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  • View in gallery

    Frequency distributions of near-surface rainfall rates over (a) oceans and (b) land between 1998 and 2010. Blue and red bars indicate distributions for V6 and V7, respectively, with bin sizes of 2 mm h−1, except for the rightmost bin where the frequency shows the number of pixels with near-surface rainfall rates of 300 mm h−1. Average values (unconditional) are also shown in each figure.

  • View in gallery

    Global distribution of regional extreme surface rainfall rates (mm h−1) on a 2.5° × 2.5° grid extracted from the TRMM PR 2A25 products (a) V6 and (b) V7. Regional extreme rainfall rate is defined as the average of the maximum estimated surface rainfall rates in the upper 0.1% of severe storms. (c) Differences between the V6 and V7 regional extreme rainfall rates.

  • View in gallery

    Example of the first type of suspicious V7 extreme rainfall. (top) Horizontal distributions of near-surface radar reflectivity (dBZ) and (bottom) along-track vertical cross-sectional radar reflectivity (dBZ) for (a) V6 and (b) V7. Contours show the topography (m) derived from the Shuttle Radar Topography Mission 30 arc s dataset (SRTM30; Farr et al. 2007) at 1000-m intervals. (c) Vertical profiles of reflectivity (dBZ; red) and rainfall rate (mm h−1; black) for V6 (dashed, see note at bottom of caption) and V7 (solid) at the pixel for which the near-surface rainfall in V7 is determined as a regional extreme. Circles (see note at bottom of caption) and crosses show the V6 and V7 echo boundaries, respectively. Note that a dashed line and circles are not shown in (c) because V6 has no rain echo for type 1 cases; the dashed lines and circles are used in subsequent Figs. 46 that use the format of Fig. 3.

  • View in gallery

    As in Fig. 3, but for an example of the second type. The solid and dashed lines in Fig. 4c overlay each other and are thus indistinguishable both for the rainfall rate and reflectivity as the V6 and V7 rainfall rates and reflectivities are identical between the V6 echo boundaries.

  • View in gallery

    As in Fig. 3, but for an example of the third type.

  • View in gallery

    As in Fig. 3, but for an example of V7 extreme rainfall over the ocean. This case is categorized as the second type of suspicious extreme rainfall (Fig. 4).

  • View in gallery

    (a) Geographical distribution of extreme rainfall events in 2000 and 2008 for type 1 (red crosses), type 2 (blue triangles), and type 3 (gray circles). (b) As in (a), but for the Asian region.

  • View in gallery

    Frequency distributions of extreme rainfall events over land in 2000 and 2008 as a function of the (a) mean and (b) standard deviation of the surface elevation. The mean and standard deviation of surface elevation are calculated over an 11 km × 11 km box centered at the corresponding PR footprint using the SRTM30 dataset. Bars show the total frequencies of type 0–3 extreme events (left axis), and black-dashed, red, blue, and green solid lines show the proportions of type 0, 1, 2, and 3 events, respectively (right axis).

  • View in gallery

    Frequency distributions of extreme rainfall events over land in 2000 and 2008 as a function of rain-top height (above ground level). Black-dashed, red, blue, and green solid lines show the frequencies of type 0, 1, 2, and 3 events, respectively. Each frequency distribution is normalized to its corresponding maximum value.

  • View in gallery

    Frequency distributions of the extreme rainfalls extracted from V7 for each angle bin. Each frequency distribution is normalized to its corresponding maximum value.

  • View in gallery

    Differences between the V6 and V7 vertical reflectivity profiles at the extreme pixel indicated by the solid rectangle in Fig. 5b (solid line) and its surrounding four pixels (dashed, dotted, dashed–dotted, and dashed–two dotted lines). Each pixel location (cross track and along track) is indicated as the relative location to the extreme pixel.

  • View in gallery

    Scatter diagrams of (a),(b) the ratio of near-surface rainfall rate to the average of its four surrounding pixels (SRR), and (c),(d) the near-surface rainfall rate vs the vertical reflectivity gradient calculated using the two lowest bins (VGZ) for the extreme rain samples extracted from (left) V7 and (right) V6. Red, yellow, green, and blue dots in the V7 distribution indicate type 1, 2, 3, and 0 samples, respectively. Note that the SRR value 104 is assigned to the samples that have four surrounding pixels with an average value of 0. The horizontal and vertical black lines in (a) outline the threshold values for filtering out suspicious extreme data.

  • View in gallery

    (a) Location of data filtered out using the filter developed in this study during 1998–2010. Red circles, blue triangles, and black crosses indicate the data with near-surface rainfall rates of 300, 100–300, and <100 mm h−1, respectively. (b) As in (a), but for the Asian region (Fig. 7).

  • View in gallery

    Frequency distributions of occurrences of the data filtered out using the filter developed in this study over land during 1998–2010 as a function of the (a) mean and (b) standard deviation of surface elevation.

  • View in gallery

    As in Fig. 1, but for the original V7 dataset and filtered V7 dataset (V7f).

  • View in gallery

    As in Fig. 2, but for regional extreme rainfall distribution for V7f.

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A Removal Filter for Suspicious Extreme Rainfall Profiles in TRMM PR 2A25 Version-7 Data

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  • 1 Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, Japan
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Abstract

This study reports on the presence of suspicious “extreme rainfall” data in the 2A25 version-7 (V7) product of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset and introduces a simple method for detecting and filtering out the suspicious data. These suspicious data in V7 are found by comparing the extreme rainfall characteristics in the V7 and version-6 products. Most of the suspicious extremes are located over land, especially in mountainous regions. Radar reflectivities in the suspicious extremes show significant monotonic increases toward the echo bottom. These facts indicate that the suspicious extremes are mainly caused by contamination from ground or sea clutter. A simple thresholding filter for eliminating the suspicious extreme data is developed using common characteristics in the horizontal and vertical rainfall structures and reflectivities in the suspicious extremes. The proposed filter mitigates deformations in the frequency distribution of the surface rainfall rate in the 2A25 V7 product.

Corresponding author address: Atsushi Hamada, Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: a-hamada@aori.u-tokyo.ac.jp

Abstract

This study reports on the presence of suspicious “extreme rainfall” data in the 2A25 version-7 (V7) product of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) dataset and introduces a simple method for detecting and filtering out the suspicious data. These suspicious data in V7 are found by comparing the extreme rainfall characteristics in the V7 and version-6 products. Most of the suspicious extremes are located over land, especially in mountainous regions. Radar reflectivities in the suspicious extremes show significant monotonic increases toward the echo bottom. These facts indicate that the suspicious extremes are mainly caused by contamination from ground or sea clutter. A simple thresholding filter for eliminating the suspicious extreme data is developed using common characteristics in the horizontal and vertical rainfall structures and reflectivities in the suspicious extremes. The proposed filter mitigates deformations in the frequency distribution of the surface rainfall rate in the 2A25 V7 product.

Corresponding author address: Atsushi Hamada, Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: a-hamada@aori.u-tokyo.ac.jp

1. Introduction

The precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite successfully provides unique three-dimensional rainfall structure measurements between latitudes 36°S and 36°N. More than 15 yr of global observation with a footprint of ~5 km has enabled the construction of high-resolution rainfall climatology datasets (e.g., Nesbitt and Anders 2009; Biasutti et al. 2012). TRMM’s non-sun-synchronous orbit captures the diurnal variation in rainfall (e.g., Hirose et al. 2009).

The accumulation of long-term homogeneous TRMM measurements not only enables the examination of interannual variation in monthly, seasonal, and annual mean rainfalls, but also reveals heavy or extreme rainfall characteristics both on global and on regional scales. Zipser et al. (2006) showed the global distribution of the world’s most intense convection by the combined use of TRMM instruments. They indicated that the geographical distribution of extreme convection is rather different from that of the total rainfall or the overall convective activity, and the strongest convective storms are often found in semiarid regions. Romatschke et al. (2010) have defined some specific types of extreme convective systems using three-dimensional PR measurements. Their regional characteristics were examined over South Asia (Romatschke et al. 2010) and South America (Romatschke and Houze 2010). Sohn et al. (2013) examined the PR echo profiles over the Korean Peninsula and demonstrated that heavy rainfall in this region is associated more with low-level clouds rather than deep convective clouds.

The current TRMM PR 2A25 product version 7 (V7) was released in July 2011. Among many improvements over the previous version 6 (V6), the estimated rainfall rates in V7 are generally higher, especially over land, mitigating the underestimation of rainfall rates over land of V6 (Seto et al. 2011, 2013; Amitai et al. 2012; Kirstetter et al. 2013), although the underestimation still remains, especially for extremely heavy rainfall (Rasmussen et al. 2013). Kirstetter et al. (2013) compared the 2A25 V6 and V7 products with a precipitation product based on a ground radar network and rain gauge measurements over the United States. They concluded that the bias in the rain-rate estimates of V7 have been improved from those of V6, and that overestimation of light rain rates (<10 mm h−1) and underestimation of heavy rain rates (>30 mm h−1) are mitigated simultaneously. They also indicated that the former and latter improvements are likely a result of the recalibration of the reflectivity–rainfall rate (ZR) relation equation over land and the nonuniform beam-filling (NUBF) correction, respectively. Seto et al. (2011) compared the frequency of heavy surface rainfall rates (above 50 mm h−1) in V6 and V7 and showed that heavy rainfall over land is more frequent in V7 than in V6. They concluded that this increase is partly caused by improvements in the surface reference technique (SRT; Seto and Iguchi 2007); however, they pointed out a possibility that SRT in V7 mistakenly gives extremely heavy rainfall estimates (~300 mm h−1).

The TRMM PR 1B21 algorithm was also improved by replacing the high-resolution surface elevation dataset that is used to determine the bottom level free from mainlobe surface clutter, over Asia and South America (JAXA 2011). Although this replacement is believed to increase the accuracy of the clutter detection routine, it has been found that there is a problem caused by insufficient use of the recently introduced surface elevation dataset (JAXA 2011). Also, a programming error was found in the computer code for the clutter detection routine. The occurrence of false rainfall due to these issues is found to be infrequent but might significantly affect the extreme rainfall statistics.

We conducted a visual inspection of extremely heavy rainfall events in V7 and found many suspicious data that are unlikely to be caused by natural changes associated with the updating of the rain profiling algorithm. Contamination by large erroneous values due to bugs in the retrieval program or inadequate quality control procedures may have some impacts on rainfall statistics, especially in studies focusing on extreme rainfall. In this study, we examined the occurrence and characteristics of suspicious extreme rainfall data in the PR 2A25 V7 product. We developed a simple thresholding filter to eliminate the suspicious data using common characteristics for the three-dimensional rainfall and radar reflectivity structure. Note that hereafter we use the term extreme rainfall to refer specifically to extremely heavy rainfall that is defined on a local basis at each 2.5° × 2.5° grid in latitude and longitude.

Section 2 describes the data used in the study. Section 3 describes a detailed investigation of the extreme rainfall profiles in the V7 dataset to identify the characteristics common to the suspicious extremes. Section 4 introduces and evaluates a simple method for filtering out suspicious extreme data. Section 5 presents the summary and conclusions.

2. Data

We use the current (V7) and previous (V6) versions of the TRMM PR 2A25 product (Iguchi et al. 2000). We also use the TRMM 1B21 product to obtain information about the surface elevation that was used to determine the bottom level free from mainlobe surface clutter (Awaka et al. 2000). The 2A25 product contains three-dimensional rainfall rates and effective radar reflectivities derived from the TRMM PR measurements at 13.8 GHz as well as the parameters for their retrieval. The TRMM satellite has continuously provided unique measurements from a non-sun-synchronous orbit with an inclination angle of 35° since its launch in November 1997. The orbiting altitude of TRMM was increased from 350 to 402.5 km in mid-August 2001 to extend its mission life; this increased its swath width from 220 to 247 km and its footprint from 4.3 to 5 km. The vertical resolution is 250 m from 0 to 20 km throughout the entire period. The TRMM orbit boost maneuver decreased the PR’s sensitivity and, consequently, affected the frequency distribution of the radar reflectivity (Short and Nakamura 2010; Hirose et al. 2012). The differences in the PR rain profiling algorithm from V6 to V7 are described in Iguchi et al. (2009). Major changes include the implementation of a new NUBF correction formula, which was disabled in V6; introduction of a new phase-state model of hydrometeors, which is related to the vertical profile of specific attenuation; recalibration of parameters in the kPZe and RZe relations (kP is specific attenuation due to precipitation and Ze is effective reflectivity factor) by introducing a new drop size distribution model along with the adoption of a nonspherical drop model, which affects path-integrated attenuation (PIA) and rainfall-rate estimates; and an improvement in PIA correction based on a modified SRT. Although these changes in the rain profiling algorithm may introduce additional uncertainties into the rainfall-rate estimates, several evaluation studies using independent measurements indicate an improvement of V7 rainfall estimates from V6 (Seto et al. 2013; Amitai et al. 2012; Kirstetter et al. 2013).

We used the entire rainfall-rate and radar reflectivity dataset that had rain-certain flags in V6 and V7 of the PR 2A25 dataset for 13 yr, from 1998 to 2010. In the V6 dataset, there are spurious echoes with a 300 mm h−1 rain rate at all levels, which were reported in Hirose et al. (2009). We found 51 scans (1314 pixels), containing such spurious echoes during the analysis period, and manually removed them from the V6 dataset. Detailed visual inspections were conducted on the 2000 and 2008 data to describe the common characteristics in the suspicious extreme data and to determine the threshold value for filtering out suspicious extreme data. These two specific years are chosen to allow for the assessment of the effects of the PR sensitivity change before and after the orbit boost in 2001, under similar meteorological conditions.

3. Differences in extreme characteristics between V6 and V7

Figure 1 shows histograms of the numbers of pixels with nonzero near-surface rainfall rates in V6 and V7 over the oceans and over land across the entire TRMM observation area (37.5°S–37.5°N) for 13 yr. The frequency of near-surface rainfall rates over the oceans is slightly higher in V7 than in V6 for rainfall rates between 10 and 60 mm h−1. In ranges other than the 300 mm h−1 bin, there is almost no significant difference between V6 and V7. The frequency of rainfall rates over land decreased (increased) in V7 relative to V6 for almost all ranges below (above) 20 mm h−1. The average rainfall rates (unconditional) are higher in V7 than in V6 over both ocean and land due to the intensity shift to higher rainfall rates, but the differences are only a few percent because of the decrease in the total number of rainy pixels. The difference in the frequency distributions of V6 and V7 is slightly complicated in coastal regions; the frequency of rainfall rates between 20 and 130 mm h−1 tended to be higher in V7, and lower in V7 for other rainfall ranges except for the 300 mm h−1 bin (not shown). The differences between V6 and V7 over ocean, land, and coastal regions are basically consistent with the expected differences caused by the updating of the 2A25 retrieval algorithm (JAXA 2011).

Fig. 1.
Fig. 1.

Frequency distributions of near-surface rainfall rates over (a) oceans and (b) land between 1998 and 2010. Blue and red bars indicate distributions for V6 and V7, respectively, with bin sizes of 2 mm h−1, except for the rightmost bin where the frequency shows the number of pixels with near-surface rainfall rates of 300 mm h−1. Average values (unconditional) are also shown in each figure.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

In the difference in rainfall frequency distributions between V6 and V7, a substantial increase in the 300 mm h−1 bin in ocean, land, and coastal regions is noticed. This may be attributed mainly to the ceiling of the rainfall rates in the 2A25 dataset, where the estimated rainfall is reset to 300 mm h−1 if it exceeds 300 mm h−1. However, as shown later, there are also many suspicious data that cannot be considered to be a natural outcome of the retrieval algorithm; we focused on these data in this study.

It should be noted that the ratios of 300 mm h−1 rainfall frequencies to the total number of rainy pixels in V7 are still on the order of 10−5% and 10−3% over the oceans and over land, respectively. Hence, the extreme rainfall frequencies have little impact on rainfall statistics that use the entire range of rainfall rates, such as the average rainfall over long periods or large regions. However, the apparent increase in the extreme rainfall frequencies in V7 may have a significant effect on the heavy or extreme rainfall statistics. Figure 2 illustrates geographical distributions of regional extreme rainfall rates. The extreme rainfall rate is calculated as the average of the maximum estimated surface rainfall rates for each of the most intense (the upper 0.1% of the 13 study yr) “storms” in a 2.5° × 2.5° grid. Each storm is defined as a set of contiguous rainy pixels with estimated surface rainfall rates above 0.5 mm h−1, and storm intensity is defined by the maximum estimated surface rainfall rate in the storm. We hereafter call the upper 0.1% intense storms extreme events. Overall, it is noticed that the regional extreme rainfall rates in V7 are significantly higher than in V6 over land, and are lower than in V6 over the oceans with higher rainfall rates (Fig. 2c). Over the ocean, the regional extreme rainfall rates in V7 tend to be lower than V6 for regions with rainfall rates heavier than about 80 mm h−1, and to be slightly higher than V6 for regions with lighter rainfall rates. The regional differences between V6 and V7 are qualitatively consistent with the updating of the retrieval algorithm, although these differences are not small. Over land, the extreme rainfall rates in V7 are significantly higher in almost all regions and are qualitatively consistent with the increase in rainfall rate of the frequency distribution at heavier rainfall rates (Fig. 1b). However, note that there are regions where the extreme surface rainfall rates in V7 are more than 100 mm h−1 higher than those in V6. Such significant increases are observed mainly in mountainous regions such as the Himalayas, the eastern margin of the Tibetan Plateau, the Zagros Mountains, and along the west coast of the Americas. In these regions, the near-surface radar reflectivities in V7 are also significantly higher, by more than 10 dB (not shown).

Fig. 2.
Fig. 2.

Global distribution of regional extreme surface rainfall rates (mm h−1) on a 2.5° × 2.5° grid extracted from the TRMM PR 2A25 products (a) V6 and (b) V7. Regional extreme rainfall rate is defined as the average of the maximum estimated surface rainfall rates in the upper 0.1% of severe storms. (c) Differences between the V6 and V7 regional extreme rainfall rates.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Some changes in the 2A25 rain profiling algorithm, such as the NUBF correction and PIA correction based on a modified SRT, might increase the near-surface radar reflectivity and consequently increase the rainfall rate. However, it is possible that contamination with strong nonprecipitating echoes due to ground clutter artificially increases the radar reflectivity and rainfall rates for “extreme rainfall.” It is crucial to investigate whether the significant increases in radar reflectivities and rainfall rates for extreme rainfall are caused by inadequate retrieval or quality control procedures. We carefully examined the horizontal and vertical structures of the rainfall and reflectivity profiles for V7 extreme rainfall events by comparing the profiles of the V7 extreme rainfall events with the corresponding V6 profiles at the same location (note that the corresponding V6 profile would not necessarily be determined to be a V6 extreme rainfall event). We first conducted a feasibility study to find common three-dimensional echo structure characteristics in V7 extreme events in which there is large discrepancy in reflectivity at the echo bottom between V6 and V7. All V7 extreme events observed over the Asian region during two years (2000 and 2008; approximately 3500 out of 16 937 extreme events) are compared with the corresponding V6 rainfall events. The years 2000 and 2008 were chosen to examine the effects of the PR sensitivity change before and after the orbit boost in 2001. These two years were also chosen to reduce the effect of El Niño–Southern Oscillation (ENSO), which is one of the major natural factors influencing the frequency and intensity of extreme precipitation events (e.g., Lyon and Barnston 2005; Grimm and Tedeschi 2009). Careful visual inspections revealed that approximately 12% of the V7 extreme rainfall events had substantially higher near-surface rainfall rates and reflectivities than did the corresponding V6 events. These events can be classified into three categories on the basis of the differences in the horizontal and vertical structures of the rainfall rates and reflectivities near the ground surface.

In the first type of V7 extreme rainfall events, extremely heavy near-surface rainfall is observed at a pixel in V7, but no rainfall is observed at the same location in V6. As an example of this type, Fig. 3 shows the V6 and V7 horizontal distributions of the near-surface radar reflectivity, along-track vertical cross sections of the radar reflectivity, and vertical profiles of the rainfall rates and the effective radar reflectivities at the extreme rainfall pixel. The pixel at the center of Fig. 3, surrounded by a black border, is determined to be a V7 extreme rainfall event. A 300 mm h−1 near-surface rainfall with a rain-top height of approximately 6.5 km is observed in V7, but no rainfall is seen in the vertical at the same location in V6. In almost all cases of the first type, a nonrainy pixel in V6 turns into a rainy pixel with extremely heavy near-surface rainfall in V7. The radar reflectivity shows an almost monotonic and linear increase toward the echo bottom, and consequently the rainfall rate increases exponentially toward the echo bottom. We further examine the vertical profiles of the PR-received power at the extreme pixel, as well as the surrounding four pixels, for this case. We confirmed that such monotonic increases are observed in the PR-received power at all of five pixels, but these increases are determined to be surface clutter at the surrounding pixels (not shown). It is important to note that the bottom height of the rainfall and radar reflectivity in the extreme pixel is more than 2 km below the lowest level at which nonmissing data are stored (Fig. 3c). The lowest vertical bin that contains meaningful data in the 2A25 dataset is defined as one bin above the highest bin that is cluttered with either surface echo or noise. This definition could affect the near-surface rainfall estimates, since radar reflectivity profiles generally do not stay constant, but still have positive or negative slopes below the freezing level (Liu and Zipser 2013). It should also be noted that the near-surface rainfall rate in the first type is not always 300 mm h−1; it sometimes takes a relatively small value, approximately 100 mm h−1.

Fig. 3.
Fig. 3.

Example of the first type of suspicious V7 extreme rainfall. (top) Horizontal distributions of near-surface radar reflectivity (dBZ) and (bottom) along-track vertical cross-sectional radar reflectivity (dBZ) for (a) V6 and (b) V7. Contours show the topography (m) derived from the Shuttle Radar Topography Mission 30 arc s dataset (SRTM30; Farr et al. 2007) at 1000-m intervals. (c) Vertical profiles of reflectivity (dBZ; red) and rainfall rate (mm h−1; black) for V6 (dashed, see note at bottom of caption) and V7 (solid) at the pixel for which the near-surface rainfall in V7 is determined as a regional extreme. Circles (see note at bottom of caption) and crosses show the V6 and V7 echo boundaries, respectively. Note that a dashed line and circles are not shown in (c) because V6 has no rain echo for type 1 cases; the dashed lines and circles are used in subsequent Figs. 46 that use the format of Fig. 3.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

The second type of extreme rainfall has similar characteristics to the first type, except that V6 has nonzero rainfall. A typical example is shown in Fig. 4, in which the near-surface reflectivities are substantially higher in V7 in one or several pixels within a wide area of rainfall. The vertical profiles of the rainfall and radar reflectivity at the extreme pixel show different characteristics above and below the V6 echo-bottom level. Above this level, the V6 and V7 rainfall rates and reflectivities are identical (the solid and dashed lines in Fig. 4c overlay each other both for the rainfall rate and the reflectivity). However, the echo-bottom level for V7 is more than a few kilometers lower than that for V6 at the same location, 2.5 km in this case, and the rainfall rate and reflectivity for V7 increase almost monotonically toward the echo bottom. There is no difference in the echo-top height for V6 and V7. It should again be noted that relatively small near-surface rainfall rates, approximately 100 mm h−1, are often observed.

Fig. 4.
Fig. 4.

As in Fig. 3, but for an example of the second type. The solid and dashed lines in Fig. 4c overlay each other and are thus indistinguishable both for the rainfall rate and reflectivity as the V6 and V7 rainfall rates and reflectivities are identical between the V6 echo boundaries.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

In the third type of extreme rainfall events, there are nonzero near-surface rainfalls in both V6 and V7, similar to the second type. However, the changes in the vertical rainfall and reflectivity profiles in V7 relative to those in V6 at the same location are slightly different from the second type and difficult to explain. A typical example is shown in Fig. 5. In almost all cases, the echo tops of the V6 and V7 profiles are at the same height. The echo-bottom heights in the V7 extreme profiles are just one bin (250 m) above the corresponding echo-bottom heights in the V6 profile for more than 70% of the cases of the third type, and are the same for almost all other cases. The vertical V6 and V7 profiles are almost identical in the upper troposphere, approximately above 7 km. Below 7 km, the V7 rainfall and reflectivity profiles gradually diverge from the V6 profiles. Below 5.5 km, both rainfall and reflectivity for V7 continue to increase toward the echo bottom, but those for V6 remain almost constant. The difference in radar reflectivities between V6 and V7 is approximately 15 dB at the echo bottom. These changes are qualitatively consistent with the modifications to the rain profile model (Iguchi et al. 2009), which have been introduced to mitigate the underestimation of rainfall rates over land in V6.

Fig. 5.
Fig. 5.

As in Fig. 3, but for an example of the third type.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

All three types of extreme rainfall events examined above are also found over the ocean, although the number of cases is considerably smaller than that over land. Figure 6 shows an example of extreme events over the ocean. This case is categorized as the second type of extreme rainfall event (Fig. 4). The V6 and V7 rainfall rates and reflectivities are almost identical between the V6 echo-top and echo-bottom levels (Fig. 6c). However, the bottom height of the V7 echo is one bin lower than that of the V6 echo, and the rainfall rate and reflectivity for V7 increase sharply at the V6 echo-bottom height. Such a discontinuous increase in the reflectivity profile near the echo bottom is unlikely to be related to natural variation, such as raindrop size distribution, although the echo profile above the V6 echo bottom is characteristic of typical convective rainfall in mesoscale convective systems (Houze 1993, chapter 9). Relatively high amounts of extreme events are found around the PR swath edges for type 2 and 3 extreme events.

Fig. 6.
Fig. 6.

As in Fig. 3, but for an example of V7 extreme rainfall over the ocean. This case is categorized as the second type of suspicious extreme rainfall (Fig. 4).

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

To confirm that the suggested types are applicable to other regions, we also conduct same visual inspection of 500 V7 extreme events that are randomly picked up in the region containing the Americas (37.5°S–37.5°N, 120°–60°W). We also found all three types of extreme rainfall events in this region and confirmed that all of the extreme events can be classified into the same three categories defined here.

On the basis of the results from the above feasibility study, we objectively classified all extreme rainfall profiles extracted from V7 into the three types described above using the following criteria.

  • Type 1—the V7 near-surface rainfall rate has a nonzero (extreme) value, but the V6 value is zero at the same location.

  • Type 2—the near-surface rainfall rates are nonzero in both the V6 and V7 profiles. The echo-bottom height is lower for the V7 profile, and the radar reflectivity profiles are identical above the V6 echo bottom. The difference between the reflectivities at the V6 and V7 echo bottoms is higher than 5 dB.

  • Type 3—the near-surface rainfall rates are nonzero in both the V6 and V7 profiles. The difference between the V6 and V7 echo-bottom heights is not considered. Their reflectivity profiles are not identical within the range where both V6 and V7 contain meaningful data. The difference between the reflectivities at the V6 and V7 echo bottoms is higher than 5 dB.

  • Type 0—the profiles do not fall into the above three categories.

Profiles classified as type 0 do not have significant differences in rainfall and reflectivity and, therefore, can be considered to be normal. Profiles classified as types 1 and 2 are very likely to be artificial, although some normal profiles may be classified as these types. Profiles classified as type 3 are generally normal, but some profiles are likely to be artificial, as will be shown in section 4.

There are 14 870 (87.8%), 144 (0.9%), 540 (3.2%), and 1383 (8.2%) profiles classified as types 0, 1, 2, and 3, respectively, during the 2 yr examined (Table 1). The geographical distributions of type 1–3 samples are shown in Fig. 7b for the Asian region. Almost all samples categorized into types 1–3 are distributed over land, and there are clear differences between the distributions of each type. Almost all type 1 samples are located over the Tibetan Plateau and along the Himalayas–Hengduan Mountains in southwest China. Type 2 samples are widely distributed over mountainous regions and islands, such as western Sumatra, central New Guinea, Taiwan, and Japan. Type 3 samples are more broadly distributed than the other types, over both continents and islands. Such characteristics can be found in other regions (Fig. 7a), where most of the type 1 and 2 extreme events are distributed over land and islands, especially in mountainous regions in Asia and the Americas, as well as the highlands of Africa.

Table 1.

The number of extreme cases extracted from V7 in 2000 and 2008 for four near-surface rainfall-rate categories. The numbers in parentheses are the frequencies relative to the total for each type (%), except for the second column, which is the frequency for each type relative to the total number of extreme samples.

Table 1.
Fig. 7.
Fig. 7.

(a) Geographical distribution of extreme rainfall events in 2000 and 2008 for type 1 (red crosses), type 2 (blue triangles), and type 3 (gray circles). (b) As in (a), but for the Asian region.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Figure 8a shows the frequency distributions of extreme rainfall events during 2 yr as a function of mean surface elevation, which is calculated over an 11 km × 11 km box centered at the corresponding PR footprint. In total, the number of extreme events decreases exponentially with increasing mean elevation, with some exceptions around 5 km. The proportions of type 1 and 2 cases are less than 5% at the lowest elevation bin (0–500 m), but tend to be high at higher-elevation bins, with peaks at the 5.5–6- and 3–3.5-km bins for types 1 and 2, respectively, except for the highest-elevation bin. Type 0 and 3 cases are also distributed in the high-elevation range; however, there is a notable difference between types 1 and 2 and types 0 and 3, when comparing the frequency distributions of the standard deviation of the surface elevation as an index of terrain complexity (Fig. 8b). The proportions of the type 0 and 3 cases show an almost monotonic decrease with increasing standard deviation. On the other hand, the proportions of the type 1 and 2 cases tend to be high at a higher standard deviation range.

Fig. 8.
Fig. 8.

Frequency distributions of extreme rainfall events over land in 2000 and 2008 as a function of the (a) mean and (b) standard deviation of the surface elevation. The mean and standard deviation of surface elevation are calculated over an 11 km × 11 km box centered at the corresponding PR footprint using the SRTM30 dataset. Bars show the total frequencies of type 0–3 extreme events (left axis), and black-dashed, red, blue, and green solid lines show the proportions of type 0, 1, 2, and 3 events, respectively (right axis).

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Differences in several factors among the three types are also observed. Table 1 shows the frequency distribution of the near-surface rainfall rate for each type. The type 0 frequency is higher at lower rainfall rates. In contrast, the frequency distributions for types 1–3 are less dependent on the rainfall rate.

Table 2 shows the frequency distribution for the size of the contiguous rainy pixels for each type. The distribution for type 1 is quite different from that for the other types. It should be noted that more than 80% of type 1 cases are just one pixel in size. The cases for other three types generally tend to be more frequent for larger rainfall areas, but the ratio of the cases with a smaller rainfall area for types 2 and 3 is larger than that for type 0. Type 1 cases also show a notable difference in the vertical structure in comparison with the other three types. Figure 9 shows the frequency distribution of rain-top heights for each type. Almost all type 1 cases have rain-top heights below 5 km. Type 2 cases also tend to have lower rain-top heights than type 0 and 3 cases, although they have broad distributions up to 18 km. There is no significant difference between types 0 and 3.

Table 2.

As in Table 1, but for the four size categories of the rain area (in pixels).

Table 2.
Fig. 9.
Fig. 9.

Frequency distributions of extreme rainfall events over land in 2000 and 2008 as a function of rain-top height (above ground level). Black-dashed, red, blue, and green solid lines show the frequencies of type 0, 1, 2, and 3 events, respectively. Each frequency distribution is normalized to its corresponding maximum value.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Table 3 shows the surface- and rain-type frequency distributions. A notable feature is that a large portion of the type 1–3 extreme cases occur over land. If all type 1 and 2 extremes were artificial, approximately 18% (522 cases) of the extreme events (2938 cases) over land determined from V7 would turn out to be abnormal cases.

Table 3.

As in Table 1, but for surface and rain types.

Table 3.

Table 4 shows the frequency for each type for 2000 and 2008 (i.e., the years before and after the satellite orbit boost). A roughly 34% decrease in the total frequencies may have been caused by the decrease in the PR sensitivity after the orbit boost. There is no significant difference in the relative frequencies of each type between the two years.

Table 4.

As in Table 1, but for before and after the satellite orbit boost. The number in parentheses is the frequency for each type relative to the total number of extreme samples.

Table 4.

We further compare clutter-free bottom levels of extreme rainfall events with the surface elevation data that are stored in the PR 1B21 dataset and that are used to identify the position of the mainlobe clutter. Table 5 shows the frequency distribution of extreme events for the difference between the range bin numbers corresponding to a clutter-free bottom level and the maximum surface elevation in an 11 km × 11 km box centered on the PR footprint. There is significant difference between types 1 and 2 and types 0 and 3. For 62.9% of type 1 and 88.2% of type 2 extreme events, the difference is less than or equal to zero; that is, the clutter-free bottom level is lower than the maximum surface elevation within the PR footprint.

Table 5.

As in Table 1, but for the difference between the clutter-free bottom level and DEM maximum surface elevation (in an 11 km × 11 km box centered on the PR field of view) stored in the 1B21 dataset. The difference is shown as the number of range bins, and negative values indicate that the clutter-free bottom level is lower than the DEM maximum surface elevation. The numbers in parentheses are the frequencies relative to the total for each type (%).

Table 5.

The frequency distributions of the radar-beam incidence angle for each type also show notable differences (Fig. 10). The type 0 extreme cases are observed at all angle bins, with a slight decrease toward the swath edge. This feature is generally consistent with the incidence-angle dependency of the rainfall estimates from PR (e.g., Seto et al. 2011; Hirose et al. 2012). On the other hand, the frequency distributions for types 1–3 are quite different from that for type 0. Types 1 and 2 have more cases in the near-nadir (25th bin) region and the swath edge, while the type 3 frequency peaks only at the nadir. In the near-nadir region, the frequency distributions for types 1–3 are slightly different from each other, with type 1 extremes being less frequent at the nadir than at the nearby bins. The frequency distributions for type 1–3 extreme cases may look similar to the incidence-angle variation of normalized radar cross section of the surface (Meneghini et al. 2000), which contributes to the uncertainty in the reflectivity estimates through the uncertainty in the path-integrated attenuation estimates by the SRT (Meneghini et al. 2000, 2004), but further investigation is required to support this speculation.

Fig. 10.
Fig. 10.

Frequency distributions of the extreme rainfalls extracted from V7 for each angle bin. Each frequency distribution is normalized to its corresponding maximum value.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

The PR 2A25 product contains information about the quality and/or reliability of the estimated rainfall and effective radar reflectivity, such as “qualityFlag” and “reliab,” as well as the estimated rainfall rate. We examined these quality flags for the type 0–3 extremes but could not obtain any useful information that would help us to distinguish the erroneous values from the normal values.

The characteristic geographical distribution of type 1 and 2 extreme events, where almost all cases are observed in high and complex topography over land (Figs. 7 and 8; Table 3), suggests a possibility that most of them are likely to be false extreme events due to misidentification of surface clutter as a rain echo. The fact that a large portion of the type 1 and 2 events have clutter-free bottoms lower than or equal to the maximum surface elevation within the corresponding PR footprints (Table 5) also supports this hypothesis. There still remains a possibility that type 1 cases may be isolated convective cores, since most of the type 1 cases occur on very small horizontal scales (Table 2). However, it is highly unlikely that isolated convection with rain-top heights lower than 5 km (Fig. 9) can produce rainfall with intensities higher than 100 mm h−1. Type 2 extreme events might also be related to a local enhancement of low-level convective cores. However, it would be more likely that the monotonic increase of reflectivity toward the echo bottom is related to surface clutter, since such a monotonic increase is sometimes observed in the PR-received power over complex topography (Awaka et al. 2000). Type 3 extreme events also show different characteristics compared to type 0; this is considered to be normal for some factors such as most of those observed over land (Table 3), and particularly those in the nadir angle bin (Fig. 10). However, we could not identify probable cause to reject (all or part of) type 3 cases as false extremes, except for in some cases that show discontinuous increases in their reflectivity profiles near the echo bottom, as shown in Fig. 6. In the following section, we will develop a simple thresholding method for filtering out suspicious extreme rain events. The design concept of the filter is set to reject as many type 1 and 2 cases as possible and to keep as many type 0 and 3 cases as possible.

4. Filtering out the suspicious extreme rain profiles

The number of suspicious extreme cases is quite small compared to the total number of rainy pixels in V7. Therefore, these suspicious extreme values are expected to have minimal impact when entire rainfall rates are used to calculate rainfall statistics for long periods or over large regions. However, these suspicious extreme values may have a critical impact on the heavy or extreme rainfall statistics, as shown in Fig. 2. Therefore, it is crucial to correctly identify and appropriately eliminate the artificial values. However, some of the type 1 and 2 suspicious extreme cases have relatively low near-surface rainfall rates, less than 100 mm h−1 (Table 1), which makes it difficult to judge the normality of these extreme rainfall values. Therefore, discarding rainfall rates exceeding specific thresholds as erroneous is not appropriate, especially when focusing on regional extreme rainfall characteristics. In this study, we propose a simple method for eliminating the suspicious values by using common characteristics in the horizontal and vertical structures of the rainfall and reflectivity in the suspicious extreme rainfall cases.

There are two common characteristics in the suspicious extreme rainfall profiles. First, the effective radar reflectivities increase almost monotonically toward the echo bottom, resulting in a very large vertical reflectivity gradient at the echo bottom as described above. Second, the radar reflectivities in the surrounding pixels do not exhibit significant increases compared to the extreme rainfall pixels, resulting in very large horizontal reflectivity and rainfall gradients at the echo bottom. Such a characteristic horizontal rainfall and reflectivity pattern is observed in most of the type 1 and 2 extreme cases and in some of the type 3 extreme cases. Figure 11 shows the differences in the vertical reflectivity profiles between V6 and V7 at the extreme rainfall pixel and the four surrounding pixels for the case illustrated in Fig. 5. At the echo bottom, the difference in reflectivity reaches above 15 dB at the extreme pixel, but the differences in the surrounding pixels are less than 3 dB, despite deep rainfall profiles in the surrounding pixels. This result transforms the horizontal near-surface rainfall distribution into a spikelike pattern (Fig. 5b). It should be noted that highly isolated convection that occurs within the PR footprint resolution and brings heavy rainfall estimates in the 2A25 product could have a huge horizontal gradient of near-surface reflectivity and rainfall rates. However, the occurrence of such cases seems to be very rare, except for type 1 cases (Table 2), which are likely to be false extremes as described above.

Fig. 11.
Fig. 11.

Differences between the V6 and V7 vertical reflectivity profiles at the extreme pixel indicated by the solid rectangle in Fig. 5b (solid line) and its surrounding four pixels (dashed, dotted, dashed–dotted, and dashed–two dotted lines). Each pixel location (cross track and along track) is indicated as the relative location to the extreme pixel.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Figure 12a clearly depicts the differences in the two characteristics described above for the type 0–3 extreme cases. The abscissa shows the vertical gradient of the effective radar reflectivity calculated using the two lowest bins at the extreme pixel (referred to as VGZ), while the ordinate shows the ratio of the near-surface rainfall rate at the extreme pixel to the average near-surface rainfall rate in the four surrounding pixels (referred to as SRR). VGZ values are generally smaller in type 1 and 2 cases than in type 3 and 0 cases. The fractions of cases with VGZ values smaller than −20 dB km−1 to all cases for types 1 and 2 are about 65.3% and 70.1%, respectively. Type 3 cases have a similar distribution to type 0 cases but have larger SRR values. Some of type 3 cases have large negative VGZ values. Figure 12b is the same scatterplot as shown in Fig. 12a, but for all the extreme cases that are independently determined from the V6 dataset. The distribution shows fairly similar patterns in the V7 type 0 extreme cases. Almost all of the V6 cases have VGZ > −20 dB km−1 and SRR < 300.

Fig. 12.
Fig. 12.

Scatter diagrams of (a),(b) the ratio of near-surface rainfall rate to the average of its four surrounding pixels (SRR), and (c),(d) the near-surface rainfall rate vs the vertical reflectivity gradient calculated using the two lowest bins (VGZ) for the extreme rain samples extracted from (left) V7 and (right) V6. Red, yellow, green, and blue dots in the V7 distribution indicate type 1, 2, 3, and 0 samples, respectively. Note that the SRR value 104 is assigned to the samples that have four surrounding pixels with an average value of 0. The horizontal and vertical black lines in (a) outline the threshold values for filtering out suspicious extreme data.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Provided that almost all extreme cases determined in the V6 rainfall rates as well as almost all type 0 and most of type 3 extreme cases determined in the V7 rainfall rates are normal, there should be discernible similarities in the three-dimensional structures of the V6 and V7 rainfall and reflectivity profiles, despite having different estimated rainfalls and reflectivities. This may justify the consideration that the V7 extreme rainfall cases with significantly different distributions than the corresponding V6 dataset are artificial. In this study, we propose a simple thresholding method for filtering out the suspicious extreme values, where the cases with SRR values larger than 300 or VGZ values smaller than −20 dB km−1 are rejected as artificial values (Fig. 12a). These threshold values are determined subjectively to reject as many type 1 and 2 cases as possible and retain as many type 0 and 3 cases as possible. The SRR threshold value of 300 is chosen based on the result from visual inspection of all extreme events with SRR higher than 100, showing that all of the type 1 and 2 and most of the type 3 events are likely to be artificial. For the cases shown in Fig. 12, the percentages of type 0–3 cases that are rejected by this thresholding filter are 0.3%, 89.8%, 70.7%, and 6.4%, respectively. The filter was applied to all rainy pixels with near-surface rainfall rates above 40 mm h−1 in the 2A25 V7 dataset irrespective of their classification as extreme rainfall, because the SRR values tend to be high even for normal cases when the near-surface rainfall rates are below 40 mm h−1. There are no type 1–3 extreme cases in regions with near-surface rainfall rates below 40 mm h−1 (Fig. 12c). It should be noted that such a simple thresholding method might have possibilities for occasionally removing normal values as an error, especially for cases that are highly isolated convection events occurring within the PR footprint resolution and bringing heavy rainfall estimates in the 2A25 product, and for type 3 cases that may be related to the correct result from the improvement in the 2A25 algorithm. A simple thresholding method might also present another potential issue wherein false extreme events most likely due to misidentification of surface clutter, such as type 1 and 2 cases distributed along the vertical line with VGZ = −20, might be kept as normal.

The method utilizing the VGZ and SRR results to filter out the suspicious values proposed in this study is based on the methods for detecting and eliminating nonprecipitating echoes due to ground clutter. Steiner and Smith (2002, and references therein) present an excellent review of the methods used for eliminating nonprecipitating echoes due to ground clutter as well as the anomalous propagation of radar signals. The spatial variability, vertical gradient, and stationarity of the observed reflectivity are key factors for identifying ground clutter. Additional information on Doppler velocity, Doppler spectrum width, and polarization, if available, enable more effective detection and elimination of ground clutter (e.g., Sachidananda and Zrnić 2000; Zrnić et al. 2006). Because TRMM PR is a spaceborne radar and does not have the capability to measure Doppler spectrum and polarization, abnormal echoes due to ground clutter can be identified only from the spatial and vertical instantaneous echo structures.

A more reliable approach may be found in the comparison of the rainfall and reflectivity profiles at all rainy pixels in the V7 dataset with the corresponding V6 dataset and their elimination if the profiles are judged to be abnormal. However, this approach still does not assure the complete detection of artificial values and is quite laborious. Also, this approach may still have potential issues similar to those of the thresholding method introduced in this study, in which the correct V7 profiles showing large discrepancy from V6 could be removed as error, while the false V7 profiles showing an intermediate vertical gradient of reflectivity at the echo bottom could be kept as normal. Therefore, it is better to further develop the method for eliminating erroneous values using only the V7 dataset.

The filter was applied to the entire 2A25 V7 dataset during 1998–2010. It detected 11 294 pixels as being erroneous, which corresponds to approximately 0.001% of the total rain-certain pixels (~1.07 × 109) during that period. The geographical distribution of the rejected data in the Asian region (Fig. 13b) is similar to the distribution of the type 1 and 2 extreme cases determined in the feasibility study (Fig. 7). A high proportion of the rejected data are distributed over land, especially in mountainous regions in Asia and the Americas and across the highlands of Africa (Fig. 13a). The distribution of the rejected data with near-surface rainfall rates of 300 mm h−1 fairly corresponds with regions having particularly large differences between the V6 and V7 extreme rainfall rates (Fig. 2). Figure 14 shows the occurrence frequency of removed pixels as a function of the mean and standard deviation of the surface elevation. A large portion of the removed pixels is located in the surface elevation range above 500 m (Fig. 14a). Although about 1000 events are observed in the region with surface elevations below 500 m, most of the removed pixels are distributed in the higher standard deviation range (Fig. 14b). These characteristics are more similar to those for type 1 and 2 extreme events than those for type 0 and 3 events (Fig. 8). These facts indicate that the removal filter developed in this study basically works well in removing the false rainfall profiles due to misidentification of surface clutter.

Fig. 13.
Fig. 13.

(a) Location of data filtered out using the filter developed in this study during 1998–2010. Red circles, blue triangles, and black crosses indicate the data with near-surface rainfall rates of 300, 100–300, and <100 mm h−1, respectively. (b) As in (a), but for the Asian region (Fig. 7).

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Fig. 14.
Fig. 14.

Frequency distributions of occurrences of the data filtered out using the filter developed in this study over land during 1998–2010 as a function of the (a) mean and (b) standard deviation of surface elevation.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

Frequency distributions of the global near-surface rainfall rate for the 13 yr before and after the filter was applied are shown in Fig. 15. The frequencies of 300 mm h−1 rainfall rates over ocean and land decreased by ~33.1% and ~56.6%, respectively. Note that the protruding frequency of 300 mm h−1 remaining in the V7f (filtered) dataset is considered to be attributed to the ceiling of the rainfall rates, which is not in the target area of the filter introduced in this study. There is very little difference between V7 (unfiltered) and V7f (filtered) over the oceans, except for the frequency of the 300 mm h−1 rainfall rates. Over land, there is a gradual decrease in the frequency of near-surface rainfall rates above 70 mm h−1. Because the number of rejected pixels is very small compared to the total number of rain-certain pixels, as described above, applying the filter should have little impact on the rainfall statistics that use the entire range of rainfall rates over long time periods or large regions. For example, the zonal averages (unconditional) of near-surface rainfall rates in the 1° latitude bands for 13 yr only differ by at most 0.1% and 0.4% over the oceans and over land, respectively, except for around 28°N, where the difference is ~0.9% because more pixels are rejected here than at other latitudes (Fig. 13). When the 13-yr averages are calculated for the same 2.5° × 2.5° grids as shown in Fig. 1, the differences between the V7 and V7f datasets are less than 0.5% for most regions (not shown). The differences are at most a few percent around the Himalayas, the eastern margin of the Tibetan Plateau, the Zagros Mountains, and the southern Andes, with some exceptions for the regions with very small average rainfall rates.

Fig. 15.
Fig. 15.

As in Fig. 1, but for the original V7 dataset and filtered V7 dataset (V7f).

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

The regional extreme rainfall distribution in the V7 dataset after applying the filter and its differences from the V6 dataset are shown in Fig. 16. The regional distribution is smoother in the V7f dataset than in the original V7 dataset (Fig. 2b). The increment in the extreme surface rainfall rate in V7f over V6 is suppressed in comparison to that in the original V7, particularly over the regions where sharp increases are observed in the original V7 data compared to V6, such as the Himalayas, the eastern margin of the Tibetan Plateau, and the west coast of the Americas (Fig. 2c). Over the oceans, the differences in the regional extreme rainfall amounts between the V7 and V7f datasets are very small for most regions, while an effect of the removal filter is shown in some regions (e.g., around 10°S and 140°W).

Fig. 16.
Fig. 16.

As in Fig. 2, but for regional extreme rainfall distribution for V7f.

Citation: Journal of Applied Meteorology and Climatology 53, 5; 10.1175/JAMC-D-13-099.1

5. Summary and concluding remarks

The presence of suspicious “extreme rainfall” data in version 7 of the TRMM 2A25 product has been examined. The vertical rainfall and radar reflectivity profiles for these suspicious rainfall events were classified into three types by comparing these profiles with the V6 profiles at the same location. In type 1, the V6 dataset has no near-surface or vertical rainfall but the V7 dataset has extremely heavy near-surface rainfall. The echo-bottom height of the V7 profile is lower than the lowest level at which V6 has meaningful data. Below this level, the reflectivity of the V7 dataset almost monotonically and linearly increases toward the echo bottom, and consequently the rainfall rate increases exponentially. In type 2, both V6 and V7 have nonzero near-surface rainfall. The echo-bottom height of the V7 profile is lower than that of the V6 profile. The V6 and V7 vertical rainfall and reflectivity profiles are identical above the V6 echo bottom, but below this level the reflectivity and rainfall rates increase almost monotonically toward the V7 echo bottom, similar to the first type. In type 3, both V6 and V7 also have nonzero near-surface rainfall. The V7 echo-bottom heights in almost all cases in the third type are at the same bin or the bin above the corresponding V6 profiles. The V6 and V7 vertical rainfall and reflectivity profiles are almost identical in the upper troposphere, but the V7 profile gradually diverges from the V6 profile and is very different around the echo bottom. An important characteristic common to the three suspicious types is that relatively small near-surface rainfall rates (approximately 100 mm h−1) are often observed.

A large proportion of the suspicious profiles are found over land, especially in mountainous regions. The radar reflectivities for the suspicious profiles increase almost monotonically toward their echo bottom. These facts imply that suspicious profiles mainly consist of artificial values due to contamination by ground clutter. We developed a simple thresholding method for filtering out the suspicious profiles by utilizing the horizontal and vertical rainfall and reflectivity structures that are found in the suspicious extreme profiles. The proposed filter basically works well in mitigating the inaccuracies in the frequency distributions of near-surface rainfall rates in the V7 product. We set the threshold value to 300 by conducting careful visual inspection of all extreme events with SRR > 100; however, it should be noted that our method might have the potential to occasionally remove normal values as errors, especially for type 3 cases that may be related to correct results from the improvement in the 2A25 algorithm. Our method might also have another potential issue: false extreme events most likely due to misidentification of surface clutter, such as types 1 and 2 cases distributed along the vertical line with VGZ = −20, might be kept as normal.

The ratio of the number of suspicious extreme profiles to the total number of rainy profiles in the V7 dataset should be quite small and, therefore, would have little impact on rainfall statistics that use all rainfall rates for long periods or over large regions. However, the suspicious extremes may substantially affect observational and statistical studies of heavy or extreme rainfall.

Using the V6 dataset instead of the V7 dataset may avoid contamination by suspicious rainfall extremes. However, this approach is inadvisable because the 2A25 V6 dataset more significantly underestimates the rainfall rate over land than does V7 (Kirstetter et al. 2013). The filter introduced in this study detects and eliminates suspicious extreme values reasonably, but it cannot reject all suspicious values and may reject a small proportion of normal data as false positives. We expect that the retrieval algorithms for 2A25 and its associated products will be investigated and improved carefully, so as not to generate artificial extreme rainfall values.

Acknowledgments

The authors express their gratitude to Dr. Toshio Iguchi of the National Institute of Information and Communications Technology and Dr. Shinta Seto of Nagasaki University for their helpful comments and discussions. This study is supported by the sixth GPM/TRMM RA of the Japan Aerospace Exploration Agency (JAXA).

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