October
Journal of2021
the Meteorological Society of Japan, 99(5),M. 1231−1252,
HIROSE et 2021.
al.
doi:10.2151/jmsj.2021-060
Special Edition on Global Precipitation Measurement (GPM): 5th Anniversary

1231

Refinement of Surface Precipitation Estimates
for the Dual-frequency Precipitation Radar on the GPM Core Observatory
Using Near-Nadir Measurements
Masafumi HIROSE
Faculty of Science and Technology, Meijo University, Nagoya, Japan

Shoichi SHIGE
Graduate School of Science, Kyoto University, Kyoto, Japan

Takuji KUBOTA
Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Japan

Fumie A. FURUZAWA, Haruya MINDA, and Hirohiko MASUNAGA
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan(Manuscript received 1 October 2020, in final form 21 May 2021)

Abstract
Precipitation statistics from Global Precipitation Measurement Core Observatory Dual-frequency Precipitation
Radar (GPM DPR) are underestimated due to systematic bias depending on the scanning angle. Over five years
of GPM DPR KuPR Version 06A data, the precipitation anomaly is −7 % and −2 % over land and ocean, respectively. This study improves the estimation of low-level precipitation-rate profiles and the detection of shallow
storms (with top heights of ≤ 2.5 km), using reference datasets of near-nadir measurements.
First, the low-level precipitation profile (LPP) is updated using an a priori near-nadir database generated fromstructural-characteristics related variables of the precipitation and environmental parameters. The LPP correction
increases precipitation over areas where downward-increasing precipitation profiles are dominant below 2 km,such as at high elevations and at middle and high latitudes. Globally, the LPP correction increases precipitation
by 5 %. Second, the effect on precipitation data of missing shallow storms is estimated using the angle-bin difference in the detectability of storms with a top height of ≤ 2.5 km. The effect of the shallow-precipitation deficiency (SPD) is comparable in magnitude to that of the LPP correction. A priori lookup tables for the SPD correction,
constrained by the clutter-free bottom level and spatially averaged shallow-precipitation fractions, are constructed
so that the correction applies to gridded statistics at 0.1° and three-month scales. The SPD correction enhances
precipitation by 50 % over specific low-rainfall oceans in the sub-tropics and at high latitudes, where shallow
precipitation dominates. Based on these two corrections, precipitation increases by 8 % and 11 % over land andocean, respectively. At latitudes between 60°N and 60°S, the difference in KuPR compared with satellite-gauge
blended products is reduced from −17 % to −9 %, whereas with gauge-based products is reduced from −19 % to−15 % over land.
Corresponding author: Masafumi Hirose, Faculty of Scienceand Technology, Meijo University, 1-501, Shiogamaguchi,
Tempaku, Nagoya, Aichi 468-8502, Japan
E-mail: mhirose@meijo-u.ac.jp
J-stage Advance Published Date: 8 June 2021
©The Author(s) 2021. This is an open access article published by the Meteorological Society of Japan undera Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0).

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Keywords spaceborne precipitation radar; retrieval error; low-level precipitation profile; shallow storm; surfaceclutter mask; incidence-angle dependence
Citation Hirose, M., S. Shige, T. Kubota, F. A. Furuzawa, H. Minda, and H. Masunaga, 2021: Refinement of surface precipitation estimates for the Dual-frequency Precipitation Radar on the GPM Core Observatory using nearnadir measurements. J. Meteor. Soc. Japan, 99, 1231–1252, doi:10.2151/jmsj.2021-060.

1.

Introduction

The geographical distribution of precipitation canbe delineated using high-volume data obtained fromspaceborne precipitation radars: the Tropical Rainfall
Measuring Mission Precipitation Radar (TRMM PR)(Kummerow et al. 1998; Kozu et al. 2001) and the
Global Precipitation Measurement Core Observatory
Dual-frequency Precipitation Radar (GPM DPR)
(Kojima et al. 2012; Hou et al. 2014; SkofronickJackson et al. 2017; Iguchi 2020; Nakamura 2021).
Compared to the ground observations, restricted by
the locations of the measuring stations, satellite products are superior in representing spatial information(Beck et al. 2019; Sun et al. 2018). Spaceborne radars
onboard non-sun-synchronous satellites detect echoes
regardless of surface type and local time zone and caninvestigate the striking dependence of precipitation
on environmental features (e.g., Hamada et al. 2015;
Liu and Zipser 2015; Hirose et al. 2017). However,the current observational precipitation datasets needrefinement to understand the complete magnitude of
the global water and energy cycles (Adler et al. 2017a;
Behrangi et al. 2014; Stephens et al. 2012). Advancesin fine-scale precipitation mapping have increased theneed to mitigate regional retrieval errors. In addition
to problems of insufficient sensitivity and sampling
uncertainty, attention needs to be paid to attenuation
corrections, varying drop-size distributions, nonuniform beam-filling effects, and bright band detection (Iguchi et al. 2009; Seto et al. 2013, 2021; Kubota
et al. 2014, 2020a; Awaka et al. 2016; Meneghini
et al. 2015, 2021). Improvements in algorithms andstatistical evaluations have been ongoing since the late
20th century. The underestimation of precipitation in
spaceborne radar data has been highlighted in several
studies (e.g., Kirstetter et al. 2013; Oki et al. 2020;
Heymsfield et al. 2020). Despite a diverse array ofcomparative studies over the last two decades, thealgorithmic issues responsible for the known precipitation bias have not been entirely resolved.
Some of the retrieval properties of spaceborne
radars depend on the incidence angle. A regional bias

in the TRMM PR data is identified using statisticaldifferences between incidence angles (Hirose et al.
2012, hereafter, H12). An accurate observation of theshallow storms becomes difficult at large incidence
angles because the interference caused by surface clutter increases toward off-nadir angles. H12 and a shortreport (Hirose 2011) noted that near-nadir statistics areless affected by removal masks designed to eliminate
main-lobe clutter. The authors showed that approximately 5 % of the total precipitation is underestimatedin the TRMM PR data because of the deterioration ofradar estimates in its off-nadir scans. Also, they showthat half of the underestimates are caused by missing
shallow storms, whereas the remainder is caused byother reasons, including low-level profile assumptions.
Deep clutter-free bottom (CFB) levels could affectsurface precipitation estimates, especially verticallyvarying precipitation, below the melting layer (e.g.,
Kobayashi et al. 2018; Liu and Zipser 2013; Teraoet al. 2017; Sohn et al. 2015). The GPM DPR products provide two types of precipitation rates at theestimated surface: “precipRateESurface” and “precipRateESurface2”. In “precipRateESurface” (standard
output), the algorithm estimates a precipitation rate
at the surface by assuming that the effective radarreflectivity factor (Ze) at the surface is identical tothat at the lowest point free from main-lobe clutter(Seto et al. 2021). This assumption can be challenging
because of regional variations in the low-level precipitation profiles (LPP), as identified by H12. Therefore,
an extrapolated surface precipitation rate, “precip­Rate­
Esurface2”, is prepared as an experimental output of
the DPR product Version 06A (Iguchi et al. 2018). The
correction in “precipRateESurface2” is based on an apriori low-level profile dataset classified by the aloft
precipitation rate for each 5° grid cell. However, thedevelopment of dynamic correction methods remainsa challenge.
Shallow storms interfered with the surface-clutter
mask of radar are difficult to detect, especially overmountainous areas/higher latitudes (Aoki and Shige
2021; Barros and Arulraj 2020; Casella et al. 2017).
Factors causing underestimation were analyzed inOctober 2021

M. HIROSE et al.

H12 following statistics from coarse grids or the
global mean, because samples of near-nadir statistics
must ensure the reliability of the reference data. This
sampling issue is mitigated to some extent by accumulating data over a long period. Additionally, highlatitude observations by GPM DPR increased theneed to evaluate the effect of missing shallow storms.
Therefore, analyses of shallow storm detection basedon previous work, involving TRMM PR observations,need to be updated.
This study aims to enhance the consistency of
precipitation estimates across angles by reducing theoff-nadir underestimation and to improve surface
precipitation estimates from spaceborne radars. This
paper examines the incidence-angle dependency of
spaceborne radars and suggests two correction methods for the incidence-angle dependency of GPM DPR.
One is the substitution of the instantaneous estimated
precipitation profiles near the surface using an a priori
database. The other is a statistical evaluation of the
impact of missing shallow storms and the retrieval ofthe missing data from a lookup table.
The remaining part of this paper is organized asfollows: Section 2 describes the incidence-angle dependency of precipitation estimates from spaceborne
radars and the two correction methods. The impactsof the corrections are examined in Section 3, while
conclusions are given in Section 4.
2. Incidence-angle dependency and correction
methods
2.1 GPM DPR data
This study uses five years of GPM DPR KuPR Version 06A (hereafter, KuPR) data, from June 2014 to
May 2019. In addition, GPM DPR KuPR Version 05A
and TRMM PR Versions 7 and 8 products are used
for some comparisons. The KuPR sensor is similar to
TRMM PR in terms of frequency, horizontal footprintsize, and scan width (245 km). However, its sensitivity is superior at approximately 15.5 dBZ against 18
dBZ, and its areas and levels of observation have been
extended (Masaki et al. 2020; Iguchi 2020; Kojima
et al. 2012; Hou et al. 2014; Hamada and Takayabu
2016). Its horizontal resolution is approximately 5 km.
The vertical resolution of both radars is 250 m. Moreover, the sampling intervals of the TRMM PR and
KuPR data are 250 m and 125 m, respectively. The
number of gridded samples obtained from KuPR isapproximately half that obtained from TRMM PR. For
instance, TRMM PR and KuPR observe 2.0 and 1.0
samples, respectively, at a 0.1°-grid resolution per day
over 20°S – 20°N. On average, an overpass observation

1233

provides five to six samples for a 0.1° grid cell, i.e.,
the GPM Core Observatory passes over a certain pointin the tropics, once every five to six days. The numberof overpasses per day is 0, 1, and 2, accounting for 78(32), 21 (53), and 1 % (15 %), respectively, for a 0.1°(5°) grid cell over 20°S – 20°N. At latitudes between
61.6° and 65.8°, the number of samples obtained from
KuPR exceeds that from TRMM PR in the tropics.
Furthermore, the surface precipitation rate(“precip­RateESurface”) is estimated using the radarreflectivity factor extrapolated down to the main-lobe
clutter region. As explained in Section 1, Ze below
the CFB level is set to a constant (Seto et al. 2021),
except in the TRMM PR Version 7 product, which
includes a downward-decreasing Ze in the clutterinterfered levels for stratiform precipitation over land,
reflecting the evaporation effect assumptions (TRMM
Precipitation Radar Team 2011). The precipitation rate(R) is calculated for a given R – Dm relation once themass-weighted mean diameter (Dm) is retrieved fromthe radar reflectivity factor (Iguchi et al. 2018; Setoet al. 2013, 2021). Also, the pressure correction is performed on the terminal velocity for the precipitation
estimates, which is a possible factor in downwarddecreasing precipitation rates for vertically constant
Ze . This study focuses on the acceptability of thisassumption concerning low-level precipitation profiles(LPPs).
Figure 1 shows the CFB levels. The average levels
are approximately 1 km near nadir and 2 km at theedge of the swath. To mitigate contamination byground clutter, these levels are slightly higher thanthose for TRMM PR Version 7 over a large part of theland area (Iguchi et al. 2018; Kubota et al. 2016). Notethat the topography modulates the spatial pattern. For
example, a 0.1° average CFB level over the Himalayas
is approximately 1 km higher than that over flat landand oceans, such that the precipitation at altitudes
below 2 – 3 km from the surface is not detected, evenat nadir. At latitudes higher than 65°, the average CFBlevel is high due to the lack of data at low incidence
angles.
2.2 Angle-dependent retrieval uncertainties
Precipitation statistics based on TRMM PR and
KuPR differ by angles and products, as shown in Fig.
2. Precipitation off nadir is underrepresented whencompared to the near-nadir data, except over the ocean
at low latitudes. This off-nadir underestimation correlates with the findings of H12 from the TRMM PR
data. At high latitudes, estimates of the precipitation
decrease, forming a parabola away from nadir. An

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Fig. 1. The level of the lowest range bin free from surface clutter over the entire observational area and the Himalayas
based on the five-year GPM Dual-frequency Precipitation Radar (DPR) KuPR Version 06A data using all-angle
bins (top), those near nadir (middle), and the swath edges (bottom). The level indicates the height above the surface.
The spatial resolution is 0.1°. The near-nadir statistics use data in bins 21 – 23 and bins 27 – 29 and the swath-edge
statistics are for data in bins 1 – 2 and bins 48 – 49.

Fig. 2. Surface precipitation over land and ocean foreach angle bin. The thick and thin solid lines indicate the averages between 35°S and 35°N (LOW)
using the 5-year KuPR Version 06A and 16-year
TRMM PR Version 8 data, respectively. The statistics are obtained over the ranges of 66 – 35°S and
35 – 66°N (HIGH) using the five-year KuPR Version
06A (dotted lines).

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M. HIROSE et al.

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Fig. 3. Vertical distribution of precipitation over the land (upper) and ocean (lower) for each angle bin. From left toright, the different panels show data composites of various surface precipitation rates: all, < 1, 1 – 10, 10 – 50, and≥ 50 mm h−1, respectively.

update in the algorithm reduces the side-lobe clutter(Kubota et al. 2016); however, the contamination atbins 20 and 30 remains a positive bias. Precipitation
estimates increase by more than 20 % at the nadir
angle over land. Improvements by each algorithm
update have been identified about the reduced incidence-angle difference; however, an angle-dependentbias remains. The TRMM PR Version 7 productreduces side-lobe clutter but leaves an asymmetricbias resulting from the beam-mismatch correction
errors (H12, Hirose 2011), different from other DPR
algorithm-based products (not shown). Moreover,differences in the near-nadir precipitation between
products are seen. This study focuses on mitigating
the incidence-angle dependency in the KuPR precipitation data rather than the examination of error factors
in different products.
Figure 3 shows that the KuPR precipitation rate of> 50 mm h−1 is remarkably high at a nadir over landbecause of uncertainties in the path-integrated attenuation (PIA) estimates that need to be specified for
non-precipitating surface cross-sections in such areas(Meneghini et al. 2015, 2021). Over the land, allprecipitation products are underestimated off-nadir,especially at the swath edge, because of the reduced
ability of the sensor to detect downward-increasing
precipitation rates and its failure to detect shallow
storms against the relatively deep CFB. The overall
features of the angle-bin difference are consistent withthe results of H12, using TRMM PR, except for the
clutter impacts from the antenna sidelobes and right–
left asymmetric pattern, following the orbit boost in
2001. Over the ocean, weak and moderate precipitation rates smaller than 10 mm h−1, are frequently
observed in the inner swath. However, the off-nadir
underestimates are unclear, except at the swath edge
due to the predominant stratiform systems and thecross-track-dependent algorithm in the attenuation-

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Table 1. Precipitation and anomalies for the near-nadir statistics between 35°S and 35°N (the parentheses show the statistics
for 66 – 35°S and 35 – 66°N). The values are from the 16-year TRMM PR data and the 4- and 5-year GPM DPR KuPR
Version 05A and 06A data.
Ocean
−1

Precipitation [mm d ]Anomaly [%]

Land

PRv.7

PRv.8

KuPR05A

KuPR06A

PRv.7

PRv.8

KuPR05A

KuPR06A

2.6
−4.0

2.9
−0.2

3.1 (−1.9)
1.7 (−7.9)

3.0 ( 1.8)
0.7 (−8.8)

2.2
−4.7

2.0
−5.1

2.1 ( 1.1)
−11.8 (11.6)

2.0 ( 1.0)
−6.4 (−8.8)

correction procedure. Overestimates of strong precipitation rates in the outer swath, over the ocean, could
be attributed to the attenuation-correction effect basedon the differential surface cross-section estimatedfrom the along- and cross-track reference data, and thefitting function, referred to as the hybrid surface reference technique (Meneghini et al. 2004, 2015; Setoand Iguchi 2007), similar to the results in H12 from
TRMM PR.
The reference in this study is the near-nadir statistics. In the cross-track direction, 1st – 49th angle-bin
data are observed with an interval of 0.71° from thenadir (bin 25). H12 set their reference data to bins
23 – 24 out of 49 over land and bins 23 – 25 over theocean considering the lowest CFB, insignificant sidelobe contamination, uncertainty in the heavy precipitation over land, and non-symmetric patterns due to thebeam-mismatch effect. In this study, given the updateto the algorithm and consistency of the statistics, we
use the reference data from the precipitation data inbins 21 – 23 and bins 27 – 29. Additionally, the nadir
precipitation rate of < 50 mm h−1 over the land is used
to generate the low-level profile database.
Compared to the near-nadir statistics, the underestimation bias in the surface KuPR precipitation is 7 %and 2 % over the land and ocean (66°S – 66°N), respectively. At low latitudes, significant underestimates
are found over land; however, the oceanic precipitation is slightly higher than the reference (Table 1).
Regarding the middle-to-high latitudes, precipitation
is 9 % lower than the near-nadir statistics over landand ocean. The differences are reduced at higher
levels. Highlighting the vertical cross-section of average precipitation between angles, Fig. 3 shows thatthe remarkable incidence-angle difference appears at
lower levels. Several features such as the off-nadir increase in intense precipitation over the ocean and theirpeak at a nadir over the land are observed. However,the largest impact on the total amount of precipitation
is by moderate precipitation of less than 10 mm h−1,
and the total incidence-angle dependency results inunderestimation.

2.3 Low-level precipitation profile (LPP) database
As noted in Section 2.1, in the current GPM DPR
algorithm, precipitation-rate profiles in the clutter
region are estimated by assuming a constant Ze andmodified drop-size distribution parameters; however,various low-level profiles have been observed. This
study prepares near-surface profiles, below the CFBlevel, based on a LPP database generated from nearnadir statistics. The constraints of the LPP database are
the surface type (2 bins), precipitation type (2 bins),
0°C levels (8 bins), storm top height (STH: 6 bins),
and vertical gradients of the precipitation rate (VGP:
7 bins) at 2 – 2.5 km or 3 – 3.5 km above the surface(Fig. 4). The STH is the highest of three consecutive
meaningful precipitation echoes. We define the VGP
as the gradient of the regression line using precipitation rates in five range bins between 2 km and 2.5 km(in most cases). The present LPP database contains
1,344 profiles corresponding to the abovementioned
variables. In addition, another database for CFB levels
at 2 – 3 km and their average profiles are prepared. Fora shallow storm where VGP at 2 – 2.5 km is not obtained, the reference profile (averaged without VGP)
is used as a substitute. This substitution is applied to
profiles with a CFB level of < 2 km. In mountainous
regions where CFB ≥ 2 km, another LPP databasemeasuring values below 3 km with VGP at 3 – 3.5 km
is applied. The near-nadir profile information in the
clutter region, approximately below 1 km, is derived
based on the standard algorithm. Therefore, thiscorrection enables the mitigation of different LPPs
between angles.
Figure 5 shows a simplified version of LPP averag­ed separately for stratiform and convective precipitation over land and ocean. These profiles are averaged
for different conditions: deep and non-deep storms(threshold STH, 6 km), downward increasing and constant or downward-decreasing profiles (threshold VGP
at 2 – 2.5 km, −0.5 mm h−1 km−1), and warm and cold
environments (threshold 0°C level, 2 km). The original LPP database is further subdivided using these parameters. Figure 5 shows that LPPs are characterizedOctober 2021

M. HIROSE et al.

Fig. 4.

1237

Flowchart of the low-level precipitation profile correction.

Fig. 5. Examples of simplified LPP average for stratiform (left) and convective (right) precipitation over land (upper) and ocean (lower). Solid-dashed lines indicate LPP when the storm top height is ≥ 6 km (high STH) and < 6
km (low STH), respectively. Thick lines correspond to the downward-increasing (DI) profiles with VGP < −0.5
mm h−1 km−1, whereas thin lines correspond to the constant or downward-decreasing (DD) profiles with VGP ≥−0.5 mm h−1 km−1. Lines with marks represent LPP average for 0°C levels of ≥ 2 km, whereas those without marks
represent LPP average for 0°C levels of < 2 km.

1238

Journal of the Meteorological Society of Japanby significant downward-increasing patterns and slightdecreasing patterns. For downward-increasing profiles
with storm top heights of ≥ 6 km and 0°C levels of< 2 km, as shown by the thick blue solid lines, theprecipitation rates at the surface are approximately
1.5 times greater than those at 2 km. Significant
differences in the precipitation rates, below 2 km, areobserved for deep convective storms, as indicated by
the solid lines. In convective precipitation, LPPs fortropical deep storms (solid orange and red lines) have
similar precipitation rates at 2 km but differ considerably near the surface, implying the significant impact
of VGP aloft. The precipitation rate does not change
significantly for stratiform precipitation, except for
the abovementioned deep storms with downwardincreasing VGP at high latitudes. Consistent withearlier studies (Hirose and Nakamura 2004; Liu andZipser 2013; Kobayashi et al. 2018; Porcacchia et al.
2019), precipitation rates are possibly increasing
toward the surface in low-temperature regions. The
low-level peaks below 1 km often appear with evenmoderately downward-decreasing VGP. Thus, theprofile of near-surface precipitation rate is not fully
determined as an extension of the upper part of the
profile. The LPP database includes profiles with bimodal structures at high latitudes.
The height-smearing effect is not considered in the
LPP correction. Therefore, vertical properties, suchas VGP, become slightly blurred at its off-nadir. For
example, VGP at the scan edge (17°) could be approximately 4 % smaller than that at the nadir. In thisstudy, it is expected that the height-smearing effect on
the LPP correction will be small, since the absolutevalue of the vertical information is roughly divided.
For example, VGP is classified into seven categories
with the thresholds of −5, −2, −0.5, 0.5, 2, and 5mm h−1 km−1. Thus, this LPP correction incorporates
patterns of various low-level profiles, which could beconservative for extreme cases with few samples.
Note that the LPP correction does not ensure consistency with attenuation estimates. The PIA estimateof the single-frequency algorithm is operationallybased on the Hitschfeld–Bordan (HB) method andsurface reference technique (SRT) (Iguchi et al. 2018;
Meneghini et al. 2021). Although the forward procedure based on the former method works for lighter
49

∑ ∑
SPD =
i=1

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precipitation, it increases the retrieval uncertainties
in cumulative attenuation near the surface. The PIA
estimate given by SRT is based on the difference inthe surface echoes with and without precipitation.
The backward-correction method with a constraint
on the total attenuation estimates is stable for intense
precipitation. The LPP correction below the CFB level
could affect PIA estimates through the forward approach and the adjustment factor of the modified HB–
SRT method, particularly for heavy precipitation. An
iterative approach using profile databases of measured
radar reflectivity (Zm) might represent the coherentspatial structure of precipitation; however, in thisstudy, a posterior scheme based on the LPP database isapplied for simplicity. Moreover, the issue regarding
attenuation correction is addressed in Section 3.3a.
2.4 Shallow-precipitation deficiency (SPD)H12 investigated the idea that the underestimationof off-nadir precipitation from shallow storms iscaused by the smaller number of observed shallow
systems and that the impact of this effect is high over
the ocean. Significant angle-bin differences in thehistogram of STH appear approximately below 2 km.
This study applies the same approach to estimate theimpact of missing shallow storms on the KuPR data.
The incidence-angle discrepancy of the surface precipitation from the difference in the number of missing
shallow storms is retrieved using the near-nadir (nn)–precipitation intensity at the surface (Rs), conditionedon the STH and stratiform/convective (sc) types,
and the categorized storms detected at each angle, as
shown in Eq. (1), where N is the number of storms
for each angle, STH, and precipitation type. The
near-nadir N is an average of N at the 21st – 23rd and27th – 29th angle bins. The equation shows the fractionof missing precipitation resulting from the angle-bin
differences in the number of STH ≤ 2.5 km and corresponding surface precipitation intensity at each gridto the near-nadir total precipitation. In this study, theeffect estimated from fewer samples because of thedeteriorating CFB levels is referred to as the SPD. In
H12, a threshold of 3 km above the surface was set as
the STH of shallow storms, allowing some amount ofsampling at all-angle bins, consistent with the natural
statistics of shallow storms along with trade wind

2.5 km∑ [ N (i, h, k ) − N (nn, h, k )] R (nn, h, k ) ,49∑∑ N (nn, h, k ) R (nn, h, k )h=surf

top

s

k =sc

h=surf

k =sc

s

(1)

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1239

inversion. In this study, shallow storms are definedas precipitation echoes with an STH of ≤ 2.5 km to
mitigate the height-smearing effect resulting frommatching per-level samples in each 125-m range bin
between angles. The previous study used the data in
the first half scan in their bias estimation to isolate
the beam-mismatch effect (for details, see Section 2bof H12). This study uses all-angle data observed by
KuPR, considering the symmetric pattern shown in
Fig. 2. It is easy to see that SPD has a negative value.
In this study, the SPD effect indicates the effect of thecomplement processing of SPD as a positive value, as
shown in Eq. (2):SPD effect = −100SPD.
1 + SPD(2)

For example, the SPD effect is 25 % for the casewhere SPD is −0.20.
3. Impacts of the LPP and SPD corrections
3.1 Correction of LPPs
LPP corrections are made for every profile using
the a priori LPP database. Thus, the vertical precipitation-rate profiles around the CFB level have beencorrected to enable smooth connection by the substitution, according to the structural and environmental
variables. The positive values in this correction
indicate that the low-level profile values are corrected
upward compared to the current algorithm assumptions. Figure 6 shows a snapshot of the precipitationand correction effect in the case of Typhoon Megi hitting Taiwan on September 26, 2016. The effect is lesssignificant for moderate precipitation over flat areasin the inner swath where the CFB levels are relatively
low. The individual effects often exceeded 10 %. Mostof the profiles with downward-increasing precipitation
rates near 2 – 2.5 km show increased precipitation
at 1 km. A downward-increasing trend appears insituations, such as orographic precipitation, shallow
storms, and mid-latitude disturbances. The LPP correction decreases precipitation lower than 1 mm h−1, at
the outer edge of the precipitation area, by more than
5 %. The downward-decreasing profiles below CFB
are selected from the a priori database, particularly
at the edges of stratiform precipitation areas overarid regions, implying a significant evaporation andpressure-correction effect for different precipitation
rates. The regional characteristics of low-level profilesof light precipitation with downward-decreasing VGP
and the database validity need to be further examined.
On average, the decrease in precipitation is negligible,
except in low-rainfall areas, as will see in the sequel.

Fig. 6. Snapshot of the (a) rainfall rate and (b)effect of the LPP corrections on the surface precipitation estimates at each 0.05° grid cell for thecase of Typhoon Megi hitting Taiwan on September 26, 2016. The positive values in panel (b)indicate that the LPP corrections increase surface
rainfall. Pixels where the surface precipitation
rate is less than 0.2 mm h−1 are blanked out.

On average, the LPP corrections conspicuously
increased precipitation in areas where shallow storms
are dominant, except for low-rainfall areas, as shownin Fig. 7a. The effect is obvious at the swath edge andover steep terrain, even at nadir (Figs. 7b, c). Morethan 20 % of the amount of precipitation increased
over the northern Atlantic, at latitudes near 60 – 50°S,
along with high mountain ranges, and the northernand southern edges of the orbit. However, the difference is not significant over the Antarctic Ocean, wheremost storm top heights are lower than 3 km and most
low-level profiles are lost because the clutter interference has been removed. Over arid regions, the corrections decreased precipitation by several percentages,
indicating a drastic decline in the near-surface precipitation because of evaporation. Over wide areas of the
Tibetan Plateau, where shallow storms dominate, the
LPP correction increases precipitation by 20 %. One
can see a discrete pattern in the north–south-direction
tracing satellite orbits with an inclination angle of65°. This reflects the latitude-dependent regional
differences in the CFB levels of individual samples,
constrained by the orbit. Note that the precipitation
profiles vary with season (not shown). For example,
the LPP effect around Japan is insignificant in summer

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Fig. 7.

Vol. 99, No. 5

Effect of the LPP correction (a) between 70°S and 70°N, (b) near nadir, and (c) at the swath edge.

but reaches approximately 20 % in winter. Also, thenegative effect or the downward-decreasing trend issignificant over the Taklimakan Desert in summer andmoderate over land areas during dry winter.
It is worthy to note that VGP or the low-levelvertical-gradient information effectively determines
the downward increasing and decreasing patterns
at low levels. The removal of VGP from the LPP
database constraints blurs this effect, particularly inthe correction of instantaneous snapshots, as shownin Fig. 6. Over the Sahara (10°W – 30°E, 15 – 30°N)
where downward-decreasing profiles prevail, the VGP
information on the current LPP correction decreased
surface precipitation by approximately 4 % comparedwith that without this information. The VGP calculated at 3 – 3.5 km represents the low-level patterns,
while the VGP at 2 – 2.5 km is necessary to perform
corrections, particularly for shallow storms at highlatitudes. The removal of the VGP parameter at 2 – 2.5
km from the correction procedure in Fig. 4 deteriorates the positive effect of LPP corrections byapproximately 5 % of the total precipitation due to theextratropical cyclones at 60 – 50°S (not shown).
Our preliminary investigation shows that the spatial
pattern of the LPP correction for the TRMM PR Version 8 product is similar to that of the KuPR Version
6 product since their the algorithms are basically thesame. However, for the TRMM PR Version 7 product,
the positive effect over the land areas is notable (notshown). This may be attributable to the assumptionthat stratiform precipitation over land decreases in
the clutter region by 0.5 dB km−1 in the TRMM PR
Version 7 algorithm. In addition, the Dm profiles of
KuPR may result in differences in the LPP databasesof KuPR and TRMM PR Version 7. The source of
the inconsistency between these algorithms requires
investigation.
Furthermore, the precipitation amount and incidenceangle dependency differ considerably with altitude.
This correction modifies the estimated precipitation
profiles at levels between approximately 1 km and
2 km and increases the precipitation amount at levels
around and below 1 km at off-nadir angles (Figs. 8a,b). Figure 8c shows the impact of the LPP correction

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Fig. 8. Latitudinal cross-section of (a) precipitation based on the original data, (b) the LPP-corrected precipitation,and (c) the correction effect based on the KuPR data using all-angle bins (top), those near nadir (middle), and theswath edges (bottom). The ordinate represents the height from the surface up to 2 km.

for each latitude and level above the surface. Even atnear-nadir angles, the estimated precipitation below
the CFB level slightly increases at high latitudes because some samples contain slightly high CFB levels,
as also shown in Fig. 7b. At the swath edge, the nearsurface precipitation increases by 5 – 10 % in thetropics and more than 50 % at 60 – 50°S because ofthis correction. The correction has more influencesat the northern and southern edges of the observabledomains, where no nadir observation is attainable. In
addition, the incidence-angle difference before andafter the LPP correction is due to the changes madeby this correction. The underestimation bias to thenear-nadir statistics is noticeable near 1 km before thecorrection. The LPP correction improves the consistency across the incident angles, while maintaining anegative anomaly. An evaluation of this is summarized
in Section 3.3.
3.2 Evaluation and estimation of the SPDa. Effect of the long-term mean SPD
This section presents the observational limitationsof spaceborne radars in terms of missing shallow
storms. As described in Section 2.4, SPD is derived
from the difference in the detection of per-angle
shallow storms with an STH of ≤ 2.5 km and thecorresponding precipitation rate conditioned on the
STH and precipitation type. In this study, a local
reference dataset, based on the near-nadir observation,is constructed at a 0.5°-grid resolution. Figure 9a indicates that the shallow-precipitation fraction is a key
determinant of the effect of the SPD corrections. For alarge part of the high precipitation areas in the tropics,
the fraction of precipitation contributed by shallow
storms is only a few percentages. This fraction is highin low-rainfall areas over oceans, at high latitudes,
over the Tibetan Plateau, and in some mountainous
areas (Figs. 9a, b). The correction effect in Fig. 9c ismore significant over oceanic low-rainfall areas and atslightly higher latitudes than the LPP correction (Fig.
7a). The impact is less than 1 % in most tropical areas
with high precipitation and reached 62 % and 98 %over subtropical oceans where shallow storms contribute 50 % and 80 % of total precipitation, respectively.
Over these subtropical oceans, shallow isolated
convective-type storms are missing. Precipitation isbarely detected at the swath edges over the oceans off
the coasts of Peru, Chile, Angola, and Namibia, where
the shallow-precipitation fraction is approximately
100 %. At high latitudes, the missing shallow storms
consist of both stratiform and convective types. In
terms of the zonal average, the highest impact appears
around Antarctic waters. These spatial features are
similar in all datasets, including TRMM PR Versions
7 and 8 (not shown).
b.

Correction based on a lookup table
The abovementioned SPD calculation requires

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Fig. 9. Maps of the (a) the shallow-precipitation fractions, (b) total precipitation, and (c) effect of the SPD corrections at each 0.5° grid cell. The shallow-precipitation fraction is the proportion of precipitation from storms with
top heights of ≤ 2.5 km with respect to the total precipitation.

sufficient samples at moderate spatiotemporal scales.
The distribution of the SPD effect corresponds to thefraction of shallow storms and CFB levels. Therefore,
the effect is retrieved based on these two parameters
using a lookup table (LUT) of the SPD effect (Fig.
10). Higher CFB levels and shallow-precipitation fractions result in a larger SPD correction. For example,
for areas with the CFB level 2 km, more than 20 % of
precipitation is missing where the shallow-precipitation
fraction is > 20 %, whereas more than 50 % of precipitation is missing where the shallow-precipitation
fraction is > 40 %. Figure 10 shows that a positiveeffect is expected even at near-nadir angles where theaveraged CFB levels are low because the CFB levels
are uneven at the same angle. For instance, LUT
shows that a 10 % increase in precipitation is required
over areas where the CFB level is approximately 700
m, and shallow-precipitation accounts for half of thetotal precipitation.
The LUT-based retrieval can be performed wherev-

Fig. 10. Lookup table for the SPD effect given theshallow-precipitation fraction and the CFB level.

er the shallow-precipitation fraction is obtained. The
SPD correction needs to be extended in short-term andhigh-resolution statistics to account for seasonal andregional differences in the precipitation structure at
middle-to-high latitudes. The availability of near-nadir

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Fig. 11. (a) Fine-scale shallow precipitation fraction mapping by near-nadir data (1998 – 2013); (b) the number ofprecipitation samples; (c) the sampling radius ensuring 1,000 precipitation samples; (d) the estimated shallowprecipitation fraction using the neighboring samples within a given radius; and (e) the effect of the LUT-based
SPD correction prepared at a resolution of 0.1° for the region 0 – 70°N and 130 – 145°E.

shallow-precipitation fraction data at fine spatiotemporal scales is key to short-term SPD corrections.
Figure 11a shows the fraction at a scale of 0.1° forthe entire period. Grids with extremely high shallowprecipitation fractions occur sporadically, even in thefive-year accumulated data, because the number ofprecipitation samples at each grid is insufficient, asshown in Fig. 11b. Moreover, the spatial distributionof the shallow-precipitation fraction is influenced bya small sample of significant precipitation events. Aninterpolation scheme has been developed to extract
the fine-scale features and ensure sufficient sampling.
Herein, we calculate the shallow-precipitation fractions based on 1,000 neighboring precipitation samples for a given period to enhance the sampling ability
in order to detect local climatic signals, including
high-impact precipitation systems, such as large-scale

systems > 100 km with more than 400 precipitation
samples. Figure 11c shows the area-equivalent radiusof an adjacent area, containing 1,000 precipitation
samples for the entire period. Most of the samplingradii are less than 0.6°, except those near the northernand southern edges of the swath and in low-rainfall
areas. The radius is shortened according to the data
accumulation. The estimated shallow precipitation
fraction covers the entire area, including the grids atthe edge of the orbit (Fig. 11d). Thus, the SPD effectis retrieved using LUT with the 0.1°-scale CFB levels
and the estimated shallow-precipitation fraction data
(see Fig. 11e).
The input data need to be spatially averaged tomitigate sampling issues, especially for a short-term
dataset. For three months, the fine-scale shallowprecipitation fraction could not be obtained because

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Fig. 12. As contained in Fig. 11 but using data of three months from December 2014 to February 2015.

the near-nadir precipitation samples on a 0.1° gridwere scarce and fragmented over most regions (Figs.
12a, b). In other words, sampling gaps need to be
filled to perform LUT-based correction. In the threemonth data, the area-equivalent radius of the 1,000
precipitation samples ranged from 0.1° to 10°, which
are mostly within 2° (Fig. 12c). The sampling radius
is approximately 1° over a wide area with moderate
and abundant precipitation. Figures 12d and 12e show
the estimated shallow-precipitation fraction and the
SPD effect, respectively. The shallow-precipitation
fraction in Fig. 12d is spatially coherent compared tothat in Fig. 12a. The estimated SPD effect for winterprecipitation in the northern part of Japan is found to
be greater than 50 % (Fig. 12e).
When the sampling radius is set to zero, that is,
without the spatial average of the regional shallowprecipitation fraction at a scale of 0.1°, the spatial
continuity of the SPD effects is very low, even over
the ocean for five-year accumulated data. This study

generated 0.1°-grid maps of the shallow-precipitation
fractions averaged for each set of 1,000 adjacent
precipitation samples. Considering the spatially averaged fraction data and the instantaneous CFB-level
information, the SPD effect for every three months
could be retrieved from the LUT. This is because theretrieved SPD effect in Fig. 11e matches the analytically derived SPD effect in Fig. 9a. In addition, theLUT-based SPD estimates derived the bias over themountainous areas where the CFB levels are high,even at nadir. This procedure increases the near-nadir
precipitation over the ocean where shallow storms
prevail. This implies that a slight increase in the CFBlevels could deteriorate the statistics by tens of percentages over low-rainfall oceans, as expected from
Fig. 10.
3.3 Bias resulting from the main-lobe clutter maska. Total correction amountThis subsection describes the total bias based on

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M. HIROSE et al.

Fig. 13. (a) Map of the combined corrections, (b) zonally averaged precipitation before (Org) and after (Cor) the
corrections, and (c) impact of the LPP and SPD corrections on the zonally averaged corrected precipitation. The
units are percentages in panels (a) and (c) and mm d−1 in panel (b).

Table 2. Mean precipitation and effects of the LPP and SPD corrections over areas between lower latitudes (LOW: 35°S –35°N), higher latitudes (HIGH: 66.3 – 35°S and 35 – 66.3°N), and the entire area (ALL: 66.3°S – 66.3°N).
Ocean

Land

All

LOW

HIGH

ALL

LOW

HIGH

ALL

LOW

HIGH

ALL

Precipitation [mm d−1]

Original
Corrected

2.97
3.19

1.97
2.39

2.62
2.92

2.05
2.12

0.94
1.12

1.55
1.67

2.75
2.93

1.60
1.94

2.29
2.54

Correction [%]

LPP
SPD
Total

3.0
4.4
7.4

11.3
10.5
21.8

5.1
6.0
11.1

2.7
0.8
3.5

7.6
11.5
19.1

4.0
3.7
7.7

2.9
3.7
6.6

10.5
10.7
21.2

5.0
5.7
10.6

the LPP and SPD corrections. Note that there are two
types of SPD correction: the gridded analytical solution according to Eq. (1) and the SPD effect referring
to LUTs with a per-angle CFB level and estimatedshallow-precipitation fractions based on spatiallyintegrated precipitation samples. The latter fine-scale
LUT-based estimate is hereinafter the SPD correction.
The total bias map is shown in Fig. 13a. The eventual accumulation effect increases precipitation by lessthan 10 % in tropical rainy regions and considerablyincreases precipitation at high latitudes and elevations.
On average, these two types of corrections increase
the area-weighted mean precipitation by approximately 7, 21, and 11 % over 35°S – 35°N, 66.3 – 35°S
and 35 – 66.3°N, and 66.3°S – 66.3°N, respectively(see Table 2). An increase in the zonal precipitation isdiscerned at middle and high latitudes (Fig. 13b), with
impact reaching 56 % in the region with 65 – 60°S,
where shallow storms prevail (Fig. 13c). The aver-aged correction coefficient is 119 % at latitudes of
66.3 – 66°S, at the edge of the orbit. The LPP and SPD
correction effects are comparable over wide areas,
except in subtropical oceans with low precipitation
and at high latitudes. At 20°S, the impact on the zonally averaged precipitation is 11 %, whereas the averageof the gridded correction coefficient is 26 % (notshown). At high latitudes where the largest effects of
the SPD corrections prevail, the LPP correction effectiveness decreases because of lack of observations at
levels lower than 1 km. For 65 – 60°S, thin shallow
storms with top heights lower than 2 km provide 90 %of the surface precipitation (not shown). The nearsurface precipitation maxima from low-level raindrop
growth at high latitudes are difficult to detect.
The revised incidence-angle difference is shownin Fig. 14 and Table 3. The side-lobe effects remain;
however, the LPP correction compensates for the bulkof the incidence-angle differences, i.e., the difference1246

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bias over the land from −8.8 % to −4.1 %, which is a
53 % reduction in the incidence-angle differences. The
revised incidence-angle dependency is −4 % and 6 %over the land and ocean, respectively. Therefore, themitigation in the off-nadir underestimation sheds lighton the issue of off-nadir estimations of intense precipitation, i.e., overestimation over the ocean and underestimation over the land. This is not directly associatedwith the levels of the main-lobe clutter interference
but with the attenuation correction per angle over landand ocean. Approximately half of the remaining bias
over land can decrease when the off-nadir intense precipitation is equivalent to the near-nadir statistics. It is
worthy to note that the LPP-corrected surface precipitation data at angle bins 20 and 30 are 20 % and 16 %greater than the data in the other bins for moderate
and all precipitation, respectively, over the land but
7 % and 13 % larger, respectively, over the ocean. The
mitigation of the side-lobe contamination reduced theaveraged surface precipitation by 0.5 – 1 % over theocean. In addition, the LPP-corrected data indicatethat the extraordinary peak at a nadir over the land is
2.4 times larger than the data in the other bins, and it
is 1.9 times larger than the near-nadir data for intense
precipitation (Fig. 14c). This remains to be determinedin further discussions of extreme events based on asingle-frequency radar.
Regarding the precipitation with low attenuation,
given by the final estimate of PIA < 1 dB, fractions inthe sample, precipitation amount, and LPP correction
effect account for 92, 52, and 66 %, respectively (notshown). The fraction in the LPP correction effect rises
to 89 % for profiles with PIA < 6 dB. Considering thelow usage of SRT for light precipitation in the KuPR
06A algorithm (Seto et al. 2021), the impact of PIA
inconsistency, due to the LPP correction, is limited
in total precipitation amount. The retrieval of heavy
precipitation demands the continual refinement ofschemes on attenuation estimates, as well as otherissues, such as the non-uniform beam-filling effect.

Fig. 14. The per-angle surface precipitation overareas between 66.3°S and 66.3°N for the land (L:warm colors) and ocean (O: cool colors). LPP
and SPD in the legend indicate the results fromthe LPP and the SPD correction, respectively.
ORG is the results of original data with no correction. COR indicates the results from data with
the LPP and SPD corrections. The plots are basedon (a) all the precipitation rates at the surface, (b)the light precipitation rate, < 10 mm h−1, and (c)the intense precipitation rate, ≥ 10 mm h−1. Panels(b) and (c) show the results of ORG and the LPP
correction alone.

between the all-angle precipitation amount and reference near-nadir data, for moderate precipitation rates< 10 mm h−1 (Figs. 14a, b). The per angle inconsistency in the moderate precipitation, i.e., underestimation
concerning the near-nadir statistics, is mitigated by approximately 80 % and 91 % over the land and ocean,respectively. However, positive anomalies occur for
intense precipitation (≥ 10 mm h−1) at off-nadir angles
over the ocean. The remarkable incidence-angle difference in the intense precipitation remains over land.
The correction mitigates the off-nadir underestimation

Table 3. Precipitation anomaly in the near-nadir statistics over areas between 66.3°S and 66.3°N. The
unit is %. LT10 and GE10 indicate the statistics from instantaneous surface precipitation rates lower
than 10 mm h−1 and greater than or equal to 10 mm h−1, respectively.
Original
Land
Ocean

SPD

Total

All

LT10

GE10

All

LT10

LPP
GE10

All

All

−8.8
−1.6

−2.1
−4.5

−23.9
4.9

−6.2
3.2

−0.4
−0.4

−19.1
11.1

−6.8
0.6

−4.1
5.6

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Table 4. Summary of the global precipitation data used in this study.

Acronym
CPC_Global
GPCC_MPCMORPH_CRT
GSMaP_Gauge
PERSIANN_CDR
GPCP_CDR
IMERG_Cal
GSMaP_MWR
GSMaP_MVKIMERG_HQ
IMERG_Uncalb.

Full nameClimate Prediction Center unified gauge-based analysis ofprecipitation
Global Precipitation Climatology Centre Monitoring productBias-corrected CPC MORPHing technique
Gauge-adjusted Global Satellite Mapping of Precipitation(GSMaP)
Precipitation Estimation from Remotely Sensed Information
using Artificial Neutral Networks‒Climate Data Record
Global Precipitation Climatology Project Climate Data Record
IMERG with gauge calibration
GSMaP based on MicroWave Radiometers
GSMaP based on a Moving Vector with Kalman filter
IMERG microwave-only precipitation estimates
IMERG without gauge calibration

Comparison of global precipitation products
In this section, we compare the abovementioned
corrected KuPR precipitation data with other highresolution multisatellite and gauge-based precipitation
datasets. The 11 products used for the comparison provide latest observational data between 60°S and 60°N
from June 2014 to May 2019 (Table 4). The datasetsare grouped into three types: gauge-based products,
including CPC_Globe and GPCC_MP; satellite-gauge
blended datasets, including CMORPH_CRT, GSMaP_
Gauge, PERSIANN_CDR, GPCP_CDR, and IMERG_
Cal; and satellite-only precipitation estimates, including GSMaP_MWR, GSMaP_MVK, IMERG_HQ, and
IMERG_Uncal. These state-of-the-art precipitation
datasets are used by various scientific communities,
and various studies are ongoing regarding their error
performance and algorithm development (e.g., Wang
and Yong 2020; Yuan et al. 2019; Beck et al. 2020;
Kubota et al. 2020b). Precipitation data at different
spatial resolutions are averaged over the range of 1°scale grid, while ocean and land grid pixels are identified on the basis of 5-min gridded global-relief data
(ETOPO5), according to the dominant surface types
for each 1° scale grid. The areal-weighted averages
over the land and ocean at latitudes between 60°S and
60°N are summarized in Fig. 15. The KuPR precipitation without and with the correction are 2.41 mm d−1
and 2.65 mm d−1, respectively. The satellite-gauge
blended and satellite-only products estimated precipitation of 2.91 mm d−1 and 3.09 mm d−1, respectively.
The difference in KuPR with respect to the satellitegauge blended products improves from −17 % to −9

VersionReference

V1.0

Xie et al. (2007)

V6
V1.0ADJ

Schneider et al. (2018)
Xie et al. (2017)

V7

Mega et al. (2019)

V1R1

Ashouri et al. (2015)

V1.3
V6
V7
V7
V6
V6

Adler et al. (2017b)
Huffman et al. (2019)
Aonashi et al. (2009)
Ushio et al. (2009)
Huffman et al. (2019)
Huffman et al. (2019)%, whereas that with satellite-only products improves
from −22 % to −14 %. Thus, a large reduction in the
differences at high latitudes exists.
Furthermore, the mean precipitation of the gaugebased, satellite-gauge blended, and satellite-only
products are 2.10, 2.26, and 2.58 mm d−1, respectively.
The land precipitation of the satellite-only products
over land is 23 % greater than the gauge-based analysis. The satellite-gauge blended precipitation data
reduce this inconsistency, but they are still 8 % greaterthan the gauge-based data. The CPC_Global precipitation is 15 % less than GPCC_MP precipitation. The
satellite-gauge blended products adjusted using the
CPC unified daily gauge analysis (CMORPH_CRT
and GSMaP_Gauge) have 13 % less precipitation thanthose adjusted using the GPCC data (PERSIANN_
CDR, GPCP_CDR, and IMERG_Cal). In contrast, theoriginal KuPR precipitation (KuPR_ORG) and corrected KuPR precipitation (KuPR_COR) are 19 % and
15 % less than the gauge-based mean precipitation,
respectively. The differences between KuPR_ORG
and CPC_Globe, and KuPR_COR and CPC_Globe
are −12 % and −7 %, respectively. Therefore, thedifference between the corrected KuPR precipitationand gauge-based analysis is near 10 %, whereas the
KuPR precipitation remains smaller than the near-nadir statistics and other satellite datasets, as indicatedin Figs. 14 and 15. For areas between 50°S and 50°N,
the KuPR_COR precipitation increases by 5 % compared to those between 60°S and 60°N (not shown).
The non-negligible differences from the correction
and different latitudinal zones underscore the need to1248

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Fig. 15. Mean precipitation using KuPR without (KuPR_ORG) and with (KuPR_COR) corrections and the gaugebased observational data (CPC_Global and GPCC_MP), the satellite-gauge-blended precipitation datasets(CMORPH_CRT, GSMaP_Gauge, PERSIANN_CDR, GPCP_CDR, and IMERG_Cal), and the satellite-only
precipitation products (GSMaP_MWR, GSMaP_MVK, IMERG_HQ, and IMERG_Uncal) for the period of June2014 – May 2019. The data are gridded at a resolution of 1° and averaged over land and ocean between 60°S and
60°N.

obtain robust precipitation estimates at high latitudes.
The KuPR_ORG, KuPR_COR, and other satellitebased mean precipitation rates over ocean are 2.67,
2.96, and 3.20 mm d−1, respectively. The KuPR precipitation increases by 11 % because of the correction.
The KuPR_ORG and KuPR_COR precipitation over
the ocean are 16 % and 7 % less than those of othersatellite-related products, respectively. The refined
KuPR precipitation is nearly equivalent to several
oceanic precipitation estimates and the conventional
precipitation climatology (Behrangi et al. 2014); however, there remain uncertainties to be addressed, e.g.,
detecting limits of light precipitation at high latitudes.
Attention should be paid to the remaining incidenceangle dependency of the KuPR_COR precipitation
over the ocean, especially for intense precipitation, as
shown in Fig. 14.
4.

Conclusions

GPM DPR detects precipitation profiles over landand ocean, and over complex terrain, from 66.3°S
to 66.3°N. This study investigated regional retrieval
uncertainties in near-surface precipitation statistics
caused by main-lobe clutter removal routines andincidence-angle differences. The GPM DPR KuPR
precipitation data are 7 % and 2 %, respectively, lower
than the reference near-nadir data over land and ocean.
The underestimation bias increased to 9 % at latitudes

higher than 35°. This study investigated the low-levelprofiles and missing shallow storms to mitigate suchinternal inconsistencies. Our results show that the LPP
and SPD corrections increase the land-surface precipitation by 8 % and reduce the angle-bin difference byhalf for the total precipitation over land and 80 % formoderate precipitation of < 10 mm h−1. This mitigatesthe off-nadir underestimation of the surface precipitation statistics from the different CFB levels to someextent and reduces the retrieval differences between
the precipitation datasets. The inconsistency between
the KuPR precipitation and gauge-based analysis
decreased by half. Precipitation increased because ofcorrections by 11 % and 21 % at latitudes between
66.3°S and 66.3°N and above 35°, respectively. Table
2 summarizes results from the retrieval process andthe observational limits, with respect to low-level
precipitation. The refined KuPR precipitation is 9 %lower than that of the satellite-gauge blended products. The uncertainties retrieved by the LPP and SPD
corrections are large at high latitudes, where shallow
storms consist of the bulk of the total precipitation,
because the profiling capability near the surface andsensitivity with light snowfall detection are insufficient. Therefore, these retrieval uncertainties are present in the current product. The assumption with theconstant Ze below the CFB level in the DPR algorithm(Seto et al. 2021) needs to be improved. The LPP-

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corrected surface precipitation data are incorporated asa variable named precipRateESurface2 in a new version (Version 07) of the GPM DPR product released
in 2021.
Therefore, the corrections indicate that the off-nadir
overestimation of intense precipitation retrieval over
the ocean needs to be resolved because of its blur by
the off-nadir underestimation of light and moderate
precipitation. This reaffirms the importance of careful
investigations of attenuation corrections in singlefrequency analyses (Meneghini et al. 2015, 2021). The
incidence-angle dependence of intense precipitation
over land remains one of the most important challenges to achieving robust estimations and understanding
extreme precipitation biases (Masunaga et al. 2019).
Other remaining issues are related to the LPP correction of the vertical variation in precipitation adjacentto the surface, i.e., at altitudes of < 1 km, which
require comprehensive validation studies into phasetransitions and hydrometeors. Note that a detailed
examination of the impact of specific profiles archived
in the current LPP database is beyond the scope of thisstudy. However, an accumulation of various samples
will increase our understanding of the specifying
factors of the precipitation-rate profiles. Additionally,
the differences in the near-nadir data produced by different sensors need to be examined. Our preliminary
study demonstrates that the TRMM PR Version 8
product data based on the DPR Version 06A algorithm
differ considerably from the TRMM PR Version 7
data, regarding the incidence-angle pattern of the surface precipitation. Efforts to mitigate biases resulting
from the algorithms will fill these gaps in the data.
Approximately 60 % of corrections that reduce theunderestimation of the original data are attributed tothe observational limits of SPD, although the fractions
vary by region. This study suggests SPD corrections
for short-term data using LUTs and the spatial averaging of shallow-precipitation fraction data. For instantaneous estimates, the use of additional information,such as brightness temperatures or assimilation techniques, is necessary. The effect of the missing shallow
storms is conspicuous over complex terrain and athigh latitudes, where uncertainties in observations arelarge. Finer-resolution and higher-sensitivity sensors,
capable of detecting the caps of storms, are needed toevaluate global and local water budgets.
Acknowledgments
The authors express gratitude to members of the
TRMM and GPM projects. We are grateful to thereviewers for their constructive comments, which

improved the manuscript. This study was supportedby the JAXA eighth GPM/TRMM research announcements, the JAXA second research announcement on
the Earth Observations, and JSPS KAKENHI Grant
Number 19H01969.
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