dataesr/scientific-paragraphs-categorization huggingface dataset:
RGD (Rainfall Gridded Datasets) 3. 2. 1. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) PERSIANN-CDR is a multi-satellite product that provides daily precipitation estimates with a spatial resolution of 0. 25 • from 1983 onwards  (Ashouri et al. , 2015). The PERSIANN artificial neural model is pre-trained with NCEP (National Centers for Environmental Prediction) stage IV hourly precipitation radar data. Then, PERSIANN-CDR is generated by the PERSIANN algorithm using GridSat-B1 infrared data  Knapp (2008) ). The final product is adjusted with the monthly GPCP (Global Precipitation Climatology Project) product to ensure that both datasets are consistent on a monthly scale.
Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) CHIRPS precipitation estimates (version 2) are available from 1981 onwards with a spatial and temporal resolution of 0. 05 • /24 h  (Funk et al. , 2015). This database uses monthly precipitation climatology CHPClim (Climate Hazards Group Precipitation Climatology), quasi-global geostationary thermal infrared satellite observations, Tropical Rainfall Measuring Mission's (TRMM) 3B42 product, atmospheric model rainfall fields from NOAA CFS (Climate Forecast System), and precipitation observations from different sources, including national or regional meteorological services. The CHIRPS production requires two steps. First, pentad (five-day) rainfall estimates are created from CCD (Cold Cloud Duration) which are obtained from regression models, and calibrated by using TRMM 3B42 pentad precipitation; these estimates are expressed as a percentage of normal precipitation by dividing the estimated values for regression models by their long-term averages (this outcome is names CHIRP).
Multi-Source Weighted-Ensemble Precipitation (MSWEP) The second version of MSWEP, as described by  Beck et al. (2017)  is a global gridded dataset for the period 1979-2017 with a spatiotemporal resolution of 0. 1 • /3 h. This dataset is derived by merging rainfall estimates based on rain gauges coming from different databases: [WorldClim  (Fick and Hijmans, 2017) , GHCN-D  (Menne et al. , 2012) , GSOD (https://data. noaa. gov), and national databases from different country], different SRE: [CMORPH  (Joyce et al. , 2004) , GridSat  (Knapp et al. , 2011) , GSMaP  (Ushio et al. , 2009) , and TMPA 3B42RT  (Huffman et al. , 2007) ] and reanalysis: [ERA-Interim  (Dee et al. , 2011)  and JRA-55  (Kobayashi et al. , 2015) ]. To build MSWEP,  Beck et al. (2019)  was evaluated the performance of the gridded datasets using the correlation coefficient of the gridded data with the 3-day mean rainfall data.
, 2014). The GPM IMERG products, available from 2000 onwards, offer a relatively fine spatial resolution of 0. 1 • × 0. 1 • and high temporal resolution of 30 min. The GPM IMERG Day 1 Final Run V6 data is used in this study. The Final Run product introduces GPCC rainfall dataset  (Schneider et al. , 2014)  for bias correction, generally considered to be more accurate results than the near real-time products (Early and Late Run), and is thus widely used in hydrology and climate researches  (Huffman et al. , 2015). Table  A. 1 (in appendix) briefly lists the main characteristics of the selected gridded rainfall data -PERSIANN-CDR, CHIRPS, MSWEP, ERA-5 and GPM IMERG.
Point-to-pixel analysis A point-to-pixel analysis was applied to compare the time series of the reference rain gauge stations (i. e. the points) with the corresponding grid cell values (i. e. the pixels). This approach has been chosen over the pixel-to-pixel methodology  (Saemian et al. , 2021)  given the sparse rain gauges density over the studied region. Thus, the application of a pixel-to-pixel methodology requires to have a gridded reference dataset or to spatially interpolate reference rain gauges data. In the Lesser Antilles, particularly in the Martinique and Guadeloupe French Islands (cf. Figure A. 3 in appendix), more than one rain gauge is counted on the same RGD pixel. For each RGD pixel containing more than one rain gauges, an area-weighted average of the considered rain gauge series has been performed using the Thiessen polygons (e.
RGD upscaling The four RGD considered have different spatial resolutions. The point-to-pixel analysis has been performed (i) to the original RGD resolution, and also (ii) at a similar resolution of 0. 25 • , which is the resolution of the ERA-5 and PERSIANN-CDR coarser RGD considered. Thus, an upscaling of the CHIRPS, and MSWEP datasets has been performed to a new spatial resolution of 0. 25 • by using bilinear interpolation  (Baez-Villanueva et al. , 2018).
Use of two different rain gauges sets The MSWEP RGD has been produced considering different ground datasets, including several rain gauges (see subsection 3. 1. 3). Thus, this bias-corrected dataset is expected to better perform than the RGD that do not use rain gauge stations (e. g. Baez-Villanueva et al. , 2018). To ensure the independence of the rain gauge stations used as reference in our study and those used during the RGD construction, we calculated the performances of the RGD on two different rain gauges sets. First, 103 rain gauges are considered excluding the 24 rain gauges identified as positioned within a 1-km radius from rain gauges mentioned as used within the MSWEP V2. 8 datasets. Then, all the 127 rain gauges are considered. The excluded rain gauges are identified in the Figure A.
Rainfall seasonality Annual rainfall cycles are calculated with the 15-day aggregated rainfall data for the 103 reference rain gauges and on the RGD pixels corresponding to these reference data. Below the 15-day aggregation, the annual rainfall patterns are noisy and of little interest. The finest timescale relevant to present the annual rainfall pattern is the 15-day aggregation. The result is averaged over the Greater and the Lesser Antilles to highlight the annual rainfall pattern and to assess the ability of the RGD to reproduce the rainfall seasonality in the area study.
Heavy rainfall statistics The ratios of the 90th and 99th rainfall percentiles and the maximum annual rainfall of each reference rain gauge to the corresponding RGD are calculated to estimate the ability of the RGD to assess heavy rainfall in the Greater and Lesser Antilles. A ratio greater than 1 indicates an overestimation of RGD and a ratio below 1 indicates an underestimation of RGD. Lastly, the summary statistics such as average, maximum and standard deviation were calculated for selected rainfall percentiles of the rainfall datasets.
RGD general performance on a daily timescale Fig. 3  summarizes the performance of the RGD in estimating daily rainfall in the Greater and Lesser Antilles through the KGE criterion and its components (correlation coefficient, dispersion and bias) and the mean square error, using the 103 considered rain gauges. Overall, the performance over the Greater and Lesser Antilles are not significantly different. MSWEP is the most correlated with the reference data with a correlation coefficient of 0. 5 in the Greater Antilles and slightly less in the Lesser Antilles. MSWEP has a good dispersion and a low bias, with α and β coefficients close to 1. These relatively good values of R, α and β coefficients combine to produce a relatively good KGE for MSWEP, with values between 0. 45 and 0. 5 in the Greater and Lesser Antilles.
ERA-5 is the second most correlated RGD with the reference data with correlation coefficients between 0. 35 and 0. 4 but that, the dispersion around the mean value of ERA-5 is low (α near to 0. 5) and a priori the estimates of ERA-5 are relatively uniform and are unable to capture heavy rainfall well, as shown in Fig. 8  and Fig. 9. ERA-5 underestimates the reference data with biases (β) between 0. 65 and 0. 75. This weakness in the dispersion around the mean value and underestimation of the reference rainfall makes ERA-5 the worst performing RGD, with KGE between 0. 1 and 0. 15. PERSIANN-CDR has a better KGE score than CHIRPS in the Lesser Antilles, mainly due to a better correlation of PERSIANN-CDR compared to CHIRPS. However, in the Greater Antilles, the R, α and β coefficients and thus the KGE scores of PERSIANN-CDR and CHIRPS are similar.
The annual rainfall cycle The reference rainfall series as well as the corresponding RGD pixels were aggregated over a 15-day period, and the annual average rainfall cycle was calculated for each aggregated data series. Then, the annual rainfall cycles were averaged for the Greater and Lesser Antilles (see Fig. 7 ). The results show that the average annual rainfall cycle in the Greater Antilles is divided into 4 seasons with two rainfall peaks during the year. Winter dry season (WDS) from late November to mid-March with an average rainfall of 3 mm/d, early rainy season (ERS) from mid-March to mid-May with a peak rainfall of about 7 mm/d, mid-summer drought (MSD) from mid-May to early August which is a less rainy season interspersed between the two heavy rainy seasons with a rainfall of a little over 4 mm/d and late rainy season (LRS) from early August to late November with an average rainfall of 7 mm/d.
These boxplots offer a summary of the distribution of the scores calculated on each of the 103 considered rain gauges: the center line indicates the median, the red point indicates the mean, the edges of the rectangle are the 1st quartile and the 3rd quartile, the ends of the lines define a threshold that cannot exceed 1. 5 times the interquartile range, and the black points, when they exist, are points that are outside the threshold of the box. Greater Antilles, but all the RGD systematically overestimate the reference data in the north of Haiti and south-east of Cuba, and sometimes in Jamaica. Although RGD tend to underestimate the 90th percentile rainfall, MSWEP and CHIRPS have a good ability to estimate the 90th percentile rainfall (r close to 1) in the eastern part of the Greater Antilles and in the northern part of the Lesser Antilles.
5 times the interquartile range, and the black points, when they exist, are points that are outside the threshold of the box. ERA-5 underestimates by half the percentiles of heavy rainfall in the Greater and Lesser Antilles and is the least efficient of the four RGD. The 5 %, 25 %, 50 %, 75 % and 95 % rainfall percentiles are calculated for the 103 considered rain gauges and corresponding RGD pixels in the Greater and Lesser Antilles. The mean, maximum and standard deviation values of each rainfall database used are presented in Table  A. 3 in appendix. The results show that on average more than 75 % of the days during our study period had rainfall less than 5 mm/d and these low rainfall events are well captured by ERA-5 and PERSIANN-CDR. In contrast, CHIRPS, MSWEP and PERSIANN-CDR tend to capture the heavy rainfall percentiles, with CHIRPS overestimating heavy rainfall percentiles and MSWEP and PERSIANN-CDR underestimating them.
Overall, the upscaled RGD have a better ability to detect rainy days and dry days, represented by a higher POD and CSI, despite the fact that the FAR increased for CHIRPS in the Greater Antilles. Fig. 9. Ratio of 90th and 99th rainfall percentiles and annual maximum of the RGD to the reference rainfall data in the Greater Antilles and Lesser Antilles. These boxplots offer a summary of the distribution of the scores calculated on each of the 103 considered rain gauges: the center line indicates the median, the red point indicates the mean, the edges of the rectangle are the 1st quartile and the 3rd quartile, the ends of the lines define a threshold that cannot exceed 1. 5 times the interquartile range, and the black points, when they exist, are points that are outside the threshold of the box.
Comparison of RGD performances The performance of the RGD over the studied region reflects the philosophy behind their design. MSWEP was designed for operational research in hydrology to improve our understanding of hydrological processes and enhance hydrological model performance  (Beck et al. , 2019). Thus, MSWEP has been produced by merging gridded rainfall data and daily rainfall data. Therefore, MSWEP performs well in most of the statistical metrics considered. CHIRPS is designed for monitoring extremes, which is why CHIRPS gives good estimates of heavy rainfall percentiles over the studied region. The performance of PERSIANN-CDR is poor over our study area. This poor performance of PERSIANN-CDR might be due to the data used to construct precipitation datasets. PERSIANN-CDR has used an algorithm based on artificial neural networks with trained by the stage IV radar data but this radar data itself is biased  (Habib et al.
Conclusion In this study, the performance of five Rainfall Gridded Datasets (RGD) -namely MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 and GPM  IMERG were evaluated using 103 rain gauges in the Lesser and Greater Antilles, with the aim of highlighting the qualities and shortcomings of these RGD on a daily timescale and to guide researchers in the choice of RGD to use for hydrometeorological applications in this study area. To our knowledge, this is the first work done on RGD data on daily timescale in this region. For hydrological applications in the Greater and Lesser Antilles or on a watershed scale, the use of MSWEP is recommended due to its high KGE score and high spatio-temporal resolution (0. 1 • and 3 h). On the other hand, CHIRPS, ERA-5, GPM IMERG and PERSIANN-CDR are not recommended due to their low KGE score and/or coarse spatial resolution, which are not appropriate for the Greater and Lesser Antilles countries.
Acknowledgements The authors thank all weather services that provided rainfall data. In particular,  Météo-France  for rainfall data from Guadeloupe and  Martinique ,  UHM  for rainfall data from  Haiti, InsMet  for rainfall data from Cuba, ONAMET for rainfall data from Dominican Republic, CIMH for rainfall data from several Caribbean islands, MetService for rainfall data from Jamaica and  NOAA  for rainfall data from Puerto Rico. Special acknowledgement to  Cedric Van Meerbeeck  and  Adrian Trotman  who helped us to find the contacts of some weather services in the Caribbean and to  Hylke E. Beck  for the MSWEP data. The PhD thesis of Ralph BATHELEMY is funded by the  French Embassy in Haiti and Institut de Recherche pour le Développement  (the  French National Research Institute for Sustainable Development).
Introduction An optical Imaging Science Subsystem (ISS)  (Porco et al. , 2004)  mounted on the Cassini orbiter has taken more than 440,000 images. These images have become an important resource for the astrometry of natural satellites. For example,  Cooper et al. (2006)  reduced ISS images of inner Jovian satellites;  Cooper et al. (2014)  performed mutual-event astrometry of ISS images of the mid-sized Saturnian satellites. A selection of ISS images of some main icy Saturnian satellites has been reduced by  Tajeddine et al. (2013 Tajeddine et al. ( , 2015) )  and  Zhang et al. (2018a,b). In 2018,  Cooper et al. (2018)  released a software package to the community, Caviar (https://www. imcce. fr/recherche/equipes/pegase/caviar), which is dedicated to the astrometric reduction of Cassini ISS images.
Observations We gathered all ISS images of Phoebe taken between 2004 and 2017 from the Planetary Data System (https://pds-imaging. jpl. nasa. gov/). The total number of Phoebe images is 1880, including 1651 images taken by the ISS Narrow Angle Camera (NAC) and 229 images taken by the Wide Angle Camera (WAC). Through careful checking, we discarded all WAC images because either Phoebe's signal is very weak, or there are no reference stars in them. In all NAC images of Phoebe, firstly, those reduced by  Tajeddine et al. (2015)  have been excluded because they have been measured and the centring technique used in this paper is not suitable for them. Then, some images with poor quality due to the short exposure duration, noise corruption, scattered light and so on have been discarded. Finally, a total of 834 ISS NAC images of Phoebe were successfully reduced.
Data reduction We used the dedicated package of the astrometry of ISS image, Caviar, to reduce all ISS images of Phoebe. The whole reduction procedure includes three main steps: pointing correction, target centring and target centre's conversion. The details are given below. (1) In pointing correction, the nominated pointing of the ISS camera is corrected by matching image stars with catalogue stars. At first, we detect all possible star-like objects in an ISS image and compute their centres by the modified moment method. They are referred to as image stars. Secondly, all image stars' coordinates are corrected by the geometric distortion model given in Owen  (2003). Thirdly, all possible stars in the field of view (FOV) of NAC are extracted from Gaia Early Data Release 3 (Gaia EDR3)  (Gaia Collaboration, 2016 , 2021)  according to the nominated pointing of the camera, and their image coordinates are derived from their ICRS celestial coordinates in Gaia under the help of the NAIF (Navigation and Ancillary Information Facility) SPICE library  (Acton (1996) ;  Acton et al.
Filters Of all 834 observations, 351 images were taken with CL1 and CL2, and the remaining images were taken with a few other filter combinations. According to whether the images of Phoebe were taken with (CL1, CL2), we divided the observation positions of Phoebe into two classes: (CL1, CL2) and non (CL1, CL2). The relevant statistics are displayed in Table  5. The table shows that the data from non (CL1, CL2) has a slightly greater standard deviation than those from (CL1, CL2). It indicates non (CL1, CL2) probably produce slightly bigger errors. It should be noted that the exposure duration of an image with non (CL1, CL2) was often longer than that with (CL1, CL2). It probably increased the positional error. But it is not entirely clear because the observation conditions varied during the observation period, and the positional uncertainty can be caused by many conditions, not only filter combinations.
Acknowledgements This work has been partly supported by the  Joint Research Fund in Astronomy  under cooperative agreement between the  National Natural Science Foundation of China and Chinese Academy of Sciences  (No. U2031104 ), and  National Natural Science Foundation of China  (No. 11873026 ). This work has also been supported by the  European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  263466  for the  FP7-ESPaCE project. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (htt ps://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
However, this poses tremendous challenges regarding equipment sourcing, equipment cost, and software integration. When a functional platform is established, upgrade and maintenance of the corresponding hardware and software are also highly challenging. Accordingly, very few examples have been reported to date. For instance, only two groups have demonstrated automated cytometry and reactive optogenetic control of bacteria  14  or yeast  7  cultures, with setups limited to either a single continuous culture  14  or multiple cultures with limited continuous culture capabilities  7. One group has also demonstrated automated microscopy and reactive optogenetic control of a single yeast continuous culture  15. Here, we present ReacSight, a generic and flexible strategy to enhance bioreactor arrays for automated measurements and reactive experiment control.
Results Measurement automation, platform software integration, and reactive experiment control with ReacSight. The ReacSight strategy to enhance bioreactor arrays for automated measurements and reactive experiment control combines hardware and software elements in a flexible and standardized manner (Fig. 1 , Supplementary Note 1). A pipetting robot is used to establish, in a generic fashion, a physical link between any bioreactor array and any plate-based measurement device (Fig. 1a ). Bioreactor culture samples are sent to the pipetting robot through pump-controlled sampling lines attached to the robot arm (sampling). A key advantage of using a pipetting robot is that diverse treatment steps can be automatically performed on culture samples before measurement (treatment). Samples are then transferred to the measurement device by the pipetting robot (loading).
1b ). First, programmatic control of all instruments of the platform (bioreactors, pipetting robot, measurement device) is required. Second, a single computer should communicate with all instruments to orchestrate the whole experiment. ReacSight combines the versatility and power of the Python programming language with the genericity and scalability of the Flask web application framework to address both challenges. Indeed, Python is ideally suited to easily build APIs to control various instruments: there exist well-established, open-source libraries for the control of micro-controllers (such as Arduinos), and even for the 'clicking'-based control of GUI-only software driving closed-source instruments lacking APIs (pyautogui). Importantly, the open-source, low-cost pipetting robot OT-2 (Opentrons) is shipped with a native Python API.
Hamilton robots can also be controlled with a Python API  16. Flask can then be used to expose all instrument APIs for simple access over the local network. The task of orchestrating the control of multiple instruments from a single computer is then essentially reduced to the simple task of sending HTTP requests, for example using the Python module requests. HTTP requests also enable user-friendly communication from the experiment to remote users using the community-level digital distribution platform Discord. This versatile instrument control architecture is a key component of ReacSight. Two other key components of ReacSight are (1) a generic object-oriented implementation of events (if this happens, do this) to facilitate reactive experiment control and (2) an exhaustive logging of all instrument operations into a single log file.
ReacSight software as well as source files for hardware pieces are made openly available in the ReacSight Git repository. Reactive optogenetic control and single-cell resolved characterization of yeast continuous cultures. Our first application of the ReacSight strategy is motivated by yeast synthetic biology applications. In this context, it is critical to accurately control synthetic circuits and to measure their output in well-defined environmental conditions and with sufficient temporal resolution and scope. Optogenetics provides an excellent way to control synthetic circuits, and bioreactor-enabled continuous cultures are ideal to exert tight control over environmental conditions for long durations. To measure circuit output in single cells, cytometry provides both high sensitivity and high throughput.
We thus resorted to the ReacSight strategy to assemble a fully automated experimental platform enabling reactive optogenetic control and single-cell resolved characterization of yeast continuous cultures, using a benchtop cytometer as a measurement device (Fig. 2a ). Detailed information on the platform hardware and software is provided in Supplementary Note 2, and we discuss here only key elements. Eight reactors are connected to the pipetting robot, meaning that each time point fills one column of a sampling plate. While three columns of the cytometer input plate are accessible to the robot, we use only one column, washed extensively by the robot to achieve less than 0. 2% carry-over, as validated using beads (Supplementary Fig. 8 ). We typically fit two tip boxes and two sampling plates (2 × 96 = 192 samples) on the robot deck, therefore enabling 24 time points for each of the eight reactors without any human intervention.