Atmospheric Research 197 (2017) 1–20

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Atmospheric Research
journal homepage: www.elsevier.com/locate/atmosres

Invited review article

Global precipitation measurements for validating climate models
a,⁎

a

b

c

d

MARK
d,e

F.J. Tapiador , A. Navarro , V. Levizzani , E. García-Ortega , G.J. Huﬀman , C. Kidd ,P.A. Kuceraf, C.D. Kummerowg, H. Masunagah, W.A. Peterseni, R. Rocaj, J.-L. Sánchezc,
W.-K. Taod, F.J. Turkka

University of Castilla-La Mancha (UCLM), Department of Environmental Sciences, Institute of Environmental Sciences, Toledo, Spain
National Council of Research, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Bologna, ItalycInstitute of Environment, University of León, SpaindNASA-Goddard Space Flight Center, Greenbelt, MD, USAe
University of Maryland, College Park, MD, USAf
National Center for Atmospheric Research, Boulder, CO, USAg
Colorado State University, Ft. Collins, CO, USAhInstitute for Space-Earth Environmental Research, Nagoya University, Japani
NASA-Marshall Space Flight Center, Huntsville, AL, USAj
OMP/LEGOS, Toulouse, FrancekNASA-Jet Propulsion Laboratory, Pasadena, CA, USAb

A B S T R A C T
The advent of global precipitation data sets with increasing temporal span has made it possible to use them forvalidating climate models. In order to fulﬁll the requirement of global coverage, existing products integrate
satellite-derived retrievals from many sensors with direct ground observations (gauges, disdrometers, radars),
which are used as reference for the satellites. While the resulting product can be deemed as the best-available
source of quality validation data, awareness of the limitations of such data sets is important to avoid extracting
wrong or unsubstantiated conclusions when assessing climate model abilities. This paper provides guidance on
the use of precipitation data sets for climate research, including model validation and veriﬁcation for improving
physical parameterizations. The strengths and limitations of the data sets for climate modeling applications arepresented, and a protocol for quality assurance of both observational databases and models is discussed. The

Abbreviations: 4D, Four-dimensional; 1DD, GPCP One-Degree Daily; AIRS, Atmospheric Infrared Sounder; ANN, Artiﬁcial neural networks; APHRODITE, Asian Precipitation - HighlyResolved Observational Data Integration Towards Evaluation; AR5, IPCC Fifth Assessment Report; ARM, Atmospheric Radiation Measurement; ATBD, Algorithm Theoretical Basis
Document; BIAS, Bristol-NOAA InterActive Scheme; C3VP, Canadian CloudSat/CALIPSO Validation Program; CaPPM, Cloud and Precipitation Processes Mission; CDR, Climate Data
Records; CESM, Community Earth System Model; CFAD, Contoured-Frequency-by-Altitude Diagram; CFSR, Climate Forecast System Reanalysis; CHIRPS, Climate Hazards group Infrared
Precipitation with Station; CMAP, CPC Merged Analysis of Precipitation; CMORPH, CPC MORPHing technique; CORDEX, Coordinated Regional Climate Downscaling Experiment;
COSMO, Consortium for Small-scale Modeling; CPC, Climate Prediction Center; CRM, Cloud Resolving Model; CRU, Climate Research Unit; CSH, Convective-Stratiform Heating; CST,
Convective-Stratiform Technique; DPR, Dual-frequency Precipitation Radar; DSD, Drop Size Distribution; ECA, European Climate Assessment; ENSO, El Nino-Southern Oscillation; ERA-I,
European Centre for Medium-Range Weather Forecast (ECMWF) interim reanalysis; GCE, Goddard Cumulus Ensemble; GCM, General Circulation Model/Global Climate Model; G-CRM,
Global Cloud Resolving Model; GHCN, Global Historical Climatology Network; GMI, GPM Microwave Imager; GPCC, Global Precipitation Climatology Centre; GPCP, Global Precipitation
Climatology Project; GPI, Global Precipitation Index; GPM, Global Precipitation Measurement mission; GPROF, Goddard proﬁling algorithm; G-SDSU, Goddard Satellite Data Simulator
Unit; GSMAP, Global Satellite Map Product; GV, Ground validation; HH, Hydrometeor Heating; HR-GCM, High Resolution General Circulation Model; HRPP, High-Resolution
Precipitation Products; iFloodS, Iowa Flood Studies; IMERG, Integrated Multi-satellitE Retrievals for GPM; IPCC, Intergovernmental Panel on Climate Change; ipHex, Integrated
Precipitation and Hydrology Experiment; IPSL, Institut Pierre Simon Laplace; IR, Infrared radiation; ITCZ, Intertropical Convergence Zone; JCR, Journal Citation Reports; JRA25,
Japanese 25-year Reanalysis; LH, Latent Heat; LMDZ, Laboratoire de Météorologie Dynamique Zoom; MERRA, Modern-Era Retrospective analysis for Research and Applications; MP,
Microphysics; NRL, Naval Research Laboratory; NU-WRF, NASA-Uniﬁed Weather Research and Forecasting; OLYMPEX, Olympic Mountain Experiment; PBL, Planetary Boundary Layer;
PDF, Probability distribution function; PERSIANN, Precipitation Estimation from Remote-Sensed Information using ANN; PMIR, Passive Microwave-InfraRed; PMW, Passive Microwave;
PR, Precipitation Radar; PRESTORM, Preliminary Regional Experiment for STORM-Central; PRH, Precipitation Radar Heating; QA, Quality Assurance; QC, Quality Control; RAMS,
Regional Atmospheric Modeling System; RCM, Regional Climate Model; REFAME, Rain Estimation using Forward Adjusted-advection of Microwave Estimates; RSS, Remote Sensing
Systems; SBM, Spectral bin microphysics; SG, Satellite-Gauge; SLH, Spectral Latent Heating; SSM/I, Special Sensor Microwave Imager; SSMIS, Special Sensor Microwave Imager/Sounder;
TAMSAT, Tropical Applications of Meteorology using SATellite data and ground-based observations; TMI, TRMM Microwave Imager; TMPA, TRMM Multi-Satellite Precipitation Analysis;
TMPI, Threshold-Matched Precipitation Index; TOVS, TIROS Operational Vertical Sounder; TRMM, Tropical Rainfall Measuring Mission; VIS, Visible; WRF-SBM, Weather Research andForecasting – Spectral Bin Microphysics⁎

Corresponding author.
E-mail address: Francisco.Tapiador@uclm.es (F.J. Tapiador).

http://dx.doi.org/10.1016/j.atmosres.2017.06.021
Received 7 April 2017; Received in revised form 21 May 2017; Accepted 19 June 2017
Available online 20 June 20170169-8095/ © 2017 Published by Elsevier B.V.

Atmospheric Research 197 (2017) 1–20

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paper helps elaborating the recent IPCC AR5 acknowledgment of large observational uncertainties in precipitation observations for climate model validation.

1. Introductionhydrological cycle, complete, global precipitation data sets are required
and much eﬀort is spent on providing climatologically-sound data sets,with particular attention paid to avoid possible inconsistencies in suchproducts. However, note that at present no single-source of global
precipitation measurements exists (Michaelides et al., 2009). Many
single-source data sets exist that provide climate-scale precipitation
products, some of which are combined to provide multi-sourced global
precipitation products.
In terms of coverage, surface data per se essentially refers to landonly measurements (including islands). Even over the land areas there
is great variation in the availability and density of the observations(Kidd et al., 2017), which aﬀects the representativeness of the measurements. Over the oceans, the few islands that provide measurements
do not adequately represent the precipitation over the surrounding
oceans or even accurately represent the immediate oceanic surroundings. Some land areas are now covered by surface-based radar networks; these regions tend to have also adequate gauge measurements,
but gauges don't adequately capture the spatial variability of rainfallthat radar can provide. Satellite data sets, although touted as ‘global’
are usually nearly global, typically being limited to 60°S to 60°N due tothe extent of the available satellite observations, or to the limitations in
the retrieval schemes.

Precipitation is a major element in the Earth's hydrological cycle
and at the same time is tied dynamically to the atmospheric circulation
by redistributing the latent heating through the troposphere.
Precipitation thus serves as a critical linkage between the global waterand energy cycles.
Among the key questions in this outstanding research topic is how
much global precipitation has been changing over time in association
with global warming. It is known that precipitable water has increased
with temperature nearly as rapidly as predicted from the ClausiusClapeyron eq. (6–7% K− 1), while the rate of global precipitation
change is expected to be only a few % K− 1 at best (e.g., Allen andIngram, 2002). The lower amplitude increase in precipitation may beunderstood in terms of the energy budget constraint that latent heatingmust be balanced primarily by the atmospheric radiative cooling(Mitchell et al., 1987).
The fact that water vapor increases faster than precipitation on a
global scale suggests that as a whole the Earth's hydrological cycle
slows down in the warming climate. On the other hand, future climate
projections also imply that on a regional scale precipitation intensiﬁes
where it is already moist in the present climate (IPCC, 2014). As such, afull understanding of the nature of precipitation under delicate balance(or short-term imbalance) in the global water and energy budget remains a major challenge. We are therefore in urgent need of long term,
continuous and accurate measurements of global precipitation to better
document how the climate system behaves and better prepare for the
future climate change (cfr. Michaelides et al., 2009; Michaelides,
2013a, 2013b, 2014, 2016).
However, although precipitation measurements are often considered as the “truth” to validate models against, it is important to beaware that measurements have their own uncertainties of diﬀerent
kinds. Rain gauge analyses such as the Global Precipitation Climatology
Centre (GPCC) product (Becker et al., 2013; Schneider et al., 2014,2017), for example, have spatial representativeness issues since theground stations are highly inhomogeneously distributed over land andare totally absent over oceans (Kidd et al., 2017).
Satellite data products are superior to gauge products in spatial
coverage over the globe but are subject to retrieval errors and biases.
Merged data products such as Global Precipitation Climatology Project(GPCP; Adler et al., 2016; Huﬀman et al., 2009) and Climate Prediction
Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin,1997) have been among the most extensively used products for model
validation purposes. Their use in climatological studies is in constant
growth as the temporal coverage of the data sets increases (Kidd, 2001).
In those products, multiple satellite and gauge measurements are
combined so as to maximize the spatial and temporal sampling, but
retrieval errors are generally even more diﬃcult to track down in themerged products owing to the complexity of the algorithm.
The purpose of this paper is to discuss how currently available
precipitation data sets may be used to validate climate models, to illustrate the uncertainties and limitations of the products and simulations, and to propose a common set of standards for both reference data
sets and climate models in order to avoid pitfalls and issues arising from
diﬀerent practices between the observational and modeling communities.

2.1. Surface-based data sets
2.1.1. Rain gauge-based products
A great number of instruments are designed to provide in situ
measurements of precipitation. The most common and longest-serving
is the rain gauge. Gauges designed for measuring precipitation (rainfall
and snowfall) represent the fundamental, de facto standard of precipitation measurements across the globe. Sevruk and Klemm (1989)
and New et al. (2001) put the number of gauges worldwide at morethan 150,000, while Groisman and Legates (1995) estimated thenumber of ‘diﬀerent’ gauges to be as many as 250,000. While it iscertain that many gauges exist, these numbers depend largely upon
their construction principles (i.e., what is considered as a valid gauge),
their density and or gauge record; in particular, not all gauges haveoperated continuously or simultaneously. Indeed, the number of gauges
available at a particular temporal resolution, for a speciﬁc period, orwith a certain temporal latency depends greatly upon regional/national
data policies. The reader can ﬁnd an up-to-date appraisal of gauge
coverage and distribution in Kidd et al. (2017).
Despite the impressive number of gauges, their availability andtherefore representativeness across the Earth's surface is highly variable(see Kidd et al., 2017). The vast majority of gauges over the Earth's
surface are concentrated in populated regions. Over the oceans very few
gauges exist with most being ‘coastal’ and not necessarily representative of the open ocean. Furthermore, while the number ofgauges that report daily accumulations of precipitation might be considered adequate, gauges that report sub-daily precipitation (critical for
extreme pluvial events) are very limited in number (Kidd et al., 2017).
The basic rain gauge has a number of limitations. Sevruk andKlemm (1989) noted more than 50 diﬀerent types of gauge design(whether aerodynamic or not) with diﬀerent oriﬁce size and heights
above ground. At the gauge-scale, the ‘capture’ of precipitation by arain gauge is aﬀected by the wind ﬂow around the oriﬁce (Duchon andEssenberg, 2001). Turbulence induced by the wind-gauge interaction
interrupts the ﬂow across the gauge oriﬁce aﬀecting light precipitation
the most but to some extent also heavy rainfall (Duchon and Biddle,2010), resulting in an under-catch at low intensities and higher wind

2. Data sets of global precipitation
For climate-scale comparisons of the precipitation component of the2

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Other global data sets include the CPC Gauge-Based Analysis of
Global Daily Precipitation (Xie et al., 2010) and the Global Historical
Climatology Network (GHCN; Menne et al., 2012), both of which provide daily gridded precipitation products derived from meteorologicalobservations worldwide. The number of available gauges in the CPC
data set varies considerably by year (and by region/year) with a maximum number of precipitation observations of just over 30,000 stations,about half of which are in the US. The GHCN collects precipitation datafrom approximately 7500 gauges while snow depth is measured fromabout 17,000 stations, again virtually all in the US.
The Climate Research Unit (CRU, at the University of East Anglia)
gauge product (Mitchell and Jones, 2005) aims to provide a consistent
precipitation data set at a resolution of 0.5° × 0.5° globally (excluding
Antarctica) from 1901 to 2001 (New et al., 2000). The number ofgauges used varies over time, ranging from a minimum of 4957 in 1901
to a maximum of 14,579 in 1981. To interpolate these gauge data to the
0.5° × 0.5° grid, a minimum 30-year record is required for a particulargauge.
Although the products mentioned above provide similar global
monthly precipitation totals (e.g., Chen et al., 2002), these products
tend to diﬀer at regional scales due to contributions from diﬀerent
gauges, particularly where the gauge density is low such as over tropical Africa and tropical South America (Qian et al., 2006) and diﬀerences in the analysis procedures. For example, issues in validation are
expected in the CRU product over the Amazon between 1901 and 1921
when only one station was available for this large region.
While not being global, regional data sets, such as the APHRODITE
product (Yatagai et al., 2012) based over SE Asia, the China Gaugebased Daily Precipitation Analysis (CGDPA; Shen and Xiong, 2016), theEuropean Climate Assessment (ECA) for Europe (Klein Tank et al.,2002), and the database described by Liebmann and Allured (2005) forSouth America are often able to obtain a greater number of regional
gauges through local sources and tailor the ﬁnal product to particularregion characteristics, such as topography. For example, due to thecomplex topography in the domain of the APHRODITE product, topographic information is used to better constrain the interpolation of thegauge information to the ﬁnal gridded product.

speeds (Nešpor et al., 2000).
Although pit gauges are generally deemed to produce as close to thecorrect ‘surface’ precipitation as possible, these are diﬃcult to maintainand are therefore extremely few in number. Errors may also arise due tophysical problems (such as blockages of the gauge oriﬁce and evaporation of precipitation from the funnel/collector) or electronic/recording issues (problems with data loggers, or written records). Most
gauges have diﬃculty in accurately recording snowfall since most areprimarily designed for the collection and measurement of rain; thelower fall speed of snow compounds wind-oriﬁce eﬀects and thus affects the (in)eﬃciencies of the gauges (Goodison et al., 1998;
Rasmussen et al., 2012). While rainfall can be usually measured towithin 10–20% in one-minute intervals (Vuerich et al., 2009), windeﬀects may result in less than 25% of the snowfall being caught(Goodison et al., 1998).
At the local scale, the exposure of the gauge within the local environment is critical to the spatial representativeness of the gaugemeasurement. Standards set by the WMO are designed to ensure consistency among gauge measurements to reduce inherent errors causedby siting or exposure (WMO, 2008). However, the auto-correlation
length of precipitation is surprisingly small particularly for instantaneous precipitation (Habib et al., 2001); even gauges in close
proximity exhibit a good degree of variability (Ciach, 2003). Accumulating precipitation over time increases the correlation length (Bellet al., 1990), although the correlation length is dependent upon themeteorology of the precipitation event and the local topography.
The conversion of the gauge information into the ﬁnal precipitation
product can lead to diﬀerences, not only related to the number ofgauges available. A number of schemes exist to take the point measurements of the gauges and provide a spatial estimate of precipitation.
Although a simple task at ﬁrst glance, particularly where gauge density
is high, the use of diﬀerent schemes and criteria may lead to diﬀerent
end results. Interpolation schemes such as kriging, are used to try andreduce processing errors, although the interpolation scheme itself may
introduce biases in the ﬁnal product.
A more “subtle” aspect needs also to be considered regarding thetiming of the gauge observations, which notably diﬀers between thevarious national networks. This sometimes creates untagged multi-day
accumulations with clear implications for the comparisons with othersources of data (e.g., Viney and Bates, 2004).
While gauge data are generally used “as is”, errors and uncertainties
associated with such precipitation measurements are reasonably wellunderstood and corrections or further quality control measures can beapplied. However, despite errors, rain gauges remain arguably the mostaccurate instrument by which to measure rainfall at the surface.
A number of global daily gauge data sets exist, as summarized in
Tapiador et al. (2012) (Tables 1–5), that provides precipitation products at varying temporal and spatial resolutions. Despite the number ofgauge products, it should be noted that many gauges contribute to one
or more gauge products and thus such data products are not necessarily
independent. Indeed, much of the variations in the gauge analyses
originate in the quality control and the point-spatial conversion methodology.
The Global Precipitation Climatology Centre (GPCC; Becker et al.,2013) provides perhaps the foremost repository of global precipitation
data derived from gauges. The GPCC gridded product is generated fromdata obtained from 180 institutions, providing a total of about 85,000
gauge locations having provided observations at least once since thestart of the data set in 1901. The Full (long-term or climatological)
GPCC analysis is designed to provide a high-quality product. To ensure
continuous records of precipitation from any single station, the GPCC
imposes a 10-year minimum constraint. This limits the number ofavailable stations as of 2013 (2015) to 67,298 (75,165) for the bestmonth, or 67,149 (75,033) for the worst. The total is 65,335 (73,586)
stations across all 12 months of the year (Becker et al., 2013; Schneider
et al., 2015). This Full product is updated about every 2 years.

2.1.2. The role of radars and disdrometers
Other surface-base instruments, such as radars and disdrometers arealso widely used for measuring precipitation, but lack the coverage andlongevity of record necessary for wide-area analysis. However, these
are included for completeness and in recognition that they may provide
useful information on precipitation intensities and characteristics that
could be useful in validation climate models locally or regionally.
Surface-based radars now provide good quality spatial measurements over most of the US/Canada, Europe, Western Russia, Japan,
Korea, Australia, and New Zealand. Weather radars oﬀer frequentspatial observations of precipitation over relatively large areas in nearreal time for time-critical applications, such as ﬂash ﬂoods, etc. They
can provide useful information to modelers on the spatial and temporalvariability of precipitation together with rain intensity distribution.
However, radars do have a number of limitations, such as range eﬀects,
blockages of the radar beam, and imprecise backscatter/precipitation
rate (Z-R or Z-S) relationships. Calibration against surface rain gauges
together with improvements in radar technology have reduced some ofthe errors associated with the Z-R relationships, allowed the phase of
precipitation to be retrieved and the variability of the DSD to be assessed (e.g., Chandrasekar et al., 2012). Polarimetric measurementsallow signiﬁcant steps forward in precipitation estimation (e.g., Bringi
and Chandrasekar, 2001). At the same time, dual-polarization radarmeasurements of precipitation are inﬂuenced by size, shape, orientation, and phase of the hydrometeors, and thus these measurements are a
powerful tool for identifying hydrometeor characteristics along with
precipitation intensity (e.g., Herzegh and Jameson, 1992;
Vivekanandan et al., 1999).
3

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have been ﬂown on a number of missions. The Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) carried the Ku-band
Precipitation Radar (PR), CloudSat carries a W-band cloud radar(Stephens et al., 2002), while the Global Precipitation Measurement
mission (GPM; Hou et al., 2014; Skofronick-Jackson et al., 2017) carries
the Ku/Ka band Dual-frequency Precipitation Radar (DPR). These radars provide not only a measure of surface precipitation, but also thevertical distribution of precipitation in the atmosphere; the disadvantage is that the global coverage is limited, requiring signiﬁcanttemporal averaging to provide useful (i.e., sample-limited) products.
Combining multi-satellite information has proved useful, as evidenced
by the A-train (Stephens et al., 2002), allowing complementary atmospheric observations to be exploited.
A number of satellite-derived global precipitation products areavailable (e.g., Kidd and Levizzani, 2011), although data availability
and retrieval accuracy typically limits these products to the latitude
band 60°S to 60°N. Furthermore, estimates from any single sensor are
infrequent (typically less than twice a day from low Earth orbiting satellites) and may be deemed to be only a snapshot of precipitation,posing a severe drawback for documenting the evolution of precipitation. The relative high frequency sampling of geostationary visible/infrared sensors is negated by the indirectness of these observations. The
ability to exploit a number of diﬀerent precipitation-oriented sensors
greatly improves the number of samples, while incorporating the more
direct, but sample-poor microwave estimates with the less direct, but
sample-rich infrared observations promises much improved precipitation estimates.
A number of techniques have been devised to combine informationfrom both visible/infrared and microwave observations into Level-3(i.e., gridded daily time scale or ﬁner) products. The Bristol-NOAAInterActive Scheme (BIAS; Barrett et al., 1987) replaced VIS/IR estimates of precipitation with those derived from the passive microwave;
cloud development and movement was then used interactively to advect the estimates over time. Adler et al. (1993) calibrated IR cloud top
temperatures with PMW-derived estimates to correct for regional-scalevariations in the cloud-top temperature/rain rate relationships, although these calibrations were generated at relatively coarse resolutions (2.5° × 2.5° monthly). Since most general circulation models(GCM) assessments use Level-3 products, it is important to understandthe procedures of how these are created.

Disdrometers are designed to retrieve key physical properties ofprecipitation particles, such as the drop size distribution and the particle shape, across a range of precipitation types (Chang et al., 2009;
Adirosi et al., 2016; Thurai et al., 2016), temporal scales (Tokay andShort, 1996), and spatial scales (Tapiador et al., 2010; Jaﬀrain et al.,2011). However, there are known biases due to the small measurement
area of the instruments (Tapiador et al., 2017), with single disdrometers
severely incorrectly estimating instantaneous rain rate (up to 70%) andunderestimating the mean diameter of the rain drop size distribution.
For climate-scale veriﬁcation, however, these instruments have thepotential to investigate the representation of precipitation particle types
within models and also to help understanding the precipitation processes that generate these particles.
2.2. Satellite estimates
Satellite estimates of remotely-sensed precipitation have been
available over much of the globe for almost four decades and have thepotential to be available on a truly global scale. Critically, satellite estimates have a distinct advantage for assessing precipitation over datasparse land and ocean regions. Satellite observations from visible, infrared, and in particular, passive and active microwave systems areused to generate precipitation estimates using a number of techniques(Kidd and Huﬀman, 2011), although techniques diﬀer in performanceregionally and temporally. Simple techniques based upon the calibration of the observations using surface (or satellite) radar may yielduseful results, although greater insight into the precipitation characteristics may be obtained through the use of more complex physicalmodeling techniques (e.g., Kummerow et al., 2000, 2001, 2007).
2.2.1. Infrared-based methods
Observations in the (thermal) infrared channels (IR) allow cloud top
temperatures to be retrieved, and through a simple relationship between cloud top temperatures and surface precipitation may be used toretrieve precipitation. However, the actual relationship is considerably
more complicated than this simple information can describe.
Nevertheless, techniques such as the Global Precipitation Index (GPI;
Arkin and Meisner, 1987), the Convective/Stratiform (CST; Adler and
Negri, 1988), the Auto-Estimator (Vicente et al., 1998), and the HydroEstimator (Scoﬁeld and Kuligowski, 2003) have proved eﬀective atlarge-scale precipitation estimation. Some visible/infrared fused techniques have utilized artiﬁcial neural networks (ANN), and for example,
the Precipitation Estimation from Remote-Sensed Information using
ANN (PERSIANN; Sorooshian et al., 2000) uses multi-source information from satellite and surface data sets to establish, and update therelationship between surface precipitation and IR observations.
Tapiador et al. (2004) also used ANN to merge PMW with IR data. The
usefulness of observations in the visible (VIS) spectrum is limited bysolar radiation and so these tend to be combined with IR observations.
Several new global datasets dwelling on IR observations have been
proposed recently with the aim of supporting ﬂood and drought studies.
Among them, it is worth mentioning the Climate Hazards group Infrared Precipitation with Stations (CHIRPS; Funk et al., 2015). Local
datasets are also available and widely used such as, for example, theTropical Applications of Meteorology Using Satellite Data and GroundBased Observations (TAMSAT; Tarnavsky et al., 2014).

2.2.3. IR-PMW combined methods
Current combined IR–PMW techniques fall into two main categories. The ﬁrst rely upon the calibration of the IR observations by
PMW estimates: The NRL-Blended technique (Turk et al., 2000) and thePassive Microwave-InfraRed (PMIR) technique (Kidd et al., 2003) use amoving spatial and temporal window to generate a local relationshipbetween the fast-refresh IR observations and the precipitation estimates
sourced from the Level-2 (i.e., instantaneous swath-level) PMW observations. The second methodology relies upon the PMW to provide
the estimate of precipitation and the IR data to track the movement of
the precipitation between adjacent PMW estimates. These are usuallytermed advection morphed or Lagrangian time-interpolation schemes.
Examples of advection morphed schemes include the Climate Prediction Center (CPC) Morphing (CMORPH; Joyce et al., 2004), the Global
Satellite Map Product (GSMaP; Kubota et al., 2007) and the Integrated
Multi-satellitE Retrievals for GPM (IMERG; Huﬀman et al., 2015), while
the Rain Estimation using Forward Adjusted-advection of Microwave
Estimates (REFAME; Behrangi et al., 2010) is an example of a Lagrangian scheme. The PMW estimates may be derived from a number oftechniques. The IR information is used to derive motion vectors along
which to advect the PMW estimates. These motion vectors may begenerated through correlation or mesh-based techniques (Bellerby,2006) or physically-based approaches (Tapiador, 2008). Additional
steps are used in CMORPH and IMERG to ‘morph’ the estimates between the starting and ending PMW estimates. The most recent merged

2.2.2. Microwave-based methods
Passive microwave observations provide a more direct measure of
precipitation since the upwelling microwave energy is linked more directly to the precipitation-sized hydrometeors. While simple techniques, such as frequency diﬀerences highlighting the scattering fromfrozen hydrometeors may be used, more complex techniques using radiative transfer calculations can be used to build a database of simulated observations and their associated geophysical parameters(Elsaesser and Kummerow, 2008). Spaceborne precipitation radars
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2.2.4. Known issues in using precipitation databases for validation
Issues with satellite data products relate primarily to the indirectness and the temporal sampling of the estimates. Although PMW estimates relate to well-founded knowledge of radiation-hydrometeor interactions, signiﬁcant variability occurs due to the usefulness of theobservations over diﬀerent surfaces. Over land, only high frequency
channels are useful, and these sense frozen hydrometeors, which means
they sense the ice at the top of the precipitating system rather than thesurface precipitation. Over the ocean, both high and low frequencies
are used, where the latter respond to liquid hydrometeors. Gauge information to provide a calibration reference may be used to correct
regional and seasonal variations, although the accuracy of the gauge
information is critical.
For proper assessment of GCM's, it is important to understand theprocesses that go into the production of global-scale satellite-based
Level-3 precipitation datasets. The quality of the Level-3 products
mentioned above is driven by continuous, short revisit microwavebased observations. These data originate from a variety of diﬀerent
sensors and satellites, with diﬀerent spectral and spatial resolutions,and quality over land and water surfaces (Turk et al., 2016). Given thevarying revisit time and diﬀerent sensitivity to precipitation intensity,
the error characteristics associated with merging these many disparate
datasets are not well known (Maggioni et al., 2016). One of the fundamental contributors to the overall error Level-3 characteristics istherefore the properties of the Level-2 precipitation algorithms that are
applied to the diﬀerent microwave data sources (e.g., Elsaesser andKummerow, 2015). Many of these techniques are built upon Bayesianestimation schemes which, while appropriate, are fully dependent upon
the quality and accuracy of a-priori precipitation estimates provided by
the GPM DPR (Grecu et al., 2009). For example, assumptions made forthe drop size distribution characteristics of the GPM radar-based estimates, therefore “trickle down” and inﬂuence the radiometer-based
precipitation retrievals, and eventually the Level-3 merged productsthat get used for GCM model veriﬁcation. As the quality of the GPM
Level-2 products improves, further improvement should result in theLevel-3 merged products.
In summary, as with any data sets, precipitation products have anumber of error sources, although, due to the nature of these products,
quantifying these is problematic. Understanding the provenance of the
input data and the processing scheme is crucial to understanding theparticularities of each precipitation product to ensure that the correctconclusions on model performance are provided.
With some exceptions, the uncertainties in these data sets are
seldom considered in climate model validation, rendering the exercise
prone to justiﬁed criticisms. Also, it is worth mentioning that the majority of the merged products, either CDR or HRPP, were never intended
to be used for trends as their input data sources drift.

techniques provide precipitation estimates at resolutions down to
10 km, 30 min over 60°S–60°N.
A number of mature satellite-based precipitation techniques incorporate surface precipitation data sets, allowing high spatial andtemporal resolution precipitation products to be generated (e.g.,
Huﬀman et al., 2009) using surface gauge measurements to provide theanchor points. Algorithms that emphasize homogeneous input andprocessing are referred to as Climate Data Records (CDR), while thosethat give the best short-interval estimate are referred to as High-Resolution Precipitation Products (HRPP).
The TRMM Multi-satellite Precipitation Analysis (TMPA; Huﬀman
et al., 2007) provides 3-hourly 0.25° × 0.25° resolution precipitation
data from 50°N–50°S 1998–present, and consists of four products: amerged-microwave product, a microwave-calibrated IR product, acombined calibrated-IR/merged-microwave product, and a rain gaugeadjusted product. The analysis incorporates as many satellite-based
precipitation estimates as possible calibrated to a single sensor. The
near-real time product (3B42RT) is calibrated against the Goddard
Proﬁling (GPROF) algorithm (Kummerow et al., 1996) TMI data
(2A12RT), while the post-realtime research-grade product (3B42) iscalibrated against the TMI/PR estimates (2B31; Haddad et al., 1997).
Other PMW estimates are then histogram-matched against these products, before being merged into 3-hourly windows centered on thenominal meteorological observation times (i.e., 0000, 0300, 0600 …
2100 UTC). These combined PMW estimates are the used to calibrate
the IR observations over overlapping 3° × 3° regions. These IR estimates are used to ﬁll gaps in the PMW coverage in each 3-hourly time
step. A ﬁnal bias adjustment against the monthly GPCC monitoringgauge analyses is carried out to generate a gauge adjusted 3-hourly
0.25-degree product (3B43; Huﬀman et al., 2010).
As part of the Global Precipitation Climatology Project (GPCP)
several products are generated and designed to address Climate Data
Record standards. A merged Satellite-Gauge (SG) precipitation analysis(Huﬀman et al., 1997, 2010; Adler et al., 2003) provides complete
global monthly 2.5° × 2.5° estimates from 1979 to the present. The
product uses the precipitation estimates from the SSM/I and SSMIS
passive microwave sensors to calibrate geostationary IR observations
over 40°N–40°S. Poleward of this latitudinal band the passive microwave estimates are combined with those generated from TOVS or AIRS.
Over land the satellite precipitation is then adjusted against the full
GPCC rain gauge analysis.
The GPCP One-Degree Daily (1DD) precipitation analysis (Huﬀman
et al., 2001, 2010), available from October 1996 forward, uses a
Threshold-Matched Precipitation Index (TMPI) between 40°N–40°S toproduce instantaneous precipitation from the geo-IR observations. A
regionally-varying IR threshold is matched against the fractional coverage of the SSM/I and SSMIS-derived GPROF precipitation estimates.
The rain rate of the IR pixel is then computed so that the TMPI monthly
precipitation equals that of the corresponding SG monthly precipitation
total. Outside 40°N–40°S, precipitation estimates from the TOVS and
AIRS sensors are adjusted by the precipitation occurrence of GPROF at
40° latitude.
The GPCP 2.5° × 2.5° pentad precipitation analysis (Xie et al.,2003) uses the satellite-gauge product to adjust the pentad CPC Merged
Analysis of Precipitation (CMAP) pentad product so that the overallmagnitude of the product matches at the monthly scale but with the
sub-monthly variability of the pentad CMAP product.
The CPC Merged Analysis of Precipitation (CMAP; Xie et al., 1996)
generates 2.5° × 2.5° monthly global precipitation derived from rain
gauges and satellite precipitation estimates. The method of combining
the gauge and/or satellite information uses a maximum likelihood
based upon weights inversely proportional to the square of the random
error of the individual sources. Over land gauge analysis is used as thereference, while over the oceans the Paciﬁc Atoll data set is used.

3. Direct veriﬁcation of climate model precipitation outputs
Most of the databases in the previous section have been comparedwith climate models for a variety of purposes, for instance for uncertainty analyses. In the case of RCM, the majority of such exercises
are aware of the diﬀerence between reanalysis-driven and GCM-driven
RCMs, as the overriding inﬂuence is the diﬀerences in the lateralboundary conditions, and validation in only meaningful for the resulting aggregated climatologies and not for the temporal sequence of
events.
The models can be validated and veriﬁed at several temporal andspatial scales. Direct comparisons of highly aggregated data (such as themean annual precipitation in latitudinal bands) are useful as a ﬁrst
approximation, but they do not suﬃciently prove that a climate model
correctly represents the climate as even early energy balance models
such as Paltridge's (1975) compared favorably with observations at
those scales. Neither is reproducing the annual cycle in mid latitudes amajor challenge, as models can be easily tuned to favor the main mode5

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(say 5 km/1 h). Otherwise, unsubstantiated claims are likely to appear.
This is important as sometimes RCMs are coupled with, for instance,crop models with the intent of providing detailed projections of yields
in future climate conditions. If the climate model has not been validated
at the intended working resolution it is doubtful that the results can beof any practical use, notwithstanding the existence of additional,
stronger issues to question such practice (cfr. Oettli et al., 2011).
There are a number of possible ways in which climate models can becompared to observations. Satellite estimates are convenient in that,like the climate models themselves, they tend to be global and homogeneous. Critical in any such endeavors, of course, is that the “observations” are in fact trusted, and do not themselves have biases,
trends or artifacts that would cloud such comparisons.
Here, we discuss three separate topics: absolute magnitudes, spatial
variability, and trends plus temporal variability. These can be viewed as
the ﬁrst order outputs of the climate models and tests their ﬁdelity in amacroscopic sense. More complex, but also potential metrics could beframed around process-related veriﬁcation as might be related to climate models' ability to capture the diurnal cycle of precipitation, theproper partition of convective versus stratiform precipitation or ﬁner
scale triggers such as orography or coastal inﬂuences on convection.
A ﬁnal, but perhaps even more complex form of veriﬁcation could
be framed around societal question such as a climate models' ability tocapture regional trends in precipitation, reproduce drought indices, or
capture trends in regional precipitation extremes. As these latter
properties would require higher resolution in-situ observations forveriﬁcation that have their own uncertainties, we limit ourselves here
only to the ﬁrst category – the physical evaluation of macroscopic
precipitation properties.
While there are many satellite products available, there are relatively few classes of products that the community has converged upon.
Following Ebert et al. (1996), it is generally accepted that passive microwave sensors have better instantaneous skill at sensing rainfall thantheir VIS or IR counterparts. This is because microwaves are attenuatedonly weakly by clouds and can thus sense the broad column of hydrometers (e.g., Wilheit et al., 1977). Although VIS/IR methods are generally limited to cloud top information (e.g., Platnick et al., 2003) theiravailability from geostationary satellites allows much better samplingof the relatively infrequent precipitation events. This led to better
correlations of precipitation at daily and monthly scales (Ebert et al.,of variability, and even models without microphysics parameterizations
can simulate quite well the seasonal cycle of precipitation. Coarse aggregations (e.g. 5°× 5°) are not especially useful either as many basinsare smaller than that or a major driver of precipitation research is hydrological applications at local scales.
The minimum set of statistics for validating precipitation in climate
models are the annual and seasonal comparisons, the analyses of bias
and correlations (usually through scatterplots or Taylor diagrams), theuse of probability distribution functions (PDFs), latitudinal and longitudinal transects, and time-latitude or longitude diagrams. Spatial indexes such as Geary's or Moran's have also been used (Tapiador, 2010)
to gauge spatial decorrelation. More sophisticated plots, such as the
Hovmöller diagram, are useful for model outputs, but seldom suited forcomparing with small or non-homogenous observational data sets.
Grid point to grid point comparison is a natural test for validatingclimate models since most applications of GCM outputs in mitigation
and adaptation are most useful locally. The spatial variability of the
precipitation makes instantaneous comparisons diﬃcult, but climatologies are fully comparable. Fig. 1 shows a comparison of a standard
Community Earth System Model (CESM) simulation of precipitation forpresent climate with several observational data sets, and for the same
period (2000–2015). It is apparent that there is an overall consensus
albeit diﬀerences appear (Table 1). The well-known issue of incorrectly
having a split ITCZ appears in the model, but the observational databases agree on the major features of the global precipitation climatology. Fig. 2 shows the corresponding scatter plots for several comparisons between model and observational databases.
A tenet of such direct validation is that the spatial and temporalscales at which the comparison is performed represents a limit for
judging a model (‘scope principle’). One can hardly claim that a modelwith a spatial/temporal resolution of 25 km/6 h performs well unlessthe validation has been performed at such scales and reasonable scores
have been found. In other words, the ability of the CESM model toreproduce the main precipitation climatology at 1° resolution in Figs. 1
and 2 does not necessarily imply that the model is suitable at, for instance, daily scale and 25 km spatial resolution.
Favorable scores at such coarse aggregations only show that themodel can be safely used at those scales. Any claim of potential applicability or positive assessment on the model ability at 25 km/6 h
resolution should be backed by a validation at that scale or at ﬁner scaleFig. 1. A comparison of the global annual mean
(2000–2015) of precipitation as simulated by the
CESM model (top) and four diﬀerent observational
databases. The third row depicts the diﬀerences
between the observations (second row) and themodel (top row). The GCM simulation includes
Carbon and Nitrogen cycles.

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Table 1
Cross-correlations of the CESM precipitation estimates with observational data in Fig. 2 (2000–2015 climatology).
Total annual precipitation (at 2.5° spatial resolution)

CMAP

GPCP

PRECLANDCRU

CESM
GlobalNH

SH# Points = 13,824
y = 0.817x + 0.207
R2 = 0.747# Points = 13,824
y = 0.787x + 0.339
R2 = 0.737# Points = 6561
y = 0.89x + 0.249
R2 = 0.708# Points = 4759
y = 0.94x + 0.139
R2 = 0.681# Points = 6912
y = 0.903x + 0.111
R2 = 0.818# Points = 6912
y = 0.83x + 0.368
R2 = 0.798# Points = 3865
y = 0.947x + 0.048
R2 = 0.741# Points = 3673
y = 0.958x + 0.136
R2 = 0.687# Points = 6912
y = 0.725x + 0.342
R2 = 0.671# Points = 6912
y = 0.747x + 0.312
R2 = 0.679# Points = 2696
y = 0.838x + 0.476
R2 = 0.688# Points = 1086
y = 0.946x − 0.001
R2 = 0.576

Fig. 2. Scatter plots comparing the annual mean (2000–2015) atevery grid point for diﬀerent ground reference data (GPCP, CRU,
PRECLAND and CMAP) and a GCM simulation (CESM model); forthe whole planet (left column), the Northern hemisphere(middle) and the Southern hemisphere (right).

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GPM which also shows good agreement in the tropics. Fig. 3 shows thecurrent zonal mean estimates of precipitation from GPM's Ku radar(Iguchi et al., 2000), passive microwave radiometer (Kummerow et al.,
1996, 2001), GPCP (Huﬀman et al., 2009), and CMAP (Xie et al., 1996)
estimates in the tropical band [35°N–35°S]. Agreement is good overboth ocean and land as shown in panel’s b and c respectively. The good
agreement over land is particularly interesting in that GPCP and CMAP
include rain gauge adjustments while the GPM Ku radar and radiometer
estimates are independent of gauges. Notwithstanding that algorithms
were designed with knowledge of reference values, the good agreement
would suggest that tropical precipitation is relatively well measured
and that bias adjustments from gauge analyses are relatively minor at
global scales. The agreement is good even in areas of large rain gauge
concentrations (e.g., US, Europe, Japan and China) where the optimal
interpolation essentially reproduces the rain gauge estimate within a
few percent of the mean rain (Wang et al., 2014).
Matters become more complicated when higher latitudes are included. Both active and passive sensors face unique challenges related
to the shallow and often very light precipitation common at high latitudes. Passive microwave sensors are relatively skilled at retrieving thetotal water content over oceans. However, small drops associated withdrizzle at high latitudes oﬀer little contrast with cloud droplets at microwave frequencies. As such, it has always been diﬃcult to relate theobserved emission signal into clouds and precipitation. Hilburn andWentz (2008) used observations along the west coast of the UnitedStates to determine the cloud threshold that corresponded to the correct
frequency of precipitation as observed on the ground. Further emission
was then partitioned between cloud water and precipitation to optimize
correlations with surface observations of precipitation.
Other methods such as GPROF (Kummerow et al., 1996, 2001) haveused cloud resolving model simulations to perform the partition between cloud water and actual precipitation but the method relies onlimited high latitude simulations and may not be universally applicable(Elsaesser and Kummerow, 2015).
Active sensors on TRMM and GPM have limited sensitivity to
drizzle. While the GPM radar can detect precipitation down to approximately 0.2 mm h− 1 (Hou et al., 2014), this lower threshold corresponds to assumed tropical drop size distribution and not to the verysmall drizzle drops that are often reported at high latitudes. CloudSat,
in contrast, has enough sensitivity to detect all clouds and drizzle(Stephens et al., 2008) but cannot detect echoes near the surface when
the attenuation becomes too large, as in an area of heavy rain. CloudSat

1996) than the more precise but infrequent microwave observations
from low Earth orbiting satellites. The general approach followed byalgorithm developers has thus been to merge the instantaneous microwave estimates with more frequent infrared estimates to overcome
sampling limitations.
As mentioned above, because rain gauges are readily available overland and are still considered to be the reference standard against whichsatellite estimates are evaluated, most producers of multi-sensor, multisatellite products have also incorporated rain gauge estimates into theirproducts. This is generally done through some form of optimal interpolation scheme to ensure that areas with many gauges do not diﬀer
signiﬁcantly from their measurements while the satellite has greater
weight in areas with few or no gauges. Adjustments are usually made atmonthly or climatological time scales, although climatological adjustment factors can be applied to instantaneous estimates.
The TMPA (Huﬀman et al., 2007), GSMAP (Kubota et al., 2007),and CMORPH (Joyce et al., 2004), IMERG (Huﬀman et al., 2015), GPCP
(Adler et al., 2003; Huﬀman et al., 2009) and CMAP (Xie et al., 1996)
are all well-known examples of such techniques that blend microwave
with infrared sampling and gauges for bias corrections. The diﬀerencesamong these products thus stem from the subtleties employed to merge
the infrared and passive microwave as well as the bias adjustment togauges. These subtleties are beyond the scope of this paper but have to
be considered when comparing climate models with observations. Instead, and as a ﬁrst approximation to the problem we focus here primarily on areas of broad agreement among these products as a measureof where observations can be considered robust (Hegerl et al., 2015).
In this section, we focus on individual sensor estimates along with
the GPCP and CMAP products described above. While TMPA, GSMAP,
CMORPH and IMERG are all high-quality products that may also be
used in some of these applications (Kim et al., 2017; Li et al., 2017a;
Guo et al., 2016), they are designed primarily to produce the highest
possible quality snapshot rainfall product. These products blend allavailable information at any given time to optimize sampling and noeﬀort is generally made to homogenize the time series. This can cause
variations in the product over time as old satellites drop out and new,generally more capable satellites are introduced into the time series.

3.1. Absolute magnitudes
The TRMM mission did much to bring together diﬀerent estimatesof precipitation in the tropics. These products have been transferred toFig. 3. Zonal means for GPM Ku PR V4, GPM GPROF V4, GPCP and CMAP for Sept. 2014–Aug. 2015. Panel (a) is for all surfaces; panel (b) is for land only; while panel (c) is for oceans
only.

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nevertheless represents perhaps the best estimate of climatologicalprecipitation at these high latitudes.
Fig. 4 focus on the southern hemisphere to show how the GPM
radar, CloudSat, GPCP and CMAP all compare over high latitude
southern oceans. ERA-Interim (Dee et al., 2011) and MERRA-2 (Gelaro
et al., 2017) are also included for reference. The CloudSat zonal means
are still a bit noisy due to the limited sampling of the nadir-only radar
but the general trend can already be discerned. The southern oceans areshown to illustrate a worst-case scenario as there are no gauges against
which the global merged products such as GPCP may calibrate.
Those issues have to be considered when validating climate models.
Given the extent of tuning that models have there is a danger of overﬁtting parameters to observations that are known to present its own
uncertainties, biases and limitations. For example, the number of preferred conically-scanning microwave radiometers is expected to decrease in the coming decade as the legacy SSMIS sensors reach end-oflife (Huﬀman et al., 2015). To maintain global coverage, this implies
more reliance upon the large number of cross-track scanning, coarser
resolution passive microwave sounders. While these relatively abundant observations are more indirectly related to near-surface precipitation (Kidd et al., 2016), they could also provide measures of thedepth and intensity of convection (Haddad et al., 2017).

3.2. Spatial variability
While issues related to the detection thresholds of active and passive
sensors, as well as attenuation at high rain rates (particularly for Wband radars) require some mitigation or at least merging of diﬀerent
satellite estimates, spatial variability is generally quite consistentamong diﬀerent satellite estimates. This is particularly true for TRMM
and GPM that sample across the entire diurnal range. To illustrate thisbehavior, Fig. 5 shows an annual mean map for GPCP and the GPM Ku
radar products. While a diﬀerence map would largely highlight theFig. 4. Zonal ocean means precipitation from − 35 to − 90 S (i.e. Antarctic shelf) for 6
estimates: GPCP, GPM- Ku radar, CMAP, ERA and MERRA-2, and CloudSat.

Fig. 5. Annual mean daily rainfall (mm/day) for the GPCP and GPM
Ku-based radar algorithm (V4).

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radiometer estimates when averaged over tropical oceans as shown in
Fig. 8 By using a well-characterized dual-polarized radar at Kwajalein,
it is possible to explain the diﬀerences in terms of the frequency ofoccurrence of unique precipitation states, deﬁned as shallow, isolated
deep convection, and organized convection. When viewed by convective organization, the ENSO related biases can be explained as mere
shifts of deep isolated convection to more organized convection (largelyin the Central Paciﬁc) as the ENSO persists. This sensitivity of microwave sensors (both active and passive) remains an issue that the GPM
dual frequency radar is in the process of untangling.
At this time, it is somewhat problematic to use these global scale
trends and variations to validate climate models as there is still somedisagreement among the satellite products themselves. However,looking at the envelope of the observational products, models that lie
outside such envelope need to be treated with care. In particular, ENSO
moves around the precipitation without driving large global changes,
according to the satellites. We note similar diﬃculties with rain gauge
only products, where wind corrections can sometimes alter the conclusions about precipitation trends at the global scale (e.g., Metcalfe
et al., 1997; Ungersböck et al., 2001).

smaller sampling of the radar product compared to the multisatellitebased GPCP, the very good agreement is clearly visible. Other products
have similar agreements (Katsanos et al., 2016; Retalis et al., 2016).
This can be attributed largely to accumulations resulting largely frommoderate rainfall (1–10 mm h− 1) that are retrieved quite well by mostsensors.

3.2.1. Trends and temporal variability
It is important to diﬀerentiate trends and variability at the global
scale, which may be due to changes in the overall climate system, from
temporal changes at regional scales that tend to follow the generalcirculation and are closely related to the spatial variability. The latter,characterized by such eﬀects as interannual variability, or the manifestation of El Nino Southern Oscillation (ENSO) in the Central Paciﬁc,are easily detectable in the various satellite records as illustrated bytime series of TRMM radar, radiometer, GPCP and CMAP times series ofprecipitation over a 20° × 20° area centered over the central Paciﬁc[10°S to 10°N; 170°E to 170°W] as shown in Fig. 6.
In contrast to regional variability, trends and variability at global
scales are more diﬃcult to assess and there is less agreement among
satellite products. This diﬃculty arises because the global trend andvariability are the residue of the relatively large, opposing-sign variations at the regional scale. A comparison among GPCP, CMAP, theTRMM passive microwave product and the RSS products (Hilburn andWentz, 2008) illustrates this fact. Fig. 7 shows these products averaged
over the tropical oceans for a 15-year period corresponding to the
TRMM era.
In terms of overall magnitudes, the agreement among GPCP, theTRMM product and the RSS product are quite good while the CMAP
product produces 10% more precipitation. If land is included (notshown), the agreement between GPCP and CMAP improves as bothproducts rely on more or less the same gauges for bias adjustments.
However, CMAP also has more pronounced variability over this time
period that is not seen in the other products. The GPCP time series
appears to have the least variability of the remaining three products.
This may be related to the nature of the radiometer scheme used in the
GPCP algorithm itself as diﬀerent algorithms can have diﬀerent sensitivities to changes in precipitation system structure and morphology.
A possible explanation for these diﬀerences can be found in the
TRMM record which also shows disagreement between its radar and4. Validation of the physics
Apart from performing direct validation against precipitation databases, there are other strategies for using observations to improveclimate models. In this respect, the limitations of models include theprecise modeling of the ENSO phenomenon (Neale et al., 2008), therepresentation of the diurnal cycle of rainfall (Betts and Jakob, 2002) or
the frequency of occurrence of high- and low- intensity rainfall events(Sun et al., 2006). All these issues have been identiﬁed long ago, still
persist, and are directly related with the representation of subgrid-scale
processes such as convection. Note that the validation of precipitation
physics may be premature for GCMs, but is included here as the GCMs
are quickly reaching resolutions where cloud permitting parameterizations become a reality (e.g., Bretherton, 2015).
A major research topic to improve the water cycle modeling andaddressing such issues is comparison of modeled and observed latent
heat (LH). Latent heat release is a consequence of phase changes between the vapor, liquid, and frozen states of water, which cannot bemeasured/detected using present observational instruments. The

Monthly Mean Rainfall(Selected Region: 10S-10N, 170E-170W)

15

TMI GPROF 2010 V2TRMM 3A25 V7
GPCP V2.2
CMAP Obs only V1201

10

5

0
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12

Mean Rainfall (mm/day)

20

10

Fig. 6. Time series of TRMM radiometer (TMI GPROF 2010,
V2), radar (3A25, V7), GPCP V2.2 and CMAP (observation
only, V1201) times series of precipitation over a 20 × 20° area
centered over the central Paciﬁc [10°S to 10°N; 170°E to
170°W].

Atmospheric Research 197 (2017) 1–20

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Fig. 7. Time series of tropical oceanic precipitation from the
TRMM mission radiometer product (TRMM 3A12), Remote
Sensing Systems (RSS), GPCP and CMAP.

vertical distribution of LH has a strong inﬂuence on the atmosphere,
controlling large-scale tropical circulations, exciting and modulating
tropical waves, maintaining the intensities of tropical cyclones, andeven providing the energetics of midlatitude cyclones and other midlatitude weather systems (Li et al., 2017b; Melcón et al., 2017).
Another direction is in the ﬁeld of microphysics (MP). This is theframework through which to understand the links between interactive
water vapor, aerosol, cloud and precipitation processes. Global measurements of microphysics are important to avoid overﬁtting themodels to speciﬁc places when tuning the empirical parameters, whichis the standard procedure to adjust models to observations (Voosen,2016).
A third way of dealing with evaluating climate model physics issetting up dedicated ground validation (GV) campaigns designed toprovide multisource, complementary information on precipitation
processes. Here the combined use of databases from instrumented

research aircraft, radars, and satellites is invaluable, but complicatedlogistics, costs and technical diﬃculties both limit and make extremely
valuable existing campaigns such as iFloodS (Ryu et al., 2016), ipHex(Barros et al., 2014) or OLYMPEX (Houze et al., 2017).

Monthly Mean Rainfall (ocean only)

Fig. 8. Time series of TRMM radiometer (TMI GPROF 2010
V2), and radar (3A25, V7), averaged over global tropicaloceans.

4.1. The use of latent heat (LH) measurements to evaluate models
The launch of the TRMM satellite in November 1997 provided amuch needed and accurate measurement of rainfall as well as theability to estimate the four-dimensional (4D) structure of latent heating(LH) over the global tropics (Simpson et al., 1988, 1996).
The success of TRMM made it possible to have another major NASAprecipitation measuring mission, the GPM mission. GPM is consideredby NASA to be the centerpiece mission of its Global Water & Energy
Cycle research program. Cloud Resolving Models (CRMs) have been
identiﬁed as being a valuable tool for algorithm developers and is(Selected Region: 40S-40N, 0-360E)

3.4

3.0
2.8
2.6
2.4

TMI GPROF 2010 V2TRMM 3A25 V7

11

14
20

12
20

10
20

08
20

06
20

04
20

02
20

00
20

98

2.2
19

Mean Rainfall (mm/day)

3.2

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from CSH and SLH and Q1 from ERA-I (Fig. 9). The double peak in theheating ﬁeld has previously been observed and discussed (Zhang and
Hagos, 2009; Takayabu et al., 2010). It is reasonable to say that theupper peak is related to precipitation by cold (ice or mixed phase)clouds and the lower one to precipitation by warm (liquid phase)clouds. LH in TRAIN and CFSR and Q1 in JRA25 do not have any obvious double-peak structure.
Ling and Zhang (2011) also pointed out that the discrepancies
among the heating data sets are not merely between the TRMM andreanalysis data sets or between LH and Q1. Diﬀerences within the
TRMM and reanalysis products, respectively, and within various products of LH or Q1 are no less than those between the TRMM and reanalysis data and between LH and Q1. These diﬀerences reﬂect ourcurrent level of estimating diabatic heating ﬁelds: we may get somebasic properties of the heating ﬁeld (e.g., longitudinal locations ofmaxima) correct, but there are many details with large uncertainties.
These uncertainties should by no means stop us from cautiously using
the currently available heating products to provide as much information as they may credibly provide.

considered a key component for one of the major GPM ground validation (GV) sites. In addition, CRMs are one of the most important tools
used to establish quantitative relationships between diabatic heating
and rainfall. Thus, simulated data from the Goddard Cumulus Ensemble(GCE) have been used extensively in TRMM for the development ofboth rainfall and heating retrieval algorithms (Simpson et al., 1996; Taoet al., 2006).
Five diﬀerent TRMM LH algorithms designed for application withsatellite-estimated surface rain rate and precipitation proﬁle inputs
have been developed, compared, validated, and applied for over two
decades (Tao et al., 2001, 2006, 2016b). They are the: (1) Goddard
Convective-Stratiform Heating (CSH) algorithm, (2) Spectral Latent
Heating (SLH) algorithm, (3) Goddard Trained Radiometer (TRAIN)
algorithm, (4) Hydrometeor Heating (HH) algorithm, and (5) Precipitation Radar Heating (PRH) algorithm. The strengths and weaknesses of each algorithm are discussed in Tao et al. (2006).
Ling and Zhang (2011) compared the heating proﬁles between
TRMM retrieved (CSH, SLH and TRAIN) and global re-analyses (ERA-1,
JRA25 and CFSR). All heating data exhibit three longitudinal maxima
but with diﬀerent amplitudes; for example, heating over South Americaand Africa is much stronger in three models (CSH, SLH, and CFSR) thanthe others. Heating is obviously weaker over the Maritime Continent
than over the eastern Indian Ocean and western Paciﬁc in some data
(e.g., Q1, TRAIN LH, ERA-I Q1, and JRA25 Q1) but not so in others.
Among all, TRAIN has the largest low-level heating over the east Paciﬁc, which might be an overestimate owing to shallow convection(Grecu et al., 2009). Low-level heating over the eastern Paciﬁc is alsopresent with smaller amplitudes in Q1 from ERA-I and LH from CFSR.
The distribution of boundary heating of the LH from CFSR is almost
the same, and it may also be related to precipitating marine stratus
clouds over the ocean (VanZanten et al., 2005). The two heating peaks
are more obvious in some TRMM and other reanalysis data, such as LH

4.2. Helping in the development of microphysics (MP) schemes
CRMs with advanced microphysical schemes have been used to
study the interactions between aerosol, cloud and precipitation processes at high resolution. These processes play a critical role in the
global water and energy cycle. Validation of CRMs with observational
databases is important both to ascertain the ﬁdelity of the outputs andto improve the models.
The interest on this topic lies in that there are still many uncertainties associated with various microphysics schemes. In part, thisreﬂects the fact that microphysical processes cannot always be measured (or observed) directly. Herein cloud properties, which can be
Fig. 9. A comparison of latent heat (LH) retrieval from (a) Goddard
Convective-Stratiform Heating (CSH) algorithm, (b) Spectral Latent
Heating (SLH) algorithm, (c) Goddard Trained Radiometer (TRAIN)
algorithm and observations: ERA-I Q1 mean, CFSR LH mean andJRA25 Q1 mean. The good performances illustrate the usefulness ofobservations to improve the physics within climate models.

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models are rapidly approaching those of the traditional CRMs and thus
may soon replace traditional limited-area Regional Climate Models(RCMs), which by their nature present several limitations.
In spite of the demonstrated past ability of RCMs to derive climatologies suitable for geographical applications (Tapiador et al.,2011), and of existing eﬀorts to coordinate RCMs ensembles at regional
scale (such as CORDEX) escalating computing resources are making
RCMs redundant as coupled GCMs and G-CRMs can now be run at highspatial and temporal resolutions. Besides, there are strong conceptual
reasons that favor global modeling and variable-resolution, convectionpermitting models such as COSMO (cfr. Nuissier et al., 2016), namely
RCM issues in providing correct large-scale atmospheric circulation
across a region (Trenberth, 2007); and the critical dependence on thetype of driving data, the climate variable, and the region used (Di Lucaet al., 2016). Moreover, some RCMs perform poorly (correlation − 0.2)
in describing key hydrological variables such as the dependence between the number of snowfall days and temperature (Pons et al., 2016),
and in modeling evaporation/precipitation feedbacks (Lucarini et al.,2007) raising serious concerns about its use to inform policies.
To conclude this section, it is worth mentioning that a prospective
NASA satellite mission called the Cloud and Precipitation Processes
Mission (or CaPPM) is currently being proposed. Such a mission could
provide global estimations of cloud and precipitation properties, which
are needed to evaluate and improve dynamical and microphysicalparameterizations, and the feedbacks between them in both CRMs andglobal G-CRM.

estimated, have been used to validate model results.
The spectral bin microphysical (SBM) schemes represent the mostsophisticated representations of microphysical processes. They generally perform better in simulating realistic cloud properties and surface
precipitation compared with bulk microphysical schemes (Li et al.,2010). SBM schemes have helped to improve the bulk scheme (Langet al., 2014; Tao et al., 2016a, also RAMS microphysics used bin scheme
to parameterize their cloud activation).
However useful, SBM schemes are not perfect. For example, inFig. 10 we show nine years of TRMM PR and 85 GHz TMI observationsof squall systems during late spring/early summer over the central US
compared against a SBM simulated squall line case (PRESTORM campaign of 1985; Li et al., 2010). The ﬁgure shows improvements of aspectral bin microphysical scheme using long-term TRMM satellite
observations (fully described in Li et al. (2010)). The columns are forthree diﬀerent instruments: the ground-based C-band radar (ﬁrstcolumn), the TRMM Precipitation Radar (second column), and the
TRMM microwave imager at 85 GHz (third column). Note that the
ground-based radar refers to the same case as the model simulation,
whereas TRMM PR and TMI are a 9-year composite of TRMM observations at the same location (the Southern Great Plains) and period(late spring/early summer).
Comparisons against a surface C-band radar (ﬁrst column) and the
TRMM PR radar (second column) show an over estimation of radar
reﬂectivity in the original scheme (second row). To improve the simulated radar reﬂectivity proﬁles, the temperature dependence of thecollection eﬃciency between ice-phase particles, especially those of theplate-type, was modiﬁed. This improvement reduced the coalescence ofvarious ice-phase particles and produced smaller aggregates, resulting
in better radar CFADs comparisons in the stratiform region, as shown in
the third row. Note that the SBM MP scheme is more direct (and realistic) than the bulk MP parameterizations used in GCMs. Uncertainties
in these can be expected to be larger.
With the increase of computer power, one of the fastest developments in the modeling community is the high-resolution global cloudresolving models (G-CRM). The spatial resolutions of these global

4.3. The role of ground validation (GV) campaigns
On one hand, CRMs provide simulated 4D data sets for improving
the performances of rainfall, snowfall and LH retrieval algorithms. On
the other hand, CRMs (and G-CRMs) need to be validated and improvedby observations. To leverage resources, ground validation (GV) sites are
set. These are intended to collect high quality, detailed information anddata from several sources during the periods of interest. They are a third
way of improving GCM physics.
Fig. 10. An example of the improvements and sensitivity of aspectral bin microphysical scheme using observations. The top
row is the observations; the middle row is the original SBM
simulations; the third row is the improved SBM simulations bymodifying the size threshold separating aggregates fromgraupel, and the ice crystal collection of cloud droplets. Eachcolumn represents one instrument. The left column is thesurface C-band radar reﬂectivity CFADs in stratiform region
during the PRESTORM ﬁeld campaign; the middle column is
the CFADs of the averaged TRMM PR observations for all thesquall cases identiﬁed over a 9-year period; the right column isthe distributions of TRMM TMI 85 GHz polarization corrected
brightness temperature for the same cases as the middle
column. Elaborated from Li et al. (2010).

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SBM, i.e. the control run including snow riming eﬀects (blue dots) and atest run excluding snow riming eﬀects (green dots); both control andtest runs employed the Mellor-Yamada-Janjic PBL (Planetary Boundary
Layer) scheme. The right panel shows the scatter plots simulated in a
WRF-SBM run employing Medium Range Forecast PBL scheme (greendots) as well as that from the aircraft measurements (red dots).
The broad distribution of the red dots in vertical directions indicatesthat the snowstorm is composed of non-rimed low-density and rimed
dense snow particles. The WRF-SBM control run (blue dots) was notable to simulate non-rimed low-density particles included in the measurement plots. This bias is related to the strength of heat/moistureﬂuxes from the lake surface through the PBL process to form the lakeeﬀect snowstorm.
The existence of the relationship between the riming process andthe PBL process is demonstrated by results of the sensitivity test employing the diﬀerent PBL scheme (green dots in the left panel). Onceagain, the case illustrates the many nuances in using observations tovalidate and improve climate models.
These exercises illustrate not only the challenges faced while validating climate models with observations, but also the potential andusefulness of such endeavors. The examples are numerous. Thus for
instance, and regarding just another component of the hydrologicalcycle, Lin (2014) illustrated the value of the estimates of subgrid-scale
humidity variability to improve the representation of clouds in GCMs.

In addition to collecting datasets conducive to physically validating
GPM precipitation retrieval algorithms, some of their major objectives
include the use of CRMs in precipitation science such as: (1) testing theﬁdelity of CRM simulations via intensive statistical comparisons between simulated and observed cloud properties and LH ﬁelds for avariety of case types, (2) establishing the limits of CRM space-time integration capabilities for quantitative precipitation estimates, and (3)supporting the development and reﬁnement of physically-based GMI(GPM Microwave Imager), DPR and DPR-GMI combined retrieval algorithms using ground-based GPM GV Ku-Ka band radar and CRM simulations. Both GCE (Tao et al., 2014) and NU-WRF (Peters-Lidard
et al., 2015) were used for GPM GV campaigns in real time forecasts(Tao et al., 2013; Wu et al., 2016) and for validating and improving theperformance of model simulations (Shi et al., 2010; Iguchi et al., 2012a,
2014).
Fig. 11 exempliﬁes the need of such exercises to improve climate
models. It shows a case that used GPM GV to validate and improve theperformance of simulating snow events during of the GV ﬁeld campaign(C3VP). The upper part of the ﬁgure shows horizontal distribution ofvertically maximum C-band radar reﬂectivity (in dB) over an area between the Georgian Bay of Lake Huron and Lake Ontario at 1200 UTC
on January 20, 2007 derived from King-City radar measurements(right) and simulated by the WRF-SBM and Goddard Satellite Data Simulator Unit (G-SDSU; left).
The G-SDSU (Matsui et al., 2014) computes satellite-consistent radiances or backscattering signals from simulated atmospheric proﬁlesand condensates consistent with the microphysics. Fig. 11 shows how aband of high reﬂectivity forming from the south edge of the Georgian
Bay and extending to Lake Ontario side. This band was due to asnowstorm formed by the interaction between cold dry air blowing overthe lake and heat/moisture supplied from the relatively warm water
surface of the lake (lake-eﬀect snowstorm). The lower part of the ﬁgureshows the scatter plot diagrams between airborne-instrument-based
bulk density (vertical axis) and bulk eﬀective radius (horizontal axis) of
snow particles sampled in the lake-eﬀect snowstorm case. The left panel
shows the scatter plot derived from actual aircraft measurements on theday (red dots) and those simulated in the two simulations of the WRF-

5. Discussion: On the need of quality control (QC) standards forclimate model validation
As seen above, validating precipitation estimates from climate
models requires a good knowledge of the details of the observational
databases in terms of their limitations, applicability and uncertainties.
In order to make such comparisons as transparent as possible, and todrive model improvements in models using a physical approach insteadof relying on statistical tuning, it is necessary to establish a set of welldeﬁned protocols and procedures. These have to be objective, transparent, traceable and easy to apply for everyone involved in thisemerging ﬁeld.
Fig. 11. (top) Horizontal distribution of vertically maximum Cband radar reﬂectivity (dB) at 1200 UTC on January 20, 2007
derived from King-City radar measurements (upper left) andsimulated using WRF-SBM and G-SDSU (upper right). (bottom)
Scatter plot diagrams between airborne-instrument-based bulk
density (vertical axis) and bulk eﬀective radius (horizontalaxis) of snow particles sampled in the lake-eﬀect snowstorm
case; the plots are derived from aircraft measurements (reddots) and those simulated in the three types simulations for
WRF-SBM, i.e. the control run including snow riming eﬀects(blue dots), a test run excluding snow riming eﬀects (green dotson lower-left side), and a run employing Medium Range
Forecast PBL scheme in place of the Mellor-Yamada-Janjic PBL
scheme (green dots on lower-right side). From Iguchi et al.,2012b. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the web version of thisarticle.)14

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Documentation, however, is just a ﬁrst step for building conﬁdence
in data sets and models. Additional requirements can be proposed to reinforce credibility. Table 2 gathers three tiers of those possible requirements divided into basic, extended and full.
The basic requirements are very simple. In addition to being
documented, the products (databases and models) should have been
described in scientiﬁc journals. The standard criteria for quality here ispeer-review and indexing such as in Journal Citation Reports (JCR).
Journals in the top of the categories are obviously more credible thanthose in the bottom, but as a basic requirement, publication suﬃces. It
is important however that the model or the algorithm is fully described:
cases in which the model is used with others and provides similar
performances are not enough. The algorithm or the modeling strategy
has to be submitted to public scrutiny in order to evaluate the applicability of the product for a certain application.
The second basic requirement refers to more in-depth scientiﬁcdiscussion. Proprietary systems and ‘in house’ algorithms are useful for
preliminary climate research but can provide only a minimum conﬁdence until the data are public and can be confronted into the peerreview and post-publication processes. Algorithms and models fallingunder the ‘basic requirements’ tag indicate little more than the existence of the product, but the tier is nonetheless relevant as some climate models and data sets do not even satisfy this simple speciﬁcation.
The extended level of conﬁdence includes more requirements.
Extended conﬁdence is tied to how the product compares with others.
Data or models yielding radically diﬀerent results from similar products, or models just replicating the annual cycle should be taken withcaution. Replicability is one of the cornerstones of science. Within thistier, results from case studies are a plus as they allow evaluating performances that can remain hidden to statistical comparisons.
Full conﬁdence includes additional, stronger requirements. The
probability of bugs in the code decreases exponentially with thenumber of eyes scrutinizing the code. A community of users also reduces to almost zero the chance of malpractice, incompetence or fraud.
It is therefore sensible that proprietary models whose source code noone has ever seen and which are used by just a few people in the world,
are not put on the same tier as another used by a community of hundreds of developers and whose code is downloadable for anyone tocheck.
To achieve full conﬁdence, it is also important to be able to evaluate
the degree of novelty of the product. Some allegedly independent or
new models are actually ‘avatars’ of a well-known public code which
has been tailored. There is nothing wrong in these practices as long as
there is a reason for doing so and the procedure is transparent.
However, using such models within an ensemble may not contributeadded value to the experiment because of the lack of independence and

Besides, the use of climate models for a variety of societal needs
such as dam design, urban water management and actuary activities,
has motivated the development of quality criteria so that ﬁnal users, aswell as scientists, are fully informed of the capabilities and limitationsof the products.
Both precipitation databases and outputs from climate models
should be considered within quality control (QC) standards. This is anincreasingly pressing requirement as climate becomes more and moreinterweaved with activities of mitigation and adaptation to global
warming. The public and the decision makers demand that the sciencebehind policies is traceable, transparent and auditable.
A note on terminology is in order as the meaning of ‘quality’ diﬀers
between communities. In industry ‘quality’ is precisely deﬁned by thequality assurance (QA) concept. The word is not addressed to the intrinsic value of the product but to ‘providing conﬁdence that quality
requirements will be fulﬁlled’ (e.g., ISO 9000). Thus, QA implies not
only that the product is suited for the speciﬁc purpose it was built (adata set or a climate model in our case) but also that the product has
been created following a well-deﬁned set of rules and methods thanbuilds conﬁdence in the whole production process. QA procedures aredesigned to minimize errors and mistakes, setting double-blind evaluations, sanity checks and providing a traceable ﬂow of the several
stages of the process of generating the product. It is worth clarifyingthat passing QA does not imply a good performance of the product. All
it means is that the product conforms to standards. Another issue worth
mentioning here is the topic of convergence. Convergence is not intrinsically a good estimate of observational product or model performance since all the developers are equally aware of the range of sensible values for their product and model outputs.
To achieve a QA-standard each step of the production process has to
be clearly deﬁned and subject to auditing. This is not a problem formost merged precipitation data sets since these are carefully designed
products whose science can be traced back to an Algorithm Theoretical
Basis Document (ATBD). The ATBDs are the cornerstone of the conﬁdence in merged precipitation data sets, in the same way that metadata and technical notes perform for pure observational data sets. They
provide the rationale of the many decisions taken over the process ofdeveloping the product, and allow users to trace back each step, also
permitting duplication of the product by another party.
Reputable climate models also have the equivalent to the ATBD inform of model documentation describing the physics of the dynamicalcore, the numerical methods employed, the parameterizations, and theempirical choices used to ﬁne-tune the model. A numerical modellacking such information can be considered ‘in house’, ‘internal code’ or‘research code’ but ought not be used to inform public policies or toderive consensus conclusions such as those of the IPCC.

Table 2
Basic, extended and full set of requirements of data and climate models involved in validation.
Tier

Tier 1: Basic conﬁdence

Requirements
Validation data

Climate models

ATBD available

Full technical documentation of the model core is available (incl. dynamical core,numerical methods, parameterization, and empirical coeﬃcients used for tuning)
The model has been fully described in a peer-reviewed journal

Algorithms or data set creation methodology has been describedin peer-reviewed literature
Plus
Tier 2: Extended
conﬁdence
Plus
Tier 3: Full conﬁdence

Products are publicly and freely available
There are published comparisons with other data set
Case studies are available

Outputs are publicly and freely available
Aggregated results compare reasonably well with pre-existing models
Benchmarking and case studies are available

The code for the algorithms is publicly available/replicable

Source code is publicly available
The results of the simulations are fully replicable by a third party
The model has been validated by several independent groups and the results havebeen published in reputable journals

The data set has been successfully used by several independentgroups and the results have been published in reputable journals

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As of 2017, it is safe to state that the future of climate modelingpoints towards high-resolution, coupled, convection-permitting, fullphysics GCMs projects lead by large institutions. Modeling eﬀorts are
directed towards variable resolution GCMs and HR-GCMs.
Consequently, validation eﬀorts should be directed towards improving
the physics sustaining such models, and here global precipitation databases can play a determinant role. Physical validation, more complex
and costly than direct validation, and often involving setting up

diversity, a quality very much sought when building an ensemble. Just
reporting the parameterization type is not enough: most parameterizations contain a choice of values for key variables. Since in thedevelopment phase models are evaluated in speciﬁc locations, no one
can assure that the parameterization would work in another place.
Besides, the same parameterization can be wired in the model in adiﬀerent way depending on programming practices or computing resources, and it is always helpful to have the code examined by a fresh
pair of eyes. Again, the more people have gone through thousands oflines of code, the better for the community.
The top requirements for quality assurance are in the line of theoutputs or products having been used and replicated by a third party. A
model used by several independent groups and whose results are routinely published in top journals cannot be in the same category as othermodels developed by a few people and used just by them. Ideally, thevalidation should be as independent as possible; preferably by anothergroup. Consequently, the QA for those models must be clearly diﬀerent.
At this point, it is worth mentioning that once science ventures into
providing input for policies only models with the higher conﬁdence
should be used.
Examples of full conﬁdence level data sets using those criteria
would include the GPCP database, on the data side; and the CESM on
the model side. CESM satisﬁes all the requirements described above.
Many versions of the code are routinely run in several independent
research centers, and there is a community improving the model andreporting results, study cases and the unavoidable bugs featuring incomplex and large projects.
The same applies to databases such as GPCP, which anyone withadequate technological resources can replicate, and that have been
examined by a large community. Such dissemination builds up conﬁdence in the data to be the best possible at the time, and therefore
suitable for been used to derive public policies.

Table 3
A checklist of known issues that must be considered in the ﬁeld of validation of precipitation outputs from climate models.
1. Rain gauges provide pointwise estimates that may be not fully representative of thearea, especially for large areas with a few observations (e.g., the Amazon basin).
2. Rain gauges have known technical limitations and biases and the spatial
distribution/length record of the instruments is highly variable.
3. Ground radars are characterized by many sources of uncertainty (i.e., beam
blockage, attenuation) that should be taken into account.
4. Precipitation (solid, liquid and mixed phase) has a large spatial and temporalvariability making its validation both challenging and important using
precipitation datasets in model validation.
5. Satellite estimates are indirect and have limited temporal sampling, and thisshould be considered in the comparisons.
6. Satellites estimates over land, coast and ocean are derived using diﬀerent methods
and assumptions.
7. Merged precipitation databases are not intended for trend analyses as sensors drift
and/or are available over limited time spans.
8. Many of the techniques used in Level-2 products are built upon Bayesian estimates(i.e., they require a prior estimate).
9. The quality of Level-3 precipitation products is driven by microwave observations
and therefore is dependent on their availability and quality.
10. The error characteristics resulting from the merging of disparate datasets are notwell known.
11. All climate models are tuned to observations, and this must be considered for
ensuring a truly independent validation.
12. Global measurements of microphysics are important to avoid overﬁtting models
to empirical parameters.
13. There are known uncertainties in the estimation of diabatic heating ﬁelds thataﬀect how models represent some precipitation processes.
14. Model outputs that have been bias-corrected or that are the results of model
output statistic techniques cannot be validated.
15. Series derived from GCM-driven RCMs cannot be directly compared with timeseries of observations.
16. High-resolution global cloud-resolving models (G-CRM) are becoming best suitedthan RCMs to inform policies and advance our knowledge of the physics ofprecipitation.
17. Parameterizations can only be validated with data not used in their development
and tuning.
18. ‘Scope principle’: a model cannot claim performances at better resolutions thatthose at which it has been validated.
19. Blending methods in deriving global precipitation products involves subtleties
than must be considered in any validation process.
20. There are signiﬁcant latitudinal diﬀerences in the satellite and ground based
estimates in terms of known biases and uncertainties.
21. Parameters and techniques used in the estimation process using satellites and rain
gauges may not be universally applicable, both in space and time.
22. Ground validation campaigns are essential for improving the representation of
precipitation in models.
23. End-to-end characteristics of the satellite-based retrieval process are not yet fully
understood.
24. There is less agreement among satellite products in trends and variability at
global scale than in regional variability.
25. The precise measurement of shallow and very light precipitation still represents ascientiﬁc challenge.
26. While precipitation is a key variable to validate models, there is not agreement in
the reference to be compared with. More research and targeted observations are
required to ﬁll this gap.
27. Public auditing of model code and precipitation databases algorithms is required
if models are used for policy-making and societal applications other than pureresearch.
28. Every aspect of model and database development should be subject to QC
methods and be fully traceable, transparent and auditable.
29. Models must be independently validated by scientists not involved in theirdevelopment or belonging to the same research network.
30. Users should be made fully aware of the conﬁdence level that can be attributed tomodel outputs and observational databases.

6. Conclusions
The validation of climate models is critical to improve the physics ofsuch tools and to gauge its ability to provide useful input to decision
makers. However, observational, reference data sets present a series of
uncertainties and limitations. Awareness of those is a must to avoid
pitfalls and extract wrong or unsubstantiated advice on the spatial andtemporal distribution of precipitation. Also, there is a lack of standards,
best practices and quality assurance methods for both observation data
sets and models.
This paper has elaborated over both aspects. Table 3 gathers thecentral points that require attention in the task of validating precipitation outputs from climate models.
A main conclusion is that since there are no “perfect” data sets thatare optimal in both sampling and retrieval error, it would be always
desired to use multiple precipitation products as the reference to verify
model simulations so the audience can grasp the magnitude of measurement uncertainties. The IPCC AR5 acknowledgment of large observational uncertainties in precipitation observations for climate
model validation still applies, and the more we discover about precipitation physics the more questions arise. Comparisons considering
the uncertainties and limitations of the reference data are rare andcumbersome, but nonetheless necessary.
Another major conclusion is that there is the need for setting up
quality standards to ensure products are judiciously used. Simple
schemes such as the three-tiers scheme we propose can help to understand how suited for a speciﬁc application a model or data set is. Thus, aregional precipitation data set might be helpful in the model development stages but may not be that useful for extracting conclusions at
global scale over a full climatological period. Here, topics such as the
habit of validating at a spatial or temporal resolution and then claim
performances at better resolutions are highly relevant and deserve full
attention.
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dedicated ground validation campaigns are important to advance theﬁeld.
Until the end-to-end characteristics of the satellite-based precipitation retrieval process are fully understood and parameterized in a dynamic fashion (dynamic since the overall errors are aggregated fromalways-changing satellite orbital patterns), it will be diﬃcult to quantitatively compare GCM models and satellite precipitation data beyond
more than daily precipitation totals, threshold scores and precipitation
patterns. The future lies in extracting the information content within
the multiple active and PMW data, to assess physical processes (cfr.
Michaelides et al., 2015) interior to the GCM models (SkofronickJackson et al., 2017).
Validation of climate models is full of subtleties and details thatquite often are only obvious to data set developers. Thus, for instance it
is premature to use global scale trends and variations from observations
to validate climate models as there is still some disagreement among
the satellite products themselves. Given the extent of tuning thatmodels have there is a danger of overﬁtting parameters to observations
that are known to present its own uncertainties, biases and limitations.
The application of those tuned models to a future climate under otherset of emissions and forcings may not yield robust conclusions and thus
be counterproductive.
A major reason for being prescriptive in the area of climate model
validation and veriﬁcation is reinforcing accountability. From the moment science is used to inform policies it can become political andtherefore subject to increased public scrutiny. That means increased
levels of quality assurance. Therein, awareness of the speciﬁcs of model
tuning is critical to further societal advance. In an era of information,transparency and public responsibility, it is important to base policies
on nothing but the best information. Both modelers and precipitation
data set producers should make their codes as public as possible soeveryone can check the assumptions made, evaluate the performances,and examine the empirical assumptions. It would be also advisable
requiring that validation is done independently from model developers.
Finally, it is worth mentioning the validation must be performed
with independent data not previously used for model development.
While this may seem obvious, the fact is too often forgotten. A biascorrected model cannot be fairly validated. On this respect, ongoingmissions such as GPM provide a wealth of continuously updated information and planned missions such as CaPPM can serve to test hypotheses in the extremely demanding task of precisely quantifying
precipitation over the globe in 4D.
Acknowledgments
Funding from projects CGL2013-48367-P, CGL2016-80609-R(Ministerio de Economía y Competitividad), UNCM08-1E-086(Ministerio de Ciencia e Innovación), and CYTEMA (UCLM) is gratefully
acknowledged by FJT (Tapiador) and AN. AN acknowledges FPU13/02798 grant and VL is grateful for support from the EU under grant
agreement 603608 (eartH2Observe).
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