HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-6559-2017Evaluation of GPM IMERG Early, Late, and Final rainfall estimates
using WegenerNet gauge data in southeastern AustriaOSungminsungmin.o@uni-graz.athttps://orcid.org/0000-0002-7364-2122FoelscheUlrichhttps://orcid.org/0000-0002-9899-6453KirchengastGottfriedhttps://orcid.org/0000-0001-9187-937XFuchsbergerJuergenhttps://orcid.org/0000-0002-8961-6260TanJacksonhttps://orcid.org/0000-0001-7085-3074PetersenWalter A.https://orcid.org/0000-0002-6090-7144Institute for Geophysics, Astrophysics, and Meteorology/Institute
of Physics (IGAM/IP), NAWI Graz, University of Graz, Graz,
AustriaFWF-DK Climate Change, University of Graz, Graz, AustriaWegener Center for Climate and Global Change (WEGC), University of
Graz, Graz, AustriaUniversities Space Research Association,
Columbia, Maryland, USANASA Goddard Space Flight Center,
Greenbelt, Maryland, USAEarth Sciences Office, ST-11, NASA
Marshall Space Flight Center, Huntsville, Alabama, USASungmin O (sungmin.o@uni-graz.at)22December20172112655965722May201722May20175October201713November2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/6559/2017/hess-21-6559-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/6559/2017/hess-21-6559-2017.pdf
The Global Precipitation Measurement
(GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide
quasi-global (60∘ N–60∘ S) precipitation estimates,
beginning March 2014, from the combined use of passive microwave (PMW) and
infrared (IR) satellites comprising the GPM constellation. The IMERG products
are available in the form of near-real-time data, i.e., IMERG Early and Late,
and in the form of post-real-time research data, i.e., IMERG Final, after
monthly rain gauge analysis is received and taken into account. In this
study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F)
half-hourly rainfall estimates are compared with gauge-based gridded rainfall
data from the WegenerNet Feldbach region (WEGN) high-density climate station
network in southeastern Austria. The comparison is conducted over two IMERG
0.1∘× 0.1∘ grid cells, entirely covered by 40 and
39 WEGN stations each, using data from the extended summer season
(April–October) for the first two years of the GPM mission. The entire data
are divided into two rainfall intensity ranges (low and high) and two seasons
(warm and hot), and we evaluate the performance of IMERG, using both
statistical and graphical methods. Results show that IMERG-F rainfall
estimates are in the best overall agreement with the WEGN data, followed by
IMERG-L and IMERG-E estimates, particularly for the hot season. We also
illustrate, through rainfall event cases, how insufficient PMW sources and
errors in motion vectors can lead to wide discrepancies in the IMERG
estimates. Finally, by applying the method of
, we find that IMERG-F half-hourly rainfall
estimates can be regarded as a 25 min gauge accumulation, with an offset of
+40 min relative to its nominal time.
Introduction
The Global Precipitation Measurement (GPM) mission was launched in February
2014. This international mission is led by the National Aeronautics and Space
Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA),
as a successor to the Tropical Rainfall Measuring Mission (TRMM), to continue
and improve satellite-based rainfall and snowfall observations on a global
scale . The GPM
mission consists of a core observatory satellite and a constellation of
partner satellites to collect information from as many passive microwave
(PMW) and infrared (IR) satellite platforms as available. Such a merged
PMW–IR approach can mutually enhance the respective merits of individual PMW
or IR satellite-based rainfall estimates; that is, IR satellite estimates can
be adjusted with the greater accuracy of PMW data, and, conversely, PMW
satellite estimates can be interpolated along cloud movements obtained by the
high sampling rate of IR data
.
Once observation data are received from the PMW and IR platforms, they are
combined into half-hourly gridded fields through the Integrated
Multi-satellite Retrievals for GPM (IMERG) system
. The IMERG system
mainly comprises the following rainfall retrieval algorithms: the Climate
Prediction Center Morphing-Kalman Filter (CMORPH-KF)
, the Precipitation Estimation
from Remotely Sensed Information using Artificial Neural Networks-Cloud
Classification System (PERSIANN-CCS)
, and the TRMM
Multi-Satellite Precipitation Analysis (TMPA) .
Processed differently based on user requirements in terms of data latency and
accuracy (see Sect. for details), the IMERG computes Early,
Late, and Final runs (hereafter IMERG-E, IMERG-L, and IMERG-F runs).
Since the first release of IMERG-F data in April 2014, extensive studies have
been devoted to the evaluation of the IMERG rainfall estimates compared to ground
observations such as radars and gauges, or to other existing satellite
rainfall data
e.g..
For instance, demonstrated, through an
intercomparison study between the data using a hydrological model, that the
IMERG products can adequately substitute TMPA products, both statistically and
hydrologically. Furthermore, presented a new
validation approach for tracing rainfall errors to individual platforms or
techniques within the IMERG system using ancillary variables provided in the
products. Such analyses can provide useful information, not only for further
improvements in processes of satellite rainfall retrieval but also for users
in many relevant applications, from hydrological modeling and hazard studies
to climate simulations
.
In this study, we evaluate and compare the rainfall data generated by all
three IMERG runs using rain-gauge-based gridded data from the WegenerNet
Feldbach region (WEGN) high-density climate station network in southeastern
Austria . Through this approach, the study
aims to rigorously test the performance of IMERG runs and to explore
differences between the data. The comparison is conducted over two
0.1∘× 0.1∘ IMERG grid boxes, which are fully
covered by 40 and 39 WEGN stations, respectively. We investigate the data
during April–October in the years of 2014–2015, the first two years after
the launch of the GPM Core Observatory. While IMERG-F data are available from April 2014, IMERG-E and IMERG-L data are only available from
April 2015 at the time of writing, so the evaluation of these two runs
is restricted to 2015. Note that all the IMERG data will eventually be
retrospectively processed to the start of the TRMM era.
Even though gauge data are considered to be ground reference in many existing
validation studies, it is acknowledged that gauge measurements are also
subject to uncertainties in terms of areal representativeness, owing to a
limitation in spatial coverage
. Fortunately, this
point is of much less concern for the WEGN data. Around 40 gauges in one
IMERG grid ensure a high reliability of data within the domain area,
considering that a much smaller number of gauges ranging from 5 to 15 gauges
per 2.5∘× 2.5∘ grid cell, depending on the
study ,
has been suggested to guarantee a monthly error of less than 10 %. The
variance reduction factor (VRF) , examined
using half-hourly WEGN data from the 40 gauges grid box, is about 0.02 for
10 gauges (average of 40 random combinations), and little or no improvement is
observed in the VRF beyond 10 gauges. Another concern is that the use of tipping-bucket
gauges, as employed in the WEGN network, is associated with systematic errors caused by
various factors such as wind speed and rainfall intensity
. To this end, the WEGN
data are adjusted by a correction factor described by
, who found that WEGN tends to underestimate
rainfall by about 10 % compared to reference gauges.
The paper is organized as follows. Following this introduction,
Sect. further introduces IMERG and WEGN data and
Sect. describes the methodologies adopted for the assessment
of IMERG estimates. The results are detailed in Sect. , in terms
of statistical evaluation and analysis of example rainfall events.
Section contains concluding remarks and plans for future
studies.
DataGPM IMERG satellite rainfall estimates
IMERG version 3 (V03) level 3 products are used in this study. The level 3
products include gridded rainfall and snowfall data, with
0.1∘× 0.1∘ spatial resolution and 30 min temporal
resolution, generated from combining PMW and IR data of the GPM constellation
satellites, and calibrated by gauge analysis of the Global Precipitation
Climatology Centre (GPCC) . The IMERG processing
steps include (1) the CMORPH-KF for quality-weighted time interpolation
(“morphing”) of PMW estimates following cloud motion vectors
, (2) the PERSIANN-CCS for
retrieving PMW-calibrated IR
estimates , and
(3) the TMPA for inter-satellite calibration and monthly gauge adjustment
. A more complete data and algorithm description can
be found in .
The IMERG system is run twice in near-real time (NRT), first to produce
IMERG-E data about 6 h after nominal observation time for users who need a
quick answer related to potential flood or landslide warnings, and second to
produce IMERG-L data with approximately 18 h latency for users working in
agricultural forecasting or drought monitoring. Once the monthly gauge
analysis is received, the final IMERG cycle is run to create the IMERG-F data
approximately 3 months after the observation month. Note that both IMERG-E
and IMERG-L runs only use some of the IMERG processing steps. For instance,
instantaneous PMW rainfall estimates are only propagated forward in time by
the morphing scheme of the IMERG-E run, whereas both forward and backward
morphing schemes are used in IMERG-L and IMERG-F runs. In this way, IMERG-L
and IMERG-F runs are expected to better describe changes in the intensity and
shape of rainfall features. For bias adjustment, the IMERG NRT runs use
climatological gauge data, while the IMERG-F run ingests monthly GPCC gauge
analyses, so the IMERG-F estimates are supposed to be the most accurate and
reliable . In this
study, we use the calibrated estimates (precipitationCal) for all IMERG runs.
IMERG version 4 (V04) products have recently been started to be released, but
the new data do not yet cover the time ranges used in this study at the time
of writing (as of April 2017). However, the different version should not
lead to significant changes in our conclusions, since the main aim of this
study is to evaluate the three different IMERG runs relative to each other.
Most of the changes in V04 are applied to all three IMERG runs
, so any improvements to the IMERG-F run should also result
in a similar improvement to IMERG-E and IMERG-L runs. Furthermore, it is likely
that the algorithmic and data differences between the runs (e.g., backward
morphing and gauge adjustment) have a stronger influence than any differences
between the versions.
WEGN gridded rain gauge data
The WEGN is a high-resolution network for weather and climate study and
monitoring purposes, located in the Feldbach region, southeastern Austria
.
The region is part of the southeastern Alpine foreland, characterized by the
river Raab valley and a moderate hilly landscape, with altitudes ranging from
260 to 600 m. The network comprises 153 weather stations in an area of about
300 km2 (i.e., about one station per 2 km2), collecting rainfall
measurement data every 5 min (Fig. ). A total of 151 stations employ
tipping-bucket gauges for rainfall measurements, and each gauge was equipped
with one of three different sensors during the study period
. Meanwhile, since a major sensor replacement
in 2016, all WEGN tipping-bucket gauges have employed the same type of sensor
.
WEGN climate station network in the Feldbach region (black square),
southeastern Austria. The inset plot shows an enlarged view of the number of
WEGN rain gauges that are located within GPM IMERG
0.1∘× 0.1∘ grid cells. The two red-framed grid
boxes (within 46.9–47.0∘ N, 15.8–16.0∘ E), containing
40 and 39 WEGN gauges each, are selected for the study; for brevity, these are also
termed “Grid 15.85” and “Grid 15.95” based on
mid-longitude.
Once the WEGN processing system receives “Level 0” raw observations, with a
latency of 1–1.5 h, the Quality Control System produces “Level 1”
station-level data. Then, only best quality Level 1 data are chosen to
transfer into the Data Product Generator (DPG), and the DPG generates the
general user data products, “Level 2” station time series, as well as
200 m × 200 m gridded data by an inverse-distance-weighted
interpolated method; all missing and non-best Level 1 data are filled in by
temporal and spatial interpolation as part of the DPG processing. All data
products are available online at the WEGN web portal within 2 h latency.
Since recently, based on the findings of , the Level
2 processing has started to apply a bias correction factor for part of the rain data. More
information on the WEGN data processing system and data products can be found
in and .
For the statistical comparisons and the study on rainfall events reported in
Sect. and , half-hourly WEGN gridded rainfall
data are used, which are generated by summing up the basic (5 min) gridded
data, for direct comparison with IMERG rainfall estimates on a WEGN
grid points average to IMERG grid box basis. For Sect. , on
interpreting temporal characteristics of the satellite estimates, WEGN
gridded data with 5 min native resolution are used. For computing the
area-averaged WEGN rainfall for each IMERG grid box, we simply take the
arithmetic mean of all WEGN grid points that lie within the grid box.
Furthermore, we use a threshold value of 0.05 mm 30 min-1 to define
rain/no rain for removing false alarms in half-hourly data.
Figure shows that the number of WEGN half-hourly rainfall
values retained after such threshold-clipping is only significantly reduced
for a small number of gauges (fewer than about 30 out of 150 gauges) and that
more than about 65 gauges are not affected at all. This suggests that the
chosen threshold is reasonable, leaving a high amount of reliable half-hourly
data, and that the WEGN half-hourly data exceeding 0.05 mm are very
unlikely to be false alarms from the gauges' technical limitations. We also
note that WEGN is not a member of the GPCC network, so the WEGN gauge
data are independent of the IMERG gauge adjustment process.
WEGN network-averaged half-hourly rainfall amounts as function of
the number of WEGN gauges detecting rain (gauges with ≥ 0.1 mm rainfall
are counted). The rain amounts for half-hourly time intervals over the study
period (April–October in 2014–2015) are shown, to check the reasonability
of a rain/no-rain threshold for the IMERG data evaluation; 0.05 mm is used
for the study. The inset shows the full range of rainfall
data.
Approach
We assess the performance of IMERG runs using both statistical and graphical
methods. After inspecting some basic time series differences, we compare
probability density functions (PDFs) and cumulative distribution functions
(CDFs) of half-hourly IMERG estimates and WEGN data in terms of their
distribution as function of rain rate. The PDF of rain occurrence
(PDFc) describes the percentages of rain occurrence across
the predefined bins. Conversely, the CDF of rain volume
(CDFv) indicates the relative contribution of rain rate in each
bin to the total rain volume
. The PDFs and CDFs
are computed over a binning range up to 30 mm, with a 0.5 mm bin width. We
also use scatter plots to visually evaluate how IMERG estimates are
distributed compared to the WEGN data.
Description of the data: mean, standard deviation (SD), maximum,
number of rainfall amount values (≥ 0.05 mm 30 min-1), and
percentage of no-rain data (< 0.05 mm 30 min-1). Half-hourly
rainfall data for the period of April to October in 2014 and 2015 are used.
IMERG-L and IMERG-E data are available from April 2015, i.e., used for the
second year only.
Mean (mm)SD (mm)Max (mm)Number of rain dataPercentage of no rain (%)WEGN0.821.2915.26335391.8IMERG-F1.232.0225.61267193.5IMERG-L1.332.8223.73133593.5IMERG-E1.362.8925.67124893.9
Accumulated 24 h rainfall (mm day-1) over the study periods
in 2014 (a) and 2015 (b), obtained from WEGN (top) and
IMERG runs (middle), and differences between IMERG and WEGN (bottom).
Regarding IMERG in the 2014 period, only IMERG-F data are available.
In addition, we adopt widely used statistics and contingency indices,
including relative bias (RB), mean absolute error (MAE), root mean squared
error (RMSE), Pearson correlation coefficient (r), Spearman's rank
correlation coefficient (ρ), and probability of detection (POD), for
quantifying differences in performance between the IMERG runs. These are used
with definitions as follows.
RB=∑i=1n(Ii-Wi)∑i=1nWi,MAE=∑i=1nIi-Win,andRMSE=∑i=1n(Ii-Wi)2n,
where Ii and Wi are respective rain rate values provided by IMERG
estimates and WEGN data for a single grid box, at the ith time step, with
n pairs of data. Related to the latter two, normalized MAE and RMSE
(NMAE and NRMSE) are also used, which are computed as relative values of MAE and
RMSE with respect to the mean of the WEGN data.
r=cov(I,W)var(I)⋅var(W),
where cov(X,Y) is the covariance between X and Y values, and var(X)
is the variance of X,
ρ=1-6∑j=1n(rankj(I)-rankj(W))2n(n2-1),
where rankj(X) means the rank position of X, and
POD=hitshits+misses,
where “hits” means that both IMERG and WEGN data recorded rainfall
(≥ 0.05 mm 30 min-1), and “misses” refers to the rainfall
occurrence identified by WEGN data but missed by IMERG data. The POD ranges from
0 to 1 with a perfect score of 1 (0 in the case of misses).
Furthermore, we select two example rainfall events for case-based inspections
of spatial patterns and time series, in order to visually explore some
pronounced discrepancies, especially of IMERG-E and IMERG-L estimates,
compared to WEGN data. The half-hourly WEGN gridded data from the whole network
domain and corresponding time series of both IMERG estimates and WEGN data
are used in this evaluation, including the consideration of data sources, i.e., PMW
or IR observations.
Lastly, we employ the method of to provide
an interpretation of IMERG rainfall estimates in terms of gauge accumulation
time, Δ, and the offset, δ. WEGN 5 min gridded data are used
as the basis for the purpose of test-integrating these gauge data in the range
between 5 and 100 min. The offset means the time from which the
accumulation of gauge data is started and is considered to account for time differences
between instantaneous satellite estimates and actual rainfall on the ground
surface. Consequently, we can reveal the combination of Δ and δ
which leads to a minimum RMSE and interpret it as the temporal resolution of the
IMERG rainfall estimates.
When only the pairs for which both IMERG and WEGN data exceeding the
threshold value are investigated, we generally classify the entire data into
low rain intensities (≤ 80th percentile) versus high rain intensities
(> 80th percentile), according to the percentiles of WEGN rain rates, and
also into warm season (April, May, and October) versus hot season
(June–September), following the approach of
. According to temperature measurements
collected by WEGN, the average 2 m air temperature of the study period
(2014–2015) was 12.2 ∘C in the warm season and 18.6 ∘C in
the hot season. We did not use data from the cold season, November to
March, in order to guarantee the robustness of the WEGN data as ground
reference, since most WEGN gauges are not heated and therefore do not capture
snowfall events accurately .
Occurrence probability density function of rain rates
(PDFc, dashed) and cumulative distribution functions of rain
volume (CDFv, solid) for WEGN (gray) and IMERG (red). Comparisons
are shown for IMERG-F over the full 2014–2015 period (a) and separately for
IMERG-F (b), IMERG-L (c), and IMERG-E (d) over the
2015 period.
Same layout per panel as Fig. , but restricted to the
data pairs for which IMERG and WEGN both detected rainfall
(≥ 0.05 mm 30 min-1). Comparisons are shown for
IMERG-F (a–e), IMERG-L (f–j), and
IMERG-E (k–o), using the entire data (a, f, k), the
data divided into low and high rain intensities (b–c, g–h, l–m)
based on the 80th percentile of WEGN data, and the data divided into two
different seasons (d–e, i–j, n–o), warm (April, May, October) and
hot (June to September).
Scatter plots of half-hourly rainfall amounts for
IMERG-F (a–c), IMERG-L (d–f), and IMERG-E (g–i)
versus WEGN, for the entire dataset (a, d, g), warm season
only (b, e, h), and hot season only (c, f, i). Overplotted
are, for nine bins across the range with at least 30 IMERG-WEGN data pairs in
each bin, the 50th percentile (black symbols and line) and the 25th and 75th
percentiles (gray symbols and lines) of the IMERG estimates. Total number of
data pairs (n) is indicated in the upper left of each panel. Note that a
log-log scale is used.
ResultsStatistical evaluation of IMERG rainfall estimates
Basic statistics of IMERG estimates and WEGN data are summarized in
Table . All three IMERG estimates have a higher value for
mean and maximum rain rates, and for standard deviation, compared to the
rain rates shown by WEGN data. The percentage of no rain is also slightly larger in IMERG data,
which is very likely related to limitations of satellite observations in
detecting very low rain intensities
.
Figure shows the 24 h accumulated rainfall time series
comparison (0.2 mm threshold is applied for the daily amounts). IMERG
estimates and WEGN data show good overall agreement on the occurrence of most
daily rainfall events at IMERG grid scale, although IMERG tends to
overestimate high rain rates.
Figure shows PDFc and CDFv of IMERG
estimates versus those of WEGN data. The IMERG estimates are in good
agreement with the WEGN data in terms of rain occurrence except for low rain
rates (< 0.5 mm 30 min-1). However, IMERG shows high rain rates
exceeding the maximum value of WEGN data (15.3 mm 30 min-1) and
consequently yields relatively large differences in CDFv compared
to WEGN data for the moderate to high rain rates. More specifically, rain
rates less than 15 mm 30 min-1 contribute to essentially 100 % of
the total rain volume for WEGN data, while about 95 % for IMERG-F, and
only about 75 % for IMERG NRT estimates. This shows that the satellite
estimates sometimes overestimate rainfall and produce very high values, which
have, in spite of their low frequency, a significant impact on the total rain
volume.
Figure is similar to Fig. but with the data
pairs restricted to those whose IMERG and WEGN values are both higher than
0.05 mm 30 min-1; i.e., both detect rain. Here, we also divided
the entire data into low and high rainfall intensities, and into warm and hot
seasons, as described in Sect. . Given that the disagreement of
low rain rates in PDFc reduces (see entire data), it is confirmed
that the differences seen in Fig. are due to the poor
sensitivity (misses) of satellites to low rain rates rather than due to some
general biases in the estimation. Indeed, we recomputed the POD score, only using values when WEGN is above 0.5 mm 30 min-1 (i.e., disregarding
low rain rates) and found that the POD scores are 0.70, 0.79, and 0.75 for IMERG-E,
IMERG-L, and IMERG-F estimates, respectively, while compared to WEGN of the
entire range of rain intensities above the 0.05 mm 30 min-1
threshold, the POD scores are only 0.50, 0.57, and 0.53, respectively.
In the panels of low rain intensities (< 1.2 mm 30 min-1, or the
80th percentile of WEGN data), IMERG CDFv still gets a
contribution from rain rates greater than 1.2 mm 30 min-1, even over
10 mm 30 min-1, while the corresponding PDFc has a fairly good
agreement with that of WEGN. This leads us to suspect that such big
differences could be associated with a time lag between rain peaks of IMERG
estimates and WEGN data, rather than a tendency of the satellite to
constantly overestimate rainfall; this will be further investigated and
illustrated in Sect. . For the high intensities, the IMERG
runs tend to underestimate the rain rates.
Validation statistics comparing the performance of IMERG runs at
each grid box.
Furthermore, the CDFv of the IMERG NRT estimates does not show
physically plausible shapes, e.g., a sudden rise between 10 and 20 mm. As
seen in the hot season, the comparison reveals a clear improvement in IMERG
rainfall estimates by applying more retrieval or calibration processes to the
satellite observations; CDFv moves closer to that of WEGN data,
and also, the shapes become gradually smoother from IMERG-E via IMERG-L to
IMERG-F estimates. In general, it is concluded that IMERG-F estimates have
the highest overall accuracy, followed by IMERG-L and IMERG-E estimates.
Figure shows scatter plots of IMERG estimates versus WEGN
data to enable a more quantitative understanding of the discrepancy between
the data. Although it is a common practice to conduct regression analysis
with scatter plots, we decided not to because highly skewed distributions of
rain rates (outliers) seen in the CDFv (Fig. ) can
strongly affect the results. Therefore, we chose to examine distributions of
IMERG estimates over nine predefined rain rate bins (each bin containing at
least 30 data pairs); 25th, 50th (median), and 75th percentiles of IMERG
estimates are analyzed for each bin, and the collective results are shown in
Fig. .
The IMERG runs show better performance (i.e., closer to one-to-one line) in
estimating moderate rain rates within about 0.3 to 3 mm 30 min-1 but
have a tendency to overestimate low rain rates and underestimate high rain
rates. It is worth noting that the slopes of IMERG-F percentile lines
(Fig. , top row) are consistent, with a relatively narrow
spread, across the dataset partitioning, indicating that the biases in the
IMERG-F estimates are relatively small and uniformly distributed. In
contrast, the 75th percentile line of the IMERG NRT estimates is slightly off
the 50th and 25th percentile lines, particularly in the hot season, which
indicates that the distribution of IMERG NRT rainfall estimates in the bins
is skewed toward low values.
Table provides the statistics metrics computed for each
of the two IMERG grid boxes (see Fig. ). All metrics are
improved in IMERG-F estimates, except the correlation coefficient (r) that
may not be a proper metric to evaluate the accuracy of IMERG data due to some
large outliers. Indeed, IMERG-F estimates show the highest Spearman's rank
correlation coefficient (ρ) which is known to be much less sensitive to
outliers . A somewhat
better performance in Grid 15.85 compared to Grid 15.95 may be attributed to
an Austrian national station, which is not part of the WEGN gauges, but
located within the WEGN area (over Grid 15.85), of which measurements are
integrated into the GPCC gauge product, therefore influencing IMERG over Grid
15.85.
Analysis of example rainfall events
In this subsection, we focus on diagnosing the more detailed behavior of
IMERG runs by selecting example rainfall events where the IMERG estimates
show distinct differences from the WEGN data. Note that the WEGN can give
very accurate information about the domain in terms of spatial and temporal
rainfall variability, in spite of potential overall biases of up to about
10 % in the data. Figures and show the
spatial distribution of two such rainfall events captured by the WEGN network
and the corresponding time series of IMERG estimates and WEGN data.
Example rainfall event in the warm season that occurred on 30 May
2015. (a) Spatial rainfall pattern over the WEGN network for
consecutive half-hourly periods over the two selected IMERG grid boxes (red
outline; see Fig. ). (b) Time series of IMERG-F,
IMERG-L, IMERG-E estimates, and WEGN data in each grid box during the rainfall
event, with the shaded areas highlighting the two hours illustrated by the
map sequence in (a). Solid vertical lines indicate time steps where
all three IMERG runs received PMW-based information for the rainfall
retrieval at that time step, dotted vertical lines (not applicable for this
event but for the event of Fig. below) mean that either
IMERG-E or both IMERG NRT runs did not receive PMW-based information at the
time step, and no vertical line (the case for most time steps) implies that
none of the IMERG runs received PMW-based information.
Same as Fig. , but for an example rainfall event in
the hot season that occurred on 8 July 2015.
Figure shows a rainfall event in the warm season, on 30 May 2015.
According to the spatial WEGN maps, the rain clouds arrived at Grid 15.85 first (around 21:00 UTC), and then drifted eastwards. Among the
IMERG NRT runs, the IMERG-L run is better able to describe this time lag
between the two grids. This improvement can be attributed to backward
morphing, which is applied in the IMERG-L, but not yet available in IMERG-E.
The IMERG-L run captures rainfall withdrawal at Grid 15.95 better as
well. Nevertheless, all IMERG runs tend to overestimate rainfall, with a time
difference of about 2 h earlier in starting time. This false alarm suggests
that the PMW observations (19:30–20:30 UTC) made by the IMERG runs were
likely combined with incorrect IR cloud information. However, despite the
absence of available PMW observations during the actual rainfall, the
overestimation in IMERG-F is much smaller, thanks to the adjustment by the
gauge analysis.
Figure shows a rainfall event in the hot season, on 8 July 2015.
Here, again, the onset of rainfall in IMERG estimates is ahead of that of
the WEGN data (see the shaded area around 16:00 UTC). It is interesting that
IMERG NRT runs describe the first peak (13:30–14:00 UTC) at Grid 15.85 well, albeit with overestimation, but only the IMERG-E run captures the peak
at Grid 15.95 with a half-hour time shift. We suspect that the
satellite-observed rain was morphed more slowly than the actual cloud
movement (from west to east), for example, because the cloud motion vectors
derived from IR-based data do not always accurately reflect the actual cloud
advection speed . Therefore, the peak
still remains at Grid 15.95 in the IMERG-E estimates.
When it comes to the IMERG-L and IMERG-F estimates, we assume that the backward
morphing identified the timing of the peak correctly. However, given that the
morphing weights are inversely proportional to the time difference between
the target data time and the PMW observation (i.e., higher weight is assigned
for the time step when IMERG-E depicted the peak), the backward morphing
significantly reduced the peak in the IMERG-E run (since it has a higher
weight), whereas it only slightly increased the missing peak (since it has a
lower weight). This implies a possibility of conflict between the forward and
backward morphing that can lead to error in the rainfall estimates.
Contour plots of RMSE (mm 30 min-1) between IMERG estimates
and WEGN data as a function of WEGN gauge rainfall accumulation time and time
offset (which means the time from which the WEGN gauge rainfall
accumulation is started relative to the IMERG data start time).
In Fig. , both IMERG-E and IMERG-L overestimate rainfall
during 16:00–22:00 UTC. This can be explained by differences in the number
of PMW observations conducted in each IMERG run. The IMERG NRT runs could use
four or fewer PMW observations during the period, all of which overestimated
the rain rates (no difference in data values between IMERG-E and IMERG-L once
the data are collected from the same PMW sensor), resulting in the
overestimation after the forward morphing and then even more after the
backward morphing. According to , evaporation
below cloud base can introduce large positive bias by the CMORPH morphing
method during warm and hot seasons.
Conversely, the IMERG-F run received more PMW-based information over
the same period (see 18:00–18:30 and 19:30–20:00 UTC) and the monthly
gauge analysis. Thus, it shows better performance than the IMERG NRT runs.
This demonstrates clearly the value of more PMW-based estimates in the
morphing process as well as the ability of gauge
adjustment to mitigate systematic biases . From
these two case studies, it appears that the gauges provide a greater
improvement to IMERG Final estimates. One reason the PMW observations
overestimate rainfall is likely the subgrid-scale rainfall variability. For
instance, the IMERG runs may use satellite footprints over the northwestern
corner of the grid cells, where rain is stronger
(≈ 15 mm 30 min-1 at 16:30–17:00 UTC), for their gridding
process.
Evaluation of temporal matching of IMERG estimates
used contour diagrams of RMSE as a function
of accumulation time and time offset to interpret TMPA 3-hourly rainfall
estimates as a 100 min accumulation starting between 90 and 30 min before
the nominal time. Here, we use the same approach to provide an evaluation and
interpretation of the temporal characteristics for the IMERG estimates in
terms of rain gauge accumulation on ground. The WEGN 5 min gridded data are
used and integrated over accumulation times from 5 min (native sampling) to
100 min (twenty 5 min samples) for time offsets from -20 to +60 min
(in 5 min steps). Figure shows the resulting RMSE of IMERG
rainfall estimates versus WEGN data as a function of the gauge accumulation
time, Δ, and the time offset, δ.
The minimum RMSE value for the IMERG-F estimates of the entire dataset
(Fig. , top left) occurs at a Δ of about 25 min and a
δ of about +40 min. This offset of 40 min exceeds the 30 min time
resolution of IMERG, which means that the IMERG-F estimates are, on average,
displaced by more than one time step. This suggests, for example, that IMERG-F
rainfall estimates during 09:00–09:30 UTC can be considered as gauge
measurements during 09:40–10:05 UTC. The positive offset is consistent with
the early bias in rainfall onset found in Sect. . The hot
season shows a shorter offset for the minimum RMSE compared to the warm
season (Fig. , top middle and right), which agrees with the
results of .
Intercomparing the IMERG products for their common period in 2015
(Fig. , bottom row), it is visible that longer Δ values
are needed to minimize RMSE of IMERG-E and IMERG-L rainfall estimates, while
optimal δ values are obtained at around +20 min for the both
datasets. This analysis identifies possible sources of error that should be
considered in the context of hydrological applications of IMERG data. For
instance, biases (overestimation in this case) in IMERG rainfall estimates
will inevitably propagate through hydrologic models, and consequently this
would lead to larger errors in runoff. The magnitude of biases can be reduced
when IMERG Final estimates are used. Time offset bias, however, remains
relatively stable across all three IMERG runs, especially in the warm season.
Therefore, comparison or adjustment of IMERG estimates using local ground
reference (if available) in terms of biases, not only in amount of rainfall
but also in its timing, should be considered as an approach to reach the
required level of accuracy in rain data.
In general, the IMERG NRT estimates show higher RMSE values compared to the
IMERG-F estimates, as expected. Also, they show relatively indistinct patterns
and even multi-minimum RMSE values (in the case of IMERG-E). As such, this
approach of interpreting the rainfall estimates may not be sufficiently
constrained by the NRT estimates, due to the limited sample size from only
7 months of data and also due to larger errors. More years of data are
needed before such an approach can provide a robust interpretation of the NRT
estimates.
Conclusions
In this study, we evaluated half-hourly rainfall estimates from the IMERG-E,
IMERG-L, and IMERG-F runs using gauge measurement data from the WEGN network
in southeastern Austria for the period of April–October in 2014 and 2015. The
dense WEGN gauge network provided a unique opportunity for a direct
grid-to-grid comparison over two selected IMERG
0.1∘× 0.1∘ grid boxes. This evaluation work
provides valuable insights and input to improve satellite rainfall retrieval
processes, to further intercompare data among satellite-based rainfall
products, and to achieve a better product quality, in particular of IMERG
NRT, for various data applications such as flood and landslide warning or
agricultural drought forecasting and monitoring.
First, thorough statistical comparisons between IMERG estimates and WEGN data
show the biases of IMERG both in rainfall occurrence and in intensity
distributions. Nonetheless, we find that the IMERG-F run considerably
outperforms the NRT runs. IMERG-E and IMERG-L runs overestimate low rain
rates, leading to large discrepancies in accumulated rainfall, which result
in a lower correlation with WEGN data in general. All three IMERG runs tend
to underestimate high rain rates.
Second, the study of rainfall events selected to examine large IMERG-WEGN
discrepancies reveals specific situations, e.g., a lack of PMW-based
observations during short-term rainfall, when the IMERG runs can fail to
describe rainfall features even qualitatively. Here, again, we find
significantly smaller errors in the IMERG-F estimates, by the monthly gauge
correction, compared to the IMERG NRT estimates.
Last, by calculating the RMSE of the half-hourly IMERG estimates compared to the
WEGN ground-based rainfall data as a function of gauge accumulation time and
time offset, the minimum RMSE found for IMERG-F estimates suggests these can
be regarded as a 25 min accumulation with a +40 min time offset
(preceding the time of the gauge data by this time span). For example, an
IMERG-F estimate for 09:00–09:30 UTC can be interpreted as an accumulation
over 09:40–10:05 UTC. Again, the results for the IMERG NRT estimates
suggest significantly lower confidence, both due to insufficient sample size
and larger estimation errors.
Consequently, our analysis across the different runs of IMERG demonstrates
the effects of the additional processes on the final rainfall estimates.
While the better performance of the IMERG-F run is often attributed to the gauge
adjustment procedure
,
we also identify the advantages of a greater number of PMW-based estimates.
Conversely, the inclusion of forward and backward morphing in the
IMERG-L run, with sparse PMW observations, provides only marginal benefits
over the forward-only morphing in the IMERG-E run. In fact, our case study of
example rainfall events illustrates the interesting possibility of
cancellation in the backward and forward morphing estimates for the IMERG-L
run, resulting in a performance poorer than in the IMERG-E run. These results
for the performance of IMERG runs could be representative of other regions
under similar conditions (e.g., midlatitude land areas). The study approach
is, however, not easily applicable to different precipitation regimes. This
is mainly due to the limited availability of independent ground reference
data like WEGN. As a result, WEGN offers valuable information about the
accuracy of IMERG estimates across its three different runs.
Further studies on detailed links between the errors in the final rainfall
estimates and the upstream data sources (i.e., contribution of each PMW/IR
sensor to biases in IMERG estimates) or retrieval processes, to alleviate
those issues, will contribute to improvements in the performance of the
IMERG-L run (e.g., by accounting for time-lagged peaks or improving the cloud
motion vectors) and, consequently, the IMERG-F run. Meanwhile, addressing
instantaneous satellite estimates made in the IMERG runs will help us to
understand overestimation in the PMW estimates themselves.
Our future work on the evaluation of IMERG products will place emphasis on
the IMERG-F data, in order to better understand the behavior of rainfall
estimates with various conditions, such as different temporal accumulation, varying thresholds, or
the inclusion of PMW/IR sources. Using the WEGN high-resolution data, we
can also explore rainfall uncertainty and variability at a IMERG
subpixel-scale, another intriguing prospect. Additionally, it will be
worthwhile to intercompare the version of IMERG-F data used here (V03) with
the current version (V04) or the upcoming version (V05) to be released soon
for evaluation of improvements of IMERG. IMERG V04 is the first version to
use the GPM Core Observatory as a calibrator for the constellation satellite
partners so it is expected to provide more consistent quality among the
PMW/IR estimates.
WEGN data are available in the WEGN data portal,
www.wegenernet.org. IMERG data are provided by the NASA/Goddard Space
Flight Center's PMM and PSS teams from
http://pmm.nasa.gov/data-access/.
The authors declare that they have no conflict of
interest.
Acknowledgements
The study was funded by the Austrian Science Fund (FWF) under research grant
W 1256-G15 (Doctoral Programme Climate Change Uncertainties, Thresholds and
Coping Strategies). Walter A. Petersen and Jackson Tan acknowledge the NASA GPM/PMM Programs,
and Jackson Tan acknowledges funding under the NASA Postdoctoral Program. WegenerNet
funding is provided by the Austrian Ministry for Science and Research, the
University of Graz, the state of Styria (which also included European Union
regional development funds), and city of Graz; detailed information is found
at www.wegcenter.at/wegenernet.
Edited by: Wouter Buytaert
Reviewed by: Remko Uijlenhoet and two anonymous referees
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