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History of modifications

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Version Date Description of modification Chapters / Sections v1 01/07/2021 initial version all

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Reference ID Document D1 Product Quality Assurance Document (PQAD); Copernicus Microwave-based Global Precipitation (COBRA) D2 Algorithm Theoretical Basis Document (ATBD); Copernicus Microwave-based Global Precipitation (COBRA) D3 Product User Guide and Specification (PUGS); Copernicus Microwave-based Global Precipitation (COBRA) D4 Validation Report SSM/I and SSMIS products HOAPS version 4.0, v1.2, 2017/03/15, CM SAF, https://www.cmsaf.eu/SharedDocs/Literatur/document/2017/saf_cm_dwd_val_hoaps4_1_2_pdf.pdf DOI of corresponding dataset: 10.5676/EUM_SAF_CM/HOAPS/V002 D5 Report on Updated Key Performance Indicators (KPIs): A Quality Assessment Approach for their determination and evaluation D6 Target Requirements and Gap Analysis Document (TRGAD): C3S_312b_Lot1 – Precipitation CDRs

Acronyms

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Acronym Definition AMSR-E Advanced Microwave Scanning Radiometer - Earth Observing System AMSU-B Advanced Microwave Sounding Unit – B ATBD Algorithm Theoretical Basis Document C3S Copernicus Climate Change Service CC Correlation coefficient CDR Climate Data Record CDS Climate Data Store CMSAF Satellite Application Facility on Climate Monitoring CNR National Research Council of Italy COBRA Copernicus Microwave-based Global Precipitation CONUS Continental Unites States of America DWD Deutscher Wetterdienst (Germany's National Meteorological Service) ECMWF European Centre for Medium-Range Weather Forecasts ERA5 ECMWF Reanalysis v5 EUMETSAT European Organisation for the Exploitation of Meteorological Satellites FAR False alarm rate FCDR Fundamental Climate Data Record FP False precipitation GCOS Global Climate Observing System GPCC Global Precipitation Climatology Centre GPCP Global Precipitation Climatology Project GPROF Goddard PROFiling algorithm HE Hit error HOAPS Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data HR Hit rate HSS Heideke Skill Score ISAC Institute of Atmospheric Sciences and Climate JND Joint normalised density KPI Key Performance Indicator ME Mean error MFP Mean false precipitation MHE Mean hit error MHS Microwave Humidity Sounder MMP Mean missed precipitation MP Missed precipitation MRMS Multi-Radar/Multi-Sensor System MW Microwave NIMROD Precipitation Radar Dataset for Europe NOAA National Oceanic and Atmospheric Administration OceanRAIN Ocean Rainfall And Ice-phase precipitation measurement Network PACRAIN Pacific Rainfall Database PNPR-CLIM Passive microwave Neural network Precipitation Retrieval for CLIMate applications POD Probability of detection PQAD Product Quality Assurance Document RMSE Root mean squared error RQI Radar Quality Index SMMR Scanning Multi-channel Microwave Radiometer SSM/I Special Sensor Microwave Imager SSMIS Special Sensor Microwave Imager / Sounder TMI TRMM Microwave Imager TRMM Tropical Rainfall Measuring Mission UTC Coordinated Universal Time WCRP World Climate Research Programme

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table1 table1

Table 1: ME, RMSE and CC between PNPR-CLIM and MRMS using the entire dataset.

In the following, two case studies of MHS/AMSU-B overpasses over the CONUS area are discussed. Both the PNPR-CLIM and MRMS instantaneous precipitation rates (the latter regridded to the MHS original grid) are displayed.

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Figure 4: Comparisons between PNPR-CLIM instantaneous precipitation rate retrieval (left panels) and the radar-based MRMS precipitation field regridded to the MHS original grid considering the antenna pattern (right panels) for two different scenes (upper and lower panels). The scene in the upper panels refers to the MHS, on-board MetOp-B, overpass at 02:37 UTC on 2017-04-03. The scene in the lower panels refers to the MHS, on-board NOAA19. overpass at 12:15 UTC on 2017-03-10.

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table2 table2

Table 2: Scan positions per instrument of the inner scan lines around the swath centre. As the TMI field of view is much smaller than the others (see the ATBD [D2]), we reject respective data here².

Table 3 contains the numbers of collocated data pairs, the respective detection statistics as well as mean error (ME) and RMSE and the correlation coefficient (CC) for various scenarios. Here, HOAPS v4 has been chosen as the reference dataset for PNPR-CLIM. All statistics refer to the entire timeline of collocated data pairs.

The “Swath Centre” scenario comprises all available data pairs as per the above requirements. HOAPS v4 sees on average 0.04 mm/h higher precipitation rates than PNPR-CLIM (ME). The RMSE lies at 0.24 mm/h, and the CC is above 0.6. The “Latitude” scenarios filter the “Swath Centre” pairs with respect to their zonal position in three latitude bands. Most statistics are best in low latitudes, but the RMSE is higher there, most likely due to heavy precipitation being represented differently in the two datasets. Finally, the “NOAA15” scenario uses the “Swath Centre” data pairs and retains only those for which NOAA15 is the respective PNPR-CLIM platform. This subset performs significantly worse than the “Swath Centre” set, in terms of HSS, ME, and CC. The deterioration of observations by NOAA15, whose identification led to NOAA15 data being phased out as soon as NOAA16 data were available, has already been discussed in the PUGS [D3] and will be discussed, for example, in section 2.3.2.1 of this document. The results here illustrate that the deterioration is already present in the instantaneous precipitation rate estimates.

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table3 table3

Table 3: Number of collocated data pairs, mean error (ME), root mean square error (RMSE), correlation coefficient (CC) and skill scores of PNPR-CLIM L2 data with respect to HOAPS L2 data. The number of non-precipitation events implies both datasets see zero precipitation in the respective pair.

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Figure 8: Scatter plot PNPR-CLIM vs. HOAPS (left panel) and associated histogram of differences PNPR-CLIM minus HOAPS (right panel) in the "Swath Centre" scenario. In the left panel, the grey solid line is the identity, the line of best linear fit is displayed as black dash-dotted line. 

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table4 table4

Table 4: Statistical results of the comparison of hourly gridded precipitation rate estimates in PNPR-CLIM (P) and HOAPS v4 (H). Collocated data pairs inside the specified latitudinal bands over the entire time period (2000–2017) are evaluated with the “validating dataset” as reference dataset for the “validated dataset”. ME and RMSE refer to the differences between the validated and validating datasets. Note that the scores (hit rate and HSS) vary only a little between uncorrected and bias-corrected data pairs, because only zero-precipitation events are mapped to zero precipitation during the bias correction. Small variations result from discarding certain data, see the ATBD [D2], thus reducing the database. Here, only the values for the bias-corrected data are given.

Figure 10 shows the distributions of differences in the 1DH PNPR-CLIM vs. HOAPS v4 comparison over the entire time period and the full latitudinal range, for uncorrected and bias-corrected data pairs. The distribution of differences of uncorrected values is slightly yet visibly skewed to the left (PNPR-CLIM underestimates HOAPS v4), which is not the case in the bias-corrected version, at least for small deviations from zero (≤ 0.25 mm/h).

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table5 table5

Table 5: Basic statistics for global mean differences of daily and monthly COBRA precipitation estimates with GPCP, ERA5 and GPCC datasets as references. GPCC covers land only, so COBRA data have been filtered respectively for this comparison. Minimum, maximum, mean, median, root mean square deviation and both quantiles are given in mm/d. The slope's unit is mm/d/decade. The second-to-last column indicates the fraction of temporal instances at which the target requirement of absolute differences between COBRA and a reference dataset staying below 0.3 mm/d is met. The last column ("slope") contains the linear trend in the respective time series of differences, see main text.

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Anchor note3 note3 3 Use only data since 2001-04-01, i.e. no NOAA15 data

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The error decomposition of the COBRA, GPCP and ERA5 1DD products, shown in figure 15, confirms the results outlined in section 2.3.2.2. ERA5 underestimates the convective precipitation in central Africa whereas it overestimates the precipitation over the central Pacific. The low estimates of COBRA in high latitudes, instead, are mainly due to missed precipitation, which is likely due to sensor limitations (see the final paragraph in section 2.1.2). Finally, the GPCP estimates turn out to be much more conservative with respect to the other products, manifesting in high false precipitation values of COBRA and, more severely, ERA5. Finally, the anomalous feature observed in Antarctica for COBRA, as shown in appendix 5.3 (figure 30 - – figure 32), is mainly limited to the year 2000 estimates, uniquely based on the NOAA15 AMSU-B measurements affected by large uncertainties. In conclusion, the three products show peculiar but comparable uncertainties between themselves.

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Some characteristics of the daily differences with MRMS are reported in table 6 (upper block), whereas the actual distributions (through frequencies and cumulative frequencies) are shown in figure 22. The GPCP error distribution has the widest shape, manifesting also in its extreme 5^th and 95^th percentiles. In contrast, COBRA and ERA5 distributions of errors are more concentrated around zero. In particular, 50% of the ERA5 errors are between 0 mm/d and 1 mm/d. For COBRA, this value is 40%, and for GPCP, it is 35%. Both GPCP and COBRA have 25% of their errors between -1 and 0 mm/d, while ERA5 counts less than 20% of instances in this range. Absolute errors above 4 mm/d stem from less than 20% of the entire population in each dataset. Despite the highlighted differences, all the products show similar ME and RMSE (-0.34 mm/d and 5.74 mm/d for COBRA, -0.28 mm/d and 5.83 mm/d for GPCP, -0.33 mm/d and 4.80 mm/d for ERA5). The CC, instead, are slightly different: 0.73 for COBRA, 0.67 for GPCP and 0.77 for ERA5.

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table6 table6

Table 6: Error 5^th and 95^th percentiles, ME, RMSE and CC of COBRA, GPCP and ERA5 against MRMS over the period 2016–2017 (upper block) and against NIMROD over the period 2002–2017 (lower block). Only pixels with daily average RQI greater than 0.8 have been considered with MRMS as reference.

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Figure 22: Daily Errors distribution of COBRA, GPCP and ERA5, with reference MRMS, over the period 2016–2017. Colored bars and dashed lines denote frequencies (left y-axis) and cumulative frequencies (right y-axis) respectively. Only pixels with daily average RQI greater than 0.8 have been considered.

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