Environmental Research Letters

LETTER • OPEN ACCESSInter-product biases in global precipitation extremes
To cite this article: Hirohiko Masunaga et al 2019 Environ. Res. Lett. 14 125016

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Environ. Res. Lett. 14 (2019) 125016

https://doi.org/10.1088/1748-9326/ab5da9

LETTERInter-product biases in global precipitation extremes
OPEN ACCESSRECEIVED

12 April 2019

Hirohiko Masunaga1 , Marc Schröder2 , Fumie A Furuzawa1, Christian Kummerow3,
Elke Rustemeier4 and Udo Schneider4
1

REVISED

7 November 2019
ACCEPTED FOR PUBLICATION

29 November 2019
PUBLISHED

19 December 2019

Original content from thiswork may be used under
the terms of the Creative
Commons Attribution 3.0
licence.
Any further distribution ofthis work must maintainattribution to theauthor(s) and the title of
the work, journal citation
and DOI.

2
3
4

Institute for Space-Earth Environmental Research, University of Nagoya, 464-8601 Nagoya, Japan
Satellite-Based Climate Monitoring, Deutscher Wetterdienst, D-63067 Offenbach, Germany
Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States of America
Global Precipitation Climatology Centre, Deutscher Wetterdienst, D-63067 Offenbach, Germany

E-mail: masunaga@nagoya-u.jp
Keywords: climate extremes, global precipitation, satellite meteorology, rain-gauge networks
Supplementary material for this article is available online

Abstract
Biases in climatological and extreme precipitation estimates are assessed for 11 global observational
datasets constructed with merged satellite measurements and/or rain gauge networks. Speciﬁcally,
the biases in extreme precipitation are contrasted with mean-state biases. Extreme precipitation isdeﬁned by a 99th percentile threshold (R99p) on a daily, 1°×1° grid for 50 °S–50 °N. The spatial
pattern of extreme precipitation lacks distinct features such as the ITCZ that is evident in the global
climatological map, and the climatology and extremes share little in common in terms of the spatial
characteristics of inter-product biases. The time series also exhibit a larger spread in the extremes
than in the climatology. Further, when analysed from 2001 to 2013, they show relatively consistentdecadal stability in the climatology over ocean while the dispersion is larger for the extremes overocean. This contrast is not observed over land. Overall, the results suggest that the inter-productbiases apparent in the climatology are a poor predictor of the extreme-precipitation biases even in aqualitative sense.

Climate predictions
Operationalized

Appendix: Glossary

AMSR-E

Advanced Microwave
Scanning Radiometer
for EOSDMSPDefence Meteorological
Satellite Program

CDR

Climate Data Record

ENSO

CHIRPSClimate Hazards Group
InfraRed Precipitation
with Station

El Niño Southern
Oscillation

EOS

Earth Observing System

ETCCDI

Expert Team on Climate
Change and Detection
Indices

FROGS

Frequent Rainfall
Observations on GridS

GPCC

Global Precipitation
Climatology Centre

GPCP

Global Precipitation
Climatology Project

GPM

Global Precipitation
Measurement Mission

CMORPH

CPC MORPHing
technique

CM SAF

Satellite Application
Facility on Climate
Monitoring

CPC

Climate Prediction
CenterDAPAGLOCO Daily Precipitation Analysis for the validation of
Global medium-range© 2019 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 14 (2019) 125016

GSMaP

Global Satellite Mappingof Precipitation

HOAPS

Hamburg Ocean Atmosphere Parameters andFluxes from Satellite Data

IMERG

Integrated Multisatellite
Retrievals for GPM

PERSIANN

Precipitation Estimationfrom Remotely Sensed
Information using Artiﬁcial Neural Networks

SSM/I

Special Sensor Microwave/Imager

SSMIS

Special Sensor Microwave
Imager/Sounder

TAPEER

Tropical Amount ofPrecipitation with an
Estimation of ERror

TRMM

Tropical Rainfall Measuring Mission

There exist some studies that focus on extreme precipitation at regional scales (e.g [6, 7]). Systematic assessments
related to extremes on global scale, however, are few inthe literature (e.g [8]). Yet, knowledge related to meaninter-product differences are not necessarily applicable
to extremes, given that satellite-derived extreme precipitation is often associated with some cloud property(e.g. cloud top height or precipitation water content)
rather than the intensity of precipitation itself [9].
The main goal of this article is a general description of inter-product precipitation biases for extreme
precipitation as compared to the mean-state biases,
providing a concise overview of the consistency
among a broad range of global precipitation datasets incontrast to a large body of relevant work in the literature where selected products were examined in depth.
The inter-comparison is made using different aspectsof the precipitation such as precipitation histograms
and global distribution patterns to identifying systematic similarities and differences among the products. The multi-year time series of precipitation are
also intercompared and stability estimates are examined for the period of 2001–2013, for which a majorityof the products currently analysed are available.

1. Introduction
Accurately measuring precipitation on a global scaleremains a challenge. The ground-based capability formonitoring precipitation is limited to the land portion of
the Earth’s surface. Most countries operate some form ofrain-gauge network, but only a few countries have theopportunity to combine these with networks of local
weather radars. The ground-based datasets used here rely
on collections from these various networks of raingauges. Thus, they are limited to coverage over land, withsome regions having relatively sparse coverage. Satellite
observations offer a complementary means of precipitation measurements beyond the reach of in situ weather
stations, but satellites estimates are not free of errors.
Geostationary (GEO) satellites have the ability to uniformly and continuously monitor clouds, but not withan instrument sensitive to raindrops beneath the cloudlayer they observe. Low Earth orbiting (LEO) spacecrafts
carrying microwave instruments are superior in thedetection skill of precipitating particles, but sample onlyintermittently and thus can miss important components
of precipitation’s inherent diurnal cycle or intense shortterm events. Among the precipitation datasets widelyadopted across the user community are ‘merged’ products constructed with different observations frommultiple GEO and/or LEO satellites with or without
gauge networks in the hope to compensate for the
drawbacks inherent in individual observations (seesection 2 for a list of such datasets).
Signiﬁcant effort has been devoted to assess the consistency of these ‘merged’ precipitation datasets ([1–5]among others). Most of the work in this area, however, isfocused on the ‘mean’ precipitation accumulation rather
than the properties of the precipitation distribution itself.
2

2. Data
In total, 11 products are selected for this work,
consisting of 9 global multi-satellite datasets, with orwithout gauge calibration over land, and 2 gauge-based
products used internally in some of those satellite
products. The multi-instrument datasets included inthis analysis are CMORPH v1.0 [10], GPCP v1.3 daily[11], GSMaP v6 [12], HOAPS v4.0 [13], IMERG v5 [14],PERSIANN-CDR v1r1 [15], TAPEER v1.5 [16], andTRMM 3B42 v7 [17]. Three land-only products,
CHIRPS v2.0 [18], CPC v1.0 [19], and GPCC Full Data
Daily v2018 [20, 21], are also analysed. All precipitation
estimates are adjusted to a daily 1°×1° grid, whichaccommodates the native resolution of all products inthe current inventory. This choice is not necessarily
optimal for assessing extreme precipitation [8] but iscrucial to ensure the consistency in spatial and temporal
resolutions for the inter-comparison. The statisticalproperties of extremes can be distorted for the mostintense events when projected onto a coarse grid. As
such, the current comparison may not precisely reﬂect
the characteristics intrinsic of the original products attheir native resolutions (see section 3.1). The productversions and resolution adopted here are fully compliant with the datasets stored in the FROGS archives [22].
Also, data from 2015 is not an ofﬁcial CM SAF HOAPS
product and not part of FROGS. It was provided as abeta version of HOAPS version 4. We do not analyse all
the products available from FROGS. The products on
FROGS that are regional or ground-based without
being used for any satellite-based dataset are outside ourscope. Upstream products (e.g. microwave-only andEnviron. Res. Lett. 14 (2019) 125016

no-gauge products) of 3B42, GSMaP, and CMORPH,
regional products, and reanalysis precipitation are also
not included in the current study.
CMORPH, GPCP, GSMaP, IMERG, TAPEER, andTRMM 3B42 are all constructed with satellite microwave and infrared measurements merged on a daily
basis, where details of the merging techniques differ
from one product to another. GSMaP, IMERG, and
3B42 each contain gauge-adjusted estimates over land,
which are used in this study. GPCP daily precipitation isscaled to its monthly, gauge-calibrated product. PERSIANN-CDR is an infrared-based product adjusted to
GPCP monthly precipitation. HOAPS precipitation is
obtained from DMSP SSM/I and SSMIS observations
over the global ice-free ocean. GPCC and CPC are gridded rainfall products constructed with data from gauge
networks over the globe. CHIRPS is a satellite infraredbased product blended with station data.
IMERG is available only after the GPM launch in2014, so we deﬁne the full year of 2015 as the reference
period for detailed inter-comparisons. The time series
analysis in section 3.3 is performed without IMERG.
The latitudinal band of 50 °S–50 °N is analysed except
for TAPEER (30 °S–30 °N) unless otherwise noted.

3. Climatology versus extremes
3.1. Histogram
Figure 1 shows log–log daily precipitation histograms
computed from latitudinally-weighted statistics ofdaily values on a regular 1°×1° grid for different
datasets. The histogram is not normalised, so excesses
and deﬁcits do not cancel each other out. Nonetheless,the inter-product bias characteristics are inhomogeneous across magnitudes of precipitation. For
instance, GPCP has higher frequency in the intermediate range of precipitation over ocean but quickly drops
below all other datasets once daily precipitation
exceeds approximately 30 mm d−1. In contrast,
HOAPS has a large tail of high precipitation rates at theexpense of a relatively low occurrence of intermediate
precipitation. This demonstrates that biases inextremes can be fundamentally different from thosethat constitute the climatology (light and moderate
precipitation). The spread among the products issmaller over land than over ocean.
It is noted that because precipitation estimateshave been averaged onto a common 1°×1° grid inﬁgure 1, products with a higher native resolution
would originally have a better capability of capturing
extremes (ﬁgure S1 and table S1 is available online atstacks.iop.org/ERL/14/125016/mmedia in the supplementary material). Comparing ﬁgures 1 with S1shows that the precipitation histograms at the original
resolution are broadly spread across different products, where high-resolution datasets are indeed able
to capture higher extremes. The spread is reduced with3

the signals of heaviest rains averaged out at the 1° × 1°
resolution (ﬁgure 1). The inter-product spread, however, does not entirely vanishes, implying productspeciﬁc biases as described above.
In the ﬁgures that follow, extreme precipitation isdeﬁned by a 99th percentile threshold (or R99p
according to the ETCCDI indices [23],) on a daily,
1°×1° scale. We have tested the 30 and 20 mm d−1
thresholds for comparison (see the supplemental material). These two ﬁxed-value thresholds correspond todifferent percentiles in the individual products andhence could potentially yield different extreme statistics from R99p. The zonal-mean extreme precipitation
varies in a quantitative sense with threshold deﬁnitions, while the overall pattern stays qualitatively consistent among different thresholds.
3.2. Global distribution
Figure 2 shows the global distribution of the 2015
annual climatology of precipitation. Each product ispresented as the anomaly ﬁeld from the productensemble mean (top left). Note that the magnitude of
the anomaly ﬁeld stays much smaller than theensemble mean and all the products agree well in thegeneral pattern as well as the zonal means (ﬁgures 4
and 5 below). Regional precipitation biases nonetheless show some interesting features. Oceanic precipitation from some products (GSMaP, HOAPS, and3B42) is systematically higher in the ITCZ and lower in
mid-latitudes than the ensemble mean, while others(CMORPH and IMERG) show contrasting spatial
patterns in the anomaly ﬁeld. Such a systematic bias
pattern might be related to the built-in statistical
relation of IR radiance with surface rainfall because
dominant convective systems change from one region
to the other. Deep organised systems are typical of thetropics while subtropical rainfall mostly comes fromshallow cumulus. Mid-latitude storms are typicallyassociated with synoptic-scale frontal systems. Otherwise it is not obvious why some products exhibit
certain regional bias patterns given that the basicarchitecture of the retrieval algorithms is similar.
Characteristic regionality in the global distribution ofinter-product biases was also shown by [3]. Small-scale
structures with alternating signs dominate outside the
ITCZ in GPCP and PERSIANN.
African and South American precipitation is either
largely overestimated or underestimated dependingon the products, with the consensus being poor evenamong the two gauge-based products (CPC and
GPCC). The disagreement in the Congo is due largely
to the known unavailability of data there in 2015 for
GPCC and CPC, while origins of the discrepancy inSouth America are less clear. CMORPH, GPCP,
GSMaP and PERSIANN exhibit a feature in the difference to the ensemble mean from 40 °S southward(GPCP and GSMaP also from 40 °N northward). This

Environ. Res. Lett. 14 (2019) 125016

Figure 1. Log–log daily precipitation histogram for global (50 °S–50 °N) precipitation over ocean (top) and over land (bottom).

coincides with the transition from utilisation of
GEO+LEO to LEO data.
The global distribution of extreme precipitation isqualitatively different from the climatology (ﬁgure 3).
Subtropical bands of enhanced precipitation in theensemble mean of extremes (top left) are in sharp contrast to the ITCZ, which stands out in the climatology.
The spatial structure of extreme precipitation bias is
more or less homogeneous although the sign and magnitude differs vastly among products. Figures 2 and 3
together show that extremes and climatology bear noapparent similarity in the geographical pattern eitherof the ensemble mean or the anomaly ﬁeld.
To summarise the inter-product differences, zonal
mean precipitation climatology and extremes are plotted over the ocean in ﬁgure 4. While there is reasonable agreement for the climatology, different datasets
have large differences in their extremes. The spread isrelatively modest over land (ﬁgure 5) as expected fromﬁgure 1. Interestingly, most satellite-based products
are bound between the two gauge products (CPC and
GPCC) for the climatology over tropical land, presumably because the gauge network there is so sparse4that two gauge products disagree from each other,
depending sensitively on the choice of stations andinterpolation schemes. The currently analysed version
of GSMaP is heavily adjusted to the CPC product andclosely follows the CPC rain in the zonal-mean climatology. Note that latitudes south of 45 °S over landconsist almost solely of the southern tip of SouthAmerica and suffer from larger statistical noise thanother domains. Zonal-mean extremes with ﬁxed rainrate thresholds (20 and 30 mm d−1) exhibit qualitatively consistent spatial patterns in comparison with
the R99p extremes although there are differences inabsolute values (ﬁgures S2 and S3 in the supplementary material).
3.3. Time series
The analysis of time series is based on the same data
records as in previous sections, i.e. data records fromthe FROGS archive [22]. The time series are computed
either as weighted averages from daily values on aregular 1°×1° grid per month or as 99th percentiles
for a maximum period from January 1979—December 2018. Besides global means within 50 °N/S, means

Environ. Res. Lett. 14 (2019) 125016

Figure 2. Product ensemble mean of 2015 annual precipitation climatology (top left) and anomaly from the ensemble for eachproduct. TAPEER is not included in the ensemble mean because of the unavailability beyond 30° in latitude.

from three zonal bands are analysed: tropics within±10°, northern hemisphere within 10 °N and 35 °N,
and southern hemisphere within 10 °S and 35 °S.
TAPEER does not fully cover these regions and is notpart of the global analysis. Results related to the zonal
bands are shown in the supplementary material. The
averaged time series were further processed in two
different ways: (1) For each individual data record,
region, and metric (here, mean and 99th percentile),
the following processing was applied: the climatological mean and annual cycle were computed. Then, theclimatological annual cycle was removed. The resulting
time series exhibits anomalies around a mean precipitation level of 0 mm d−1. Finally, the mean precipitation
was added again. This approach was also applied by[24]. (2) For each region, for each metric and for theperiod January 2001—December 2013, the ensemble
mean was computed. Then, the difference between eachdata record and the ensemble mean was normalised tothe ensemble mean. This relative anomaly (or bias) timeseries is also used to estimate stability. Here, stability
was computed as the change of the relative bias over theperiod 2001–2013 using a least absolute deviation
method (adapted from the routine MEDFIT on page703 in [25]). Associated uncertainties were computed
following [26, 27] (see equation (6) of [27]). The null
hypothesis is that the stability is different from 0%/
decade and the alternative hypothesis is that the stability
is not signiﬁcantly different from 0%/decade. The null
hypothesis is rejected if the coverage probability >95%
(or p<0.05).
5

We show results from approach (1) ﬁrst, as this analysis is deﬁned over the full temporal coverage of eachdata record. Figure 6 shows such time series for the global ocean and land within 50 °N/S. The vertical axis
was optimised to allow a maximum zoom into theﬁgure at the expense of a variable y-axis range. In general, the monthly means exhibit fairly good agreement
over the ocean and only GSMaP is biased low. For themonthly means over land two clusters are evident
corresponding to CPC and GPCC. Clustering aroundthe gauge-based products may be partly explained by
the gauge adjustment procedures: GPCP via its
monthly adjustment, while 3B42 use a previous version
of GPCC, GSMaP and CMORPH adopt CPC, PERSIANN was adjusted to GPCP monthly precipitation
and CHIRPS utilises gauge data (see [28] for details on
CHIRPS). The difference between the clusters isapproximately 0.5 mm d−1 in the 2000s, associatedwith a bias between CPC and GPCC. This bias is smallerin the 1980s though. Over land PERSIANN exhibits
good agreement with GPCP and GPCC, except prior to
the late 1980s. Furthermore, it can be seen that monthly
means of HOAPS exhibit anomalies that coincide with
ENSO variability. For PERSIANN, anomalies in May
and June 1984 and June and July 2017 are evident.
These anomalies coincide with systematic regional data
gaps over Africa, Europe, and the Indian Ocean (Mayand June 1984) and anomalously low precipitation overthe ITCZ in June and July 2017.
The results for the 99th percentile exhibit lessagreement, i.e. a larger spread, and a lower level ofclustering than for mean precipitation. In particular,

Environ. Res. Lett. 14 (2019) 125016

Figure 3. As ﬁgure 2 but for extremes.

the clustering around GPCC and CPC is no longer evident. Over the ocean, maximum 99th percentiles ofprecipitation are similar among 3B42, CMORPH,
GSMaP and HOAPS while GPCP exhibits minimum
99th percentiles. An apparent jump to lower 99th percentiles for GPCP over the ocean occurs in late 2008/
early 2009. It is noteworthy as it is not present in theother data records. Within GPCP the utilisation of
SSM/I data ends in December 2008 and transfers to
SSMIS in January 2009 [25]. For the GPCP Daily analysis the SSMI/SSMIS data is used to establish a precipitation frequency or precipitation/no precipitationthreshold, with mean precipitation intensity determined by the GPCP monthly precipitation. This produces a conservative 99th (and other high percentile)
values. The 2009 shift is likely related to a slight shift inthat SSMI/SSMIS precipitation threshold that went
undetected. This discontinuity may be sensitive to thethreshold for extremes and needs further investigation. Over land 99th percentiles are lowest for PERSIANN and CHIRPS and highest for GPCC, with peakvalues in early 1982. This maximum coincides with theeruption of El Chichon in March 1982. However,values for January and February 1982 are the second
and third highest values. Finally, a decrease in the 99th
percentile of 3B42 over land between the start of thetime series and 2005 is observed which is not evident
in the other data records.
Results shown in ﬁgure 6 are discussed further
because several minima and maxima of globally(within 50 °N/S) averaged values coincide with ENSO:
Maxima and minima in mean precipitation and in
99th percentiles over the ocean are observed in 1998,
62010 and 2016 as well as in 1999, 2008 and 2011,
corresponding to El Niño and La Niña events, respectively. Mean precipitation minima over land coincidewith El Niño events in 1983, 1992, 1997, and 2016.
Maxima are present in 1999/2000 and 2010/2011,
coinciding with La Niña events. It is noticeable that the99th percentiles over land do not exhibit minima/
maxima in coherence with ENSO. The imprint of
ENSO on precipitation was discussed, e.g. in [29, 30]
and the contrasting behaviour in mean precipitation
between land and ocean was also described, e.g. by [30][31]. identiﬁed the impact of El Chichon and Pinatubo
as minima in 1983 and 1991 in precipitation fromGPCP [29]. observed coherent variability between
precipitation extremes and ENSO over the tropicalocean and increased amplitudes of this variability withincreasing percentiles [32]. emphasised that the
response of precipitation to ENSO has a strong regional imprint which can locally exceed the expectationfrom Clausius–Clapeyron due to ampliﬁcations of
ENSO dynamics by atmospheric feedbacks. They further concluded that the response of precipitation in
the warm, moist ENSO regions is similar to theextreme response discussed in [29] while the tropicwide response to ENSO is small due to compensating
moistening and drying effects. Results from analysis ofscaling or regression between precipitation and sea
surface temperature data can be found in papers mentioned above and in particular in [33] who also utilised
data records from FROGS. Note that the minimum inmean precipitation over land in 2005 does not correlate with ENSO or volcanoes. At present, this anomaly
cannot be explained. Finally, it is noted that results

Environ. Res. Lett. 14 (2019) 125016

Figure 4. Zonal mean precipitation (2015 annual mean) over ocean for climatology (top) and extremes (bottom).

presented in sections 3.1 and 3.2 are affected by the El
Niño in 2015/2016. The observed response of HOAPS
to ENSO partly explains the observed behaviour presented in these previous sections.
Time series of anomalies relative to the ensemble
mean for the global ocean and land within 50 °N/S are
shown in ﬁgure 7. Overall features are fairly similar toresults shown in ﬁgure 6. This is in particular valid for theoverall agreement in mean precipitation over the ocean,
the clustering in mean precipitation over land and thelarger spread in biases for 99th percentiles than for mean
precipitation. To some extent the small bias among themean precipitation over ocean can be explained with theadjustment of PERSIANN and CMORPH to GPCP (thelatter over ocean only, see [34]). Maximum temporalvariability in mean precipitation over ocean is observedfor HOAPS and 3B42, with opposing minima and maxima between both data records reﬂecting maximum difference in the response to ENSO. The 99th percentile
anomaly of CHIRPS over land exhibits a pronounced
annual cycle. This indicates that CHIRPS exhibits the largest amplitude of the annual cycle. While hardly evident
in ﬁgure 6, it can be seen in ﬁgure 7 that CHIRPS and
7

CMOPRH are not closely following the 99th percentile
anomaly of CPC over land.
Stability estimates were computed for all time series
shown in ﬁgure 7 with results provided in table 1. The
spread in stability estimates over the ocean is fairly small,with generally non-signiﬁcant differences among the products. An exception is the stability of 3B42. Prior to 2008
3B42 anomalies are smaller compared to the period after
2010 (see ﬁgure 7). In June 2009 elements of the precipitation hardware of TRMM were switched from nominal to back-up (https://pmm.nasa.gov/sites/default/
ﬁles/document_ﬁles/TRMMSenRevProp_v1.2.pdf).
However, a clear temporal coincidence between a jumpin anomalies and this event is not evident. Stability estimates for 99th percentiles over ocean exhibit a larger
spread with values ranging from −6.7%/decade (GPCP)
to 7.4%/decade (3B42). Here, stability estimates are generally signiﬁcantly different. GPCP exhibits the largest
difference in stability between mean precipitation and the99th percentile: while the mean precipitation of GPCP
exhibits the highest level of stability, the low stability of
the 99th percentile time series is explained by the jump inthat time series occurring in late 2008/early 2009 (seeEnviron. Res. Lett. 14 (2019) 125016

Figure 5. As in ﬁgure 4 but over land. The range for the ordinate is chosen to be identical to ﬁgure 4.

second panel of ﬁgures 6 and 7). As mentioned earlier,this jump coincides with the change in the utilisation of
SSM/I and SSMIS data.
The stability estimates for mean precipitation overland exhibit a fairly small spread, though with quite afew signiﬁcant differences. CHIRPS, CMORPH and
GPCC exhibit non-signiﬁcant and low stability
estimates while the stability estimate of GSMaP(5.3%±0.52%/decade) is the largest observed estimate over land. GPCP and GSMaP show maximum
absolute stability estimates for 99th percentiles overland. The spread in 99th percentiles is smaller for landbased results than the equivalent ocean-based values. A
possible explanation could be the direct or indirect useof rain gauges in the various products. While differences still exist because of the way the rain gauges areincorporated into products, an overall reduction in theland-based variability is not surprising.
It is emphasised that the ensemble mean contains
contributions from all data records, including artiﬁcial
trends and anomalies. Thus, the above stability discussion cannot address the true stability of the data
records. However, the presented stability results allow
8

the identiﬁcation of stability issues in a relative sense
and support the identiﬁcation of spurious changes in
the mean relative bias.

4. Discussion and summary
This work highlights precipitation differences among
global gridded products in terms of regional patterns
and multi-year time series, with a focus on thequalitative contrast between climatology and extremes.
Precipitation extremes are deﬁned by the 99th-percentile threshold (R99p) on a daily 1°×1° grid. All
products agree on some fundamental characteristics of
extremes, such as subtropical maxima over ocean. On
the other hand, the inter-product spread is signiﬁcantly
larger for extremes than for climatology particularly
over the ocean, perhaps because there are no rain
gauges available to bias-correct products. This suggests
the importance of analysing multiple precipitation
products at a time instead of relying on a single,
arbitrarily chosen, product.

Environ. Res. Lett. 14 (2019) 125016

Figure 6. Time series of monthly mean precipitation with the annual cycle being removed: global ocean (top two panels) and global
land within 50 °N/S (bottom two panels), mean over all deﬁned values (ﬁrst and third panel), and 99th percentile (second and fourth
panel).

Although this article is not intended to rank the
products for accuracy, it would be beneﬁcial to comment on potential issues or strengths of selected datasets. HOAPS exhibits anomalies which coincide with
ENSO but are in contrast to 3B42 anomalies. Figures
9S6–S8 of the supplement show that the ENSO
response of HOAPS is dominated by observations
over the tropics within 10 °N/S. Input observationsfor HOAPS are obtained exclusively from the SSM/I
and SSMIS sensors. When using the DAPAGLOCO

Environ. Res. Lett. 14 (2019) 125016

Figure 7. Time series of anomalies relative to the ensemble mean for the period 2001–2013: global ocean (top two panels) and global
land within 50 °N/S (bottom two panels), mean over all deﬁned values (ﬁrst and third panel), and 99th percentile (second and fourth
panel).

dataset (DOI:10.5676/DWD_CDC/HOGP_100/V002)
which utilises SSM/I, SSMIS, AMSR-E and TMI observations and the HOAPS retrieval [13], the mean precipitation over the global ice-free ocean is similar
between the HOAPS and the DAPAGLOCO products(not shown), with slightly smaller anomalies during
10

La Niña events. Thus, the temporal sampling in HOAPS
only partly explains HOAPS’ ENSO response, i.e. thesensitivity of the retrieval needs to be examined in orderto understand this feature. Over oceans, GSMaP exhibits
the lowest mean precipitation. In ﬁgures S6–S8 of the
supplement GSMaP is characterised by only slightlyEnviron. Res. Lett. 14 (2019) 125016

Table 1. Stability estimates and associateduncertainties for global means and 99th percentiles
within ±50° over ocean and land. Signiﬁcant stability
estimates are printed bold. Due to the limited
temporal coverage (2001–2013) and the utilisation of
the ensemble mean as reference the stability is only an
estimate of the true stability.

Data availability statement

Global ocean
Stability/%/decade
DatasetGlobal mean

GPCP
HOAPS
3B42
CMORPHPERSIANN
GSMaP−0.7±0.35−6.7±0.87−1.3±0.75
3.6±0.60
5.5±0.75
7.4±0.75−0.8±0.29−3.7±0.72−2.1±0.34
1.8±0.39−1.1±0.54−1.9±1.10
Global land
Stability/%/decade
Global mean
99th percentile−3.1±0.69
0.0±0.44
0.3±0.55
0.8±1.00
2.5±0.87
2.1±0.69−1.5±0.41−4.0±0.58−0.2±0.43−1.0±0.43
1.5±0.56
0.0±0.69−2.5±0.51−2.8±0.43
5.3±0.52
4.3±0.35

DatasetGPCC
CHIRPS
CPC
GPCP
3B42
CMORPHPERSIANN
GSMaP

JAXA and M Schröder acknowledges the ﬁnancial
support by the EUMETSAT member states throughCM SAF. We are grateful to Bob Adler and two
anonymous reviewers for their helpful comments.

99th percentile

lower mean precipitation over the tropics and the northern and southern hemisphere up to 35 °N/S than theother data records. Thus, the reason for the bias observed
in ﬁgure 6 must mainly be caused by observations
beyond 35 °N/S. For 2015, zonal means of GSMaP exhibit minimum mean precipitation beyond ∼40 °N/S
(ﬁgure 4) and it seems that this is valid more generally forthe considered period.
GPCP precipitation is distinctly lower for
extremes over the ocean than any other product analysed. Furthermore, GPCP exhibits a jump to lower
99th percentiles over the ocean in late 2008/early
2009, coinciding with the change in utilisation of
SSM/I and SSMIS data. The potential negative bias
and jump in GPCP extremes does not have any visible
impact on the climatology. GPCP, whose main scopes
include the construction of a reliable climate data
record (CDR), has been developed with priority given
to the stability of data over years [35]. The presented
results indicate that this might not apply to extremes.
Certain products, on the other hand, are not intended
for CDRs but may be targeted more on high-resolution mapping of precipitation. The product users areadvised to bear in mind that some products are tailored for speciﬁc purposes and may not necessarily be
optimal for all applications.

Acknowledgments
H Masunaga and F A Furuzawa acknowledge support
by JSPS KAKENHI Grant Number 19H05704 and
11

The data that support the ﬁndings of this study are openly
available at https://doi.org/10.14768/06337394-73A9407C-9997-0E380DAC5598 [22].

ORCID iDs
Hirohiko Masunaga https://orcid.org/0000-00026336-5002
Marc Schröder https://orcid.org/0000-00029693-7812
Elke Rustemeier https://orcid.org/0000-00017657-1588

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