dataesr/scientific-paragraphs-categorization huggingface dataset:
A list and descriptions of the rainfall-related climate indices used in this study are given in section 2, along with a description of the study area and the statistical methods used to assess the models' performance. In section 3, we present the observed spatial distributions of the climate indices in SEA. Furthermore, we present the performances of the models in terms of how well they simulate the climate indices. Section 4 discusses the similarities of the three gridded observational datasets that are used to determine the climate indices. We also discuss the performance of the models for a number of climate indices. In the last section, we draw conclusions from our findings.
| Observations In SEA, the development of a comprehensive dataset based on in situ surface observations of daily precipitation amounts is challenging because of the limited density of gauges and the limited long-term availability of rainfall time series  (Van den Besselaar et al. , 2017; Singh and Xiaosheng, 2019). Similar to the method of  Hariadi et al. (2021) , three gridded daily observational datasets are combined to provide the observed rainfall and an uncertainty estimate. The first dataset is the Southeast Asia Observation (SA-OBS) dataset. SA-OBS is a daily high-resolution land-only observational gridded precipitation and temperature dataset covering the SEA region  (Van den Besselaar et al. , 2017). This dataset is based on the observation data collected for the South Asian Climate Assessment and Dataset (SACA&D) (Van Den  Besselaar et al.
, 2015; Marjuki et al. , 2016). The second dataset is the Asian Precipitation Highly Resolved Observational Data Integration toward Evaluation of Water Resources (APHRODITE) dataset  (Yatagai et al. , 2012). This dataset is based on rain gauge observations across Asia. The third dataset is the Climate Hazards Group Infrared Precipitation with Stations v2. 0 (CHIRPS) dataset  (Funk et al. , 2015). This dataset is based on rain gauge data collected from Food and Agriculture Organization (FAO) and Global Historical Climate Network (GHCN), together with cold cloud duration information from Climate Prediction Center (CPC) and the National Oceanic and Atmospheric Administration's (NOAA) National Climate Data Center (NCDC).
| Model results Our study utilizes two datasets of climate model results. The first dataset consists of results from the Coupled Model Intercomparison Project. This study uses the downscaled version of the CMIP5 results from the Coordinated Downscaling Experiment (CORDEX) SEA simulations. Six global climate models are used. The Centre National de Recherches Météorologiques (CNRM), Commonwealth Science and Industrial Research Organisation (CSIRO), European Community Earth-System (EC-Earth), and Max Planck Institute (MPI) models were downscaled using the RegCM4 model  (Giorgi et al. , 2012)  from CORDEX SEA  (Ngo-Duc et al. , 2017; Supari et al. , 2020; Tangang et al. , 2020). The HadGEM model was downscaled using the regional Weather Research and Forecasting (WRF3. 5) model  (Skamarock et al.
, 2008)  from the Asia-Pacific Economic Cooperation Climate Centre  (Yang, 2012). We will refer to this model generation as CORDEX. The second set of models comes from the High-Resolution Model Intercomparison Project (HighRes-MIP)  (Haarsma et al. , 2016). These models have a spatial resolution comparable to that of the CORDEX dataset and are available from the H2020-founded Primavera project  (Roberts et al. , 2020). This dataset contains two experiments at two resolutions. The first HighResMIP experiment is the historically forced atmosphere run for the period from 1950 to 2014 (HighResSST). This experiment used the daily 0. 25 HadISST2-based dataset for the Sea Surface Temperature (SST) and sea-ice forcing. The second HighResMIP experiment contains coupled historic runs for the period from 1950 to 2014  (Hist-1950).
We use high-resolution and low-resolution datasets from both experiments. The six models that we use from HighResMIP are EC-Earth  (Haarsma et al. , 2020) , MPI  (Müller et al. , 2018) , Had-GEM  (Roberts et al. , 2019) , CMCC  (Cherchi et al. , 2019) , CNRM  (Voldoire et al. , 2019) , and ECMWF  (Roberts et al. , 2018). We used bilinear interpolation to interpolate the model and observation datasets to a uniform grid resolution; the reference resolution is 0. 25. Due to the limitations of the models that are available from CORDEX and HighResMIP, in this study, only four models are available for both model generations; these models are CNRM, EC-Earth, HadGEM, and MPI. CSIRO and GFDL are only available for CORDEX, and CMCC and ECMWF are only available for HighResMIP. Table  1  summarizes the resolutions of the available models.
| Validation methods The Taylor diagram  (Taylor, 2001)  and Kolmogorov-Smirnov (K-S) statistic values are used for the validation of the models. The Taylor diagram used the ensemble mean of the observed climate indices. This is because this analysis focuses on the pattern similarity between the models and observations. The K-S statistic values were calculated individually using the simulated climate indices from the models and the observed climate indices from the three observational datasets (SA-OBS, APHRODITE, and CHIRPS); then, the final K-S values were calculated from the mean value of these individual K-S scores. This step is intended to avoid biases among the three observational datasets. We used the Taylor diagram to compare the climatology of the climate indices for the models and observations.
In terms of R95pTOT, all CORDEX models show median K-S values  We used three gridded precipitation observational datasets in this study to account for the low dispersion of gauge data in SEA. SA-OBS and APHRODITE were developed from rain gauges. SA-OBS, which was developed specifically for the SEA region, has a higher station density compared to APHRODITE, which was developed for the Asia region. However, due to the restriction in the interpolation method, the spatial coverage of SA-OBS is lower than that of APHRODITE over SEA. The CHIRPS dataset is based on a satellite rainfall retrieval algorithm that combines climatology data, satellite precipitation estimates, and an in situ rain gauge. This means that CHIRPS provides more complete coverage over SEA compared to SAO-BS and APHRODITE. However, the amount of rain gauge data used for calibration in CHIRPS is considerably lower than the amount of rain gauge data used in SA-OBS and APHRODITE.
For the extremes, the precipitation value of a grid square should be seen as an area average for that grid square. This means that extremes will be "smeared" out more in datasets with a coarser resolution  (Van den Besselaar et al. , 2017). We determine the similarity of the three gridded precipitation observational datasets using K-S values for the eleven rainfall-related climate indices. We find that the similarity between APHRODITE and CHIRPS is the lowest; they have the highest K-S index in this comparison of observational datasets. SA-OBS and APHRODITE have the highest similarity (Figure  S12 ). This confirms the results of a previous study by  Van den Besselaar et al. (2017) , who found that the similarities between gauge-based datasets are higher than the similarities between gauge-based datasets and satellite-based datasets.
Based on the K-S indices for the observational datasets, we find that the three observational datasets have a low similarity in the area over south China and the southwest coast of Myanmar (Figure  S13 ). The area of low similarity is spread over more of SEA for APHRODITE and CHIRPS. For SA-OBS and APHRODITE, some low-similarity zones are also found in some areas of Indonesia, such as the centre of Sumatra. For SA-OBS and CHIRPS, zones of low similarity were also found in some areas in Vietnam, Cambodia, and Indonesia (Papua Island). The low similarity between the observational datasets for some areas like Papua and Myanmar is caused by the low density of the data available over the area that are used to develop the observation datasets. However, the areas where the observational datasets diverge most are not the areas where the spatial patterns of the model precipitation fields differ most from the mean in the observational datasets.
Nevertheless, this shows the need for the further development of the observational datasets for this region. In addition, the use of multiple observational datasets of the region can reduce the uncertainty. Eleven different rainfall-related climate indices were used to investigate the performance of different generations of climate models. Among the 11 indices, the CORDEX and HighResMIP models are better at simulating rainfall indices that show climatological characteristics (climatic rainfall indices: CDD, CDD5D, CWD, CWD5D, and R95pTOT) and slightly worse at simulating rainfall indices that are more directly related to the rainfall intensity (R10mm, R20mm, Rx1day, Rx5day, and R5day50mm). For the climatic rainfall indices, both the CORDEX and HighResMIP models are better at simulating R95pTOT and worse at simulating CWD compared to the other indices.
Juneng et al. (2016)  found a large positive precipitation bias due to the convection scheme that was used in the RegCM4 configuration. This explains the large precipitation biases in the CORDEX-SEA simulation that were found in previous studies  (Amsal et al. , 2019; Hariadi et al. , 2021; Nguyen-Thi et al. , 2021). Furthermore,  Nguyen et al. (2022)  found more intensity of daily precipitation in CORDEX-SEA than their forcing GCM. They suggest that the RCM setup (e. g. , parameterization scheme) has a more significant role in the model's ability to simulate precipitation compared to its forcing GCM. region is also recommended for the further use of the dataset  (Ratri et al. , 2019; 2021; Trinh-Tuan et al. , 2019; Tangang et al. , 2020). Tangang et al. (2018)  investigated anomalies in four indices of extreme precipitation: the annual total precipitation on wet days (PRCPTOT), the CDD index, the annual number of days on which the daily precipitation exceeded 50 mm (R50mm), and RX1day.
I G U R E 1 Climatological map of the observed annual maximum dry spell length (CDD), annual number of CDDs >5 days (CDD5D), annual maximum wet spell length (CWD), and annual number of CWDs >5 days (CWD5D). The indices represent the mean values of indices obtained from observational datasets (SA-OBS, APHRODITE, and CHIRPS) for the period from 1981 to 2005 [Colour figure can be viewed at wileyonlinelibrary. com].
I G U R E 2 Climatological map of the observed annual number of days on which the precipitation is ≥10 mm (R10mm) and annual number of days on which the precipitation is ≥20 mm (R20mm). The indices represent the mean values of indices obtained from observational datasets (SA-OBS, APHRODITE, and CHIRPS) for the period from 1981 to 2005 [Colour figure can be viewed at wileyonlinelibrary. com] R20mm, R5D50mm, and SDII values, the spatial correlation is high (>0. 50) for most of the models. Based on the normalized SD values of the spatial distributions, the models are close to the observations (0. 8 < normalized SD < 1. 2) in terms of the CDD, CDD5D, CWD5D, and R95pTOT indices.
Tangang et al. (2020) discussed the need to improve the RCM simulation in the future for the CORDEX-SEA model and the need to use the latest CMIP models, which helps to reduce the biases. Bias correction on climate model results dataset over this F I G U R E 1 1 Kolmogorov-Smirnov index map of Hist-1950 HR simulations of (a) climate indices that are related to climatological conditions (CDD, CDD5D, CWD, CWD5D, and R95pTOT) and (b) climate indices that are directly related to the rainfall intensity (R10mm, R20mm, Rx1day, Rx5day, R5day50mm, and SDII) [Colour figure can be viewed at wileyonlinelibrary. com].
ACKNOWLEDGEMENTS The first author received funding from the  Indonesia Endowment Fund for Education (LPDP)  ( S-353/ LPDP. 3/2019 ) for his PhD program. The second author acknowledges the support of the  Royal Netherlands Embassy in Jakarta, Indonesia , through a  Joint Cooperation Programme between Dutch and Indonesian research institutes. We are grateful for the SA-OBS dataset and the data providers who are part of the  SACA&D  project (http://sacad. database. bmkg. go. id/). The HighResMIP simulations have been made available through the  PRI-MAVERA  project. This project received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 641727 , which also provided support to authors  Marie-Pierre Moine ,  Alessio Bellucci ,  Retish Senan ,  Etienne Tourigny , and  Dian Putrasahan.
F I G U R E 1 Climatological map of the observed annual maximum dry spell length (CDD), annual number of CDDs >5 days (CDD5D), annual maximum wet spell length (CWD), and annual number of CWDs >5 days (CWD5D). The indices represent the mean values of indices obtained from observational datasets (SA-OBS, APHRODITE, and CHIRPS) for the period from 1981 to 2005 [Colour figure can be viewed at wileyonlinelibrary. com].
F I G U R E 2 Climatological map of the observed annual number of days on which the precipitation is ≥10 mm (R10mm) and annual number of days on which the precipitation is ≥20 mm (R20mm). The indices represent the mean values of indices obtained from observational datasets (SA-OBS, APHRODITE, and CHIRPS) for the period from 1981 to 2005 [Colour figure can be viewed at wileyonlinelibrary. com] R20mm, R5D50mm, and SDII values, the spatial correlation is high (>0. 50) for most of the models. Based on the normalized SD values of the spatial distributions, the models are close to the observations (0. 8 < normalized SD < 1. 2) in terms of the CDD, CDD5D, CWD5D, and R95pTOT indices.
METHODOLOGY In this section, we detail the general methodology followed during this work from the establishment of an acquisition protocol to the extraction of BIM objects. The acquisition protocol addresses four cases of scenes including a single wall, a corridor, a room (with different levels of detail) and two adjacent rooms. According to each scene, several experimentations were performed to find out the optimal way for accurate data acquisition. Next, an image processing was performed for the room (the least cluttered) and the corridor which were the most problematic. Two paths were carried out: one included filtering the depth images and another one that worked with raw images, in order to test the efficacy of depth images filtering. Then, we performed a 3D reconstruction followed by point cloud subsampling and denoising.
The second sequence lasted height days, from 26 July to 02 August 2020, and recorded the emergence of the first 2020 heat wave (HW) felt in Europe. These datasets contain intense temporal and vertical temperature gradients. They present an opportunity to compare PRR lidar temperature to the level 2 products of IASI  (Schlüssel et al. , 2005)  and to check the coherence of operational modelling with the lidar measurements. Hence, we consider products of the European Centre for Medium-Range Weather Forecasts (ECMWF) IFS forecast outputs. We also study the improvement in terms of temperature following the reanalysis process via the ERA5 reanalysis product  (Hersbach et al. , 2020) , as previously performed for atmospheric water vapour  (Chazette, Flamant, et al. , 2016). The paper is structured as follows.
| The winter 2018 event In Figure  3a , lidar-derived temperature is shown in colour from 22 to 27 February 2018 up to 3 km AMSL. This 5-day period contains strong vertical and temporal temperature gradients. Several features can be detailed based on these observations. First, the diurnal cycle of the temperature in the PBL is clearly noticeable. The F I G U R E 2 Diagram of the production procedures for the IFS operational forecast outputs. Two distinct products are available: A forecast run launched at t 0 = 0600 UTC and one at t 0 = 1800 UTC, both for 18 h/forecast steps (green). At the same time, assimilation windows occur every 12 h for a duration of 12 h development of the PBL is decreasing in altitude during this period subsequently to the settlement of a highpressure system triggering a cold wave perceptible at the end of the dataset (purple area).
In the middle of the dataset, just above the PBL top, a warm air mass is advected. This atmospheric situation leads to a strong temperature inversion (+10 C/300 m) at ~1-1. 5 km AMSL on 24 February 2018. On the night of 25 February to 26 February, the temperature at 2 km drops by 15 C with a Siberian air mass surging over Western Europe. The resulting vertical gradient of temperature of À10 C/ km contrasts with the À5 C/km gradient that occurred during the un-disturbed period of 22 February to 23 February, in line with seasonal norms. It is worthwhile to note that although the temperature field is disturbed and unstable (especially in the middle of the dataset) the whole period remains cloud-free. This clear sky may result from a particularly dry air and a subsidence induced by the anticyclonic conditions prevailing over the Paris area.
| The July 2020 event As for the previous dataset, in Figure  4a , lidar-derived temperature is shown in colour from 26 July to 02 August 2020 (8 days) up to 3 km AMSL. More clouds perturbed the lidar observations than during the winter event and even some rains are highlighted where the white area connects with the blind area of the lidar. In this case too, one can see the diurnal cycle of the PBL. From this summer typical cycle, with afternoon temperature up to ~25 C, two anomalies stand out: a first little heat stroke was felt on 27 July in the afternoon with surface temperature above 30 C and finally a short but intense heatwave from 30 July to 1 August. On this last event, surface temperatures in the Parisian basin were above 35 C and approached 40 C on 31 July, without any night cooling.
| Temporal averaging Drawing conclusions from profile-to-profile comparisons can be a delicate task when the datasets compared have different resolutions, and in particular different time steps. Such a comparison with lidar data does not necessitate the high temporal resolution associated with this instrument and a decrease of its temporal resolution using a low-pass filter can enhance its altitude range and accuracy. However, depending on the averaging time window considered, discrepancies can arise from the natural variability within this time window. In order to reduce the natural variability effect on our comparisons, we have defined a threshold on the statistical indicator ξ used by  Behrendt et al. (2015)  and defined by: ξ τ ð Þ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi < T t þ τ ð ÞÀT t ð Þ ð Þ 2 > t q À ξ 0 , ð1Þ where τ is the delay, t the time, T the lidar-derived temperature and ξ 0 the value of ξ for a null delay.
5 and 1 km. The indicators used in Figure  8  are averaged to build Figure  9  (dashed lines), showing the comparison between lidar-derived temperature and the forecasts (red dotted line). The variation interval is materialized by the coloured areas (in light blue). In the same figure, the comparison between reanalysis and WALI is plotted in black. The forecasts and the reanalysis are rather consistent in their comparison with the lidar-derived temperature. A better correlation is found for both the CW and HW in the PBL, up to 1. 5 km AMSL. The MB and RMSE decrease also in this region but only for the HW case. It seems that the overestimation highlighted in Figure  8  might be mitigated through data assimilation. However, for the CW case, the MB (Figure  9b ) is slightly greater for the reanalysis in the PBL and lower above 1.
| Statistical comparison The COR, MB and centred RMSE profiles between IASI and WALI are plotted in Figure  10. The statistics on the 26 available profiles during the CW episode are shown in Figure  10a  where IASI-derived vertical profiles of temperature are compared with the concomitant lidar datasets up to 3 km AMSL. In Figure  10b , the same computation is made for the 48 profiles of the HW measurement period. During the winter case (Figure  10a ), the correlation drops from ~0. 95 to ~0. 7 below 1. 5 km AMSL, as well as the MB, which is within the [À1 C; +1 C] interval above F I G U R E 1 0 Profiles of correlation (COR, red), mean bias (MB, blue) and centred root-mean-square error (RMSE, green) between IASI temperature profiles and corresponding lidar measurements: (a) for all the 26 lidar-IASI profiles of the cold wave period (CW), (b) for all the 48 lidar-IASI profiles of the heatwave period (HW) 1.
5 C) and a little decrease of the COR (~0. 97 to ~0. 94). Above this PBL top altitude, the bias is centred around 0 C and below it reaches 2 C near ground, associated with a correlation still larger than 0. 85. Despite the fact that the larger number available profiles during the HW (near twice as for the CW) could tend to smooth the statistical indicators, the operational temperature profiles derived from IASI seem to better apprehend the low troposphere structure when the PBL is more developed and presents smaller temperature inversions. As demonstrated, IASI does not capture sharp vertical temperature gradients, its profiles being too smooth. However, in the FT, where such gradients are not expected, WALI and IASI data sets present a good match. It is worth noting that the IASI-lidar comparison results regarding bias are higher than expected, knowing the results of  Pougatchev et al.
It should be noted that cloudy conditions limited the lidar range to ~2 km. The IASI retrievals could have been affected by these conditions too. On the same panel (Figure  11e ), one can also notice a temperature inversion modelled by ERA5 close to the surface, which seems to not exist according to lidar observations. This feature can also be noted in Figure  4. While the ERA5 and WALI data are well related, this case is remarkable and astonishingly associated with the warm outbreak. The other cases of Figure  11  agree very well except near the PBL top, with temperature inversions that are smoother in ERA5. This smoother aspect recalls the results highlighted on the IASI profiles, which are too smooth to capture low-temperature inversions. It raises the question of a circulatory issue as ERA5 assimilates IASI radiances.
| CONCLUSION WALI's features allow temperature measurements with an uncertainty of less than 1 C within both the and the lower/middle free troposphere (FT) (~3/6 km from ground for day/night), with a vertical resolution of 50 m and a temporal resolution of 20 min. Such capabilities meet the WMO observational requirements for the improvement of high-resolution forecasting and near-real-time forecasting. Hence, the lidar characteristics allow trustworthy and continuous temperature measurements in the PBL and the low troposphere with high temporal and vertical resolutions. These two case studies tend to demonstrate that a PPR lidar can be a reference tool in order to evaluate the representativeness of ETEs as reproduced by operational modelling and IASI spaceborne observation. The growing reliability of lidar systems and especially PPR lidars is about to allow to gather numerous and continuous datasets to serve as observational references and enter operational monitoring networks.
The ability of WALI to continuously trace the diurnal cycle of the PBL fills an observational gap and allows (i) to complement spaceborne instruments like IASI and (ii) to provide a strong constraint in the lower layers for meteorological models; in particular during ETEs. This capacity could benefit future spaceborne missions such as IASI-NG  (Crevoisier et al. , 2014)  and the new generation of numerical forecast models  (Brousseau et al. , 2016)  through data assimilation and parameterization validation  (Flamant et al. , 2021).