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Depending on the data record producer, different product requirements may be applied and they are used to evaluate validation results. An often-used way to handle this is to define several levels of requirements where each level is linked to specific needs or priorities. A three-level approach like the following is rather common:
Requirement | Description |
|---|---|
Threshold requirement: | A product should at least fulfill this level to be considered |
Target requirement: | This is the main quality goal for a product. It should reach this level based on the current knowledge on what is reasonable to achieve. |
Optimal requirement: | This is a level where a product is considered to perform much better than expected given the current knowledge. |
Satellite product levels
Satellite-based products are often described as belonging to the following condensed description of processing levels, each one with different complexity and information content:
Level | Description |
|---|---|
Level-0: | Raw data coming directly from satellite sensors, often described as sensor counts. |
Level-1: | Data being enhanced with information on calibration and geolocation. Level-1a: Data with attached calibration and geolocation information Level-1b: Data with applied calibration and attached geolocation information Level-1c: Data with applied calibration and additional layers of geolocation, satellite viewing and solar angle information |
Level-2: | Derived geophysical variables at the same resolution and location as L1 source data. An often-used Level-2 variety is the following: Level-2b: Globally resampled images, two per day per satellite, describing both ascending (passing equator from south) and descending (passing equator from north) nodes. Resampling is based on the principle that the value for the pixel with the lowest satellite zenith angle is chosen in case two or several swaths are overlapping. |
Level-3: | Gridded data with results accumulated over time (e.g., monthly means). |
A more comprehensive definition of all processing levels is given here: https://www.earthdata.nasa.gov/engage/open-data-services-and-software/data-information-policy/data-levels.
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Table 2‑1: Summary of periods covered by GPCP TCDR and ICDR data as well as their respective reference datasets. The ICDR overview is valid for the monthly and daily GPCP CDR. TMPA is the TRMM Multi-satellite Precipitation Analysis. The definition and use of the reference datasets are further explained in the text.Anchor table2_1 table2_1
CDR type | CDRs Temporal resolution | Period | Reference dataset |
TCDR | Monthly | Jan 1st, 1979 – Dec 31st, 2017 | n/a |
Daily | Oct 1st, 1996 – Dec 31st, 2017 | n/a | |
ICDR | Monthly and daily | Jan 1st,2018 – Dec 31st, 2019 | TMPA |
Jan 1st,2020 – Jun 30th, 2022 | ERA5 |
For the GPCP TCDR v1.0, the targeted values for accuracy, i.e. absolute difference (or mean absolute error according to General definitions) and decadal stability are given in Table 2-2. Since these products are brokered from an external producer, the target requirements are based on published validation results achieved by the producer.
Table 2‑2: Target requirements for the GPCP precipitation product.Anchor table2_2 table2_2
Variable | KPI: accuracy (Mean absolute error) | KPI: decadal stability |
Precipitation | 0.3 mmday-1 | 0.034 mmday-1decade-1 |
For the GPCP ICDR v1.x, we adopt the strategy outlined in the Report on Updated KPIs [D4], i.e., the ICDR targets for accuracy are based on percentiles in the respective TCDR’s spatially averaged accuracy. With the monthly TMPA 3B43 product (TRMM, 2011) as reference dataset, the targeted upper and lower bounds for differences of spatial averages over low- to mid-latitudes (±50°) of the GPCP ICDR precipitation fields are given in Table 2-3, sub-divided for monthly and daily products. The respective PQAR [D5] contains the details of the analysis of the percentiles of the TCDR performance against the TMPA products.
Table 2‑3: KPIs for the ICDR of the GPCP precipitation product based on reference to the TMPA 3B43 product from TRMM.Anchor table2_3 table2_3
Variable | KPI: lower percentile (2.5 %) | KPI: higher percentile (97.5 %) |
Precipitation | Monthly: -0.175 mmday-1 Daily: -0.332 mmday-1 | Monthly: 0.189 mmday-1 Daily: 0.343 mmday-1 |
The TMPA products used as references for evaluating GPCP KPIs have been decommissioned and are only available until 12/2019. Consequently, for GPCP deliveries covering times after that date, we compare full global means (instead of latitudes between ±50°) with corresponding values in ERA5 (C3S, 2017) and the corresponding values are given in Table 2-4. The PQAR [D5] contains the details of the analysis of the percentiles of the TCDR performance against ERA5.
Table 2‑4: KPIs for the ICDR of the GPCP precipitation product based on reference to ERA5.Anchor table2_4 table2_4
Variable | KPI: lower percentile (2.5 %) | KPI: higher percentile (97.5 %) |
Precipitation | Monthly: -0.338 mmday-1 Daily: -0.525 mmday-1 | Monthly: -0.063 mmday-1 Daily: 0.022 mmday-1 |
Note that according to the Report on Updated KPIs [D4], the GPCP ICDR needs to be inside the above upper and lower bounds in 95% of all available time slices, which is verified through a binomial test (explained in General definitions).
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Product | Property | Target requirement | Comments | GCOS requirement |
GPCP TCDR | Accuracy | 0.3 mm/d | absolute difference (or mean absolute error according to General definitions) between spatially averaged GPCP fields and reference fields at each available time | 0.5 mm/h |
Stability | < 0.034 mm/d/dec | i.e. the linear trend of the accuracy time series | 0.02 mm/decade | |
Spatial resolution | 2.5°(monthly) 1° (daily) | 25 km at the equator corresponds to 0.25° | 25 km |
2.3 Data format and content issues
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Copernicus Climate Change Service (C3S), 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 21/12/2018. https://cds.climate.copernicus.eu/cdsapp#!/home.
GCOS (2016), THE GLOBAL OBSERVING SYSTEM FOR CLIMATE: IMPLEMENTATION NEEDS, GCOS GCOS-200, GOOS-214, https://library.wmo.int/doc_num.php?explnum_id=3417
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