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History of modifications

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Version Date Description of modification Chapters / Sections 1 22/03/2019 initial All 1.1 26/03/2020 Previous assessment extended to first 4 three-monthly delivery batched of ICDR; GPCP–NIMROD comparison included; PACRAIN comparison extended (atoll-only); Literature review extended All 1.2 08/03/2021 Previous assessment extended to ICDR deliveries until 09/2020 (12/2019 where TMPA decommissioning limits the joint GPCP/TMPA/ERA5 analysis) Reference for KPI assessment from 01/2020 (new subsections 2.2.1.1.x) 1, 2

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Reference ID Document D1 Precipitation products brokered from the Global Precipitation Climatology Project (GPCP): Product Quality Assurance Document (PQAD) D2 Target Requirements and Gap Analysis Document (TRGAD): Precipitation CDRs D3 Precipitation – GPCP Monthly – Climate Algorithm Theoretical Basis Document, NOAA Climate Data Record Program CDRP-ATBD-0848 Rev. 2 (2017). Available at https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Precipitation_GPCP-Monthly/AlgorithmDescription_01B-34.pdf D4 Precipitation – GPCP Daily – Climate Algorithm Theoretical Basis Document, NOAA Climate Data Record Program CDRP-ATBD-0913 Rev. 0 (2017). Available at https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/Precipitation_GPCP-Daily/AlgorithmDescription_01B-35.pdf D5 Product User Guide and Specification (PUGS): Precipitation products brokered from the Global Precipitation Climatology Project D6 Report on Updated Key Performance Indicators (KPIs): A Quality Assessment Approach for their determination and evaluation

Acronyms

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Acronym Definition ATBD Algorithm Theoretical Basis Document C3S Copernicus Climate Change Service C-ATBD Climate ATBD CDR Climate Data Record CDRP Climate Data Record Program CDS Climate Data Store CNR Consiglio Nazionale delle Ricerche (National Research Council of Italy) DWD Deutscher Wetterdienst (Germany's National Meteorological Service) ECMWF European Centre for Medium-Range Weather Forecasts ERA5 ECMWF Reanalysis 5th Generation GCOS Global Climate Observing System GEWEX Global Energy and Water Exchanges GPCC Global Precipitation Climatology Centre GPCP Global Precipitation Climatology Project GPM Global Precipitation Measurement mission HOAPS Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data ICDR Interim Climate Data Record IMERG Integrated Multi-Satellite Retrievals for GPM ISAC Istituto di Scienze dell'Atmosfera e del Clima (Institute of Atmospheric Science and Climate) KPI Key Performance Indicator NetCDF Network Common Data Format NIMROD Precipitation Radar Dataset NOAA National Oceanic and Atmospheric Administration OceanRAIN Ocean Rainfall And Ice-phase precipitation measurement Network PACRAIN Pacific Rainfall Database PQAD Product Quality Assurance Document PQAR Product Quality Assessment Report PUGS Product User Guide and Specification RMS Root Mean Square RV Research Vessel TCDR Thematic Climate Data Record TMPA TRMM Multi-satellite Precipitation Analysis TRMM Tropical Rainfall Measurement Mission UMD University of Maryland WCRP World Climate Research Programme

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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 (2010-20162010–2016), Meteor (2014-20162014–2016), Investigator (2016-20172016–2017), and Roger Revelle (2016).

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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-DA–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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table1 table1

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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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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table2 table2

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.

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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 2.2.1.1.3KPIs for ICDR from 01/2020). 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:

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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 – 122018–12/2019) fall inside the boundaries, defined by the GPCP TCDR / TMPA comparison. For the GPCP daily product, 720 out of 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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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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table3 table3

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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For the monthly-resolved datasets and the time period 01/1979-121979–12/2019 (TMPA availability), we compute the absolute normalized difference between GPCP and the respective reference datasets as the ratio of the difference between GPCP and the respective reference dataset (∆pijk) at each available time slice k, each latitude grid node i, and each longitude node j as evaluated in the context of the collocated grid-cell comparison above and the GPCP uncertainty σ_ijk:

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table4 table4

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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table5 table5

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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