PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2013WR014555 Key Points: Spaceborne radar measured precipitation Variability of vertical structure of precipitation Climatological VPR for radar QPE
Correspondence to: Q. Cao,
[email protected]; Y. Qi,
[email protected] Citation: Cao, Q., and Y. Qi (2014), The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long-term spaceborne observations with TRMM precipitation radar, Water Resour. Res., 50, 3690–3705, doi:10.1002/ 2013WR014555. Received 8 AUG 2013 Accepted 9 APR 2014 Accepted article online 15 APR 2014 Published online 6 MAY 2014
The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long-term spaceborne observations with TRMM precipitation radar Qing Cao1,2 and Youcun Qi1 1 2
College of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China, Enterprise Electronics Corporation, Norman, Oklahoma, USA
Abstract The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)’s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with 15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin.
1. Introduction Microphysical and thermodynamical processes within precipitating clouds are critical to the evolution of precipitation systems and account for the distinctive characteristics of storm cells [e.g., Biggerstaff and Houze, 1993; Smith et al., 2009]. Understanding of these processes is helpful for developing accurate numerical prediction models in quantitative precipitation forecasts (QPF). In a macroscopic perspective, these processes lead to the specific structures of precipitation [e.g., McFarquhar et al., 2007; Smith et al., 2009]. For example, convective and stratiform systems [Yang et al., 2013] that involve different microphysical and thermodynamical processes display distinctive vertical structures of precipitation, indicating the changes of phase, size, and concentration of hydrometeors in the vertical direction [e.g., Fabry and Zawadzki, 1995; Cao et al., 2013a, 2013b]. Characterizing the precipitation structure helps to identify the precipitation type, estimate the rate/amount of precipitation, and even retrieve the precipitation microphysics with dualpolarization and/or dual-frequency observations. The vertical structure of precipitating system can be well revealed by the remote sensing instruments [e.g., Fabry and Zawadzki, 1995; Geerts and Dejene, 2005; Smith et al., 2009; Cao et al., 2012]. The vertical profile of radar reflectivity (VPR) shows the vertical variation of precipitation associated with different microphysics. Compared to ground radar, spaceborne radar has great advantages in measuring the vertical structure of storm because of the less impact from the earth curvature, mountain blockage, and beam broadening, all of which can limit the use of ground radar [Kummerow et al., 2000]. The first spaceborne weather radar is the Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) jointly operated by the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA). TRMM-PR, operating at Ku-band (13.8 GHz), was specifically designed for the global precipitation measurement [Kozu et al., 2001; Kummerow et al., 2000]. TRMM-PR scans with a swath width of 215 km and
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the vertical and horizontal resolutions of 250 m and 4.3 km, respectively, at nadir. TRMM satellite underwent an orbital boost in August of 2001. The postboost swath width and horizontal resolution have changed to 247 km and 5.0 km, respectively. Considering the fact that precipitating systems typically extend several kilometers in the vertical direction, TRMM-PR’s 250 m vertical resolution ensures the sufficient resolution to study the vertical structure of storm. The usage of VPR has recently attracted much research interest in improving the quantitative precipitation estimation (QPE). It is known that the ground radar QPE can be degraded by the bright band (BB) contamination and radar beam’s overshooting over the melting layer, both of which are common scenarios in ground radar measurements [e.g., Kitchen et al., 1994]. Since VPR shape is related to the ice aggregation and melting process in the precipitating cloud, the BB contamination and beam overshooting can be compensated with the VPR-based correction [Vignal et al., 2000; Germann and Joss, 2002; Bellon et al., 2005; Zhang and Qi, 2010; Qi et al., 2013a, 2013b; Qi and Zhang, 2013; Qi et al., 2014a, 2014b]. In the mountainous region where ground radar measurements are normally less ubiquitous near the surface, the VPR information is particularly useful for estimating the near-surface rainfall [e.g., Germann and Joss, 2002; Wen et al., 2013; Cao et al., 2014]. Our previous study [Cao et al., 2013a] has characterized climatological VPRs in the Mountainous West Region of U.S. (MWR) using 111 years of TRMM-PR observations. The climatological VPRs have been combined with the National Mosaic and Multi-sensor QPE (NMQ) products in a VPR-Identification and Enhancement (VPR-IE) scheme to enhance the ground radar QPE [Cao et al., 2014; Wen et al., 2013]. The current study will take advantage of the long-term TRMM-PR observations to characterize the precipitation in the Huaihe River Basin (HRB) of China. The major objectives include: (1) quantifying the VPR variability and precipitation properties in this region; (2) characterizing climatological VPRs that can be used in VPRbased radar QPE with China’s S-band weather radar network; and (3) comparing with the vertical precipitation structure in MWR to strengthen the understanding of natural variability of precipitation. HRB is the most important social, economic, industrial, and agricultural river basin region in China and has the highest population density (611 per km2) among river basins. Choosing HRB as the study region would have a great practical significance for the study of river basins. Furthermore, the climatological VPRs characterized in HRB can benefit the regional radar QPE with the novel VPR-based correction. The success of QPE enhancement in HRB would be a paradigm for improving the nation-wide QPE using China’s weather radar network. This paper is organized as follows. Section 2 introduces the study region and the long-term TRMM-PR data sets used for the study. The analysis results of precipitation characteristics in HRB are detailed in section 3. Section 4 presents the S-band climatological VPRs derived from Ku-band TRMM-PR observations. Section 5 provides the conclusions and discusses the issues for further research.
2. Region and Data Sets The HRB, located in eastern China with longitude 111 550 –121 250 and latitude 30 550 –36 360 , has an area of about 270,000 km2. Most region of HRB is vast plain except some mountains and foothills located at the western, southern, and northeastern HRB (as shown in Figure 1a). The mountain altitudes are normally 1–2 km above the mean sea level (MSL). The HRB belongs to the north-south climatic transition zone with an average temperature of 11–16 C and an average annual rainfall about 910 mm. The distribution of annual precipitation is generally decreasing from south to north with more precipitation in mountains than in plains, and along the coast than in inland [e.g., Chen et al., 2009; Luo et al., 2013; He and Zhang, 2010]. The precipitation distribution within a year is very uneven. In the flood season (June–September), the total precipitation accounts for 50–80% of annual precipitation. The unique climate and surface conditions in this region have caused frequent flooding, waterlogging, drought, and storm surge disasters, especially in the middle and lower reaches of Huaihe River and northern HRB. Previous studies have shown a great variation of precipitation in HRB [Zhou et al., 2008; Chen et al., 2009, 2012; He and Zhang, 2010; Liu, 2011; Xu, 2013; Luo et al., 2013]. Particularly, the deep convection in summer leads to distinct characteristics from other seasons [e.g., Chen et al., 2012; Xu, 2013]. Given the vertical structure of precipitation is less analyzed for HRB in previous studies, the current study may enhance the understanding of precipitation characteristics in this region using TRMM-PR observations. The TRMM data sets used for the current study are the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) for 15 year time span, i.e., from 11 December 1997 to 19 August 2012. The 2A-23 and
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Figure 1. (a) Terrain image of Huaihe River Basin (unit in meter). (b) The total pass number of PR with observing the precipitation over HRB. (c) The total precipitation (unit in mm) measured by PR. The calculation has assumed 1 h accumulation with the rainfall rate measured by PR. (d) The average precipitation (unit in mm) per PR pass. The spatial resolutions for (a) and (b–d) are 0.01 3 0.01 and 0.1 3 0.1 , respectively.
2A-25 algorithms are level-2 algorithms that were developed mainly with TRMM-PR measurements. The 2A23 algorithm generates the products including bright band detection and quantification, and precipitation type classification [Awaka et al., 1998, 2009]. The 2A-25 algorithm provides the range profiles of attenuation-corrected radar reflectivity and corresponding rain estimation parameters [Meneghini et al., 2000, 2004; Iguchi et al., 2000, 2009]. Compared to their previous version, the latest TRMM version 7 algorithms have introduced considerable improvements [e.g., Seto and Iguchi, 2007; Awaka et al., 2009; Iguchi et al., 2009]. For example, the 2A-23 algorithm has a better detection of bright band and shallow storms. It increases the subcategories of rain type and refines the classification as well. The 2A-25 algorithm produces the enhanced radar reflectivity profile by improving the estimate of path-integrated attenuation (PIA) and refining the attenuation correction method. The rainfall estimation is improved with the use of a better drop size distribution (DSD) model. Furthermore, the nonuniform beam filling (NUBF) correction has also been reintroduced to improve the rainfall estimation. TRMM takes about 90 min to orbit Earth with a non-sun-synchronous orbit and has a revisit time of 11–12 h depending on the latitude [Kummerow et al., 1998]. Therefore, TRMM-PR might not observe the storm that develops beyond its scan time and swath. In this study, a rainy pass is defined as the TRMM-PR pass, within which the summation of rainfall rates measured by TRMM-PR over the HRB region is greater than 50 mm h21. Such rainy passes have been searched from all the available 2A-23 and 2A-25 data sets and used for the analysis in the following sections. As to the monthly statistics shown in Table 1, there are totally 3999 rainy days and 11,050 rainy passes over the HRB region. Precipitation mainly appears in late spring, summer, and early autumn (i.e., from May–September). In particular, the convective precipitation is most frequent during summer (June, July, August) while it less likely happens in winter (December, January,
Table 1. TRMM Data (2A-23, 2A-25) Availability by Month, Where 1 Indicates January, 2 Indicates February, Etc. Month Observation days PR passes Convective VPRs Stratiform VPRs
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2
3
4
5
6
7
8
9
10
11
12
Total
198 411 15,035 249,673
230 525 22,035 333,930
331 754 37,534 493,517
363 912 50,745 588,416
409 1151 72,521 798,712
420 1308 106,387 748,785
458 1586 218,572 964,107
432 1520 187,064 842,156
376 1092 68,515 666,408
355 880 32,204 559,291
224 510 19,518 354,084
203 401 12,396 225,838
3999 11,050 842,526 6,824,917
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Figure 2. Distribution of annual precipitation in HRB (calculated with TRMM long-term observations). (a) Monthly percentage of precipitation; (b) seasonal percentage of occurrence frequency; and (c) seasonal percentage of precipitation. In Figures 2b and 2c, blue (or red) bar denotes the stratiform (or convective) precipitation with green bar showing the portion of likely stratiform or convective precipitation.
February). In comparison, stratiform precipitation dominates the annual precipitation. The numbers of convective and stratiform VPRs used for the statistical analysis are 0.84 and 6.8 millions, respectively. As shown in Figure 1b, the TRRMM-PR observed precipitation is mainly located in the region of latitude 33 N–35 N and the maximum number of TRMM-PR passes is about 4000. The figure also shows a lack of observations beyond the latitude 36 N and a trend of decreasing observations from latitude 34 N to latitude 30 N. The total precipitation amount is given in Figure 1c. Its calculation has assumed the 1 h accumulation of rainfall rates measured by TRMM-PR. The figure shows more precipitation at the region of latitude 33 N–35 N. The region with the most observed precipitation is located in the eastern HRB and has 800– 1000 mm rainfall. This figure implies that consequent statistics may better represent the precipitation in the region being frequently observed. Figure 1d shows the average precipitation observed within each TRMMPR pass, giving a similar pattern to the annual precipitation distribution in HRB, i.e., average precipitation generally increases from north to south and is higher along the coast than in the inland [Chen et al., 2009; Luo et al., 2013; He and Zhang, 2010]. According to 2A-23’s classification algorithm [Awaka et al., 1998, 2009; Steiner et al., 1995], the precipitation is identified as more than 30 subcategories. Those subcategories can be summarized with several major types such as ‘‘stratiform,’’ ‘‘convective,’’ ‘‘maybe stratiform or convective,’’ and ‘‘others.’’ The VPRs, which are identified as stratiform and convective precipitation with a high confidence, have 521,364 and 2,894,887 profiles, respectively. Compared to the number of total observed VPRs given in Table 1, many VPRs do not show the apparent convective or stratiform features and have been classified as ‘‘maybe’’ types. For example, more than half of stratiform precipitation is identified as ‘‘maybe stratiform’’ type, for which the bright band is not clearly detected. Most ‘‘maybe stratiform or convective’’ VPRs are associated with weak precipitation, which less likely shows the typical stratiform or convective features [e.g., Cao et al., 2013a]. As a result, in the rest of this paper those VPRs with uncertainty have been excluded to characterize the typical stratiform and convective VPRs. However, for the analysis of storm properties (e.g., storm top and freezing level), all the available data have been used regardless of classified precipitation types.
3. Analysis Results 3.1. Precipitation Properties According to long-term TRMM observations, the distribution of annual precipitation in HRB is derived in Figure 2a. The summer precipitation accounts for 53% of annual precipitation, to which July contributes the most. The monthly precipitation increases from January to July and decreases after July. December contributes the least portion (less than 2%) to the annual precipitation. The histograms in Figures 2b and 2c show the seasonal distribution for stratiform and convective precipitation. The green bars denote the portion with the likely type of stratiform or convection. As Figure 2b shows, the stratiform dominates the occurrence frequency of precipitation. However, the total convective precipitation is comparable to stratiform precipitation (Figure 2c). In summer, the convective events bring more rainfall than stratiform events
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Figure 3. Seasonal and spatial variation of the height of storm top. (a) Spring, (b) summer, (c) autumn, and (d) winter. The spatial resolution is 0.25 3 0.25 .
although the convective occurrence frequency is only one-fifth. In other seasons, stratiform precipitation occurs much more frequently and brings most of the rainfall in HRB. The heights of storm top (HST) and freezing level (0 C isotherm) are fundamental quantities to describe the vertical profile of precipitation. The HST is normally associated with the intensity of updraft [e.g., Jensen and Del Genio, 2006; May and Ballinger, 2007]. The depth of convective cloud may be controlled by environmental parameters, with which the convective cell might transit to a longer lived stratiform [e.g., Jensen and Del Genio, 2006; May and Ballinger, 2007]. It has been found that the HST may have a correlation with the surface rainfall and the dependence of surface rainfall on the storm height shows a variation in different regions [e.g., Furuzawa and Nakamura, 2005; Fu et al., 2006]. TRMM 2A23 HST product is based on the 18 dBZ threshold. Although the definition of storm height might vary with different reflectivity thresholds, the current study still uses TRMM’s HST definition, i.e., the 18 dBZ echo top height, for the data analysis. Figure 3 gives the seasonal and spatial variation of HST, which is evaluated in terms of MSL. The orographic effect (refer to terrain image in Figure 1a) is evident for mean HST distribution in spring (March, April, May), autumn (September, October, November), and winter. Generally speaking, mountains may enhance the ascent of precipitating clouds mainly in the western and southern HRB. The similar orographic effect on the HST has also been reported in Cao et al. [2013a]. One possible reason is that the stratiform dominates these three seasons and the stratus tends to be more susceptible to the terrain than the convective cloud that is common in summer. Recent studies on the precipitation in China have also shown the orographic effect on the variation of precipitation [e.g., Zhou et al., 2008; He and Zhang, 2010; Bao et al., 2011]. The variation in eastern China is closely associated with the mountain-plains solenoid (MPS) circulation that originated from the Tibetan Plateau in western China [e.g., He and Zhang, 2010; Bao et al., 2011]. Differential heating and moisture between plateaus and plains are two of the key factors controlling the MPS circulation and make the contribution to the seasonal and orographic variations of precipitation. As shown in Figure 3d, the winter HST is apparently higher in southern HRB than in the northern region. This trend is also observed for the spring HST although not so evident. The summer HST (Figure 3b) shows a different pattern, which displays a higher HST in the east than in the west. There are two possible reasons to explain this pattern. First, unlike in other seasons when convective available potential energy (CAPE) is weaker, the convection in summer
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Figure 4. Seasonal and spatial variation of the height of freezing level (i.e., 0 C isotherm). (a) Spring, (b) summer, (c) autumn, and (d) winter. The spatial resolution is 0.25 3 0.25 .
generally has a high cloud top (>5 km) and is less affected by the lifting effect of mountains (8 km) data sets have been mainly collected in warm season (i.e., summer) when stratiform precipitation usually occurs in the presence of convective cell/squall line. Biggerstaff and Houze [1993] have presented a conceptual model
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Figure 6. Dependence of the mean near-surface rainfall rate on the height of storm top. (a) Stratiform precipitation and (b) convective precipitation. Solid lines are linear regression results and the equations are given as well.
that explains the kinematics and microphysics for such a type of stratiform, which is initiated by casting the hydrometeors backward from the upper portion of updrafting convective clouds. The higher HST of stratiform precipitation, however, cannot guarantee more particles collected from the motion of the hydrometeors. Biggerstaff and Houze [1993] showed that the further away from the convective updraft the less precipitating particles would be collected in the stratiform region. In addition, the higher HST is usually associated with a higher freezing level, implying the evaporation might be stronger from the melting layer to the ground. Therefore, the decreasing trend shown in Figure 6a is reasonable for stratiform precipitation in summer. 3.2. Stratiform and Convective VPRs Previous studies have shown the great variability in the vertical structure of precipitation [Willis and Heymsfield, 1989; Fabry and Zawadzki, 1995; Hirose and Nakamura, 2002, 2004; Geerts and Dejene, 2005; McFarquhar et al., 2007; Smith et al., 2009]. This section presents the VPR variation observed by TRMM-PR for the precipitation in HRB. Figure 7 shows the VPR occurrence frequency, which has been calculated using 0.1 dB and 250 m intervals for reflectivity and altitude, respectively. Nine solid lines indicate mean curves with different percentiles. Compared to percentile curves observed in MWR of U.S. [Cao et al., 2013a, Figure 3], there are some differences given as follows. First, due to the terrain effect in MWR, the ground clutter has affected the data availability for the low levels (e.g., 15 mm h21). This feature suggests that given the same rainfall rate, lower number concentration but larger characteristic size would be seen in winter precipitation. Despite the seasonal variation, convective VPRs generally have an intensified precipitation below the freezing level. The weakening of reflectivity is also evident for weak precipitation (e.g., 1 mm h21). On the one hand, the cause of weakening is likely attributed with the evaporation in a drier environment. On the other hand, the weak convection is likely related to the transition zone where more particles in the upper level are projected from the convective cells [e.g., Biggerstaff and Houze, 1993]. 3.3. Rain Intensification and Weakening As we know, ground radar normally measures the precipitation aloft instead of near the ground, especially in the far range. The knowledge of rain intensification/weakening below the melting layer would be helpful to improve the rainfall estimation not only for ground radar but also for spaceborne censors such as TRMMPR. The rain-region VPR slope (similar to Figure 9 in Cao et al. [2013a] has been calculated for HRB and analyzed in this section. As a reminder, a positive (or negative) slope means that the reflectivity decreases (or increases) with reducing altitude. Figure 10 gives the seasonal variation of VPR slopes, which have been sorted and averaged with various rain intensities. As the figure shows, the VPR slope decreases with increasing rainfall rate, implying that the
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Figure 11. Seasonal and spatial variation of rain-region VPR slope. (a) Stratiform and (b) convective precipitation. Four rows denote spring, summer, autumn, and winter, respectively.
VPR intensification/reduction is influenced by the microphysical variation [e.g., Kumjian and Ryzhkov, 2010]. This trend is similar to the result found by Cao et al. [2013a] in MWR. In addition, stratiform and convective VPRs have a similar pattern that the slope is positive for weak rainfall and negative for moderate and heavy rainfall. This pattern is consistent with the mean VPRs shown in Figures 8 and 9. It is also worth noting that VPR slopes shown in Figure 10 have a feature distinct from the VPR slopes in MWR, where stratiform slopes are generally positive (as shown in Cao et al. [2013a, Figure 11]). As mentioned in previous subsections, the climate difference (arid/semiarid in MWR versus moist in HRB) is one of major reasons for this feature. The orographic effect might be another factor for the variation of VPR slope [Zhang et al., 2012; Qi et al., 2014b]. As Figure 10 shows, the rain intensification and weakening are more dependent on the rain intensity than the precipitation type. The rain intensification is normally less than 1 dB km21 for moderate rain (2.5–10 mm h21) and does not exceed 2 dB km21 even for heavy rain (10–50 mm h21). The rain weakening varies within 0–3 dB km21 and it is only for light rain (
