Abstract

A method is studied to make a nonuniform beamfilling (NUBF) correction for the path-integrated attenuation (PIA) derived from spaceborne radar measurement. The key of this method is to estimate rain-rate variability within a radar field of view from the local statistics of a radar-measurable quantity (〈Q〉) such as PIA derived from the surface reference technique. Statistical analyses are made on spatial variabilities of the radar-measurable quantities using a shipborne radar dataset over the tropical Pacific obtained during the TOGA COARE field campaign. It is found that there are reasonably good correlations between the coarse-scale variability of 〈Q〉 and the finescale variability of rain rate, and the regression coefficient (slope) of these two quantities depends somewhat upon rain types. Based on the statistical analyses, the method is tested with a simulation using the same dataset. The test result indicates that this method is effective in reducing bias errors in the estimation of rain-rate statistics. Although it is also effective to make the NUBF correction on an individual instantaneous field-of-view basis, one must account for the variability of local rainfall statistical characteristics that may cause significant errors in the NUBF correction.

1. Introduction

It is well known that nonuniform beamfilling (NUBF) is a major error source in quantitative rainfall remote measurement with a spaceborne rain radar if the footprint size of the radar is comparable to or larger than a convective cell size. The effect of NUBF depends also upon the algorithm for estimating rain rate. It has been shown that the use of the path-integrated attenuation (PIA) derived from the surface return method [surface reference technique (SRT)] suffers more from the NUBF effect than the use of radar reflectivity factor with a Z–R relation for rain-rate estimation (Nakamura 1991; Amayenc et al. 1993). However, SRT is recognized as a very important method to achieve a quantitative retrieval of heavy rain (about 20 mm h−1 or higher) because it provides a reference to stabilize the rain attenuation correction that often becomes unstable if we know only a measured and attenuated Z-factor profile (Meneghini and Kozu 1990). It is therefore crucial to develop a method to judge the existence of significant NUBF (to issue a “warning” flag), and, if possible, to develop a method to correct the SRT-derived path attenuation for the NUBF effect. Since the attenuation coefficient (in dB per unit distance) has an approximately linear relationship with rain rate, obtaining accurate PIA is also useful to estimate path-integrated rain rate.

We have studied a method for correcting the SRT-measured PIA for the NUBF effect (Kozu and Iguchi 1996, 1997) and have shown that the method has a potential to reduce bias errors in PIA estimation based upon case studies using shipborne and ground-based radar data from the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) (Short et al. 1995). This method is based upon a characteristic of spatial variability of rain intensity, which has been used for the SSM/I rain-rate retrieval algorithm by Kummerow and Giglio (1994). They showed a fairly good correlation between the normalized standard deviations (NSDs) of rain at 1-km resolution and the NSD at 12.5-km resolution, the latter being obtained from the 85-GHz channel of SSM/I, and made an NUBF correction using the estimated 1-km resolution NSD. In the case of radar measurement, however, the spatial scale of interest is approximately equal to the correlation length of individual rain cells, which is much smaller than those of SSM/I. Therefore, we may find characteristics different from the result of Kummerow and Giglio (1994). Graves (1993) also pointed out that statistical properties of rain field tend to be unstable as the size of instantaneous field of view (IFOV) becomes small, making a statistical NUBF correction difficult in his statistical modeling of NUBF for microwave radiometer measurements of oceanic rainfall.

This paper, which is the result of a study following the work of Kozu and Iguchi (1996, 1997), describes comprehensive statistical analyses of shipborne radar data obtained between November 1992 and February 1993 over the tropical Pacific. In the following sections, we describe 1) basic formulation of the problem, 2) the NUBF correction method, 3) statistical characteristics of rainfall, and 4) a simulation of the NUBF correction.

2. Rain and observation models

a. PDF and its parameters

We assume a vertically uniform, horizontally nonuniform rain model in an IFOV as shown in Fig. 1. This model is valid when we consider the SRT because it deals with only the path-integrated values. In this situation, we assume that we can measure a PIA with the SRT, a storm height, and a Z-factor profile that includes propagation loss due to rain.

Fig. 1.

Concept of storm model.

Fig. 1.

Concept of storm model.

If we assume that the probability density function (PDF) of rain rate in the IFOV is approximated by a lognormal model with the expectation of R, R, and the NSD of R, σR (i.e., we assume “uniform” statistics within the IFOV), then the PDF, f(R), is expressed as

 
formula

where μR and ξ2R are the mean and the variance of lnR, which are related to R and σR as follows:

 
formula

With this expression, the PIA measured with the SRT, ASRT, in decibels, is given by

 
formula

with

 
A(R) = 2LaRb,
(4)

where L is the storm depth, and a and b are the coefficient and exponent of the k–R relation (attenuation coefficient versus rain-rate relation). We define the uniform PIA, Au, as the PIA that corresponds to R with a usual k–R relation that can be obtained when rain is uniform within the IFOV:

 
Au = 2La
R
b.
(5)

Using R and σR as parameters, we can relate ASRT to Au. The relationship between those quantities assuming the lognormal PDF and a rain depth L of 5 km is shown in Fig. 2.

Fig. 2.

Relationship between “uniform” PIA (Au) and SRT-derived PIA (ASRT) assuming the lognormal PDF and a rain depth L of 5 km as the normalized standard deviation of rain rate (σR) as a parameter.

Fig. 2.

Relationship between “uniform” PIA (Au) and SRT-derived PIA (ASRT) assuming the lognormal PDF and a rain depth L of 5 km as the normalized standard deviation of rain rate (σR) as a parameter.

Since k–R and Z–R relations are generally expressed as power laws,

 
k = aRb and Z = αRβ,
(6)

the distributions of k and Z are also lognormal and the means and variances of lnk (μk, ξ2k) and those of lnZ (μZ, ξ2Z) are given by

 
formula

Thus, the expectation and NSD of k (k, σk) and those of Z (Z, σZ) are expressed as

 
formula

Equations (10) and (12) indicate that NSDs of k and Z are different from the NSD of rain rate but can easily be converted. Please note that the parameter σR in Fig. 2 is the NSD of rain rate (not of attenuation coefficient). In the calculation of Au and ASRT, the difference in PDF parameters is incorporated.

b. Relation between point and area-averaged quantities

Before describing the NUBF correction method, we consider the relationship between a “point” quantity Q and an “area-averaged” counterpart 〈Q〉 that is a basis of the current method. A similar problem has been discussed in the study of methods to predict PIA from a point measurement of rain rate with a rain gauge (Morita and Higuti 1978). They derived expressions for the mean and variance of PIA using a “normalized spatial autocorrelation function” (hereafter, ACF) of rain rate expressed by the form of exp(−ζr), where r is the distance between two points. In this paper, we derive an expression of Q (vertically averaged or integrated quantity with high spatial resolution) and 〈Q〉 (area-averaged value of Q over the IFOV). Note that the difference between the notation Q and 〈Q〉 is that the former is the expectation and the latter is still a statistical random variable.

First, we assume the following ACF model that depends on distance only:

 
ρ(r) = exp(−ζrη),
(13)

where r is the distance between two points on a two-dimensional rain field. As we see later in this paper, this model can well fit the “averaged” 1D ACF that is generated from the observed 2D ACF by making an “iso-range” circular averaging. Of course, there may be a wide range of directional properties in the 2D ACF, depending on storm type and orientation. Nevertheless, the approximation used here would be useful to obtain approximate properties of the relation between NSDs in connection to the ACF of rain field. Let Q(x, y) be the point value at (x, y) and 〈Q(x0, y0)〉 be the spatially averaged Q over a circular area having the diameter D centered at (x0, y0). In addition, let Q and σQ be the mean and NSD of Q. We add (′) to σQ because we implicitly assume here that rain-rate fluctuations having any spatial frequency can contribute to the variability of Q, which may be different from the variability of Q within an IFOV. Then we have the following relations:

 
formula

If we assume that ρ(r) is independent of the location (x, y), {Q2Q2} can be written as

 
formula

where P(λ, z) is the incomplete gamma function, and the double integral over θ1 and r2 is represented by a function B(D, ζ, η). Note that the above 2D integration is made over the circular region having a diameter D. A point p(r2, θ2) is first specified in the circle. The variable θ1 is a polar angle about p(r2, θ2), and r1 is defined as a distance from p(r2, θ2) to the edge of the circle in the direction of θ1. Thus the upper limit of the integration over r1 (Rm) is a function of θ1 and r2. Thus, the relation between σ′2Q and σ2Q can be expressed as

 
formula

which can be used to predict σ′2Q from σ2Q and the parameters of the spatial correlation function, although a 2D integration is needed to evaluate B(D, ζ, η). It should be noted that the PDF form has nothing to do with Eq. (16), but the PDF does affect the correction factor to estimate Au (see Fig. 2).

The difference between σQ and σQ would be small when the diameter of IFOV is much wider than the correlation length of the ACF, which may be applicable for most spaceborne radiometers; however, this may not be the case for the 4-km IFOV we consider here. We will discuss this problem later in the discussion of the result of observation data analyses.

3. Description of correction method

As we discussed earlier, our final goal is to estimate Au from radar-measurable quantities. The first step is to estimate parameters of the rain-rate PDF within an IFOV of interest, specifically R and σR. In this section we describe formulations using measurable quantities but do not include measurement errors to simplify the problem. The mean and NSD in this section represent the sample mean and sample NSD, respectively, which is different from the formulations in section 2, where we considered hypothetical random rain field with parent PDFs. To avoid a confusion, we denote a sample NSD “σ̂,” while the true NSD “σ” as was used in section 2.

Since we cannot directly measure 〈R〉, we cannot apply the direct relations connecting PDF parameters [Eqs. (2), (7)–(12)]; however, a quantity 〈Q〉 we can measure such as ASRT would serve a proxy of 〈R〉 to estimate the NSD of R. To obtain an estimate of σQ〉, we use data at nine IFOVs (eight surrounding IFOVs and the IFOV at which we want to estimate σR), as shown in Fig. 3, which illustrates the geometry of spaceborne radar IFOVs related to this problem. Specifically, we estimate σR from SRT-derived PIA, ASRT. Although ASRT is not proportional to 〈k〉, we will use this as a radar-measurable quantity. In the following, we formulate a discrete-model relationship for actual observation conditions, which can also be used for the simulation we discuss later.

Fig. 3.

Concept of down-looking rain radar IFOV used in the NUBF correction scheme.

Fig. 3.

Concept of down-looking rain radar IFOV used in the NUBF correction scheme.

Let 〈Ri (i = 1–9) be the IFOV-averaged path-averaged rain rate, which consists of n rain-rate elements (Rj,i, j = 1, n) within the IFOV:

 
formula

where wj is the weighting function representing the two-way antenna pattern. The PIA we should obtain when the rain is uniform in the IFOV, Au,i (dB), is

 
Au,i = 2LaRbi ≈ 2La′〈Ri.
(5′)

The exponent b depends upon the frequency and drop size distribution, but it is fairly close to unity for the frequency range of interest (10–35 GHz) (Olsen et al. 1978). The last approximate equality in Eq. (5′) [see also Eq. (5)] represents this fact with a slight modification of the coefficient a to a′ to get the best fit. Therefore, the NSD of PIA is generally close to that of path-averaged rain rate, and the ASRTAu relationship derived for a specific rain depth can be used for different rain depth cases. In other words, the variation of rain depth is not a problem if the rain rate averaged over a given rain depth follows the same type of PDF form as that assumed to generate the ASRTAu relationship. Note that the “given” rain depth should be equal to or higher than the maximum rain depth of interest.

Similar to Rj,i, the PIA element Aj,i is expressed as

 
Aj,i = 2Lkj = 2LaRbj,i2LaRj,i.
(18)

The SRT-measured PIA (ASRT) is given by

 
formula

The sample NSD of rain rate within the ith IFOV (σ̂R,i) is given by

 
formula

As an estimate of NSD in a coarse spatial-scale rain field (σQ), we use the nine samples of a radar-measurable quantity, 〈Q〉:

 
formula

where Q is the average of 〈Q1–〈Q9.

As the radar-measurable quantity, we will use the PIA measured with the SRT (ASRT). In the procedure for correcting ASRT, the coarse-resolution NSD, σ̂ASRT, is first obtained from the measured ASRT, where σ̂ASRT represents σ̂Q when 〈Q〉 = ASRT. Next, σR,5 is estimated from σ̂ASRT using a statistical relation (regression result) between σ̂ASRT and σ̂R (which will be described later). Finally, Au,5 is estimated from ASRT,5 and σR,5 using the relation between ASRT and Au for a given σR assuming a lognormal PDF within the IFOV. In this case, the bias error in the sample NSD (σ̂R) is neglected because the bias error in the σ̂Rσ̂ASRT relationship is expected to be very small (≈2%) and will be masked by other error sources.

4. Statistical characteristics of rainfall over tropical Pacific

a. Data source

To study the rainfall characteristics statistically, we use 42 constant-altitude plan position indicator (CAPPI) maps at 2-km altitude obtained from the Massachusetts Institute of Technology (MIT) radar on board the R/V Vickers during the TOGA COARE campaign from November 1992 to February 1993. Those rain scenes are listed in Table 1 with a “subjective” rain-type classification. Though subjective, the classification may be useful to get approximate information about the rain-type dependence of the statistical characteristics. Examples of CAPPI data for types 1–4 are shown in Fig. 4. Please note that the classification is based on the characteristics of the area having a radius of 40 km centered at the radar location. The parameters to process the CAPPI Z-factor maps are listed in Table 2.

Table 1.

Summary of rain scenes used for the current study. For example, 921112__0301 represents 0301 UTC 12 Nov 1992.

Summary of rain scenes used for the current study. For example, 921112__0301 represents 0301 UTC 12 Nov 1992.
Summary of rain scenes used for the current study. For example, 921112__0301 represents 0301 UTC 12 Nov 1992.
Fig. 4.

Example of MIT radar 2-km CAPPI images (radius = 150 km). Note that only the 80 km by 80 km square area centered at the radar site is used for the NUBF study.

Fig. 4.

Example of MIT radar 2-km CAPPI images (radius = 150 km). Note that only the 80 km by 80 km square area centered at the radar site is used for the NUBF study.

Table 2.

Parameters for the analysis of MIT radar data.

Parameters for the analysis of MIT radar data.
Parameters for the analysis of MIT radar data.

b. Spatial correlation properties of rain rate

As we discussed in section 3, the relation of variabilities between “point” rainfall quantity (Q) and area-averaged (〈Q〉) quantity can be estimated if we know the parameters of the ACF of Q, and the averaging area (represented by D). This is approximately true when we relate R and 〈Q〉, where Q is not necessarily equal to R. In other words, characterizing the ACF of R would be useful to estimate the σRσQ relationship. For this purpose, we calculate the 2D ACF of rain rate. This is then averaged over an isorange circle so that we can obtain an averaged 1D ACF. Examples of the averaged 1D ACF are shown in Fig. 5 as well as the fitting with the model function of Eq. (13). The calculation of the 2D ACF is made over two regions on a CAPPI scene;64 km by 64 km squares to the left and right of the radar site. This is because there is always a “blind” region near the radar site and sometimes to the north or south of the radar as well. It is found that Eq. (13) provides an excellent fit to the ACFs for almost all cases. Figure 6 shows the histograms of the averaged 1D ACF parameters ζ and η. On average, ζ ≈ 0.135 and η ≈ 1.13 for convective types (types 1, 2, and 4) and ζ ≈ 0.064 and η ≈ 1.10 for the stratiform type (type 3). Using Eq. (16) with D = 4 km, the relation between σR and σR can be obtained.

Fig. 5.

Example of the averaged 1D ACF of rain rate.

Fig. 5.

Example of the averaged 1D ACF of rain rate.

Fig. 6.

Histograms of parameters of the averaged 1D ACF ρ(r) = exp(−ζrη) for rain type 1. The parameters are obtained for the left and right sides of each rain scene.

Fig. 6.

Histograms of parameters of the averaged 1D ACF ρ(r) = exp(−ζrη) for rain type 1. The parameters are obtained for the left and right sides of each rain scene.

As we discussed in the last paragraph of section 2, however, we need to consider the effect of 4-km windowing to convert σR to the windowed NSD, σR. A theoretically rigorous treatment of this problem is beyond the scope of this paper; in the following, we consider this problem intuitively. Random rain-rate fluctuations, in which wavelengths are less than 4 km, clearly contribute to the variability of rain rate in a 4-km IFOV. The contribution of a fluctuation with spatial wavelength (λ) to σR decreases when λ becomes longer. A simple simulation evaluating the reduction factor versus λ has indicated that the contribution is reduced to about 70% when λ = 8 km. Thus, we assume the following approximate relation:

 
σR ≈ [σ′2Rσ2R〉(D=8km)]1/2,
(22)

where σ2R〉(D=8km) represents the σ2R with an IFOV diameter of 8 km. Using Eq. (16), the coefficient cR relating σR to σR,

 
σR = cRσR,
(23)

can be calculated and is shown in Fig. 7. It is found that cR is about 0.72. For ζ ≈ 0.135 and η ≈ 1.13 (mean values for convective rains), cR is about 0.66. Equation (23) can be generalized as follows to estimate σR from any 〈Q〉:

 
σR = cQσQ.
(23a)

Note that Eqs. (23) and (23a) will also be used to relate sample NSDs (i.e., σ̂R = cQσ̂Q) as an approximate sense.

Fig. 7.

Dependence of the coefficient cR in σRσR relation on the ACF parameters ζ and η.

Fig. 7.

Dependence of the coefficient cR in σRσR relation on the ACF parameters ζ and η.

c. Relation between finescale and coarse-scale rainfall variabilities

The relation Eq. (23a) can also be obtained directly from actual radar data by a regression analysis, although we have to notice that cQ represents the coefficient relating σ̂R to σ̂Q when we use the sample data. In the following, we use ASRT and 〈R〉; the former is used to obtain a statistical relation to estimate σR from the downlooking radar measurement, and the latter to compare with the theoretical calculation discussed above. The difference between 〈R〉 and ASRT will be evaluated in section 4e. Figure 8 shows histograms of the coefficient cASRT, which corresponds to cQ in Eq. (23a), obtained with a linear regression of σ̂R to σ̂ASRT and the correlation coefficient rASRT for the convective (types 1, 2, and 4) and stratiform (type 3) cases. For the convective cases, histograms are given for two cases: (case 1) the minimum rain rate of 0.1 mm h−1 and (case 2) the 5 mm h−1 rain rate to investigate the dependence of the regression result on rain rate since the NUBF correction is more important for higher rain rates. Note that the correlation coefficient is about 0.5 and slightly higher at the stratiform case and that in the convective case the coefficient cASRT is generally greater than it is in the stratiform case. As for the rain-rate dependence, case 2 gives slightly lower and more stable cASRT values than case 1.

Fig. 8.

(a) Histograms of correlation coefficient rASRT and the coefficient cASRT in σ̂Rσ̂ASRT relation for rain types 1, 2, and 4 and for the minimum rain rate processed with 0.1 mm h−1 (case 1) and 5 mm h−1 (case 2). (b) The same as (a) except for type 3.

Fig. 8.

(a) Histograms of correlation coefficient rASRT and the coefficient cASRT in σ̂Rσ̂ASRT relation for rain types 1, 2, and 4 and for the minimum rain rate processed with 0.1 mm h−1 (case 1) and 5 mm h−1 (case 2). (b) The same as (a) except for type 3.

Let us compare the result of the regression analysis between σ̂R and σ̂R with the result obtained from the ACF parameters (Fig. 7). Figure 9 shows the histogram of cR for 32 convective scenes. The mean value of 0.77 (case 2) compares fairly well with the calculation result (about 0.72) based on the ACF analysis shown in Fig. 7 with ζ = 0.135 and η = 1.13. On the other hand, the result of case 1 (mean = 0.96) is significantly higher than the calculation result. Although the sample NSDs should underestimate the NSD of the parent PDF by the factor of [(N − 1)/N)]⁠, where N is the number of data, the effect of this bias error, which causes a slight positive bias, would be rather small (about 2%) considering that both σ̂R and σ̂R underestimate the parent PDF counterparts. Other possible causes of the discrepancy are (i) using the averaged 1D ACF may be a too simplified way to accurately predict the σ̂Rσ̂R relationship, and (ii) more rigorous treatment is necessary of the window effect to the rain-rate variability within a 4-km IFOV. The scattergram between cR obtained from the σ̂Rσ̂R regression and the correlation distance rc derived from ACF analyses is shown in Fig. 10, where rc is defined as the distance at which the ACF is reduced to e−1. It is found that there is a clear correlation between them, suggesting the possibility to relate the the ACF properties to the σRσR relationship.

Fig. 9.

Histogram of cR for 32 convective scenes. Upper and lower panels show case 1 and case 2, respectively.

Fig. 9.

Histogram of cR for 32 convective scenes. Upper and lower panels show case 1 and case 2, respectively.

Fig. 10.

Scattergram between CR obtained from the σ̂Rσ̂R regression and the correlation distance rc derived from the ACF analysis.

Fig. 10.

Scattergram between CR obtained from the σ̂Rσ̂R regression and the correlation distance rc derived from the ACF analysis.

d. Lognormality of rain-rate PDF

To validate the assumption of lognormal distribution of rain rate within an FOV, the lognormality of rain rate for the spatial domain of several kilometers squares is examined by a χ2 test. For this test, 49 rain-rate samples over a domain of 7 km by 7 km square are used, in which the χ2 value is calculated only when at least 45 samples out of 49 are recognized as “rainy.” An example of the results is shown in Fig. 11, where χ2 values are expressed as a cumulative distribution. In the distribution, χ2 greater than about 10 is recognized as nonlognormal with the level of significance of 0.05 since we used an eight-category histogram to calculate the χ2 value that should follow the χ2 distribution for five degrees of freedom. Note that about 50% of the samples do not follow a lognormal distribution. We should note that a no-rain IFOV is not used to calculate a χ2 value since there is an ambiguity in the treatment of “zero” rain rate. Figure 12 shows a summary of lognormality check of 42 rain scenes; the histogram of percentage of rain areas judged nonlognormal with 5% of significance from the χ2 test. It is found that about 50% of the rain area may not be expressed as a lognormally distributed random field.

Fig. 11.

An example of the χ2 test results, where χ2 values are expressed as a cumulative distribution.

Fig. 11.

An example of the χ2 test results, where χ2 values are expressed as a cumulative distribution.

Fig. 12.

A summary of lognormality check of 42 rain scenes; the histogram of percentage of rain areas judged nonlognormal with 5% of significance from the χ2 test.

Fig. 12.

A summary of lognormality check of 42 rain scenes; the histogram of percentage of rain areas judged nonlognormal with 5% of significance from the χ2 test.

Including the zero rain rate in the χ2 test should increases the χ2 value, that is, nonlognormality. This suggests that the nonlognormality may be significant at storm edges and small isolated convective cells. In such cases, significant no-rain areas may exist within an IFOV, and we need to assume a delta-function PDF for the no-rain areas and another PDF for rainy areas. Such a mixed model was used in the NUBF study for microwave radiometer measurements (Graves 1993), but in the case of the spaceborne radar measurement, a model including a nonrainy region in an IFOV would be more difficult to apply because of the saturation of ASRT due to the dominance of echo power from the nonrainy region (Kozu and Iguchi 1996). It is a next-step study subject to find alternative PDF that can better describe the rain-rate variability, to develop a method to detect a mixed rainy/nonrainy IFOV, and to apply a special procedure to such a mixed IFOV.

In spite of the above problems, we will assume the lognormal PDF in the simulation (described in the next section) to relate ASRT to Au because of its simplicity.

e. Radar measurable quantity other than ASRT

In the above discussions and the investigations on rainfall characteristics, we have used ASRT as the measurable quantity 〈Q〉. In cases where either rain attenuation is too small or normalized radar cross section (σ0) of the surface varies considerably, we may not be able to use ASRT. There may be other quantities that can be used to estimate PIA, however. One possibility is the PIA obtained from the sum of attenuations at each range gate estimated from the profile of Z factor including attenuation (Zm) with a k–Z relation (ASU or AST). The quantity Zm(r) is given by

 
formula

where r0 is the range at the storm top, and γ and δ are the coefficient and the exponent of the k–Z relation. PIA estimates (ASU and AST) are expressed as follows:

 
formula

The difference between ASU and AST is that the former uses Zm factors at high altitudes only, so that it may better be used for relatively intense rain cases than the latter, although it does not use rain information at rain bottom. Since ASU and AST depend on the vertical structure of rain rate, the values obtained from the vertically uniform rain rate model assumed in this paper would not be the same as those from natural rain. Nevertheless, investigating the properties of these quantities under the vertically uniform rain would be useful for obtaining an approximate idea about the feasibility of using the Zm profile alone. To test if we can use any quantity as 〈Q〉, depending on the rain and surface characteristics, correlations between cQ’s obtained with the use of 〈R〉 and those obtained with the use of ASRT, ASU, or AST are plotted in Fig. 13, suggesting the validity of using ASRT, ASU, and AST in place of 〈R〉.

Fig. 13.

Correlations between cQ’s obtained with the use of 〈R〉 and those obtained with the use of ASRT, ASU, or AST.

Fig. 13.

Correlations between cQ’s obtained with the use of 〈R〉 and those obtained with the use of ASRT, ASU, or AST.

In the standard Tropical Rainfall Measuring Mission (TRMM) precipitation algorithm (Iguchi et al. 1997), the quantity

 
formula

is used. The factor ε is a parameter to be adjusted by using ASRT or by assuming the Z-factor profile is vertically constant just above the surface. Although we do not test this type of PIA estimate, we can expect that ξ is similar to the quantities we have tested here since ξ is the same as ASRT when SRT is reliable, and it should be generally a better estimate of PIA than ASU or AST.

5. Simulation of the correction method

a. Simulation procedure

For a test of the NUBF correction method, simulations are performed using the same radar dataset as those used in the above analyses. The procedure of the simulation is depicted in Fig. 14. For the preparation of the simulation, we generate a lookup table (actually a set of regression lines of the result shown in Fig. 2, see Table 3) to obtain Au from ASRT and σR.

Fig. 14.

Procedure of the simulation.

Fig. 14.

Procedure of the simulation.

Table 3.

Coefficients a0, a1, and a2 in log10Au = a0 + a1(log10ASRT) + a2(log10ASRT)2.

Coefficients a0, a1, and a2 in log10Au = a0 + a1(log10ASRT) + a2(log10ASRT)2.
Coefficients a0, a1, and a2 in log10Au = a0 + a1(log10ASRT) + a2(log10ASRT)2.

We first convert the 1-km spatial resolution Z-factor map to the corresponding rain rate and PIA. Next, we calculate the coarse-resolution rain rate and PIA of 4-km resolution by using Eqs. (17), (5′), and (19). At the same time, we calculate σ̂R using Eq. (20) and σ̂ASRT using Eq. (21). The relationship between σ̂R and σ̂ASRT [Eq. (23a) is also used for the relation between “sample” NSDs] has been established from a linear regression analysis of these quantities as we discussed in section 4c. In the current simulation, the mean cASRT for convective rain, case 2 (=0.723, see Fig. 8) is used for all types of rainfall.

With the above preparation, the simulation starts with an examination of the coarse-resolution radar measurement (ASRT) of each IFOV. For a specific IFOV, we obtain ASRT and σ̂ASRT. The σ̂ASRT is then converted to σ̂R using Eq. (23a). Here, ASRT and the estimated σ̂R, which is treated as σR, are then input to the lookup table to obtain an estimate of Au (Ãu).

Since we use the same dataset for the simulation as that used to derive the σ̂Rσ̂ASRT relation, this simulation may not be a fully independent test of the method. It would still be useful and necessary to confirm the validity of this method with this type of simulation, however, because the coefficient cASRT to estimate σ̂R from σ̂ASRT shows fairly wide variation when we look at the individual rain scene consisting of nine IFOVs, and the estimation of Au from ASRT and the estimated σR is a highly nonlinear, error-sensitive process. In addition, the validity of assuming the lognormal PDF can be tested with this simulation.

b. Simulation result

Following the procedure described above, simulations of the NUBF correction are performed; the results are shown in Figs. 15–18. In the figures, three scattergrams are compared for each rain type; one is the correlation between Au and ASRT (without correction), the second is that between Au and Ãu (estimated Au with the NUBF correction) using the estimated σR, and the third is the same as the second except that the “true” σR (strictly speaking, true σ̂R) is used. As shown in the figure, the NUBF-corrected results shown in panels b and c can improve the degree of underestimation of PIA, which is significant when no correction is applied. It is also clear, however, that over- and undercorrections are seen in some cases, particularly in types 1 and 2. In type 1, using the true σR significantly improves the tendency of overcorrection, suggesting the errors in estimating σR. On the other hand, in type 2, improvement by using the true σR is not significant, suggesting an improper PDF model causing an erroneous ASRTAu relation. In the cases of types 3 and 4, the result of NUBF correction is better than types 1 and 2; in particular, the NUBF effect is very small in type 3, leading to stable NUBF corrections. From the above results it is clear that the “instantaneous” or “IFOV-based” NUBF correction can have considerable errors. Nevertheless, it can be concluded that the correction scheme tested here does work in general to reduce the significant negative bias errors due to the NUBF. Note that Durden et al. (1998) also obtained an encouraging result in testing an NUBF correction scheme using the data acquired by a KU-band airborne rain radar flown in the TOGA COARE field campaign.

Fig. 15.

Scattergrams (a) between Au and ASRT (without correction), (b) between Au and Ãu (with NUBF correction) using the estimated σR, and (c) between Au and Ãu (with NUBF correction) using the“true” σR for type 1 rainfall. Solid and dotted lines represent the linear regression result and the 1:1 correspondence line, respectively.

Fig. 15.

Scattergrams (a) between Au and ASRT (without correction), (b) between Au and Ãu (with NUBF correction) using the estimated σR, and (c) between Au and Ãu (with NUBF correction) using the“true” σR for type 1 rainfall. Solid and dotted lines represent the linear regression result and the 1:1 correspondence line, respectively.

The effectiveness of the NUBF correction can be seen more clearly from statistical comparisons. Figure 19 shows the ratio of cumulative probabilities of PIA for types 2 and 4. Each cumulative probability represents the percentage of occurrence of PIA that exceeds the given PIA value. In the figure, ASRT/Au and Ãu/Au are plotted. Note that the NUBF correction can significantly improve the estimation accuracy of the cumulative distribution of PIA.

Fig. 19.

Ratio of cumulative probabilities of PIA for type 2 and type 4.

Fig. 19.

Ratio of cumulative probabilities of PIA for type 2 and type 4.

6. Conclusions

We have studied a method to make a correction of the path-integrated attenuation derived from spaceborne radar measurement for nonuniform beamfilling. The key to this method is the estimation of the finescale rain-rate variability within an IFOV (normalized standard deviation; σR) from the local statistics of a radar-measurable quantity (〈Q〉), such as PIA derived from the surface reference technique. The estimated σR is then used to obtain a correction factor to estimate a rainfall quantity such as rain rate and PIA, which should be obtained when rain rate is uniform in the IFOV. In this paper, we have focused our attention to use SRT-derived PIA (ASRT) to estimate the uniform PIA (Au).

Statistical analyses have been made on spatial variabilities of these radar-measurable quantities using a shipborne radar dataset obtained from the TOGA COARE field campaign. The analyses include the spatial autocorrelation function, the finescale and coarse-scale rainfall variabilities, and lognormality of a rain-rate PDF. There are reasonably good correlations between the coarse-scale variability of ASRT and the finescale variability in an IFOV, and the regression coefficient (slope) of these two quantities is fairly stable. Based on the statistical analyses, the method is tested with a simulation using the same dataset. The test result indicates that this method is effective in reducing bias errors in the estimation of rain-rate statistics. It is also effective in making the NUBF correction for individual IFOV basis; in this case, however, one must account for the variability of local rainfall statistical characteristics, which may cause significant errors in estimating Au. In the actual implementation of this method, therefore, some limitation on the magnitude of the correction factor (to retrieve Au from ASRT) or the magnitude of the estimated finescale variability (σR) may be needed to avoid a significant overcorrection for the NUBF.

Fig. 16.

The same as Fig. 15 except for type 2.

Fig. 16.

The same as Fig. 15 except for type 2.

Fig. 17.

The same as Fig. 15 except for type 3.

Fig. 17.

The same as Fig. 15 except for type 3.

Fig. 18.

The same as Fig. 15 except for type 4.

Fig. 18.

The same as Fig. 15 except for type 4.

Acknowledgments

We wish to thank Drs. C. Kummerow and D. A. Short of NASA/Goddard Space Flight Center (GSFC), Prof. K. Shimizu of Keio University, and Dr. N. Kashiwagi of the Institute of Statistical Mathematics for their valuable suggestions. Acknowledgment is also given to Mr. R. Okada, Remote Sensing Technology Center of Japan, who contributed to many of the statistical data analyses. The MIT radar data were kindly provided from the TOGA COARE project and TRMM Office, NASA/GSFC. This study was partly supported by National Space Development Agency of Japan (NASDA) under a joint research program between CRL and NASDA for the study of TRMM Precipitation Radar algorithms.

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Footnotes

Corresponding author address: Dr. Toshiaki Kozu, Department of Electronic and Control Systems Engineering, Shimane University, 1060 Nishi-kawatsu Matsue, Shimane 690-8504, Japan.