Contributors: H. Konrad (DWD), GT. Panegrossi Sikorski (CNR ISACDWD), E. Cattani (CNR ISAC)Jaqueline Drücke (DWD)

Issued by: SMHI/Karl-Göran Karlsson

Date: 06/10/2023

Ref: C3S2_D312a_Lot1.3.1.1-2022_TRGAD-PREC_v1.1

Official reference number service contract: 2021/C3S2_312a_Lot1_DWD/SC1

History of modifications

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related_documents related_documents

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Related documents

Acronyms

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Acronym

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Definition

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ATBD

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Algorithm Theoretical Basis Document

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C3S

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Copernicus Climate Change Service

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CDR

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Climate Data Record

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CDRP

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Climate Data Record Program

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CDS

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Climate Data Store

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CNR

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Consiglio Nazionale delle Ricerche (National Research Council of Italy)

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DWD

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Deutscher Wetterdienst (Germany's National Meteorological Service)

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GCOS

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Global Climate Observing System

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GPCC

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Global Precipitation Climatology Centre

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GPCP

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Global Precipitation Climatology Project

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GPM

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Global Precipitation Measurement mission

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ICDR

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Interim Climate Data Record

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ISAC

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Istituto di Scienze dell'Atmosfera e del Clima (Institute of Atmospheric Science and Climate)

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KPI

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Key Performance Indicator

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NOAA

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National Oceanic and Atmospheric Administration

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PUGS

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Product User Guide and Specification

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TCDR

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Thematic Climate Data Record

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TMPA

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TRMM Multi-satellite Precipitation Analysis

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TRMM

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Tropical Rainfall Measurement Mission

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UMD

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University of Maryland

General definitions

The meaning of the terms uncertainty, accuracy and error is often difficult to interpret and may be treated differently in various referred documents. In this document we adopt the following interpretation:

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1. The systematic error
2. The random error
3. The time-dependent error

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Acronyms

List of tables

List of figures

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general_definition general_definition

General definitions

Climate data records

Climate data compilations from observations are most often referred to as Climate Data Records (CDRs). However, the data records from satellites may consist of different types of quantities, from original radiances to derived products. Radiance data of climate quality are defined as Fundamental Climate Data records (FCDRs) while data records consisting of satellite-derived geophysical products are defined as Thematic Climate Data Records (TCDRs). In the ideal case the TCDRs should be derived by methods using FCDRs as input. However, if standards for the used radiances have not fulfilled the strict requirements for being classified as FCDRs, these radiances may be denoted Fundamental Data Records (FDRs). Notice that TCDRs can currently be based on either FCDRs or FDRs.

A special case of TCDRs are data records produced with short latency (e.g., shortly after the end of a month). These are called Interim Climate Data Records (ICDRs). The word Interim means that the data record has a higher uncertainty than the original TCDR since it has not been possible to use exactly the same input data as for the TCDR due to the short latency. Interim also means that a user may have to wait for the next edition of the TCDR to get a fully consistent and homogenous climate data record that includes data from the period with ICDR data. Normally ICDRs behave very similar to TCDRs but continuous monitoring of their quality is recommended.

Notice that since ICDRs are continuous extensions of the TCDR they are also delivered at subsequent times in separate batches (numbered 1,2,3,...etc.) where each one covers a certain time period (e.g. a number of months). Thus, when formally describing the full ICDR in the text (i.e., using the name specified in the delivery list), the ICDR version number is given but the batch number is written in generic form using letter x, for example ICDR v1.x. This is just to indicate that the batch number is only describing a temporal increment of the product and not any change of the product.

Uncertainty parameters

The meaning of the terms uncertainty, accuracy and error is often difficult to interpret and may be treated differently in various referred documents. In this document, we adopt the following interpretation:

The accuracy, uncertainty or error of an estimated ECV is described by three differently contributing components:

1. The systematic error

2. The random error

3. The time-dependent error

The systematic error is commonly the mean error or the Bias. For non-Gaussian distributions of the error, the median or the mean absolute error can be a more useful quantity.

The random error is commonly the root-mean-squared deviation RMSD. Sometimes, the Bias is subtracted yielding the centred root-mean-squared deviation cRMSD. Notice that if the Bias is zero, the two mentioned quantities are equal and may be interpreted as the standard deviation of the error (often denoted standard error).

The time-dependent error is commonly the change in Bias over time (for ECVs over decades). We call this parameter stability.

More details on the estimation of these parameters are given in the Report on Updated KPIs (D4).

Testing the quality and consistency of TCDRs and ICDRs

This C3S project also deals with extensions of TCDRs, i.e. products derived from continued processing of the CDRs using the same methods and algorithms as originally used for TCDR production. We denote these CDRs Intermediate Climate Data Records, ICDRs. To evaluate the ICDR compliance with original TCDRs, a different approach in terms of defined requirements is followed. The ICDR is assessed on the basis of the TCDR distribution with respect to a reference validation source. After calculation of this distribution of differences, the ICDR is evaluated against the same reference and a binomial test is applied to verify that 95 % of the difference values lie within the upper and lower bounds of the TCDR difference distribution. The lower and upper bounds of the difference distribution is defined as the 2.5^th and 97.5^th percentiles of the difference distribution.

For further clarity, a binomial test is a way to test the statistical significance of deviations by referring to a theoretically expected distribution of observations. In this case, we use the theoretically expected distribution of observation differences which is estimated from the difference between TCDR results and corresponding results from a validation source. We now want to test if a corresponding but restricted, i.e., based on a shorter time series of ICDR results, difference distribution is similar in its shape to the original TCDR difference distribution. This can be tested by selecting one upper and one lower percentile in the original distribution (here, the 2.5th and 97.5th percentiles) and check how many samples will fall within or outside this restricted distribution if randomly extracting a number of samples. The resulting distribution of yes and no answers as a function of the number of samples can be described by the binomial distribution (see statistical standard literature for its definition). Consequently, this sample-based difference distribution from the ICDR can then be numerically compared with what could be expected from the reference distribution based on the TCDR. Based on this, one can judge whether the ICDR results are representative or not for the TCDR results. Deviations here would then indicate particular problems for the ICDR products (assuming that the character of reference observations does not change). 

More details on the estimation of errors and uncertainty parameters are given in the Report on Updated KPIs (D4).

Product requirements

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:

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:

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.

Radiation terms

Since satellite measurements are primarily about radiation measurements in different parts of the spectrum, some definitions or synonyms need to be explained. Roughly, the spectrum is usually sub-divided into one part where solar radiation dominates and one part where radiation emitted by the Earth and the atmosphere dominates.

The solar part is usually referred to as “visible (VIS)” radiation and covers approximately wavelengths smaller than 1 µm. Two sub-regions are often referred to, namely “ultraviolet (UV)” for radiation below approximately 0.38 µm, and “near-infrared (NIR)” for radiation between 0.78 µm and 1 µm (but sometimes claimed to continue up to 2.8 µm).

The part dominated by emitted radiation from the Earth is often referred to as “thermal” radiation. Common synonyms used are “infrared (IR)” or “terrestrial” radiation. Also here, we have several sub-regions defined. The “short-wave infrared (SWIR)” region is approximately defined by wavelengths between 1 µm and 2.5 µm. The “medium-wave infrared (MWIR)” region is approximately defined by wavelengths between 2.5 µm and 5 µm. The “long-wave” region, often simply referred to as just “infrared” to represent the bulk majority of radiation emitted by the Earth, defines radiation from approximately 5 µm up to about 1 mm. Radiation above 1 mm up to 10 cm is denoted “microwave (MW)” radiation.

Special terms

The term “AVHRR-heritage” is frequently used in the TRGAD documents. By this is meant spectral channels of other sensors than the AVHRR which show a close similarity (or heritage) to the AVHRR channels, i.e., having almost the same spectral characteristics.

A product is said to be “brokered” when an existing data record from an external source (i.e., not produced exclusively within this C3S project) is handled. This also means that target requirements for these products are set to their achieved validation results since the product was not developed and validated in the C3S project.

We can get a better idea of how accurate the final product values are by using the method of “error propagation”. It means that the retrieval method is capable of accounting for errors or uncertainties in the measurements or products used to derive the final product, e.g., radiances, input or ancillary data. In this way, the uncertainty of the final products can be estimated.

Radiation fluxes for radiation budget estimations are sometimes described as being “balanced”. It comes from the fact that instrument uncertainties for radiation budget measurements are often too high to be capable of providing accurate estimations of the net radiation fluxes at the top of atmosphere. Thus, balancing is a form of bias correction based on investigations of energy balance from other observations and model studies.

Calibration of radiances are sometimes described as based on “vicarious” methods. This indicates that there is no on-board mechanism on the satellite that provides the necessary calibration information. Consequently, parameters used in calibration equations have to be estimated retrospectively from historic data by use of additional references. For example, Earth surfaces which are considered to be invariant or stable are often used as reference targets for calibration of visible radiances.

An “OPeNDAP” server is an advanced software solution for remote data retrieval (see https://www.opendap.org/).

“Triple Collocation (TC)” is a large-scale validation technique by which error variances and data-truth correlation coefficients of three independent datasets can be estimated without a specific reference observation. For further details, see Stoffelen (1998).

Scope of the document

This document provides relevant information on requirements and gaps for one precipitation product. The product is the Precipitation from the Global Precipitation Climatology Project (GPCP) Thematic Climate Data Record (TCDR) v1.0 and its associated Interim Climate Data Record (ICDR) v1.x product. The document is divided into three parts. Part 1 describes the relevant product. Part 2 provides the target requirements for the product. Part 3 provides a past, present, and future gap analysis for the product and covers both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).

Executive summary

Precipitation products estimated from satellite sensors complete the description of the atmospheric part of the global water cycle when combined with Clouds and Water Vapour products. To get a full picture of the global water cycle, components from the Earth surface has to be added (e.g., evaporation, snow and ice accumulations/melting and runoff estimates).

In Phase 1, two precipitation products were delivered, where one was based on a mix of infrared visible and microwave imagery and another one was based purely on microwave imagery. In Phase 2, only one precipitation product has been delivered so far to C3S and it is described in this document together with target requirements and gap analyses regarding existing gaps in temporal coverage and methods. The product is produced by the Global Precipitation Climatology Project (GPCP) and is here given the product name

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Scope of the document

This document provides relevant information on requirements and gaps for the Precipitation GPCP TCDR v1.0 and ICDR v1.x. It is divided into three parts. Part 1 describes the product the present document refers to. Part 2 provides the target requirements for the product. Part 3 provides a past, present, and future gap analysis for the product and covers both gaps in the data availability and scientific gaps that could be addressed by further research activities (outside C3S).

Executive summary

The precipitation product by the Global Precipitation Climatology Project (GPCP) is described together with its target requirement. The accuracy requirement is expressed as the absolute difference (mean absolute error) between spatially averaged GPCP fields and reference fields and is set to 0.3 mm/day. The targeted value for stability, i.e. the linear trend of the accuracy time series is 0.034 mm/day/decade.

The product is generated through merging of microwave and infrared precipitation estimates and outgoing longwave radiation from satellites, combined with rain gauge analyses from the Global Precipitation Climatology Centre (GPCC) over land. The temporal and spatial availability of all input data sources are described including a description of the evolution over time of surface measurements used by GPCC and of the satellite-based precipitation estimates. It is important to note that GPCP itself does not distinguish between a temporally fixed TCDR and a continuously updated ICDR. Instead, GPCP updates its long-term daily and monthly products continuously. The TCDR/ICDR distinction is only introduced in the scope of the brokering of the GPCP product to C3S, in order to harmonize its structure with other data products. Thus, the temporal coverage for the TCDR of monthly data is 1979-2017 while the corresponding ICDR covers 2018 onwards.

A gap in global coverage by polar-orbiting satellites with infrared information is expected to occur in 2020 which will thereafter be partially filled by data from the EUMETSAT Polar System Second Generation satellites.

It is emphasized that the dedicated effort within this C3S project for developing an algorithm for precipitation retrieval will not be based on the GPCP product. Instead, it will be a microwave-based TCDR exploiting MW imagers and sounders available at all latitudes in the time period 2000-2017. The use of both imagers and sounders will ensure high spatial coverage and temporal sampling of the precipitation. The new product will also utilize data from satellite-borne precipitation radars and improved methods for data merging and calibration.

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Precipitation GPCP TCDR v1.0 + ICDR v1.x

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The GPCP global precipitation products merge rainfall estimates from several microwave- and infrared satellite-borne sensors and the gridded rain gauge-based product by the Global Precipitation Climatology Centre (GPCC). It is created and maintained by the GPCP team at the University of Maryland (UMD), where the intellectual property rights remain with, and brokered to the Climate Data Store (CDS) by the Copernicus Climate Change Service (C3S). The GPCP TCDR v1.0 covers the period 01/1979-12/2017 (monthly data, v2.3), or 10/1996-12/2017 (daily data, v1.3), respectively. The GPCP ICDR v1.x starts in 01/2018 and at the time of writing extends until 09/2020 for both monthly and daily data.

It should be noted that, in the case of GPCP, the distinction between TCDR and ICDR at the cutoff date 2017/12/31 is only made in the scope of the brokering to C3S, as the initial data providers at UMD continuously produce new data as new satellite and other data become available (see PUGS [D3]). There is also an interim GPCP monthly product with a latency of only a few days to weeks compared to two to three months for the final GPCP product, but this interim product is replaced on the servers by the final one and, due to the envisaged latency in updating GPCP in the CDS of three months, is not included in the data deliveries to C3S. Moreover, the GPCP-Interim product is constructed in a similar manner to the final GPCP monthly analysis, except that for the input gauge analysis, the GPCC First Guess product is replaced by the GPCC Monitoring Product. For this reason, the Interim product would not be homogeneous to the provided GPCP TCDR. Finally, UMD interprets the GPCP-Interim as a provisional product and strongly discouraged from using it for months when the full GPCP is available.

Likewise, the version numbering for GPCP products in C3S (v1.0 / v1.x) is only introduced in the scope of the brokering. The actual dataset versions (v2.3 for monthly, v1.3 for daily data) are the relevant ones for outreach to users.

The spatial coverage is global, at a resolution of 2.5 degree (monthly data), or 1.0 degree (daily data), respectively. A list of input data is given in Table 1 in the PUGS [D3], based on Tables 1 and 2 in the C-ATBD [D1]. An overview is also given in this document, Section 3.1.1. The methods used for generating the dataset are described in the C-ATBD [D1] and Adler et al. (2003). Recent changes related to the upgrade to latest version (v2.3) are discussed by Adler et al. (2018).

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For the GPCP TCDR v1.0, the targeted value for accuracy, i.e. absolute difference (or mean absolute error according to General definitions) between spatially averaged GPCP fields and reference fields at each available time, is 0.3 mm/d or less. The targeted value for stability, i.e. the linear trend of the accuracy time series is 0.034 mm/d/dec or less.

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 0.189 mm/d and -0.175 mm/d, respectively. For the daily GPCP ICDR (reference dataset: daily TMPA 3B42 product; Goddard Earth Sciences Data and Information Services Center, 2016), the bounds are 0.343 mm/d and -0.332 mm/d. The respective PQAR [D5] contains the details of the analysis of the percentiles of the TCDR performance against the TMPA products.

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). The targeted upper and lower bounds for the differences between GPCP monthly data and ERA5 are -0.338 mm/d and -0.063 mm/d. For the evaluation of daily data, these bounds are -0.535 mm/d and 0.017 mm/d. The PQAR [D5] contains the details of the analysis of the percentiles of the TCDR performance against ERA5.

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.

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GCOS formulates requirements for precipitation products only for monthly resolution (GCOS, 2016, table 23). The given accuracy target of 0.5 mm/h [sic] is less strict than the one applied here; the stability target is stricter than the one applied here (0.02 mm/decade [sic]).

2.1.3 Data format and content issues

The data are provided in one NetCDF file per month (monthly product) or per day (daily product). The files are compliant with the Climate and Forecast (CF) 1.6 convention and the Attribute Convention for Dataset Discovery (ACDD) 1.3. The products are both on a global equidistant latitude/longitude grid (Level 3), with a 2.5° (monthly), or a 1.0° (daily) spacing. The C-ATBDs [D1, D2] list all input and ancillary data at the respective epochs when they were used. Errors are only propagated and provided for the monthly product.

There are no updates for the previously delivered TCDR. However, the dataset itself and thus later deliveries of the related ICDR, are updated monthly with a latency of two to three months.

The GPCP monthly product is complete within its temporal and spatial scope. At some dates, there are missing values in a small number of grid cells in the case of the GPCP daily product. Details on missing values are provided in the Product Quality Assessment Report [D5].

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The GPCP TCDR v1.0 / ICDR v1.x monthly product is generated through merging of microwave and infrared precipitation estimates, and outgoing longwave radiation retrievals from satellites, combined with rain gauge analyses from the Global Precipitation Climatology Centre (GPCC) over
land.

The microwave estimates are mainly based on:

1. Data from the Special Sensor Microwave/Imager (SSM/I) and its successor Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. The SSMI and SSMIS instruments provide the key input data that are used for low latitudes (40°N-40°S) within GPCP over both ocean and land for the period 1987–present, with the beginning of the operational activities of SSMIS sensor in 2009 (Adler et al., 2018).
2. The precipitation products from the TIROS Operational Vertical Sounder (TOVS, temporal coverage 1987-2003) and Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) flying on NASA’s Aqua satellite (operational since 2003) are exploited for filling in SSM/I-SSMIS data voids at higher latitudes (polar and cold land regions) caused by the shortcomings in precipitation retrieval over frozen surfaces (Adler et al., 2003; 2018).
3. For the whole duration of the GPCP dataset the geosynchronous IR-based estimates are obtained through the Geostationary Operational Environmental Satellite (GOES) precipitation Index (GPI; Arkin and Meisner, 1987) technique applied to GOES, METEOSAT, and Geostationary Meteorological Satellite (GMS)-Multifunction Transport Satellite (MTSAT)-Himawary-8 geostationary satellite series data.
4. In case of unavailable geostationary IR data, the NOAA Advanced Very High Resolution Radiometer (AVHRR) observations are used.
5. The Outgoing Longwave Radiation (OLR) Precipitation Index (OPI) monthly precipitation estimates based on IR data from all NOAA-series satellites are employed prior the SSM/I era to extend GPCP data back to 1979 (Adler et al., 2003; Xie and Arkin, 1998).
6. GPCC data: a new set of rain gauge data was introduced in the GPCP monthly v2.3, i.e. the GPCC Full analysis v7 for the period 1979-2013 and the GPCC Monitoring analysis v5 for 2014 and beyond (Adler et al., 2018)

The GPCP CDR (including TCDR and ICDR) is characterized by changes in the satellite instrumentations (e.g. transitions from SSM/I to SSMIS, from TOVS to AIRS/AMSU, and the sequence of satellites in the various geostationary programs), that imply variations in the sensor spectral channels exploited in the retrieval algorithms and their calibrations, and could potentially introduce inhomogeneities into the data set. This issue is considered in GPCP by considering appropriate overlap periods between the sensors in order to adjust the estimates from the newer sensors to those of the older sensors with a longer period of use, and by continuously monitoring the CDR for the presence of anomalous behaviors. In the GPCP monthly v2.3 CDR, brokered for the CDS, a small negative anomaly in the mean global ocean precipitation leading to an incipient downward trend has been solved (Adler et al., 2018). The issue was connected to a discontinuity generated by the transition from the SSM/I to SSMIS and a TOVS sensor degradation, which likely compromised the precipitation estimates at the end of the TOVS period.

The EO data relevant to the generation of the GPCP One-Degree-Daily (1DD) product are the same already described for the monthly product although the retrieval algorithm is different from the one used for the GPCP monthly analysis (Huffman et al., 2001). The GPCP 1DD makes use of the IR
brightness temperatures from the geostationary constellation and AVHRR to extract the precipitation estimates in the range 40°N-40°S through the Threshold-Matched Precipitation Index (TMPI) technique. This approach is an adaptation of the GPI, where the SSM/I-SSMIS–based precipitation frequency (from GPROF algorithm) and the GPCC monthly precipitation estimate are exploited to locally constrain the TMPI IR brightness temperature threshold and conditional rain rate, respectively. Finally, TOVS-AIRS-AMSU data are used to produce the precipitation estimates outside the latitudinal band 40°N-40°S.

Due to the orbital drift of existing satellites, the approaching expected end of satellite lifetimes and the termination of the US Defense Meteorological Satellite Program, a gap in global coverage by polar-orbiting satellites is expected to occur in 2020. As of November 2020, all DMSP platforms are beyond life time and might experience channel losses or total failure any time. Planned satellite mission to partially fill this gap are the EUMETSAT Polar System Second Generation satellites.

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The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the algorithm in its current form. The change in instrumentation mentioned in Section 3.1.1 is a certainly a challenge when developing the algorithm, but issues are constantly identified and the algorithm improved if feasible (Section 3.1.1).

Precipitation is a notoriously difficult quantity to retrieve from satellite data, due, for example, to its intermittent nature in space and time and to the many phases in which it occurs. Many research groups are dedicated to improving existing and designing new algorithms, and upcoming CDRs will profit from this development, for example also in the case of the MW precipitation product developed directly for C3S, due to be completed in 2021 (see below for more details).

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There is no uncertainty estimate provided with the GPCP daily precipitation product. The GPCP monthly precipitation estimate includes a measure for pixel-wise precision, propagated from the raw observations and intermediate satellite products. An assessment of the provided uncertainty is included in the respective Product Quality Assessment Report [D5]. The uncertainties represent at least a one-sigma range when assessed against differences between GPCP and ERA-5 or the monthly TMPA product (TRMM, 2011; Goddard Earth Sciences Data and Information Services Center, 2016), see the PQAR [D5]).

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The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the product in its current form. It should, however, be noted that the formulated GCOS requirement for spatial resolution (25 km,
corresponding to ~1/4° at the equator) is not met by the GPCP products (2.5° and 1.0°). Also, the daily product is not fulfilling the requirement on accuracy [D5].

However, there is a dedicated effort within C3S for developing an algorithm for precipitation retrieval, although not based on the GPCP product. The microwave-based (MW-based) TCDR will be based on the exploitation of MW imagers and sounders Level 1 FCDRs available at all latitudes between 2000 and 2017. As opposed to GPCP, where SSMI/SSMIS microwave frequencies < 90 GHz are used, the algorithm for the envisaged instruments, the Microwave Humidity Sounder (MHS) and its predecessor, the Advanced Microwave Sounding Unit B (AMSU-B), will be based on the exploitation of high-frequency channels (> 90 GHz), sensitive also to light precipitation at mid-high latitudes. The use of both imagers and sounders will ensure high spatial coverage and temporal sampling of the precipitation. MW imagers and sounders precipitation rate estimates will be merged and calibrated to provide global MW-based daily and monthly precipitation TCDRs on a 1°x1° grid.

DMSP satellites carrying SSM/I and SSMIS mostly have local overpassing times in the early morning. It is likely that the respective systematic sampling introduces a bias into the retrieved average daily (or even monthly) precipitation rates in areas where precipitation follows a diurnal pattern. The MW-product to be developed within C3S will circumvent these sampling preferences at least partially by including precipitation rates retrieved by MHS and AMSU-B. These are onboard NOAA and METOP satellites which, due to their orbit distribution, will introduce a more complete coverage of the day and thus improve the diurnal sampling. The originally envisaged temporal coverage for the newly developed product was 1999-2016. However, due to just one MW sounder platform available in 1999, with many observations compromised, this time span has been shifted to 2000-2017 for which the availability of high-quality MW sounder data is much better. This change has been communicated to and accepted by C3S.

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The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the product in its current form. Research related to the improvement of the precipitation CDR will be carried out within the development of the MW-based precipitation TCDR within C3S. The quality of satellite daily and monthly MW-based precipitation is critically dependent on the number and on the quality of MW radiometers used to ensure maximum spatial and temporal coverage, see section 3.4.1. The MW-based precipitation TCDR developed within C3S, and complementing the GPCP product, will be based on the exploitation of Level 1 FCDRs available for SSM/I / SSMIS MW imagers and MHS/AMSU-B MW sounders available between 2000 and 2017. There are two areas of research that will be explored
during the development of MW-based TCDRs:

1. MW-based precipitation development and quality assessment will benefit from the availability of unique cloud and precipitation observations by the two spaceborne radars currently available: the Dual-frequency Precipitation Radar (DPR) on board the NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory, available since March 2014, and the NASA CloudSat Cloud Profiling Radar (CPR) available since 2006. The multi-year, quasi-global, and complementary DPR and CPR measurements offer a unique and extensive resource to analyze spaceborne MW radiometer precipitation observational capabilities. This is particularly useful in remote areas and/or where ground-based observations are sparse or not available (e.g., high latitudes), and in conditions and regimes where MW precipitation retrieval is more challenging (e.g., light precipitation and snowfall).
2. Advanced merging and calibration procedure will be developed to account for inhomogeneity between MW imagers and sounders precipitation rate estimates related to differences in the processing algorithms, differences in sensor spectral channels, viewing geometry, spatial resolution. Error characterization will account for the outcome of the merging and calibration procedure and for the presence of anomalous behaviors in the TCDRs.

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Authority over which satellite data are used for the GPCP products lies in the hands of the GPCP development team. They have adapted new sensors and missions when feasible or necessary in the past and will likely continue to do so.

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The accuracy requirement is expressed as the absolute difference (mean absolute error) between spatially averaged results and reference measurements and is set to 0.3 mm/day. The targeted value for stability, i.e. the linear trend of the accuracy time series is 0.034 mm/day/decade.

The GPCP product is generated through merging of microwave and infrared precipitation estimates and outgoing longwave radiation from satellites, combined with rain gauge analyses from the Global Precipitation Climatology Centre (GPCC) over land. The temporal and spatial availability of all satellite-based input data sources are described as well as a description of the evolution over time of surface measurements used by GPCC and of the satellite-based precipitation estimates. It is important to note that GPCP itself does not distinguish between a temporally fixed TCDR and a continuously updated ICDR. Instead, GPCP updates its long-term daily and monthly products continuously. The TCDR/ICDR distinction is only introduced in the scope of the brokering of the GPCP product to C3S, in order to harmonize its structure with other data products. Thus, the temporal coverage for the TCDR of monthly data is 1979–2017 while the corresponding ICDR covers 2018 onwards.

A gap in global coverage by polar-orbiting satellites with infrared information has occurred after 2020 which will thereafter be partially filled by data from the EUMETSAT Polar System Second Generation (EPS-SG) satellites.

1. Product description for GPCP TCDR v1.0 + ICDR v1.x

Precipitation products estimated from satellite sensors complete the description of the atmospheric part of the global water cycle when combined with Clouds and Water Vapour products. To get a full picture of the global water cycle, components from the Earth surface have to be added (e.g., evaporation, ice and snow accumulation/melting and runoff estimates). 

The GPCP global precipitation products merges rainfall estimates derived from several microwave- and infrared satellite-borne sensor measurements combined with the gridded rain gauge-based product produced by the Global Precipitation Climatology Centre (GPCC). The product is created and maintained by the GPCP team at the University of Maryland (UMD), where the intellectual property rights remain, and it is brokered to the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). The GPCP TCDR v1.0 covers the period 01/1979–12/2017 (monthly data), and 10/1996–12/2017 (daily data), respectively. The GPCP ICDR v1.x starts in 01/2018 and at the time of writing extends until 06/2022 for both monthly and daily data.

It should be noted that the distinction between the TCDR and ICDR at the cutoff date 31/12/2017 is partly artificial since ICDR products are almost identical to the TCDR products. UMD continuously produces new products as new satellite and other data become available (see [D3]). The ICDR for the C3S project is just supplying these new results when they are made available. The only difference to the TCDR is a slightly different definition of the input rain gauge analysis.

The spatial coverage is global, at a resolution of 2.5 degree (monthly data), or 1.0 degree (daily data). A list of input data is given in Table 1 in the PUGS [D3], based on Tables 1 and 2 in the C-ATBD [D1]. An overview of the input data is also given in this document. The methods used for generating the dataset are described in the C-ATBD [D1] and in Adler et al. (2003). The C3S data record also cover changes made to improve the homogeneity of the product, especially after 2002. These changes are discussed by Adler et al. (2018).

2. User Requirements for GPCP TCDR v1.0 +ICDR v1.x

This section describes the requirements which have been set to be achieved by the described products. Requirements can be set at different levels (as explained in the section with General definitions) but here we will focus on what is called the Target Requirements. These requirements define the main goals for data producers which have to be fulfilled by their products. Requirements are specified by the use of various accuracy parameters which are also listed in the section with General definitions. Observe that for brokered products the target requirements are set to the achieved validation results since these products are not developed and tested within the C3S project.

Concerning products to be used in climate monitoring, requirements for what should be achievable through Earth Observation systems are generally defined by the World Meteorological Organisation (WMO) Global Climate Observation System (GCOS) expert panel. However, these requirements are generally oriented towards the capability and resolution of climate models with a rather course spatial resolution while many products listed here are focusing more on the monitoring of local and regional scale conditions. Also, they are often not attainable using existing or historical observing systems. Thus, GCOS requirements are not always identical to the requirements listed here since also other user groups than the climate modelling community have contributed in setting the requirements. However, the relation to GCOS requirements are discussed below for each individual product.

2.1 Summary of target requirements (KPIs)

An overview of periods, which have been defined as TCDR and ICDR, is presented in Table 2-1. It should be noted that this distinction between TCDR and ICDR is artificial in this case and only imposed here for practical reasons. Both data records consist of the same products but the first delivery to C3S, which covers data from 1979-2017, is denoted the TCDR while the extensions (delivered regularly in batches) after 2017 is named the ICDR. Thus, the original data provider does not differentiate between TCDR and ICDR. KPIs for ICDR datasets are based on binomial tests of differences between the product and an external reference (as explained in General definitions and in [D4]). In this case, several reference datasets have been used. These reference datasets combined with their period of usage are given in Table 2-1. The time periods and reference datasets of the ICDR are valid for both the monthly and daily GPCP CDRs.

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.

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.

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.

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).

2.2 Discussion of requirements with respect to GCOS and other requirements

GCOS formulates requirements for precipitation products only for monthly resolution (GCOS, 2016, table 23). GPCP TCDR and GCOS requirements are given in Table 2-5. The given accuracy target of 0.5 mm/h is less strict than the one applied here; the stability target is stricter than the one applied here (0.02 mm/decade). The GCOS requirement for spatial resolution is 25 km, which corresponds to about 0.25 degree at the equator. The GPCP monthly and daily products have a spatial resolution of 2.5 and 1 degree, respectively. Thus, they do not meet the GCOS spatial resolution requirements.

2.3 Data format and content issues

The data are provided in one NetCDF file per month (monthly product) or per day (daily product). The files are compliant with the Climate and Forecast (CF) 1.6 convention and the Attribute Convention for Dataset Discovery (ACDD) 1.3. The products are both on a global equidistant latitude/longitude grid (Level 3), with a 2.5° (monthly), or a 1.0° (daily) spacing. The C-ATBDs [D1, D2] list all input and ancillary data at the respective epochs when they were used. Errors are only propagated and provided for the monthly product.

The ICDR is updated monthly with a latency of two to three months.

The GPCP monthly product is complete within its temporal and spatial scope. At some dates, there are missing values in a small number of grid cells in the case of the GPCP daily product. Details on missing values are provided in the Product Quality Assessment Report [D5].

3. Gap Analysis for GPCP TCDR v1.0 + ICDR v1.x

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3.1 Description of past, current and future satellite coverage

The GPCP TCDR v1.0 / ICDR v1.x monthly product is generated through merging of microwave and infrared precipitation estimates, and outgoing longwave radiation retrievals from satellites, combined with rain gauge analyses from the Global Precipitation Climatology Centre (GPCC) over land.

The GCPC monthly estimates are mainly based on:

1. Data from the Special Sensor Microwave/Imager (SSM/I) and its successor Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. The SSM/I and SSMIS instruments provide the key input data that are used for low latitudes (40°N-40°S) within GPCP over both ocean and land for the period 1987–present, with the beginning of the operational activities of the SSMIS sensor in 2009 (Adler et al., 2018).

2. The precipitation products from the TIROS Operational Vertical Sounder (TOVS, temporal coverage 1987–2003) and Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) flying on NASA’s Aqua satellite (operational since 2003) are exploited for filling in SSM/I-SSMIS data voids at higher latitudes (polar and cold land regions) caused by the shortcomings in precipitation retrieval over frozen surfaces (Adler et al., 2003; 2018).

3. For the whole duration of the GPCP dataset the geosynchronous IR-based estimates are obtained through the Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI; Arkin and Meisner, 1987) technique applied to GOES, METEOSAT, and Geostationary Meteorological Satellite (GMS)-Multifunction Transport Satellite (MTSAT)-Himawary-8 geostationary satellite series data.

4. In case of unavailable geostationary IR data, the NOAA Advanced Very High Resolution Radiometer (AVHRR) observations are used.

5. The Outgoing Longwave Radiation (OLR) Precipitation Index (OPI) monthly precipitation estimates based on IR data from all NOAA-series satellites are employed prior the SSM/I era to extend GPCP data back to 1979 (Adler et al., 2003; Xie and Arkin, 1998).

6. GPCC data: a new set of rain gauge data was introduced in the GPCP monthly v2.3, i.e. the GPCC Full analysis v7 for the period 1979–2013 and the GPCC Monitoring analysis v5 for 2014 and beyond (for full descriptions of these versions, see Adler et al., 2018).

The GPCP CDR (including TCDR and ICDR) is characterized by changes in the satellite instrumentations (e.g. transitions from SSM/I to SSMIS, from TOVS to AIRS/AMSU, and the sequence of satellites in the various geostationary programs), that imply variations in the sensor spectral channels exploited in the retrieval algorithms and their calibrations, and could potentially introduce inhomogeneities into the data set. This issue is considered in GPCP by considering appropriate overlap periods between the sensors in order to adjust the estimates from the newer sensors to those of the older sensors with a longer period of use, and by continuously monitoring the CDR for the presence of anomalous behaviors. In the GPCP monthly v2.3 CDR, brokered for the CDS, a small negative anomaly in the mean global ocean precipitation leading to an incipient downward trend has been solved (Adler et al., 2018). The issue was connected to a discontinuity generated by the transition from the SSM/I to SSMIS and a TOVS sensor degradation, which likely compromised the precipitation estimates at the end of the TOVS period.

The EO data relevant to the generation of the GPCP One-Degree-Daily (1DD) product are the same as those already described for the monthly product although the retrieval algorithm is different from the one used for the GPCP monthly analysis (Huffman et al., 2001). The GPCP 1DD makes use of the IR brightness temperatures from the geostationary constellation and AVHRR to extract the precipitation estimates in the range 40°N–40°S through the Threshold-Matched Precipitation Index (TMPI) technique (Huffman et al., 2016). This approach is an adaptation of the GPI, where the SSM/I-SSMIS-based precipitation frequency (from the GPROF algorithm) and the GPCC monthly precipitation estimate are exploited to locally constrain the TMPI IR brightness temperature threshold and conditional rain rate, respectively. Finally, TOVS-AIRS-AMSU data are used to produce the precipitation estimates outside the latitudinal band 40°N–40°S.

Due to the orbital drift of existing satellites, the approaching expected end of satellite lifetimes and the termination of the US Defense Meteorological Satellite Program, a gap in global coverage by polar-orbiting satellites is expected to occur after 2020. As of November 2020, all DMSP platforms are beyond life time and might experience channel losses or total failure any time. We expect data availability until the end of 2023 and we did not detect any failures or losses of data until now. Planned satellite mission to partially fill this gap are the EUMETSAT Polar System Second Generation satellites.

3.2 Development of processing algorithms

The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the algorithm in its current form. The change in instrumentation mentioned in Section 3.1 is certainly a challenge when developing the algorithm, but issues are constantly identified and the algorithm improved if feasible (Section 3.1).

Precipitation is a notoriously difficult quantity to retrieve from satellite data, due, for example, to its intermittent nature in space and time and to the many phases (i.e., vapour, liquid, solid) in which it occurs. Many research groups are improving existing and designing new algorithms, and upcoming CDRs will take advantage of such developments.

3.3 Methods for estimating uncertainties

There is no uncertainty estimate provided with the GPCP daily precipitation product. The GPCP monthly precipitation estimate includes a measure for pixel-wise precision, propagated from the raw observations and intermediate satellite products. An assessment of the provided uncertainty is included in the respective Product Quality Assessment Report [D5]. The uncertainties represent at least a one-sigma range when assessed against differences between GPCP and ERA-5 or the monthly TMPA product (TRMM, 2011; Goddard Earth Sciences Data and Information Services Center, 2016), see the PQAR [D5]).

3.4 Opportunities to improve quality and fitness-for-purpose of the CDRs

The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the product in its current form. It should, however, be noted that the formulated GCOS requirement for spatial resolution (25 km, corresponding to ~1/4° at the equator) is not met by the GPCP products (2.5° and 1.0°). Also, the daily product is not fulfilling the requirement on accuracy [D5]. There are plans to improve both the spatial and temporal resolution in the next generation GPCP products (as outlined at https://www.earthdata.nasa.gov/esds/competitive-programs/measures/next-generation-global-precipitation-climatology-project-gpcp-data-products).

3.5 Scientific Research needs

The processing algorithm and its further development lie in the hands of the GPCP authors. In the scope of brokering the dataset to the Climate Data Store, we accept the product in its current form.

3.6 Opportunities from exploiting the Sentinels and any other relevant satellite

Authority over which satellite data are used for the GPCP products lies in the hands of the GPCP development team. They have adapted new sensors and missions when feasible or necessary in the past and will likely continue to do so.

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References

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