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  • View in gallery

    Scatterplot of Zh/Nw vs Do based on gamma fits to 2D-video (in convective and stratiform rain) and RD-69 (in stratiform rain) disdrometer data obtained during the TRMM/Brazil field campaign. Each data point (+) refers to a 2-min-averaged DSD to which a gamma DSD is fitted. There are 164 2-min samples of convective rain from the 2D-video and 152 2-min samples of stratiform rain from the 2D-video and RD-69. The power law fit is also shown

  • View in gallery

    As in Fig. 1, except Zdr vs Do. Power law fit is based on rain rates exceeding 2 mm h−1

  • View in gallery

    S-Pol radar measurements of Zdr vs Zh from stratiform rain on 15 Feb 1999, together with a mean power law fit

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    Histogram of log10Nw in stratiform rain from gamma fits to 2-min-averaged DSD data collected by the 2D-video and RD-69 during the TRMM/Brazil campaign

  • View in gallery

    PPI of reflectivity at 0350 UTC of the 15 Feb squall line. The polar area marked illustrates the region of convective rain selected for the radar-based retrieval of Nw and Do

  • View in gallery

    Histogram of (a) Do and (b) log10Nw based on radar retrievals for convective rain with R < 10 (mm h−1) from the polar area marked in Fig. 5

  • View in gallery

    As in Fig. 6, except for convective rain with R > 10 (mm;thh−1)

  • View in gallery

    Scatterplot of (a) Do vs R and (b) Nw vs R for convective rain from the polar area marked in Fig. 5. Overlaid are data from the 2D-video disdrometer in convective rain collected during the TRMM/Brazil campaign (164 2-min-averaged DSD samples)

  • View in gallery

    As in Fig. 6, except for stratiform rain at 0756 UTC 15 Feb 1999. Data are from a different polar area (not shown)

  • View in gallery

    As in Fig. 8, except data from stratiform rain. Overlaid are data from 2D-video and RD-69 disdrometers in stratiform rain collected during TRMM/Brazil (152 2-min-averaged DSD samples)

  • View in gallery

    (a) Polar area shows the gauge network. The 2D-video and RD-69 disdrometers and the NOAA profiler were located at the Ji Parana airport. (b) Time–height profile of reflectivity from the NOAA 915-MHz vertically pointing Doppler radar (or profiler).

  • View in gallery

    Time variation of (a) Do and mean areal R from gauges, and (b) log10Nw and μ. The DSD parameters are areally averaged over the polar area shown in Fig. 11a

  • View in gallery

    The piecewise linear fit, R = cKdp, needed for the areal rain-rate algorithm using Φdp. The data points are from scattering simulations based on a large database of RD-69 disdrometer DSDs from Darwin, Australia (over 2000 2-min DSD samples) in a variety of rain types

  • View in gallery

    Areal rainfall accumulation over the polar area in Fig. 11a from radar (using the areal Φdp method) and the gauge network vs time. Illustrates the use of βeff in reducing the bias in radar rainfall accumulation to less than 10%

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A Methodology for Estimating the Parameters of a Gamma Raindrop Size Distribution Model from Polarimetric Radar Data: Application to a Squall-Line Event from the TRMM/Brazil Campaign

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  • 1 Colorado State University, Fort Collins, Colorado
  • | 2 Istituto di Fisica dell'Atmosfera (CNR), Rome, Italy
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Abstract

A methodology is proposed for estimating the parameters of a gamma raindrop size distribution model from radar measurements of Z h, Z dr, and K dp at S band. Previously developed algorithms by Gorgucci et al. are extended to cover low rain-rate events where both Z dr and K dp are noisy. Polarimetric data from the S-band Dual-Polarization Doppler Radar (S-Pol) during the Tropical Rainfall Measuring Mission (TRMM)/Brazil campaign are analyzed; specifically, the gamma parameters are retrieved for samples of convective and trailing stratiform rain during the 15 February 1999 squall-line event. Histograms of N w and D o are retrieved from radar for each rain type and compared with related statistics reported in the literature. The functional behavior of N w and D o versus rain rate retrieved from radar is compared against samples of 2D-video and RD-69 disdrometer data obtained during the campaign. The time variation of N w, D o, and μ averaged over a 5 km × 5 km area (within which a network of gauges and a profiler were situated) is shown to illustrate temporal changes associated with the gamma parameters as the squall line passed over the network. The gauge-derived areal rainfall over the network is compared against radar using the areal Φdp method, and the concept of an effective slope of a linear axis ratio versus diameter model is shown to significantly reduce the bias in radar-derived rainfall accumulation.

Corresponding author address: Dr. V. N. Bringi, Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523. Email: bringi@engr.colostate.edu

Abstract

A methodology is proposed for estimating the parameters of a gamma raindrop size distribution model from radar measurements of Z h, Z dr, and K dp at S band. Previously developed algorithms by Gorgucci et al. are extended to cover low rain-rate events where both Z dr and K dp are noisy. Polarimetric data from the S-band Dual-Polarization Doppler Radar (S-Pol) during the Tropical Rainfall Measuring Mission (TRMM)/Brazil campaign are analyzed; specifically, the gamma parameters are retrieved for samples of convective and trailing stratiform rain during the 15 February 1999 squall-line event. Histograms of N w and D o are retrieved from radar for each rain type and compared with related statistics reported in the literature. The functional behavior of N w and D o versus rain rate retrieved from radar is compared against samples of 2D-video and RD-69 disdrometer data obtained during the campaign. The time variation of N w, D o, and μ averaged over a 5 km × 5 km area (within which a network of gauges and a profiler were situated) is shown to illustrate temporal changes associated with the gamma parameters as the squall line passed over the network. The gauge-derived areal rainfall over the network is compared against radar using the areal Φdp method, and the concept of an effective slope of a linear axis ratio versus diameter model is shown to significantly reduce the bias in radar-derived rainfall accumulation.

Corresponding author address: Dr. V. N. Bringi, Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523. Email: bringi@engr.colostate.edu

1. Introduction

A long-standing goal in polarimetric radar has been the retrieval of the raindrop size distribution using measurements of reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp). Early studies focused on the estimation of Do (the median volume diameter) or Dm (the mass-weighted mean diameter) using Zdr measurements alone (Seliga and Bringi 1976; Goddard and Cherry 1984; Aydin et al. 1987; Bringi et al. 1998). The functional relation between Do and Zdr is known to be dependent on the mean axis ratio versus drop diameter relation, which can deviate from the equilibrium relation due to drop oscillations [e.g., Andsager et al. (1999); see also the summary in Pruppacher and Klett (1997) and numerous reference therein]. The same is also true for the functional relation between Kdp and rain rate. While the combined use of Kdp and Zdr tends to mitigate somewhat the effects of drop oscillations on the estimation of rain rate (see Bringi and Chandrasekar 2001, chapter 7), a more satisfactory approach has been formulated in a series of papers by Gorgucci et al. (2000, 2001, 2002). The essential concept is related to the fact that drop oscillations and drop canting tend to bias the axis ratio toward sphericity, but this is generally nonlinear with respect to drop diameter. However, it is possible to define an equivalent linear model for the mean axis ratio versus D relation using an effective slope (βeff), which can be estimated from radar measurements of Zh, Zdr, and Kdp and subsequently used in the estimation of the raindrop size distribution parameters. A background section is included that provides more detail on this concept.

At low rain rates, such as in stratiform rain, the polarimetric measurements of Zdr and Kdp tend to be very noisy, unless the data are substantially averaged in space. In such cases, the effective β method cannot be applied, and in this paper a method is proposed for retrieval of the drop size distribution (DSD) parameters in the gamma model. The normalized gamma DSD with parameters (Do, Nw, and μ) is suitable for inverting the radar measurements. The normalization procedure (which defines the normalized intercept parameter Nw) can be found in Willis (1984) or Testud et al. (2001).

In this paper S-band Dual-Polarization Doppler Radar (S-Pol) data collected during the Tropical Rainfall Measuring Mission (TRMM)/Brazil field campaign are analyzed to provide the statistics of Do and Nw in samples of convective and stratiform rain during a squall-line episode on 15 February 1999 that lasted several hours. The behavior of Do and Nw versus rain rate is also analyzed and compared with 2D video (Joanneum Research) and RD-69 (Disdromet, Ltd.) disdrometer measurements of samples of convective and stratiform rain obtained during the field campaign. The hypothesis that the use of βeff in polarimetric rain-rate algorithms will reduce the bias in cumulative rainfall is tested by comparing with a network of gauges for the 15 February case.

2. Background

The radar measurement set of reflectivity at horizontal polarization (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) can in the Rayleigh scattering limit be related to the microphysics of raindrops. Specifically, the Zh is related to the sixth moment of the DSD, Zdr is related to the reflectivity-weighted mean axis ratio, and Kdp is related to the product of the water content and the deviation of the mass-weighted mean axis ratio from unity (Jameson 1983, 1985). If a model relating the axis ratio (r) of oriented oblate raindrops versus the equivolumic spherical diameter (D) is selected, then Zdr can be related to the reflectivity-weighted mean diameter of the DSD, while Kdp can be related to the product of W and Dm (the mass-weighted mean diameter of the DSD). Generally, the linear fit to the wind-tunnel data of Pruppacher and Beard (1970), r = 1.03 − 0.062D (with D in mm), or the numerical equilibrium shape model of Beard and Chuang (1987) has been used. If the DSD is modeled as a normalized gamma form (Willis 1984; Testud et al. 2001),
i1520-0426-19-5-633-e1a
with
i1520-0426-19-5-633-e1b
where Nw is the normalized intercept parameter of an equivalent exponential DSD that has the same water content and median volume diameter (Do) as the gamma DSD, then it follows that Zh = NwF1(μ)D7o, Zdr = F2(μ, Do), while Kdp = NwF3(μ, Do) where F represents a functional form. Thus, in principle, estimates of Do, Nw, and μ (and W or rain rate R) can be obtained from the radar measurement set of (Zh, Zdr, and Kdp). Note that Do and Dm are related by Do/Dm = (3.67 + μ)/(4 + μ) (Ulbrich and Atlas 1998).
The effects of raindrop shape oscillations (either due to resonance maintained by vortex shedding or due to collisions) and raindrop canting (due to turbulence) will bias the retrieval of the gamma DSD parameters under the above model assumptions (e.g., see Bringi and Chandrasekar 2001, chapter 7 and references therein). Both drop oscillations and canting angle distributions tend on average to drive the effective axis ratio toward sphericity relative to equilibrium axis ratios and perfect orientation. The rain microphysics model can be improved by accounting for drop oscillations using the axis ratio versus D fit proposed by Andsager et al. (1999) and by using a Gaussian canting angle distribution with a mean of 0° and a standard deviation (σ) in the range 5°–10° (Bringi and Chandrasekar 2001). For example, if the axis ratio versus D relation is assumed to be linear with a slope of (β), r = 1 − βD, then Kdp is modified as (see Bringi and Chandrasekar 2001)
i1520-0426-19-5-633-e2a
Gorgucci et al. (2000) recognized that drop canting and oscillations could be incorporated into an “effective” slope parameter (βeff) and proceeded to develop an algorithm to estimate βeff from the radar measurement set (Zh, Zdr, Kdp). It is important to recognize that even if drop axis ratio is in fact a nonlinear function of D, it is possible to define an equivalent linear model with a slope of βeff such that it results in the same Kdp (for a given value of the product WDo) as the nonlinear form. In subsequent articles, Gorgucci et al. (2001, 2002) developed algorithms (see summary in the appendix) for retrieving rain rate (R) as well as Do, Nw, and μ using βeff in combination with the measurement pair (Zh, Zdr). They show via comparisons with disdrometer DSD measurements that Do and Nw can be retrieved with excellent accuracy (ranging from 4%–8% for Do and log10Nw). Simulations were also used to study the effects of radar measurement error in the retrieval of Do and Nw, and it was found that the accuracy was still quite good (ranging from 5%–20% for Do and log10Nw) and, more importantly, the estimates were nearly unbiased. The μ estimator was found to be less accurate, though it may be possible to distinguish between certain ranges of μ, for example, −1 ≤ μ ≤ 2 versus μ > 5, which may be sufficient in practice.

The concept of an “effective” slope (βeff) of the mean axis ratio versus D relation is an important one since drop oscillations or canting are likely to be different in, for example, tropical rain versus rain in the midlatitudes. Oscillations/canting may be suppressed when rain is formed via melting of graupel or tiny hail as compared with warm rain formation. Gorgucci et al. (2001) applied the βeff concept to an unusual tropical-like flash flood–producing storm in Colorado and showed that rain-rate estimators based on βeff, Zh, and Zdr resulted in better agreement with gauge data as compared with the standard R(Kdp) algorithm (see, also, Petersen et al. 1999). May et al. (1999) also found that use of the Pruppacher and Beard equilibrium shape model (β fixed at 0.062 mm−1) resulted in a systematic underestimate in rainfall when using R(Kdp), as compared with a dense gauge network in the Tropics, and attributed this bias to drop oscillations causing an upward shift in mean axis ratio (toward sphericity). More recently, Fulton et al. (1999) have suggested an empirical adjustment to the R(Kdp) algorithm using a multiplicative bias correction factor, B(〈Zdr〉), which they found reduced the temporal bias in rain accumulation. This correction factor, though empirical, tends to account for the tendency of drop oscillations/canting to cause an upward shift in mean axis ratio. Thus, there appears to be sufficient evidence to warrant further application of the effective β concept to retrieve Do, Nw, and μ, as well as rain rate, and this is the principal objective of this paper.

Since βeff is estimated from the measurement set (Zh, Zdr, Kdp), and Kdp at long wavelengths (such as S band) is known to be very noisy at low rain rates, it follows that the retrieval of the DSD parameters is only practical when the rain rate is sufficiently high (typical threshold of Zh ≥ 35 dBZ). At low rain rates, such as in stratiform rain, the Zdr also tends to be noisy so that a large areal average is necessary to reduce the measurement fluctuations. Thus, it is necessary to extend the retrieval of DSD parameters (Do and Nw) at low rain rates (Zh < 35 dBZ), at which Kdp and at times Zdr are generally too noisy to be useful; this is another goal of this paper. Finally, the last goal is to compare radar-derived rain rates against a network of gauges to illustrate the application of βeff in reducing the rainfall accumulation bias. The data sources used are the S-Pol radar, 2D-video and RD-69 disdrometers, and a network of gauges deployed for the TRMM/Brazil field campaign1 held in 1999 in Amazonia. Data from a squall line on 15 February 1999 are used for analysis.

3. Data analysis methods

a. S-Pol radar

The S-Pol radar is a dual-polarized radar operating at a frequency near 2.8 GHz (S band). It uses a mechanical polarization switch and two separate receivers to measure the polarimetric covariance matrix (Randall et al. 1997). The datastream used here consists of Zh, Zdr, and Φdp (differential propagation phase), which are available every 150 m in range. For each beam of data, a “good” data mask is generated based on the standard deviation of Φdp over 10 consecutive gates (<10°), the copolar correlation coefficient (ρco ≥ 0.9), and the signal-to-noise ratio (SNR ≥ 3 dB). These thresholds tend to eliminate nearly all nonmeteorological echoes. The Φdp range profile is filtered according to Hubbert and Bringi (1995). Once the filtered Φdp range profile is obtained, Kdp is calculated based on the slope of a least squares fit line to the filtered Φdp profile in an adaptive manner (30 consecutive range samples are used in the linear fit for Zh < 35 dBZ; 20 for 35 < Zh ≤ 45 dBZ; and 10 for Zh > 45 dBZ). The Zh is corrected for attenuation using the algorithm of Testud et al. (2000) adapted for S band, while Zdr is corrected for differential attenuation using a self-consistent, constraint-based algorithm described by Bringi et al. (2001b). Corrections are significant only when Φdp ≥ 50°. The corrected Zh and Zdr range profiles are averaged in range using uniform block averaging for the different Zh ranges described earlier. The effective β is calculated based on the averaged Zh, Zdr, and Kdp data, and Do(βeff, Zh, Zdr), Nw(βeff, Zh, Zdr), and R(βeff, Zh, Zdr) are calculated using the algorithms given in the appendix. The threshold for computing βeff is based on Zh ≥ 35 dBZ, Zdr ≥ 0.2 dB, and Kdp ≥ 0.3° km−1.

When the Zh < 35 dBZ, which occurs for light rainfall (e.g., stratiform rain), a different retrieval method, which is based on disdrometer measurements, is proposed for Do and Nw.

b. Disdrometer

A 2D-video disdrometer (Schönhuber et al. 1995) and a RD-69 disdrometer (Joss and Waldvogel 1967) were available during the TRMM/Brazil field campaign. These two instruments were sited close to each other and near the National Oceanic and Atmospheric Administration (NOAA) profilers. (For a description of the 2D-video instrument refer to the Web site http://www.disdrometer.at.) Because of technical difficulties the 2D-video disdrometer was not operating continuously through the field campaign. However, it is believed that representative samples of DSD measurements were made in convective rain (164 2-min-averaged DSD samples) and stratiform rain (49 2-min-averaged samples). The stratification of rain types was based on manual examination of profiler reflectivity/velocity images, for example, absence or presence of a “bright band.” RD-69 disdrometer data were available more or less throughout the field campaign. In this study, the RD-69 DSD data were selected during those times when the 2D-video was operational. The 2D-video disdrometer has a large sample volume relative to the RD-69 disdrometer (Tokay et al. 1999). Intercomparisons between these two instruments are available in Tokay et al. (1999) and Williams et al. (2000). The latter study demonstrates the underestimation of small drops (<1.5 mm) by the RD-69 at higher rain rates (reflectivity ≥ 40 dBZ), which causes the mass-weighted mean diameter (Dm) and R to be biased low relative to the 2D-video disdrometer. At low rain rates the Dm and R from both instruments are in very good agreement. The underestimation of small drops by the RD-69 also tends to increase the convex shape of the DSD (i.e., higher μ values). The 2D-video measurements of small drops are affected by windy conditions (Nespor et al. 2000), because small drops “can get caught in a vortex that develops over the inlet. Some of them end up being counted more than once as they cross the sensing area while others are carried away and not counted at all. Also, the spatial distribution of the drops passing across the sensing area is distorted by the wind” (Nespor et al. 2000). To ensure the quality of the 2D-video data, the spatial distribution of drops across the sensor area (available during real-time operations and during postprocessing) was carefully examined, and no evidence was found of any distortion due to wind in the events analyzed. In addition, a terminal velocity filter was applied to the data; that is, any drop whose terminal velocity exceeded a prespecified “band” around the theoretical value [υ(D) = 9.65 − 10.3 exp(−0.6D), m s−1; Atlas et al. (1973)] was rejected.

In this study, only the 2D-video data in convective rain were used. In stratiform rain both the 2D-video and RD-69 data have been used, to increase the number of samples. For each 2-min-averaged DSD, the parameters of a normalized gamma DSD (Nw, Do, μ) were obtained using a method previously described in the appendix of Bringi et al. (2001a). In short, the water content (W, in g m−3) and the mass-weighted mean diameter (Dm, in mm) are calculated first, after which the Nw is obtained as Nw = (256/π)(1000W/D4m), in mm−1 m−3. The normalized DSD is constructed as N(x) = N(D/Dm)/Nw, and μ is estimated by minimizing the absolute deviation between log[N(x)] and log{f(μ)xμ exp[−(4 + μ)x]}. This method separates the estimation of μ, the DSD shape, from the normalizing parameters Dm and Nw and is philosophically similar to the method of Sempere-Torres et al. (1994). Other methods are available to estimate (Nw, Dm, μ) (see, e.g., Willis 1984; Ulbrich and Atlas 1998). For each triplet of gamma DSD parameters, the reflectivity at horizontal polarization (Zh), Zdr, and Kdp are computed at 2.8 GHz, assuming (i) mean axis ratio fit recommended by Andsager et al. (1999) for 1 ≤ D ≤ 4 mm, which accounts for transverse drop oscillations, and the Beard and Chuang (1987) equilibrium axis ratio fit for D < 1 and D > 4 mm; (ii) Gaussian canting angle distribution with mean of 0° and σ = 10°; and (iii) size integration up to Dmax = 2.5Dm. As shown in the appendix, when the simulated Zh, Zdr, and Kdp are used in (A4) the resultant βmodel is not constant but varies with Do in a regular manner (see A5). These model assumptions appear to be valid for tropical rain (Bringi et al. 2001a). Note, however, that this model will generally be used for retrieval of Nw and Do for light rain rates (Zh < 35 dBZ).

Figure 1 shows a plot of Zh/Nw versus Do where the data points are from the gamma fit to 2D-video data in convective and stratiform rain, and from the RD-69 in stratiform rain. Also shown is the power law fit Do = 1.513(Zh/Nw)0.136, where the exponent is close to the theoretically expected value of 1/7 = 0.143 expected for Rayleigh scattering by spherical drops. The exponent is slightly smaller because the drops are oblate. It is important to note that the exponent is accurately determined from the plot of (Zh/Nw) versus Do as compared to the determination of both the multiplicative coefficient and the exponent from a plot of Zh versus Do, which displays much more scatter. The disdrometer analysis in Fig. 1 shows that
DoNw−0.136Z0.136hγZ0.136h
(note that Zh here is in mm6 m−3). This fit will be used to retrieve Do from Zh for light rain rates when the measurement of Zdr falls below the threshold of 0.2 dB. However, the estimate of γ will be obtained in a manner to be described later [see (6b)].
Figure 2 shows a plot of Zdr versus Do. The power law fit to these data result in,
i1520-0426-19-5-633-e4
For radar measurements with Zh < 35 dBZ and Zdr ≥ 0.2 dB, the Do is retrieved using (4), and Nw is retrieved from (3), which is expressed as
i1520-0426-19-5-633-e5
where Zh is in mm6 m−3.
For radar measurements with Zh < 35 dBZ and Zdr < 0.2 dB the following method is proposed. Using (3) and (4), Do can be eliminated to obtain a relation between Zh and Zdr of the form Zdr = αZδh where δ is the ratio of the exponents in (3) and (4) given by δ = 0.136/0.486 ≈ 0.28. The coefficient α can be determined, in practice, from all radar measurements of Zdr with corresponding Zh < 35 dBZ. The estimate α̂ is easily determined as α̂ = 〈Zdr〉/〈Z0.37h〉 where angle brackets denote a spatial average; note that Zdr is in dB and Zh in mm6 m−3. Figure 3 shows a scatterplot of Zdr versus Zh for radar data in stratiform rain from 15 February 1999 as well as the power law fit with α̂ = 0.0741 (this value is close to that obtained from disdrometer analysis, 0.0842). The essential hypothesis is that even though Zdr measurements are noisy at low reflectivities and tend on average to near 0 dB at very light rain rates, scattering simulations based on disdrometer DSD samples and a rain model with βmodel as in (A5) indicate that the mean relation should follow a power law of form Zdr = αZ0.28h. Thus, a method exists for retrieving Do, even if the individual resolution volumes have Zdr < 0.2 dB (the prespecified threshold). First, α̂ is determined from the data, which includes all Zdr values with Zh < 35 dBZ (the lower bound of Zh is set to 0 dBZ here). Next, (4) is used with Zdr = α̂(Zh)0.28 to arrive at
i1520-0426-19-5-633-e6a
where γ̂ = 1.81(α̂)0.486. Subsequently, Nw is obtained from (3) as
Nwγ̂7.35−1−3
Note that this retrieval Nw can be interpreted as an estimate of the expected value of Nw, since α̂ is an estimate of the expected value of α. For example, the expected value of Nw for the stratiform rain data in Fig. 3 is 2920 mm−1 m−3. If σα is the standard deviation of α̂ then a range of Nw values is to be expected, and as a first approximation Nw may be assumed to be uniformly distributed between [Nw1, Nw2] where the lower and upper values correspond to using α̂ + σα/2 and α̂σα/2 in (7) and noting that γ̂ = 1.81(α̂)0.486. Analysis of a large number of volumes of radar measurements of (Zh, Zdr) pairs in both convective and stratiform rain types on 15 February 1999 suggests that σα is around 0.015, which puts the 1/2 standard deviation bounds of Nw in the range 2100–4300 mm−1 m−3. To demonstrate that these assumptions are reasonable, Fig. 4 shows a histogram of log10(Nw) from the combined set of 2D-video and RD-69 data in stratiform rain (composite from different days during TRMM/Brazil). Note from Fig. 4 that the Nw values in stratiform rain range between 250 and 6000 mm−1 m−3 with the mode being 2500 mm−1 m−3.

To summarize the retrieval of Do and Nw from radar measurements, if the measurement set (Zh, Zdr, Kdp) exceeds the thresholds of 35 dBZ, 0.2 dB, and 0.3° km−1, respectively, then the algorithms using βeff as described in the appendix are used. If Zh < 35 dBZ and Zdr ≥ 0.2 dB, then Do and Nw are retrieved via (4) and (5), respectively. If Zh < 35 dBZ and Zdr < 0.2 dB, then Do is retrieved using (6) and Nw using (7), with the added provision of distributing Nw uniformly in a prescribed range. The DSD shape parameter (μ) is not retrieved in this case and is set to zero. The rain rate is derived assuming μ = 0 (exponential shape) and using the retrieved Nw and Do and the terminal velocity relation υ = 3.78D0.67 from Atlas and Ulbrich (1977).

4. Results from 15 February 1999

a. Statistics of Do and Nw

On 15 February 1999 of the TRMM/Brazil field campaign,2 a squall line formed to the east of the S-Pol radar and moved westward, crossing the measurement area over a period of 4 hours (0300–0800 UTC; all times henceforth will be UTC). The squall line was well organized as a north–south line during the early phase (0300–0400) but became disorganized past 0430 with a number of strong cells embedded within a large area of weak echo. Past 0700, there was a transition from convective to primarily stratiform rain. Figure 5 shows the squall line as a PPI of Zh (in dBZ) at 0350. The polar area marked in the figure refers to the area where retrieval of Do and Nw was performed, and is presumed to be representative of strong convective rain within the squall line. Figure 6 shows histograms of radar-derived (a) Do and (b) log10Nw for rain rates < 10 (mm h−1), while similar histograms for R ≥ 10 (mm h−1) are shown in Fig. 7. Figure 6a shows that the spread in Do is relatively large at the lower rain rates compared to Fig. 7a, while the modal Nw in Fig. 6b is near 1000 mm−1 m−3 as compared to 20 000 mm−1 m−3 in Fig. 7b. The lower concentration of larger drops for R < 10 mm h−1 likely represents nonequilibrium distributions similar to data from positive Zdr columns (Caylor and Illingworth 1987; Bringi et al. 1991), whereas the Do, Nw histograms for R > 10 mm h−1 reflect data from more mature rainshafts (narrower spread in Do). Figure 8 shows Do and Nw versus rain rate; also plotted are data from the 2D-video disdrometer in convective rain from all times during the TRMM/Brazil project during which it was operational. The radar retrievals do not show any obvious functional dependence of either Do or Nw on rain rate, except possibly for Do at the very lowest rates. The disdrometer Do does show an increasing trend with R (for R ≤ 5 mm h−1) in agreement with the radar-retrieved Do. There is general agreement between the disdrometer and radar retrievals with regard to the spread of Do and Nw, even though the radar retrievals are from a specific convective area of the 15 February squall line, whereas the 2D-video data are from a small sample of different types of convective rain in the same region.

Figure 9 shows the histogram of Do and log10Nw from an area of stratiform rain at 0756 on 15 February. Vertical sections of Zh and Zdr (not shown here) indicated a “brightband” feature, and there was no convection that could be interpreted from the images. The spread of Do around its mode is now significantly smaller as compared to Fig. 6a. The spread of Nw around its mode (modal value ≈ 2000 mm−1 m−3) is also smaller in stratiform rain compared with Fig. 6b. The Do and Nw histograms in stratiform rain are generally comparable to those derived from airborne imaging probes by Testud et al. (2001) for stratiform rain during Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) (see their Fig. 3). In particular, their mean and standard deviation of Do (1.21 and 0.28 mm, respectively) can be compared with Fig. 9a (corresponding values of 1.34 and 0.24 mm). Similarly, for the mean and standard deviation of log10Nw, Testud et al. (2001) obtained 3.48 and 0.5 versus 3.31 and 0.28 from Fig. 9b. It is, in fact, remarkable that these statistics for a sample of stratiform rain from Brazil derived by polarimetric radar generally agree with a more complete ensemble of stratiform rain from TOGA COARE, despite large sample volume differences. Of course, it is well known that convective rain characteristics over land and ocean are very different, and the histograms for convective rain in Fig. 6 do not agree with the Testud et al. (2001) analysis of convective rain from TOGA COARE for R < 10 mm h−1. However, the statistics for R > 30 mm h−1 are in good agreement, as summarized in Tables 1 and 2.

One possible reason for the general agreement of the statistics in stratiform rain from these two climatic regimes may be due to similarity in the dominant microphysical processes leading to rain formation (i.e., rain formed from melting aggregates). As for the agreement in convective rain in the higher rain-rate regime (R > 30 mm h−1), it may be that the microphysical processes leading to an equilibrium-type DSD are similar in the two regimes (e.g., Hu and Srivastava 1995).

The biggest difference in Tables 1 and 2 is related to the case of convective rain with R < 10 mm h−1, which from the radar perspective is characteristic of a lower concentration of relatively larger drops as compared with the Testud et al. (2001) results and with the stratiform rain results. This is not surprising, given that warm rain processes as well as drop sorting (Carbone and Nelson 1978; Atlas et al. 1999) are likely to be dominant in the updraft area of the convective portion of the squall line leading to nonequilibrium-type DSD spectra, similar to those found in positive Zdr columns. Stronger updraft over land versus ocean could be one reason why the statistics of Do and Nw in the low rain-rate category (R < 10 mm h−1) from the radar retrievals are so different from the convective TOGA COARE statistics derived by Testud et al. (2001).

Figure 10 shows radar-retrieved Do and Nw versus R for stratiform rain; also plotted are the 2D-video and RD-69 data from samples of stratiform rain during times when the 2D-video was operational. Good agreement may be noted with regard to the range of Do and Nw predicted by radar and disdrometer at very low rain rates (R < 2 mm h−1). There appears to be a functional relation between Do and R at these low rain rates but for R > 5 mm h−1, if there is any correlation between Do and R and between Nw and R, it is very weak.

b. Time profile of DSD parameters

Because the 15 February squall line was long lived, it is possible to show how the DSD parameters and rain rate changed with time over a 3-h period as the squall line moved over one of the gauge networks. Figure 11a shows the location of the gauges relative to the S-Pol radar as well as a polar area surrounding the gauges. The various gauge rain rates were averaged over 2-min intervals and represent the mean areal R versus time over the polar area. The DSD parameters were also averaged over the polar area and represent areal average quantities. While the gauge locations were not uniformly distributed over the selected polar area, the storm system was large enough that a good estimate of mean areal rain rate is believed to have been obtained.

Figure 11b shows the time–height profile of reflectivity from the 915-MHz vertically pointing profiler. The profiler and the 2D-video and RD-69 disdrometers were located at the Ji Parana airport (see Fig. 11a). Three convective cells may be noted in Fig. 11b at 4.75, 5.75, and 6.5 h (times are in fractions of an hour, UTC).

Figures 12a and 12b show the time profile of areally averaged Do, Nw, and μ, as well as the mean areal R from the gauge network. Three convective rain cells can be identified in the time profile (abscissa is time in fractions of an hour; e.g., 4.5 means 0430 UTC) with Do's in the range 1.4–1.5 mm, Nw around 15 000 mm−1 m−3, and μ around 3–5 near the rain cell peaks. In the stratiform rain between the rain cell peaks, the Do's are around 0.7 mm, Nw is in the range 1500–2500 mm−1 m−3, and μ is around 1–2 (e.g., centered around times at 0515 UTC, or 5.25 and 7.125 h). Vertical profiles of the reflectivity from the NOAA profiler3 showed a bright band between the rain cell peaks (see Fig. 11b). It is emphasized that the radar-derived Do, Nw, and μ values are areally averaged (over the polar area shown in Fig. 11a).

To illustrate the use of the effective β in reducing the bias in rainfall accumulation, the areal R (or AR) is derived using differential propagation phase (Φdp) using the algorithm proposed and evaluated by Bringi et al. (2001a):
i1520-0426-19-5-633-e8
In the above, r1, r2, θ1, and θ2 describe the limits of the polar area (see Fig. 11). For a given beam with constant azimuth angle θ, AR depends on the boundary values of Φdp as well as the area under the Φdp range profile. As the azimuthal angle changes from θ1 to θ2, an areal sweep of Φdp over the rain region occurs naturally, performing a spatial integration of the rainfall. A linear relation between R and Kdp of the form R = cKdp is assumed to be valid locally to derive (8). Since the actual relation is nonlinear, a piecewise linear approximation is proposed, as shown in Fig. 13. The rain model used in the simulations is described in relation to (A5) in the appendix. Because a sufficiently large database of disdrometer data was not available from TRMM/Brazil, the simulations in Fig. 13 are based on an entire season of rain DSD measurements made with the RD-69 disdrometer near Darwin, Australia. When AR is divided by the polar area it will be termed the mean areal rain rate (R). The multiplicative coefficient c in (8) is selected based on the average Kdp along a specific beam according to the piecewise linear fit in Fig. 13. This approach avoids the necessity of assuming a priori that Kdp is constant along the various beams (Ryzhkov et al. 2000). The cumulative rainfall using the fixed rain model with βmodel as in (A5) results in a bias (overestimate) of around 20% when compared with the gauge network accumulation, as illustrated in Fig. 14. Simulations performed by Gorgucci et al. (2001) show that the R(Kdp) estimator varies as R = cKdpβ−1.5eff. To correct for changing βeff relative to βmodel in (A5), the modal value of βeff is first computed over the polar area in Fig. 11a from radar measurements of Zh, Zdr, and Kdp (see appendix), and this is done as a function of time. Next, the modal value of ξdr(Zdr in linear scale) is used to calculate βmodel using (A5). The areal rain rate is then adjusted by the factor (βmodel/βeff)1.5. During stratiform rain periods, the Zh, Kdp, and Zdr generally fall below the threshold required for the calculation of βeff and, thus, no adjustment is done during these periods. Figure 14 also shows the rain accumulation after correcting for βeff, and the accumulation bias has now been reduced to <10%. The main advantage of using the βeff-based correction is that the effects of drop oscillations and/or drop canting is implicity accounted for in the rain-rate algorithm. One simply starts with theoretical rain model or disdrometer DSD data for the regime (or, similar to the regime under consideration) to arrive at a first approximation for the RKdp relation, and then the radar data are used to correct for deviations of βeff from the assumed model value [βmodel in (A5)] in a relatively straightforward manner. The results shown here suggest that the use of disdrometer data from Darwin to arrive at the piecewise linear fit in Fig. 13 is valid for setting the initial RKdp relation, and, in fact, demonstrates the power of the effective β method. Such an approach avoids the use of empirical-based methods suggested by Fulton et al. (1999).

5. Summary and conclusions

A method is proposed for retrieving the parameters Do and Nw of a normalized gamma DSD using radar measurements of Zh, Zdr, and Kdp at S band (frequency near 3 GHz). The algorithms based on the effective β concept derived by Gorgucci et al. (2001, 2002) have been extended to low rain rates where both Kdp and Zdr tend to be noisy and preclude an accurate estimate of βeff. Disdrometer data and scattering simulations are used to retrieve the Do and Nw at low rain rates. Thus, the combined method retrieves Do and Nw over the full range of rain rates detectable by radar.

Statistics of Do and Nw in the form of histograms were presented for convective and stratiform samples of rain data in one squall-line event from the TRMM/Brazil field campaign using the S-Pol radar. The mean and standard deviation of Do and log10Nw in stratiform rain compared favorably with similar statistics presented by Testud et al. (2001) based on airborne measurements during TOGA COARE. In convective rain, the statistics for the higher rain-rate category (R ≥ 30 mm h−1) also agreed with Testud et al. (2001). This agreement, which is based on a very limited sample of radar data, may suggest that the dominant microphysical processes are similar, for example, melting of snow to form stratiform rain or the tendency to equilibrium-like distributions in heavier convective rain. The statistics were, however, quite different in lighter convective rain (R < 10 mm h−1) with much larger mean Do over land as compared with TOGA COARE and, correspondingly, much lower values of Nw. It will be necessary to repeat the radar retrievals before firmer conclusions can be made, but the methodology proposed herein is an important step and makes it possible now to proceed with such studies in different climatic regimes where high quality dual-polarized radar data are available: for example, Darwin, Australia; the South China Sea Monsoon Experiment (SCSMEX); the Texas–Florida Underflight Experiment (TEFLUN-B) in Florida; and the Severe Thunderstorm and Electrification Project (STEPS) in Colorado.

The functional behavior of the retrieved Do and Nw with rain rate in samples of stratiform and convective rain was studied and compared with samples of 2D-video and RD-69 disdrometer measurements in similar rain types during TRMM/Brazil. The agreement, in terms of the range of Do and Nw values, was good. At low rain rates (R < 5 mm h−1), there appeared to be a correlation between Do and R. Weak correlation was found between Do and R or Nw and R in both the radar retrievals as well as the disdrometer data for R > 5 mm h−1. Generally, these results are supportive of the Testud et al. (2001) analysis of airborne DSD data from TOGA COARE.

The 15 February squall-line event analyzed in this paper also shows how the βeff estimate was used to remove the bias in accumulated rainfall when using the areal rain-rate estimator based on differential propagation phase via comparison with a gauge network deployed over a 5 km × 5 km area. The time profile of areally averaged Do, Nw, and μ was determined as three consecutive convective rain cells moved over the gauge network area with periods of stratiform rain in between. Within the convective rain cells the Do values ranged from 1.4 to 1.5 mm, with Nw around 15 000 mm−1 m−3 and μ around 3–5. In stratiform rain the corresponding ranges were 0.6–0.7 mm, Nw around 1500–2500 mm−1 m−3, and μ around 1–2. These ranges are in general agreement with past studies (e.g., Cifelli et al. 2000; Tokay and Short 1996). Future research will be directed toward comparison with DSD retrievals from profilers (e.g., Williams et al. 2000).

The success of the proposed methodology in providing for unbiased estimates of Do, Nw, or μ relies on accurate calibration of the radar; specifically, the accuracy in Zh should be 1 dB or better and for Zdr it should be 0.1 dB or better. Also, to retrieve the gamma DSD parameters at low rain rates, disdrometer DSD samples in convective and stratiform rain should be available for setting algorithm coefficients/exponents, specifically in the power laws Do = aZbdr and Zh/Nw = cD7.35o. Correction for attenuation effects will be important at C band and higher frequencies. Even at S band, correction of Zh and Zdr data is important when the differential propagation phase becomes large (≥50°). Techniques to correct for attenuation are now available (Testud et al. 2000; Bringi et al. 2001b; Smyth and Illingworth 1998).

Acknowledgments

This research was supported by the NASA/TRMM Grants NAG5-7717 and NAG5-7876. VNB also acknowledges support from the National Science Foundation via ATM-9612519 for analysis of the 2D-video disdrometer data. Drs. John Hubbert, John Beaver, and Steve Bolen of CSU were instrumental in supporting the operation of the NASA 2D-video disdrometer during TRMM/Brazil. Dr. C. R. Williams provided the profiler data used to construct Fig. 11.

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APPENDIX

Retrieval Algorithm for Do, Nw, and μ

The method of retrieving Do, Nw, and μ from Zh, Zdr, and Kdp is summarized here from Gorgucci et al. (2001, 2002). A gamma DSD model is assumed with the following ranges for the parameters:
i1520-0426-19-5-633-ea1
with the additional constraint that R < 300 mm h−1. The parameters Do, log10Nw, and μ are varied uniformly over their respective ranges to form a large table of Do, Nw, and μ. Scattering calculations are performed at 2.8 GHz over a range of βeff, and nonlinear regression is used to develop an algorithm for β (henceforth, the subscript “eff” will be dropped) in terms of Zh, Zdr, and Kdp:
βZ−0.365hK0.38dpξ0.965dr
where Zh is in mm6 m−3, Kdp in ° km−1, and ξdr is the differential reflectivity expressed as a ratio (Zdr = 10 log10ξdr).
Simulations using gamma fits to measured drop size distributions (see section 3b) and scattering calculations at 2.8 GHz of Zh, Zdr, and Kdp, assuming (i) mean axis ratio versus D fit of Andsager et al. (1999) for 1 ≤ D ≤ 4 mm and Beard and Chuang (1987) for D < 1 and D > 4 mm, (ii) Gaussian canting angle distribution with mean 0° and σ = 10°, and (iii) size integration up to Dmax = 2.5Dm, show that βmodel using (A4) is generally clustered around 0.045–0.0475 mm−1 but is a nonlinear function of Do (or equivalently ξdr). A nonlinear fit to the simulations yields
i1520-0426-19-5-633-ea5
The median volume diameter is then derived as
DoaZbhξdrc
where,
i1520-0426-19-5-633-ea7
The Nw is derived as
10NwaZbhξdrc
where now
i1520-0426-19-5-633-ea11
and μ is derived as
i1520-0426-19-5-633-ea14
where
i1520-0426-19-5-633-ea15
The rain rate is derived as
Rβ0.865Z0.93hξdrc
where
cβ−0.703
In this paper, the thresholds used are Zh ≥ 35 dBZ, Zdr ≥ 0.2 dB, and Kdp ≥ 0.3° km−1 for retrieval of Do, Nw, μ, and R using the above algorithms.

Fig. 1.
Fig. 1.

Scatterplot of Zh/Nw vs Do based on gamma fits to 2D-video (in convective and stratiform rain) and RD-69 (in stratiform rain) disdrometer data obtained during the TRMM/Brazil field campaign. Each data point (+) refers to a 2-min-averaged DSD to which a gamma DSD is fitted. There are 164 2-min samples of convective rain from the 2D-video and 152 2-min samples of stratiform rain from the 2D-video and RD-69. The power law fit is also shown

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 2.
Fig. 2.

As in Fig. 1, except Zdr vs Do. Power law fit is based on rain rates exceeding 2 mm h−1

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 3.
Fig. 3.

S-Pol radar measurements of Zdr vs Zh from stratiform rain on 15 Feb 1999, together with a mean power law fit

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 4.
Fig. 4.

Histogram of log10Nw in stratiform rain from gamma fits to 2-min-averaged DSD data collected by the 2D-video and RD-69 during the TRMM/Brazil campaign

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 5.
Fig. 5.

PPI of reflectivity at 0350 UTC of the 15 Feb squall line. The polar area marked illustrates the region of convective rain selected for the radar-based retrieval of Nw and Do

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 6.
Fig. 6.

Histogram of (a) Do and (b) log10Nw based on radar retrievals for convective rain with R < 10 (mm h−1) from the polar area marked in Fig. 5

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 7.
Fig. 7.

As in Fig. 6, except for convective rain with R > 10 (mm;thh−1)

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 8.
Fig. 8.

Scatterplot of (a) Do vs R and (b) Nw vs R for convective rain from the polar area marked in Fig. 5. Overlaid are data from the 2D-video disdrometer in convective rain collected during the TRMM/Brazil campaign (164 2-min-averaged DSD samples)

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 9.
Fig. 9.

As in Fig. 6, except for stratiform rain at 0756 UTC 15 Feb 1999. Data are from a different polar area (not shown)

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 10.
Fig. 10.

As in Fig. 8, except data from stratiform rain. Overlaid are data from 2D-video and RD-69 disdrometers in stratiform rain collected during TRMM/Brazil (152 2-min-averaged DSD samples)

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 11.
Fig. 11.

(a) Polar area shows the gauge network. The 2D-video and RD-69 disdrometers and the NOAA profiler were located at the Ji Parana airport. (b) Time–height profile of reflectivity from the NOAA 915-MHz vertically pointing Doppler radar (or profiler).

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 12.
Fig. 12.

Time variation of (a) Do and mean areal R from gauges, and (b) log10Nw and μ. The DSD parameters are areally averaged over the polar area shown in Fig. 11a

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 13.
Fig. 13.

The piecewise linear fit, R = cKdp, needed for the areal rain-rate algorithm using Φdp. The data points are from scattering simulations based on a large database of RD-69 disdrometer DSDs from Darwin, Australia (over 2000 2-min DSD samples) in a variety of rain types

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Fig. 14.
Fig. 14.

Areal rainfall accumulation over the polar area in Fig. 11a from radar (using the areal Φdp method) and the gauge network vs time. Illustrates the use of βeff in reducing the bias in radar rainfall accumulation to less than 10%

Citation: Journal of Atmospheric and Oceanic Technology 19, 5; 10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2

Table 1.

Statistics of Do

Table 1.
Table 2.

Statistics of log10Nw

Table 2.

1

A detailed description of the instrumentation can be found online at http://radarmet.atmos.colostate.edu/lab_trmm.

2

S-Pol radar images for this day at 10-min intervals can be viewed online at http://www.atd.ucar.edu/rsf/TRMM-LBA/quicklook/990215.

3

Vertical profiles of reflectivity from the NOAA profiler can be viewed online at http://www.al.noaa.gov/WWWHD/pubdocs/TropDyn/trmmlba/al915/bra_b00_105_1999046.gif.

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