5 GCM grid cell samples, which constitutes a unique dataset, robust enough for generalizing conclusions on the governing terms representing the meso-scale contributions. The gustiness approach is found to work for representing meso-scale contributions to heat fluxes.