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We conclude here with a brief literature review of past assessment efforts with respect to GPCP.

1 Product validation methodology

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1.2 Overview

We evaluate the GPCP monthly v2.3 and daily v1.3 precipitation rates according to what was proposed in the related PQAD [D1], i.e. by comparing it to other publicly available datasets. A brief overview of the validation strategy is given in the following. The GPCP monthly product is available on a 2.5° latitude/longitude grid since 01/1979. The spatial resolution of the daily product is 1°, and it is available since 01/10/1996.

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Anchor note1 note1 1 This is in contrast to a simple arithmetic mean of sub-daily precipitation rates. However, as long as the sub-daily data series are complete and represent equally long time periods, the two approaches are equivalent.

1.2 Initial quality checks

As a first measure of quality, we test the number of missing values per file (i.e. per daily/monthly mean field) as well as the mean, minimum and maximum values.

1.3 Comparison with gridded datasets

Here we list first the gridded datasets that we compare the GPCP products to (hereafter labeled as "reference datasets") and their main characteristics. We then give a brief overview of the design of the comparisons that we carry out.

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1.3.1 Gridded datasets used for the comparison

TRMM Multi-satellite Precipitation Analysis (TMPA) products are available between 50°S and 50°N from 01/1998 to 12/2019 on a 0.25° equidistant grid as monthly means (product TMPA 3B43 v7; reference TRMM, 2011) and daily means (product TMPA 3B42 v7; reference Goddard Earth Sciences Data and Information Services Center, 2016).

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Met Office Rain Radar Data from the NIMROD System are available on a 5 km x 5 km grid over parts of Europe in 15-minute intervals since 04/2002 (Met Office, 2003). Because the NIMROD dataset is vastly different from ERA5 and the TMPA products in terms of spatial coverage, we address the comparison with GPCP products separately from these datasets, in Section 2.3. The original NIMROD data are first converted to mm/day and averaged to represent daily means on their native grid. Based on daily means, monthly mean values are computed, again on the native grid. In the case of daily resolution, we filter out daily mean values that are based on less than 32 of quarter-hourly observations (one third in a day). For monthly resolution, we filter out monthly mean values if they come from less than 10 days (~ one third of days in a month) or from less than 1000 quarter-hourly instances (~ one third in a month). For both temporal aggregations (monthly, daily), the data are then brought to the much coarser GPCP grids (2.5° for monthly resolution, 1.0° for daily, see above) by arithmetically averaging all daily or monthly values from the native NIMROD grid cells that fall into respective GPCP grid cells. Respective spatial averages for the GPCP grid cells are discarded if they come from less than 190 NIMROD grid cells in the case of the daily resolution, and 1140, respectively, in the case of the monthly resolution (about one third of the maximum number of NIMROD grid cells in the respective GPCP grid cells). These threshold values are defined ad hoc. They are required because of frequently occurring data gaps in space and time in the NIMROD database. In the Appendix, Supplementary Figure 2 illustrates the regridding scheme and Supplementary Figures 3-7 show how the actual coverage of the data relates to these thresholds. Stronger constraints on data availability during averaging might improve the agreement between NIMROD and GPCP but would also limit the instances of co-existing data pairs in both datasets. Note that the temporal aggregation for the daily resolution NIMROD data differs from that defined for GPCP (accumulation over one day; see Section 1.1).

1.3.2 Methodology

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1.3.2.1 Temporal evolution of spatial averages

For each GPCP time slice (daily/monthly), we compute spatial averages over the maximum area available in the respective reference datasets (i.e. between 50°S and 50°N for TMPA products, globally for ERA5, over Europe for NIMROD) for both GPCP and the respective reference datasets. Datasets with higher temporal resolution than GPCP (i.e., ERA5 hourly for comparison with GPCP daily and NIMROD) are averaged temporally to obtain daily and monthly mean values. A regridding has not been carried out at this stage because the spatial integration can be carried out regardless of the specific grids. The resulting time series for GPCP and the reference datasets are then compared. This allows us to also discuss the Key Performance Indicator (KPI) defined for the continuous monitoring of the datasets delivered to the CDS.

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1.3.2.2 Climatology

We compare the temporal mean and variability of the GPCP products to the respective reference datasets over overlapping periods. For this, the reference datasets need to be regridded to the 2.5°/1.0° grid of the GPCP monthly/daily products. As all reference datasets are of higher spatial resolution than the GPCP products, the regridding is carried out by averaging the reference datasets in spatial dimensions within each GPCP grid cell. In the case of ERA5 and NIMROD, this is done after the temporal averaging which provides the monthly and daily means addressed in Section 1.3.2.1. The temporal averaging is carried out over the period from 01/ 1998 to 12/2017 (i.e., the period of TMPA availability). Differences between the temporally averaged two-dimensional fields as well as their respective zonal means are discussed.

1.3.2.3 Single collocated grid cells

Having carried out the temporal (where applicable) and spatial regridding addressed in Sections 1.3.2.1 and 1.3.2.2, we compare GPCP with the regridded reference datasets in each grid cell and at each time in the form of histograms of differences. We also retrieve mean differences and respective root-mean-square (RMS) deviations from the mean for all available grid cells at all times, as well as for five pre-defined categories (zonal, magnitude of the GPCP precipitation rate, elevation, month, and year). The respective comparison is carried out over the full period in which the respective reference datasets are available, i.e. since 1979 for the monthly comparison with ERA5, since 10/1996 for the daily comparison with ERA5, and since 1998 for the daily and monthly comparison with the TMPA products.

We also use the per-time slice and per-grid cell comparison to assess the error budget of the GPCP monthly v2.3 product.

1.4 Comparison with in-situ data

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1.4.1 Datasets and Methodology

1.4.1.1 PACRAIN

The Pacific Rainfall Database (PACRAIN, Greene et al., 2008) is a compilation of daily precipitation data at various stations in the Pacific Ocean. Temporal overlap with GPCP exists currently from 1979 to 2015.

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Figure 1: PACRAIN stations and GPCP grid cells going into the comparison of PACRAIN and GPCP monthly v2.3 (A) and GPCP daily v1.3 (B). The GPCP grid cells qualify for this comparison if at least one PACRAIN station lies within. At each given time, precipitation rates are averaged only in the available subset of these stations and the respective GPCP grid cells. Note that we distinguish between atoll stations and respective grid cells and the remaining stations and grid cells (see main text). 

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1.4.1.2 OceanRAIN

The Ocean Rainfall And Ice-phase precipitation measurement Network (OceanRAIN, Klepp et al., 2017, Klepp, 2015) contains per-minute observations of precipitation rates from ship-borne sensors (rain gauges and disdrometers). For now, we use only the observations based on rain gauges. Data are available from the following ships/periods: Polarstern (2010-2016), Meteor (2014-2016), Investigator (2016-2017), and Roger Revelle (2016).

The routes of these ships along which the rain gauge observations are available, as well as their temporal coverage are displayed in Figure 2. Usually, the vessels, as they travel, sample precipitation in one grid cell for less than one day, so that a comparison of OceanRAIN and the GPCP monthly v2.3 product does not make sense. For the comparison with GPCP daily v1.3, we average all hourly rates during one day in one GPCP grid cell. This does not necessarily ensure an adequate spatiotemporal representation of the OceanRAIN in one GPCP grid cell, but where the daily coverage per grid cell is extensive, the comparison provides useful results, see Section 2.4.2.

1.5 Literature review

Comparisons of the GPCP products with other datasets have been published by the GPCP authors and independent ones. We include a brief summary of their findings in section 2.5. However, as the update of the monthly product from v2.2 to v2.3 and the related changes in the daily product (which is dependent on the monthly product) only happened in 2017, the amount of scientific literature covering the new versions is still limited. The literature review makes no claim to completeness.

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Figure 2: Routes of the four ships from which we use the rain gauge observations here. The routes are plotted in a projection with the centre meridian at 180°E/W (upper panel), and at 0°E/W (lower panel). The panel on the right indicates the temporal coverage by each vessel.

2 Validation results

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2.1 Initial quality checks

Figure 3 displays the results of the initial quality checks. They indicate that the data in the GPCP monthly v2.3 product are complete and always in an expected range:

• • there are no missing data (Figure 3A, values in red),
  • minimum values are virtually always zero² (Figure 3B, values in red), averages oscillate in a narrow band between 2.06 49 mm/d and 2.41 84 mm/d (Figure 3B, values in red), and
  • maximum values oscillate between 16 mm/d and 47 mm/d (Figure 3C, values in red).

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Anchor note2 note2 2 There are a couple of exceptions, where the minimum in the GPCP monthly dataset is always below 0.00018 mm/d.

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Figure 3: Number of grid cells without valid precipitation estimates (A), minimum and average values in the two-dimensional precipitation field (B), and respective maximum values (C) for the GPCP monthly v2.3 and daily v1.3 data at a given time (on the x-axis). The scales in A and C are logarithmic, but linear between 0 and 100=1 in A and between 0 and 101=10 in C, for a better illustration of all features in the datasets. The averages here have been obtained as simple averages in the respective precipitation array, without weighting by the area of the grid cells.

The results of the checks on the GPCP daily v1.3 product indicate a stable performance, too. However:

• on 147 156 days (1.7% of all dates), there are data gaps of variable extent (Figure 3A, values in blue ), peaking at 36,000 grid cells missing which corresponds to ~56% of the spatial field.
• Minimum values are always zero (Figure 3B, values in blue), and the average value oscillates between 12.75 08 mm/d and 3.00 48 mm/d (Figure 3B, values in blue).
• Maximum values (Figure 3C, values in blue) oscillate inside a higher range than in the monthly product, between 64 63 mm/d (5%-percentile) and 132 mm/d (95%-percentile), with a few prominent outliers surging above 1000 1,000 mm/d. Such high values appear to be singular because the respective averagevalues average values do not follow these surges (Figure 3B, values in blue).

There are a few dates at which all available grid cells in the daily product contain zeros – indicated by zero average and maximum value in Figure 3B and C. These are from 15/01/2010 to 26/01/2010, and on 09/02/201304/03/2021, 24/03/2021, 10/06/2021, 19/07/2021, 26/08/2021 and 26/12/2021. These days are also among the dates where the numbers of missing values peak (36,000, Figure 3A), all located at high latitudes, possibly due to the failure of retrieval of the respective polar-orbiting satellite systems [D3, D4]. Therefore, at these dates, the respective fields must be considered as effective data gaps.

The higher variability of both average and maximum values in the case of the daily product, compared to the monthly product (see blue compared with red values in Figure 3B,C), can be understood as a feature of the longer integration time and lower spatial resolution of the monthly product which smooths the temporal evolution and spatial patterns of the precipitation estimates. The very similar centering of average values around ~2.24 68 mm/d of both the daily and the monthly product is a consequence of their interdependence. As with the higher variability, the higher magnitudes of the maximum values in the daily product are due to the higher spatial and temporal resolution.

The NetCDF files for both the monthly and the daily GPCP product specify a valid range of 0-100 mm/d, which is obviously violated by the daily product (Figure 3C). The recommendation of the GPCP team (personal communication) is to use data even when they lie outside the specified validity range.

2.2 Comparison with gridded datasets (TMPA products and ERA5)

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2.2.1 Temporal evolution of spatial averages

Figure 4 shows the temporal evolution of spatially averaged (area-weighted) values in the GPCP products and the respective counterparts in the reference datasets ERA5 and the TMPA products (see Section 1.3.1). It is evident that the spatially averaged values of the GPCP products have a far smaller average deviation with respect to the TMPA products than with respect to ERA5. This is not surprising because TMPA 3B43 and GPCP are both calibrated against rain gauge-based data by the Global Precipitation Climatology Centre (GPCC). Note that TMPA products end in 12/2019, while the GPCP and ERA5 time series extend to 0912/2020 2021 here.
Table 1 contains basic statistics of the differences and shows:

1. that the spatially averaged GPCP values are not significantly biased with respect to TMPA (mean difference exceeded by far by the RMS differences),
2. that the GPCP global means are biased to smaller values with respect to ERA5 (difference < -0.22 mm/d on average in the comparison of monthly data),
3. and that the differences spread up to twice as much for daily means compared to monthly means (RMS column).

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Figure 4 - A and B: Mean values, averaged over the geographical TRMM window (between 50°S and 50°N) for the GPCP monthly v2.3 (A) and daily v1.3 (B) products and the respective TMPA products (3B43 in A; 3B42 in B). C and D: Global mean values of the GPCP monthly v2.3 (C) and daily v1.3 (D) products and the ERA5 reanalysis, agglomerated as monthly (C) and daily (D) means. E and F: Differences between the spatially averaged values of GPCP and TMPA/ERA5 as shown in panels A-D. The differences are computed between data of the same spatial coverage and temporal resolution, i.e. panel E refers to monthly data and panel F to daily data, and the difference between GPCP and ERA5 is based on global mean values whereas the GPCP/TMPA differences is based on the integration over the ±50° latitudinal window. The vertical dashed black line marks the formal transition from TCDR (until 12/2017) to ICDR (from 01/2018). 

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Table 1: Basic statistics for the differences between the GPCP and the reference datasets for the temporal coverage of both the TCDR and the ICDR, i.e. the black and blue curves in Figure 4E,F. For the minimum and maximum differences, those GPCP fields that have only zeros and missing values (Section 2.1) and which thus result in a spatial average of zero are discarded. The minimum and maximum differences, the mean, the quantiles (2.5%, median, 97.5%), and the RMS deviations are in mm/d. The RMS is with respect to the mean value. The unit of the slopes is mm/d/decade. In the second to last column, we give the percentage of values that meet the initial target requirement for the KPI accuracy of 0.3 mm/d.

The significantly larger average precipitation in ERA5 compared to GPCP will be analysed in detail in Sections 2.2.2, in which spatial patterns are compared, and 2.2.3, in which the dependence on latitude is briefly discussed.

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Note that if the periods over which GPCP and ERA5 were compared had been the same for the monthly and the daily products, one would have expected the same mean difference. However, as this is not the case, the different values are not surprising. Conversely, the consistency of the mean differences in the comparison with monthly and daily TMPA products follows from the fact that the monthly and daily TMPA products are not independent of each other.

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2.2.1.1 Key Perfomance Indicators

KPIs for TCDR

Table 2 contains the same statistics as Table 1, but limited to the temporal coverage of the TCDR (i.e. until 12/2017). The initial performance targets for the GPCP TCDR in the scope of the brokering of the data to C3S are 0.3 mm/d for the Key Performance Indicator (KPI) accuracy and 0.034 mm/d/decade for the KPI stability, see e.g. the respective PUGS [D5]. Accuracy in this context is the absolute difference between the spatially averaged value of the evaluated product (GPCP) and a reference product. The second to last column in Table 2 gives the percentage of values during the temporal evolution of the spatially averaged values that meet this target. The relatively large difference between mean values in GPCP and ERA5 leads to a much worse compliance with this accuracy target. The comparison with the TMPA products shows that the target is achieved at all times in the case of the monthly product. As discussed already in Section 2.1, the daily products naturally have a larger spread, manifesting in the violation of the 0.3 mm/d accuracy target in ~7.5% of all available days when compared to TMPA 3B43. However, we would expect the compliance with a single target (such as the 0.3 mm/d accuracy target) to be different for the monthly and daily means, so we accept the violation here.

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Table 2: Basic statistics for the differences between the GPCP and the reference datasets for the temporal coverage of the TCDR (until 12/2017), i.e. the datasets shown as black and blue curves in Figure 4E,F until 12/2017. For the minimum and maximum differences, those GPCP fields that have only zeros and missing values (Section 2.1) and which thus result in a spatial average of zero are discarded. The minimum and maximum differences, the mean, the quantiles (2.5%, median, 97.5%), and the RMS deviations are in mm/d. The RMS is with respect to the mean value. The unit of the slopes is mm/d/decade. In the second to last column, we give the percentage of values that meet the initial target requirement for the KPI accuracy of 0.3 mm/d.

Stability in this context is the absolute slope of a linear regression of the timeseries of differences in spatially averaged values. All absolute values remain below the initial performance target of 0.034 mm/d/dec (last column in Table 2).

KPIs for ICDR until 12/2019

The above discussion of KPI achievements is related to the TCDR only, i.e. all data until 12/2017. In compliance with a newly formulated KPI strategy [D6], we evaluate the ICDR not against a fixed target but against the performance of the TCDR in comparison to the respective TMPA products. As TMPA products were decommissioned during the lifetime of this project, we switched to ERA5 as reference for data after 12/2019 (see section 2.2.1.1.3). We test whether the 95% confidence interval of the TCDR differences is valid as 95% confidence interval for the ICDR, too. The boundaries of the 95% confidence interval can be seen in Table 2 as:

• the 2.5%-percentile (-0.175 mm169 mm/d for monthly resolution, -0.332 mm314 mm/d for daily resolution) and
• the 97.5%-percentile (0.189 mm185 mm/d for monthly resolution, 0.343 mm341 mm/d for daily resolution).

This check on the performance of the ICDR is verified by a binomial test at a 5% significance level, for details see [D6]. For the GPCP monthly product, all 24 temporal instances of the ICDR (01/2018 – 12/2019) fall inside the boundaries, defined by the GPCP TCDR / TMPA comparison. For the GPCP daily product, 720 out of 727 729 temporal instances fall inside the boundaries. In both cases, we can conclude that the given boundaries are valid as a 95% or higher confidence interval of the ICDR at a significance level of 5%. Consequently, the ICDR in this time period performs sufficiently well, in line with the requirements formulated in [D6].

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KPIs for ICDR from 01/2020

The decommissioning of TMPA products made it necessary to use ERA5 as the reference dataset for the evaluation of the KPI accuracy for ICDR deliveries covering the period starting in 01/2020. In accordance with our analysis above (e.g., Figure 4, Table 1, and Table 2), we now evaluate globally averaged values instead of being limited to the latitudinal TRMM window. The boundaries of the respective 95% confidence interval in Table 2 are:

• the 2.5%-percentile (-0.338 mm355 mm/d for monthly resolution, -0.535 mm528 mm/d for daily resolution) and
• the 97.5%-percentile (-0.063 mm073 mm/d for monthly resolution, +0.017 mm013 mm/d for daily resolution).

For the GPCP monthly product, 8 21 out of 9 24 temporal instances of the ICDR (01/2020 – 0912/20202021) fall inside the boundaries defined by the GPCP TCDR / ERA5 comparison. For the GPCP daily product, 269 702 out of 274 731 temporal instances fall inside the boundaries. In both cases, we can conclude that the given boundaries are valid as a 95% or higher confidence interval of the ICDR at a significance level of 5%. Consequently, the ICDR in this time period performs sufficiently well, in line with the requirements formulated in [D6].

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2.2.2 Climatology

We compare temporal averages of the spatial fields for the common temporal overlap of the three datasets (GPCP, TMPA products, ERA5), i.e. from 01/1998 to 12/2019, which is when the TMPA products were decommissioned. For this, we spatially average the TMPA and ERA5 data to match the coarser GPCP resolutions of 2.5° (monthly) and 1° (daily), see Section 1.3.2.2. TMPA and ERA5 data are already available at the required temporal resolution due to the processing leading to the results in Section 2.2.1.

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Figure 5 – A-F: Climatologies (i.e. temporal mean) for GPCP monthly v2.3, TMPA 3B43, and ERA5 (left column), and the respective standard deviation as a measure for the temporal variability (right column). Data are averaged over 01/1998 to 09/2019 (i.e., the period of TMPA data availability). Panel G shows the difference between the GPCP and the TMPA 3B43 climatology ("A minus C"), and panel H shows the same for the GPCP/ERA5 comparison. This implies that negative values in G and H occur where GPCP has lower precipitation rates than the respective reference dataset, and vice versa for positive values.

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Figure 6: The same as Figure 5, but for daily products. 

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Figure 7 – A: Zonal means of the climatologies (Figure 5 and Figure 6). In the absence of inherent differences between monthly and daily aggregations in each product, these should have the same magnitudes here, if they were evaluated on a common grid. However, as all data here are regridded to the respective GPCP grids (2.5° for monthly data and 1.0° for daily data), differences here are due to averaging over coarser/finer spatial scales for monthly/daily data. B: Differences between GPCP products and the respective reference datasets (to which the color codes refer, see legend in panel A). Negative values indicate that GPCP has lower precipitation rates than the respective reference dataset, and vice versa for positive values.

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2.2.3 Single collocated grid cells

Having the four reference datasets (TMPA, ERA5 as monthly and daily means) on a grid that matches the grids of the two GPCP products in spatial and temporal dimensions, we perform a comparison per grid cell and time slice. Respective time periods are 01/1979-12/2019 for the comparison of the GPCP monthly product against ERA5, 10/1996-12/2019 for the comparison of the GPCP daily product against ERA5, and 01/1998-12/2019 for the comparison of both GPCP products against respective TMPA products. The results are summarized in Figure 8 and Table 3. Again, it is visible that the daily products have a larger spread between them, manifesting in much larger RMS deviations. The mean difference to TMPA tends to be slightly positive. The mean difference to ERA5 is negative and of slightly higher magnitude, due to less precipitation in the tropics in the GPCP products (see Section 2.2.2).

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Table 3: Number of per time slice and per grid cell comparisons shown in the histograms of Figure 8, as well as the mean and RMS deviation (in mm/d each).

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Figure 8: Histograms of differences between the GPCP datasets and the respective reference datasets. The differences are computed for every available time step and every GPCP grid cell. Table 3 lists statistics of these distributions. The histogram bins are 0.5 mm/d wide. 

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Note that any deviation in the mean difference of GPCP and the two reference datasets at monthly and daily resolutions (Table 3 and Figure 8) does not need to stem from different periods here, as in Section 2.2.1, but can also be due to the varying spatial resolution of 1.0° and 2.5°, respectively.

2.2.3.1 Error assessment

The GPCP monthly v2.3 product comes with an uncertainty estimate that we assess in the following. No such uncertainty information is provided with the GPCP daily v1.3 product.

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Figure 9 shows the respective cumulative distribution, i.e. for every absolute normalized difference value  along the x-axis, the respective number of values according to Equation (1), for which  is given as percent of the total number of values , see Table 3. About two thirds of the data points (~ 1 sigma) are at or below an absolute normalized difference of 1. Note that we do not consider the uncertainty of the reference here. This would increase the observed fraction, indicating that the provided GPCP uncertainty is larger than the one-sigma interval [D6, based on Immler et al. 2010].

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2.3 Comparison with gridded datasets (NIMROD)

We discuss differences between the GPCP products and precipitation estimates derived from ground-based precipitation radar stations from the Met Office's NIMROD database between 2002 and 2019. These are available over central Europe and the British Isles (Figure 10), covered by a maximum of 56 grid cells in the GPCP monthly product and 349 in the daily product.

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Table 4: Results of the per-time slice and per-grid cell comparison of GPCP and NIMROD. Listed are the numbers of available data pairs (i.e. grid cells and times for which GPCP and NIMROD are available simultaneously), as well as the mean, 2.5- and 97.5 percentiles, RMS deviation, and – in the case of the monthly product – mean GPCP uncertainty (in mm/d each).

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Figure 11: Temporal evolution of spatially averaged values from the GPCP and NIMROD datasets, and respective differences, for the monthly (A) and daily (B) products. Similar to the temporal means (Figure 10), a grid cell only contributes to the spatial average at a given time, if both the GPCP and the NIMROD product are available at that time in that grid cell (see the varying number of contributing grid cells at the bottom of each sub-figure).

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The alteration of the difference between GPCP and NIMROD around zero (spatially averaged values as black lines in
Figure 11A, B) is confirmed by the relatively low mean difference in the grid cell-wise comparison (~0.3 mm/d; Table 4 similar to the GPCP–TMPA/ERA5 comparison in Section 2.2.3), less than the average GPCP monthly uncertainty (0.4 mm/d; Table 4). However, the spatio-temporal variation around zero is large, manifesting in high absolute values for the given percentiles (Table 4), as well as for RMS deviation (2.5 mm/d for the monthly GPCP–NIMROD comparison (Table 4) vs. ~1.2 mm/d for the GPCP–TMPA/ERA5 comparison (Table 3); 8.2 mm/d for the daily GPCP–NIMROD comparison vs. ~6 mm/d for the GPCP–TMPA/ERA5 comparison).

2.4 Comparison with in-situ data

2.4.1 Comparison with PACRAIN

Due to the lack of transferability of point measurements such as in the PACRAIN dataset and a relatively coarsely gridded product as GPCP (see Section 1.4.1), the comparison with PACRAIN is carried out on the basis of averages over all available stations illustrated in Figure 1 at a given time (PACRAIN) and the average over all grid cells in which PACRAIN stations are available (GPCP). An additional brief analysis is carried out for averages over all atoll-like settings and respective GPCP grid cells.

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Table 5: Statistics of the GPCP/PACRAIN comparison as shown in Figure 12 and Figure 13 (rows labelled as 'all'). All values are differences between the respective GPCP product, averaged over all grid cells in which PACRAIN stations are available at a given time, and the respective PACRAIN average over all available stations at a given time. Minimum, maximum, and mean values as well as the RMS deviation from the mean value and the mean GPCP uncertainty are in mm/d. Also included are rows where the statistics are given for the subset of temporal instances where a minimum number of PACRAIN stations is exceeded, and for the separate atoll-only comparison where only the subset of PACRAIN stations situated in atolls and the respective GPCP grid cells have been averaged for each time step. Note that these latter '> n PACRAIN stations' rows (with n=100, n=120) provide the statistics to a sub-set of the timeseries to which the 'all' rows provide respective statistics (i.e., filtering out specific points in time, depending on overall station availability), whereas the 'atoll stations only' provide statistics to the separate timeseries that is compiled by averaging over a smaller amount of stations and GPCP grid cells in the first place (i.e., filtering out specific stations/grid cells). Consequently, it is not contradictory that the numbers of values are equal in the 'all' and 'atoll stations only' rows, and that minimum/maximum values are lower/higher in the 'atoll stations only' rows.

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2.4.2 Comparison with OceanRAIN

The comparison with OceanRAIN observations is carried out only for GPCP daily v1.3 and the rain gauge observations onboard the research vessels (RV) Polarstern, Meteor, Investigator, and Roger Revelle (see Section 1.4.1.2 and Figure 2). Figure 14A and B show a large spread of differences for the complete dataset, around a mean of -4.9 mm/d (i.e. higher rates in the OceanRAIN dataset) and a RMS deviation of 62.9 mm/d. Figure 14C shows that this large spread is dependent on many factors. For example, mean and RMS deviation are -0.4 mm/d and 14.3 mm/d, respectively, for latitudes below 36.5°, and, in contrast, -15.6 mm/d and 105.0 mm/d for latitudes above 77.5°. This manifests also in a much smaller spread for RV Meteor and RV Investigator which sample less polar areas than RV Polarstern. Albeit at very low latitudes, the observations by RV Roger Revelle are only few (133 daily per-grid cell means in total), so that the higher spread can be explained by the small size of the sample.

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Figure 14 – A: Time series of differences between GPCP daily v1.3 and daily mean values in respective GPCP grid cells based on OceanRAIN observations. The different colors represent the different vessels, see x-axis annotations in panel C (ship category) and Figure 2. Note that for each day and ship there can in principle be more than one GPCP grid cell populated with OceanRAIN observations. B: Histogram of all available differences. C: Mean (red circles, left y-axis) and RMS deviation (black diamonds, left y-axis) of differences in various categories. The categories are 'all', vessels, timing in the OceanRAIN period (2010-2017), zonal, number of OceanRAIN observations (nobs) per minute in daily GPCP grid cell, and GPCP precipitation magnitude (p_GPCP). (Note: panel B is based on the category 'all') is the one shown in panel B. The blue crosses in panel C indicate number of daily per-grid cell differences in the respective categories (right y-axis, logarithmic). The limits of the bins in numerical categories (time, zonal, nobs, p_GPCP) have mostly been designed to contain a similar number of values.

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2.5 Literature review

As GPCP is one of the most used precipitation products³, earlier versions of GPCP have been compared to other products regularly. Many efforts have involved the comparison of GPCP to the same reference datasets as deployed here, or earlier versions of this, such as ERA-Interim instead of ERA5 (e.g. Bosilovich et al., 2008; Szczypta et al., 2011), earlier TMPA releases (e.g. Dinku et al., 2007; Joshi et al., 2013) or shorter versions of the PACRAIN database (Pfeifroth et al., 2013). Often, such comparisons have focused on certain regions rather than on the planet as a whole (e.g. McPhee and Margulies, 2005; Bolvin et al., 2009; You et al., 2015). Some findings discussed in this PQAR have been featured in these earlier publications, for example the reasonable correlation between GPCP and PACRAIN (Pfeifroth et al., 2013), or the higher tropical precipitation rates in ERA products (Bosilovich et al., 2008).

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Anchor note3 note3 3As indicators, the original GPCP monthly v2 publication (Adler et al., 2003) has been cited 3876 times by Feb 2019 according to Google Scholar; the original GPCP daily publication (Huffman et al., 2001) has been cited 1470 times (Google Scholar, Feb 2019).

3 Application(s) specific assessments

n/a

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4 Compliance with user requirements

There are no specific user requirements published for GPCP, to our knowledge.

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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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