dataesr/scientific-paragraphs-categorization huggingface dataset:
Monthly P-dataset estimate assessment The monthly amounts were computed for only months with more than 80% of common daily records for all datasets (reference and P-datasets). The accuracy of monthly P-dataset estimates was assessed using a quantitative statistical analysis based on the modified Kling-Gupta Efficiency (KGE), an objective function combining correlation ( ), bias ( ), and variability ( ) components  (Gupta et al. , 2009; Kling et al. , 2012)  (Eq. 1). We used KGE because water resource management requires reliable representation of precipitation temporal dynamics (measured by ) and volume (measured by and ): = 1 -( -1) + ( -1) + ( -1) , (1) where represents the Pearson coefficient (Eq. 2), is the ratio between the mean observed and predicted precipitation (Eq. 2), and is the ratio of the estimated and observed coefficients of variation (eq.
3): = 1 ( -) * ( -) * , (2) = , (3) = ⁄ ⁄ , (4) where and are the distribution mean and standard deviation, respectively; and s and o indicate the estimate and reference, respectively. KGE, , , and have their optimum at unity. The analysis was performed considering all months of 2000-2003 and the wet and dry seasons months separately. For each grid-cell, the wet and dry seasons were selected according to their corresponding climatic zone (Fig. 1 ). The Sahel seasonality was applied for the grid-cells located up to latitude 16° N. The values of KGE, , , and were computed at each grid-cell location to observe the P-dataset reliability over space, and their median values was used to observe that at the regional scale. Considering the important gaps over space and time for SM2Rain-CCI v. 2 (Fig.
Daily P-dataset estimate assessment The daily precipitation estimates were assessed on the basis of on both quantitative and categorical statistical metrics. The quantitative analysis relied on the median KGE, , , and obtained from the 187 considered grid-cells for all days and the wet and dry seasons days, separately, and from the 79 (183) grid-cells for SM2Rain-CCI v. 2 (GPCC v. 7). The categorical statistics were used to measure the P-dataset capacity for detecting the daily precipitation events. Daily precipitation events are considered as discrete values with only two observable cases: rainy or not rainy days. A rainy day was considered when the precipitation amount was greater than or equal to a prescribed threshold (mm. day -1 ). Four cases were possible (Table  2 ). Based on this characterisation, the Heidke Skill Score (HSS) (Eq.
4) evaluates the P-dataset ability for detecting precipitation events in comparison with a random based prediction. No precipitation c d !"" = 2 * ($ * % -& * ') (($ + ') * (' + %) + ($ + &) * (& + %)) (5) The HSS values range from -∞ to 1 with a perfect score of 1 and negaSve values indicaSng that random based prediction outperforms the P-dataset one. The mean HSS value was computed from those obtained for all of the considered grid-cells for threshold values ranging from 0 to 25 mm. day -1 with a 1 mm. day -1 increment. This consideration was used to assess the P-dataset performance based on light to heavy daily precipitation events. Finally, using a 1mm. day -1 , the HSS value was computed at each grid-cell location to observe the P-dataset reliability over space.
P-dataset assessment at the monthly time step With negative KGE values, three P-datasets (CMORPH-Raw v. 1, TMPA-RT v. 7, and PERSIANN-RT), were unable to represent the regional monthly precipitation (Fig. 3 ). Interestingly, their adjusted versions, CMORPH-BLD v. 1, TMPA-Adj v. 7, and PERSIANN-CDR, respectively, performed much better with KGE greater than 0. 8, correlation better than 0. 9, and bias and variability close to the optimum values (1). The same results were shown for CHIRP v. 2, GSMaP-RT v. 6, and JRA-55, which were systematically outperformed by their corresponding adjusted versions (CHIRPS v. 2, GSMaP-Adj v. 6, and JRA-55 Adj, respectively). In a general way, all P-datasets using gauges-based information present higher KGE than the others. The P-datasets developed for the African continent, TAMSAT v.
3 and ARC-2, did not outperform the global scale P-datasets. However, the TAMSAT v. 3 reliability was very close to that of the other P-datasets (KGE = 0. 8). The P-dataset performance expressed as KGE varied seasonally. P-datasets TAMSAT v. 3, JRA-55 Adj, PERSIANN-Adj, ARC-2, GSMaP-RT v. 6, and GPCC v. 7 were more effective during the wet season, and CMORPH-BLD v. 1, MERRA-2, GSMaP-Adj v. 6, CPC v. 1, and ERA-Interim had better performance during the dry season. However, the most effective P-datasets, CHIRPS v. 2, TMPA-Adj v. 7, WFDEI, PERSIANN-CDR, and MSWEP v. 2. 2, performed similarly for both wet and dry seasons. Interestingly, all P-datasets presented higher correlation coefficient and bias values during the dry season. With respect to the variability ratio, no clear seasonal trend was observed for the different Pdatasets.
Most of the P-datasets were well correlated to the reference, with correlation better than 0. 8 (Fig. 5 ). The adjusted version systematically presented higher correlation values, with MSWEP v. 2. 2 presenting the highest number of grid-cells with correlation better than 0. 9 and only one grid-cell with correlation worse than 0. 7. Interestingly, CHIRPS presented the lowest correlation score over the northern very arid region, with correlation worse than 0. 7 (Fig. 5 ). Arrows are used to highlight the potential benefit of using gauge-based information. Blue and black colours are used to highlight P-datasets using or not using gauge-based information, respectively, and italic font is used for P-datasets available in NRT latency of one to three days. The P-datasets without gauge-based information presented higher bias (Fig.
6 ). PERSIANN-RT highly overestimate precipitation throughout the region (bias > 1. 55). The bias decreased in the postadjusted version (PERSIANN-Adj) with acceptable bias estimates (1. 15 < bias < 1. 35) over many gridcells Similar results were observed for PERSIANN-CDR. CMORPH-Raw v. 1 and TMPA-RT v. 7 presented similar bias distributions, from overestimation to underestimation in the northern arid and southern humid regions, respectively. TMPA-RT gauge adjustment was highly successful, with most of the TMPA-Adj v. 7 grid-cells presenting acceptable bias values at 0. 85 < bias < 1. 15. CPC v. 1 strongly underestimates precipitation over the western region. This bias spread for all P-datasets using CPC v. 1 for their adjustement process (CMORPH-CRT and BLD v. 1, ARC-2, GSMaP-Adj v. 6 and MERRA-2).
Interestingly, the precipitation adjustment applied on GSMaP-RT v. 6 increased the bias on GSMaP-Adj v. 6. WFDEI, TMPA-Adj v. 7, and CHIRPS v. 2 presented less-biased precipitation estimates with reasonable bias values of 0. 85 < bias < 1. 15 in most of the considered grid-cells. 20 Arrows are used to highlight the potential benefit of using gauge-based information. Blue and black colours are used to highlight P-datasets using or not using gauge-based information, respectively, and italic font is used for P-datasets available in NRT latency of one to three days. Regarding the variability ratio distribution, the efficiency of using gauge-based information to retrieve the precipitation estimates was obvious when comparing PERSIANN-RT, TMPA-RT v. 7, and CMORPH-Raw v. 1 with their post-adjusted versions (Fig.
7 ). The non-adjusted products CMORPH-RAW v. 1, and TMPA-RT v. 7 strongly overestimated the precipitation variability in the majority of gridcells with variability ratios better than 1. 25. To the contrary, PERSIANN-RT strongly underestimated the precipitation variability in most grid-cells, with a variability ratio worse than 0. 85. However, when considering JRA-55 (JRA-55 Adj) and CHIRP v. 2 (CHIRPS v. 2), the use of gauge-based information did not significantly enhance the variability ratio. Finally, the two African P-datasets underestimated the precipitation variability, over most of the grid-cells (variability ratios < 0. 90).
P-dataset assessment at the daily time step At the regional scale, the ability of the P-datasets to quantify the daily precipitation amount was relatively low, with most having median KGE values worse than 0. 4 (Fig. 8 ). Only MSWEP v. 2. In term of KGE, the P-dataset accuracy was higher during the wet than that in the dry season. Interestingly, MERRA-2, WFDEI, ERA-Interim, and JRA-55 performed better during the dry season, which is line with the results obtained over the Continental United States (CONUS)  (Beck et al. , 2019). However, the performances of MERRA-2, WFDEI, ERA-Interim, and JRA-55 were very low, with KGE < 0. 2. respectively, and italic font is used for P-datasets available in NRT latency of one to three days. The graphics were inspired by  Beck et al. (2019). Most of the P-datasets presented the highest HSS scores using a threshold value of 1 mm.
day -1 (Fig. 9 ). In particular, the HSS values of CHIRP v. 2 and MERRA-2 were close to 0 when considering a 0 mm. day -1 threshold value; the values jumped to 0. 3 and 0. 36, respectively, when considering a 1 mm. day -1 threshold value. Actually, the P-datasets detected many precipitation events with less than 1 mm. day -1 which were not detected by the gauges. This can be explained by different factors: (1) The gauges are not sensitive enough to such precipitation amounts; (2) difference in the spatial scale between point (gauge) and average area (P-dataset grid-cell) measurements; (3) the P-dataset algorithm is deficient. Because these precipitation events are insignificant (< 1 mm. day-1), they should be considered as no-precipitation events. (Sun et al. , 2018a). Therefore, the gauge-based information used for P-datasets and the delivered time step (daily or sub-daily) considerably influence the P-dataset reliability at the daily time scale.
Monthly versus daily P-dataset reliability Interestingly, the P-dataset performance ranking differed at the monthly and daily timescale (Fig. 11 ). We identified two main factors to explain these discrepancies. The first is related to the gauge-based information time step used to adjust P-dataset estimates. Indeed, the five most efficient P-datasets at the monthly time step, CHIRPS v. 2, TMPA-Adj v. 7, WFDEI, PERSIANN-CDR, and MSWEP v2. 2, were adjusted using monthly gauge-based information, whereas three of the five most efficient P-datasets at the daily time step, MSWEP v. 2. 2, CMORPH-BLD v. 1, and CMORPH-CRT v. 1, were adjusted using daily gauge-based information (Fig. 11 ). Additionally, the reliability of the gauge-based information can also influence the P-dataset accuracy. Accordingly, over the Sahel region, the CHPclim monthly dataset reliability was higher than that of GPCC  (Funk et al.
, 2015). Because most of the considered grid-cells used to assess P-dataset reliability are in the Sahel region (Fig. 1 ), CHIRPS, which uses CHPclim, provides more realistic monthly precipitation estimates, at KGE = 0. 86, than WFDEI and PERSIANN-CDR, which uses GPCC and GPCP, respectively (Fig. 3 ). This demonstrates the importance of maintaining reliable gauge networks to insure accurate P-dataset estimates. The second factor is the P-datasets delivered time step. Some P-datasets are delivered at the daily aggregation level (Table  1 ), which is based on different time windows than those used for local records. For example, PERSIANN-CDR daily estimates correspond to a given 0 h to 0 h UTC aggregation time period, whereas the gauges used in this study register daily amount from 8 h to 8 h UTC.
Such temporal inconsistency can introduce large differences between the P-datasets and the gauge measurements  (Ashouri et al. , 2015; Satgé et al. , 2019). Therefore, only one of the P-datasets, CMORPH-BLD v. 1, delivered at the daily time step ranked in the top five most efficient P-datasets. On the contrary, four of the five most efficient P-datasets at the daily time step were delivered at the sub-daily time step (3-hourly) (Table  1  and Fig. 11 ). The 3-hourly time step enables matching of the computed daily estimates with the local record time windows to ensure consistent comparison between gauges and P-dataset estimates. Our results demonstrate the importance of considering both daily and monthly time steps when assessing P-dataset reliability because the latter is influenced by the gauge-based adjustment process and the delivered time step.
Generally, P-datasets using gauge-based information achieved highest KGE scores at both monthly and daily time step (Figs. 3  and 8 ), which supports previous results obtained over West African regions  (Casse et al. , 2015; Gosset et al. , 2013; Poméon et al. , 2017)  and elsewhere  (Beck et al. , 2019; Dinku et al. , 2007; Satgé et al. , 2017a). However, the use of gauge-based information for Pdataset adjustment is not always as effective. Indeed, GSMaP-RT v. 6 outperformed its adjusted version GSMaP-Adj v. 6 at the daily time step. This result is consistent with previous observation over the CONUS,  (Beck et al. , 2019)  and illustrates the potential limit P-dataset algorithm to consider the best of gauge data. GSMaP-RT v. 6 is the only P-dataset with no gauge-based information of the topfive daily ranking.
P-dataset reliability in space and time CMORPH-BLD v. 1, CMORPH-CRT v. 1, GSMaP-Adj v. 6, and MERRA-2 presented weaker performance over the western region in comparison with other P-datasets (Fig. 3 ). We identified one factor to explain this spatial inconsistency. Different from other P-datasets which use GPCC or GPCP data, CMORPH-BLD v. 1, GSMaP-Adj v. 6, and MERRA-2 use CPC data. The gauge number used to retrieve CPC is lower than that used to retrieve GPCC (Fig. 12a ). Over the Senegal, Gambia, Guinea-Bissau, and Guinea regions only two CPC grid-cells counted with more than one gauge against seven for GPCC (Fig. 12b  and c ). As a result, compared with GPCC v. 7, CPC v. 1 presents the lowest efficiency over the western region (Fig. 4 ), which propagates into the use of CPC by the P-datasets to adjust their estimates.
The available gauge information for retrieving CPC and GPCC datasets also varies with time (Fig. 12a ). Therefore, the P-dataset reliability could be better (worse) if considering a period with more (fewer) available gauges for retrieving GPCC or CPC datasets. In this context, TAMSAT v. 3 uses consistent gauge-based information in space and time rather than a continuously updated information to avoid adding any space or time discrepancy  (Maidment et al. , 2017). Actually, P-datasets present space and time inconsistencies which cannot be reported by using single temporal windows  (Satgé et al. , 2019). The authors assessed P-dataset reliability over three different four years period and one twelve years period across the Lake Titicaca region. Results show that the P-dataset reliability conclusion vary according to the considered period.
Therefore, the analysis should be conducted over different temporal windows to adequately evaluate the Pdataset space and time reliability. Such a consideration is challenging over West Africa owing to the scarcity of gauge networks and the important temporal gaps present. To overcome this issue, an alternative method could use satellite-based soil moisture estimates rather than traditional rain gauges measurements as a reference benchmark  (Massari et al. , 2017). It is worth mentioning that GPCC and CPC share common gauges with the reference network used in this study as highlighted by many overlapping between both network (Fig. 12 b  and c ). Similar observation should be done if considering the others gauges based datasets used for P-dataset calibration and presented in section 2. 2. 1.
Therefore, the gauges network used for the assessment is not totally independent of the considered P-datasets and could influence P-dataset reliability conclusions. The P-dataset reliability conclusion could have been less optimist if only based on independent gauges network. In this context, future studies should try to consider totally independent gauges network to provide more consistent feedback on actual P-dataset reliability. However, information on the shared information between national gauges networks and gauges based dataset (i. e. CPC and GPCC) is hard to obtain and compromise this kind of consideration.
P-datasets sensitivity to seasonal variation Reanalysis P-datasets, ERA-Interim, MERRA-2, JRA-55 and WFDEI, performed better during the dry than during the wet season (Fig. 3 ). This agrees with previous results obtained over the CONUS  (Beck et al. , 2019). The authors explained that reanalysis P-datasets are better adapted to detecting largescale stratiform systems, which are typical in the dry season, than unpredictable small-scale convective cells, which are typical in the wet season. On the contrary, only satellite-based P-datasets performed better during the wet than the dry season  (Beck et al. , 2019; Salles et al. , 2019; Satgé et al. , 2017a). Actually, the irregular sampling of the low earth orbiting satellites and the limited number of overpasses hardly captures short precipitation events which are typical during the dry season  (Gebregiorgis and Hossain, 2013; Tian et al.
, 2009). Therefore, GSMaP-RT v. 6 presented a better KGE value during the wet than that during the dry season (Fig. 3 ). The seasonality sensitivity of the other P-datasets incorporating satellite, reanalysis, or gauge-based information shows a greater contrast because they consider the different inputs. Despite the seasonal variation in KGE value, the P-datasets presented significantly higher coefficient correlation during the dry season (Fig. 3 ). This difference could be related to the higher monthly precipitation variability during the dry season (Fig. 1 ) tending to increase the correlation coefficient. Accordingly, all P-datasets presented higher correlation coefficients considering the entire period because the precipitation variability is even more marked than at the seasonal scale.
At the contrary, the P-datasets were more biased during the dry season (Fig. 3 ) except for CPC v. 1, GSMaP-Adj v. 6, and ERA-Interim. The P-datasets with higher (TAMSAT v. Interestingly, if considering the P-dataset bias in millimetres, all P-datasets had systematically higher bias values during the wet season (Fig. 13 ). Because lower monthly precipitation occurred during the dry season, the same volumetric error (millimetres) expressed in ratio (Eq. 2) corresponds to higher bias during the dry in comparison to that during the wet season. For most of the P-datasets, the reported bias value during the dry season was less than 5 mm. month -1 (Fig. 13 ), which should have an insignificant influence on the water budget. Therefore, despite the low KGE value during the dry season, P-datasets still provide valuable additional information to follow both temporal and volume monthly precipitation dynamics over West Africa.
P-dataset time latency Fig. 14  shows the KGE scores of the NRT P-datasets in comparison with the most accurate P-dataset at the daily time step. At the daily time step, GSMaP-RT v. 6 was the most reliable NRT P-dataset. With three days of time latency, the GSMaP-RT KGE, at 0. 44, was close to the most effective P-dataset (MSWEP v2. 2) with KGE = 0. 52, which is available with a few months of latency. It is worth mentioning that the low score achieved by the P-datasets at the daily time step is partly related to the difference between spatially averaged (P-dataset grid-cell) and point (reference gauges) measurements  (Salles et al. , 2019; Satgé et al. , 2019; Tang et al. , 2018). The P-dataset reliability increased with the number of gauges used to represent the spatially average grid-cell measurement  (Salles et al.
, 2019; Tang et al. , 2018). In this study, most of the considered 0. 1° gridcells were counted with only one gauge. Therefore, the presented KGE score may underestimate the actual P-dataset reliability. Testing the sensitivity of streamflow modelling to P-datasets at basin outlets overcome the influence of scarce and unevenly distributed gauge networks. Indeed, aggregation of precipitation at the basin scale eliminates the difference in spatial representation between point (gauge) and areal (P-datasets) measurements because both gauge and P-datasets represent precipitation at the basin spatial scale. Therefore, the reliability of P-datasets varies significantly when used to reproduce gauge precipitation estimates or streamflow observations  (Satgé et al. 2019). In this context, NRT P-dataset coverage and latency can fit the needs of an early warning system across sparsely gauged or ungauged regions.
Towards an enhanced P-dataset over West Africa This study considers an unprecedented sample of 23 P-datasets over the West African region to provide a consistent guideline for potential users. The results suggest that during  2000-2003, CHIRPS v. 2 and MSWEP v. 2. 2  showed the best estimates of monthly and daily precipitation, respectively. The most reliable P-dataset can change at the local scale. As an example, Fig. 15  shows the most suitable P-datasets for representing both monthly and daily precipitation at the grid-cell level. Interestingly, at the daily time step, MSWEP v. 2. 2 was more consistent for the western region, whereas CMORPH P-datasets provided more accurate estimates over the central and southern regions (Fig. 15 ). At the monthly time step, even if CHIRP(S) P-datasets are counted with the highest number of grid-cells, large spatial heterogeneity is observed with many grid-cells where WFDEI, JRA-55 Adj, CMORPH, and TMPA outperformed CHIRP(S) (Fig.
15 ). To take advantage of all available P-datasets, merging all Pdatasets to produce an enhanced P-dataset over the region is a good option. Previous studies have reported on the benefit of such an approach to retrieve a more realistic P-dataset over Pakistan  (Muhammad et al. , 2018; Rahman et al. , 2018) ,Tibet  (Ma et al. , 2018)  and different tropical complex terrain  (Bhuiyan et al. , 2019). These ensemble precipitation datasets enhance the regional precipitation representation and should be used as guideline over West Africa.
Conclusions The present study evaluates the accuracy of 23 gridded P-datasets over the West African region at both monthly and daily time step for the 2000-2003 period. Despite the limited coverage and scarcity of the ground reference points, some consistent features emerged from the analysis: • The P-dataset performance ranking differs at the monthly and daily timescale. P-datasets using sub-daily (monthly) gauge information perform better at the daily (monthly) time step. Additionally, for the P-datasets released at the daily time step, the temporal mismatch between gauge and satellite reporting times decrease their reliability at the daily time step. In this line, MSWEP v. 2. 2 and CHIRPS v. 2 provide the most reliable daily and monthly precipitation estimates, respectively whereas TMPA-Adj v.
7 performance is very good for both daily and monthly estimates. • The only satellite based P-datasets (CMORPH-RT v. 1, TMPA-RT v. 7, PERSIANN-RT, GSMaP v. 6-RT) performance is very low at both monthly and daily time scale. Their reliability drastically increase for their adjusted versions (CMORPH-CRT and BLD v. 1, TMPA-Adj v. 7, PERSIANN-Adj) excepted for GSMaP v. 6 at the daily time step. • All the considered reanalysis P-datasets (WFDEI, JRA-55, JRA-55 Adj, ERA-Interim) are unreliable at the daily time step. The use of monthly GPCC P-dataset to adjust their estimates considerably increase their reliability at the monthly time step (WFDEI, JRA-55 Adj). • The two African P-datasets (TAMSAT v. 3 and ARC-2) present an overall lower performance in comparison to the almost global scale P-datasets at both daily and monthly time-step.