Indian Journal of Radio & Space Physics

Vol. 37, October 2008, pp. 341-352

Estimation of rain parameters from spectral moments of L-band wind profiler

using Multi-Layer Perceptron Network model

Mahen Konwar1, Diganta Kumar Sarma

1, Sanjay Sharma

*, 1, U. K. De

2, S. Pal

3 & Jyotirmoy Das

4

1Department of Physics, Kohima Science College, Jotsoma, Kohima, Nagaland 797 002, India 2Department of Environmental Science, Jadavpur University, Kolkata, India

3ECSU, Indian Statistical Institute, Kolkata 700 108, India 4Institute of Technology and Marine Engineering, Jhinga 743368, India

Received 19 February 2007; revised 3 September 2007; accepted 11 July 2008

Two Multi-Layer Perceptron (MLP) models are developed to estimate the radar reflectivity factor (dBZ) and rain

intensity (R) from the spectral moments of an L-band wind profiler. Out of the four spectral moment inputs of the MLP

models, the backscattered power (P) and Doppler velocity (VD) were found to have better correlation with the rain

parameters. The model results were validated with the Joss Waldvogel Disdrometer (JWD) observations. For the training

and validation data sets of dBZ, the root mean square error (rmse) of estimated and observed data sets were found to be 5.05

and 5.32 dBZ with correlation coefficients of 0.90 and 0.86, respectively. Similarly, for the training and validation data sets

of R, the rmse for estimated and observed values were found to be 4.27 and 7.74 mmh-1 respectively with correlation

coefficients of 0.95 and 0.78. The developed models were validated with a rain event on 22 June 2000, that consisted of rain

from both convective and stratiform regimes. The error between the estimated and observed rain accumulation was found to

be ∼ 5%. The height profiles of estimated dBZ were able to identify the bright band during stratiform rain by virtue of high

values of reflectivity gradient at around 4.0 km height. Though ambiguity in the estimation of R was observed at bright band

level, overall estimated parameters were in good agreement with general characteristics of convective and stratiform rain.

Keywords: L-band wind profiler, Spectral moments, Joss Waldovogel Disdrometer, Radar reflectivity factor, Rain

intensity, Multi-Layer Perceptron network

PACS No.: 92.40.Ea; 92.40.eg; 92.60.gf

1 Introduction Measurement of rain parameters by remote sensing

technique is a fundamental objective in radar

meteorology. There are two types of sensors, i.e.

passive and active, for rain measurement by the

remote sensing technique. The passive sensors, such

as microwave radiometer, measure radiance that is the

end product of the integrated effects of the

electromagnetic absorption-emission and scattering

through the precipitating cloud along the sensor view

path1. However, height assignment of cloud systems

by passive remote sensing is not very specific. On the

other hand, active sensors provide specific height

information of precipitating systems based on the time

delay of back scattered return power1. Presently many

active sensor systems e.g. scanning reflectivity radar,

Doppler weather radar, polarimetric radar and

profiling micro rain radar2-5

are being utilized for rain

measurements by using back scattered power in terms

of radar reflectivity factor (Z). The common approach

for measuring rainfall intensity from backscattered

power is by utilizing the empirical Z-R relations,

where Z is calculated by using the following radar

equation2

2

2

r

ZKCPr = … (1)

where Pr is backscattered

power from the target, K the

dielectric constant of the target, C the radar constant

and r the range of the target. Here, Z is a function of

rain drop size distribution and is independent of radar

frequency.

The wind profilers were originally developed to

measure the height profiles of the three components

of wind velocity. Subsequently however, many

researchers demonstrated its utility for the

identification/classification of precipitating systems6-9

and in measuring the rain parameters, i.e. radar

reflectivity factor and rainfall intensity10-14

. The

precipitating signatures obtained from the wind

profiler are due to Rayleigh scattering from

INDIAN J RADIO & SPACE PHYS, OCTOBER 2008

342

hydrometeors2. The Rayleigh scattering component

observed by a vertically incident profiler results from

convolution of normalized atmospheric turbulent

probability density function with the hydrometeor

reflectivity spectral density10

and it is expressed as

follows

)(]2/)(exp[]2/1[

)()()(

hyd2air

2air

hydairRayleigh

vSv

vSvSvS

×σω−−πσ=

×ω−= … (2)

where v, is the fall velocity of hydrometeor; ϖ, the air

velocity either in the upward or downward direction;

and σair , the spectral broadening of clear air Doppler

spectrum. The hydrometeor spectrum in stationary air

is represented by

vDDDNvS d/d)()(6

hyd = … (3)

where N(D), D and dD/dv are number concentration

of the hydrometeor distribution, the rain drop

diameter and the coordinate transformation from

terminal fall speed to diameter space, respectively.

Extensive research has been carried out to estimate

rain drop size distribution (RDSD) from the Doppler

spectra of either individual or combined use of Very

High Frequency (VHF) and Ultra High Frequency

(UHF) wind profilers13-15

. For RDSD retrievals and

subsequently to estimate the rain parameters, the wind

profilers suffer from certain limitations, viz.

(i) Non-linear response of the receiver during rain -

During clear air measurements, clear air wind

profiler receiver has a linear response. However,

during rain, when backscattered signal is very

strong, profiler receiver has a non-linear

response and an increase in the incident power

produces only a small increase16

in the measured

signal power (S). At some point the receiver

even becomes completely saturated and further

increase in incident power leads to no

measurable increase in S. In addition, the wind

profilers are not calibrated, which implies that

radar reflectivity factor (Z) can not be

determined directly.

(ii) Another limitation is the assumptions made in

certain models17-22

, both physical and empirical.

Also, due to the natural variation of RDSD with

respect to either Z or R, there is an inherent

limitation of the parametric approach to follow a

particular model for the estimation of rain

parameters11

.

In the past, non-parametric technique based Multi-

Layer Perceptron23

(MLP) networks have been

utilized to solve many diverse problems in microwave

remote sensing24–26

. A similar approach is utilized for

the first time to estimate the rain parameters from

Doppler spectra of L-band wind profiler. The

motivating factor of the present study is to enhance

the capability of the wind profiler as a rain profiling

system during rainy situations, when clear air echoes

are completely masked by precipitation echoes. The

main objective of the present study is to develop MLP

network

models to estimate the radar reflectivity

factor (dBZ) and rainfall intensity (R) from the

spectral moments of L-band wind profiler; and to

study the characteristics of the precipitating systems

in terms of height profiles of the estimated

parameters. The multivariable MLP based non-

parametric techniques are important because of their

ability in dealing with the above mentioned errors

common to wind profiler data. First of all, the

inclusion of the multivariable parameters reduce the

total dependency of dBZ and R measurements on

normalized back scattered power. In the present

study, these parameters are estimated with the help of

combined use of all the spectral moments of the

Doppler spectra of L-band wind profiler and DSD

spectra from Joss Waldvogel Disdrometer (JWD).

Moreover, the training of the spectral moments with

dBZ as measured from the independent system will

overcome the limitation of the non-linear response of

the receiver during rainy situations to a certain extent.

Further in the proposed methodology, it is not

necessary to assume a specific model for the retrieval

of rain parameters, and to take into account the

natural variation of the rain parameters.

2 System descriptions and data preparation National Atmospheric Research Laboratory

(NARL), at Gadanki (13.5°N, 79.2°E), India, has the

facility of collocated instruments. For the present

work, observations of L-band wind profiler and Joss

Waldvogel Disdrometer (JWD) for the years 1998-

2000 were utilized. The L-band wind profilers

operated at 1357 MHz is very sensitive to

precipitation particles7

due to Rayleigh scattering,

where scattering cross-section is inversely

proportional to the fourth power of wavelength. The

system description of the L-band wind profiler is

shown in Table 1.

For the extraction of atmospheric parameters from

the back scattered signals of wind profiler it requires

KONWAR et al.: RAIN PARAMETERS FROM WIND PROFILER USING MLP NETWORK MODEL

343

extensive signal processing. The signal processing of

these signals mainly consist of estimation of 0th, 1st

and 2nd moments of Doppler spectrum27

. These

moments correspond to total power (P), mean

Doppler shift (fD) and variance (σ2) of the Doppler

spectra and they are estimated by the following

expressions27

:

∑= iPP … (4)

iiD fPP

f ∑=1

… (5)

22 )(1

Di ffPP I

−=σ ∑

… (6)

Where Pi and fi are the power and Doppler frequency

shift of the ith spectral line in Doppler spectrum,

respectively.

Further, for the vertical incident beam of L-band

wind profiler, fall velocity of the mean raindrop is

given by

)2/(λ= DD fV … (7)

where λ, is the wavelength of the L-band profiler. By

using this expression the Doppler frequency spectra

can be converted to the corresponding velocity

spectra.

Along with these moments the noise level of the

back scattered echoes were also estimated. From the

returned power (P) and noise level (L), the signal-to-

noise ratio (SNR) was calculated by the following

expression

)NFFT/log(10SNR(dB) ×= LP … (8)

where NFFT, is the number of FFT points. For L-

band profiler observations, though the measured

parameters are available from 0.3 km onward, but due

to the presence of ground clutter, data from 0.75 km

onward only have been considered. The moment

estimation of profiler spectra were carried out with

the help of Atmospheric Data Processor (ADP)

software28

developed at NARL, Gadanki.

The other system, i.e. JWD counts the number of

rain drops of different diameters with an integration

time of one minute. For the present study, the dBZ

and R obtained from JWD have been utilized.

Although the JWD underestimates smaller raindrops

during heavy precipitation period due to dead error

time29

, still it is one of the most widely used tools to

study rain characteristics. For the present data set, the

dead error correction was applied to reduce the error

due to the underestimation of the smaller drops during

heavy rains. The dBZ and R were estimated from

JWD observations, by using the following

equations30

:

)Z(log10dBZ 10= … (9)

where

DDDVNATZi

iii ∆= ∑=

20

1

6))(/()/1( … (10)

DDNATR i

i

i ∆π= ∑=

320

1

3 )/1)(10/6.3)(6/( … (11)

where A, is the collecting area of the sensor; T, the

integration time of one minute; Ni, the number of

drops in the ith channel; and Di, the mean drop

diameter of the ith channel.

To make the near simultaneous data points of L-

band profiler and JWD, a time lag due to falling rain

drops from a height of 0.75 km to the ground is

incorporated. The time lag is calculated with the help

of fall velocity parameter (VD) at 0.75 km. Averaging

of the DSD spectra was carried out from the start-to-

stop time observations of the vertical beam of the

profiler to match the different integration time of the

two systems.

3 Methodology

In this study, dBZ and R are estimated from

spectral moments of L-band wind profiler with the

help of DSD observations from JWD by utilizing the

MLP31

network. No prior assumption of any model is

made to the spectrum of the wind profiler. The MLP

networks consist of several layers of neurons, of

which first one is the input layer and the last one the

output layer and in-between layers are called hidden

Table 1―System description of the L-band wind profiler

Parameters Specification

Frequency 1357.5 MHz

Antenna Aperture 3.8m × 3.8m Phased Array

Beam width 4°

Pulse width 1 µs

Inter Pulse 80 µs

Number of FFT points 128

Number of coherent Integration 50

Duty ratio 1.25%

INDIAN J RADIO & SPACE PHYS, OCTOBER 2008

344

layers. Every node, except the input layer nodes,

computes the weighted sum of its inputs and applies

an activation function, a sigmoid function to compute

its output. This output is then transmitted to the nodes

of the next layer. The objective of the MLP learning is

to set the connection weight in such a manner as to

minimize the error between the network output and

the target. The back propagation gradient decent

method32

of learning is utilized for this purpose. The

architectural diagram of the present MLP is shown in

Fig. 1. The present MLP networks consist of three

layers, viz. input layer with four nodes, hidden layer

with 15 nodes and output layer with one node. It is to

be mentioned here that the optimum number of

hidden nodes were found to be 15 in the present MLP

network. The two MLP networks were trained

separately by using the four input vectors from the L-

band wind profiler, i.e. P, VD, σ, and SNR and output

vectors as dBZ and R from the JWD. For the

present study the whole data set consists of ∼ 1000

points. The data set is divided into two parts, i.e. 90%

is utilized for training and remaining 10% for

validation of the trained MLPs. The above network

architecture is realized through the MATLAB toolbox

based on Levenberg-Marquardt (L-M) back

propagation algorithm. The advantage of using this

algorithm is that it converges quickly as compared to

most of the other algorithms. It can train any network

as long as its weight, net input and transfer function

have derivative function. The L-M algorithm uses the

approximation to the Hessian matrix in the Newton

method, where the weight vectors Xt is updated as

t

TT

tt EJIJJXX −µ+−=+ ][1 … (12)

where J, is the Jacobian matrix that contains first

derivative of the network error with respect to weight

and bias; I, the unit matrix; Et, the error at output

node and µ, the learning parameter. Depending upon

the magnitude of µ the method transit smoothly

between the two extremes: Newton method (µ – 0)

and well known steepest decent method (µ - ∞). For

L-M algorithm the performance index to be optimized

is defined as

∑ ∑ −= ])([)( 2

kpkp odwF … (13)

where dkp and okp are the target and output values of

the kth output and pth features.

After the supervised training to the prepared input

data set with respect to target dBZ and R, the weight

matrices for input- hidden layers and hidden-output

layers in both the cases were estimated. These two

developed MLP network models were designated as

MLP_dBZ and MLP_R. Thereafter, with the help of

developed MLP network models the dBZ and R were

estimated for the given set of input spectral moments

data.

4 Results

Simultaneous observations of the L-band wind

profiler and JWD during convection-precipitation

campaign at NARL, Gadanki were utilized for this

work. A rain event on 22-23 June 2000 was selected

to study the typical characteristics of different

moments of Doppler spectra during rainy situations.

This event consists of rain from both the convective

and stratiform regimes. The height-time intensity

(HTI) plots of P, VD, σ and SNR of Doppler spectrum

are shown in Figs. 2(a), (b), (c) and (d), respectively.

On this day, the convective rain started at around

2142 hrs LT and as evident from the profiler

observations, it was characterized by high values of

backscattered power up to the upper height of ~ 6 km

[Fig. 2(a)]. In general, the convective rain is

characterized by high intensity rain at ground. The

stratiform type of rain is identified through the

presence of bright band, which is indicated by a band

of high value of back scattered powers at around 4.2

km. The bright band started to form at around 0131

hrs LT onwards on 23 June 2000. The stratiform rain

at the ground is characterized by low intensity rain for

longer duration of time. Figure 2(b) shows the mean

Doppler velocity of falling rain drops. It was observed

that Doppler velocity of rain drops vary from 1 to 10

m/s. The downward velocity is indicated by –ve sign.

Fig. 1―Architecture of three layers MLP network to estimate the

rain parameters from the spectral moments of L-band profiler

KONWAR et al.: RAIN PARAMETERS FROM WIND PROFILER USING MLP NETWORK MODEL

345

During convective regime the maximum Doppler

velocity is found to be around 10 m/s at higher

altitudes. Figure 2(c) shows the HTI plot of spectral

width of Doppler spectra. Large values of SW present

during convective rain indicate strong turbulence in

the atmosphere. This parameter has positive

correlation with rain as heavy rainfall in the ground

was preoccupied by strong turbulence in the

atmosphere. Figure 2(d) shows the HTI plot of signal-

to-noise ratio during non-rainy and rainy situations.

This parameter is a good indicator of rainy and non-

rainy situations. Though its variation with rain

intensity is not very significant, its value jumped from

-10 dB to ≥ 10 dB from non-rainy to rainy situation.

During this period, the temporal variation of dBZ and

R at ground are shown in Figs. 2(e) and (f),

respectively. During the convective spell the values of

reflectivity factor were varying in the range ∼ 40-50

dBZ, whereas during the stratiform period its values

were < 40 dBZ and minimum values observed up to

20 dBZ. Similarly, the temporal variation of R shows

three spells of convective rain with intensity ranging

from 25 to 78 mmh-1

. These spells started at

2137 hrs LT (JWD data is available from 2143 hrs LT

onwards) and ended at 0110 hrs LT followed by

stratiform type of rain with low rain intensity

< 5 mmh-1

.

The near simultaneous and collocated data points of

the wind profiler and JWD are considered to study

the characteristics of moments of the profiler with

respect to dBZ and R. For this purpose, the scatter

plots for P-dBZ, VD-dBZ, σ-dBZ and SNR-dBZ are

shown in Figs. 3(a), (b), (c) and (d), respectively. For

the scatter plots, the dBZ values were considered from

10 dBZ onward. The power law is fitted to these data

sets and their corresponding equations are provided in

the respective panels. Maximum value of exponent

(0.53) in the power law was observed for the P-dBZ

relation and its values were found to be in descending

order for VD-dBZ, σ-dBZ and SNR-dBZ. The

correlation coefficient (CC) for each scatter plot is

also provided in the respective panels. Maximum

correlation (0.70) was found for VD-dBZ and the CC

were in decreasing order for P-dBZ, SW-dBZ and

SNR-dBZ. Similarly, Figs 4 (a), (b), (c) and (d) show

the scatter plots for P-R, VD-R, σ-R and SNR-R,

respectively. For these scatter plots the rain intensity

values were considered from 0.1 mmh-1

onward. The

power law is fitted to these data sets and their

corresponding equations are provided in the

respective panels. Maximum value of exponent

(0.107) in the power law relation was observed for P-

R plot and its values were in decreasing order for VD-

R, σ-R and SNR-R. The correlation coefficient for

each scatter plot is also provided in the respective

panels. Maximum correlation (0.47) was found for

the VD-R relation and in decreasing order for P-R,

SW-R and SNR-R. Interestingly, though the SNR-R

relation has least values of exponent and correlation

coefficient (CC) but it is a good indicator of rain. It

Fig. 2―HTI plots of (a) returned power, (b) Doppler velocity, (c)

Spectral width, (d) SNR of a Doppler spectrum of L-band wind

profiler, (e) temporal variation of radar reflectivity factor and (f)

temporal variation of rain intensity from JWD

INDIAN J RADIO & SPACE PHYS, OCTOBER 2008

346

Fig. 3―Scatter plots for (a) returned power versus radar reflectivity factor, (b) Doppler velocity versus radar reflectivity factor,

(c) spectral width versus radar reflectivity factor and (d) SNR versus radar reflectivity factor during the rainy situations

Fig. 4―Scatter plots for (a) returned power versus rain intensity, (b) Doppler velocity versus rain intensity, (c) spectral width versus rain

intensity and (d) SNR versus rain intensity during the rainy situations

KONWAR et al.: RAIN PARAMETERS FROM WIND PROFILER USING MLP NETWORK MODEL

347

was observed that as soon as rain started its SNR

jumped from negative to positive values.

After developing the weight matrices of the MLP

network model with the help of supervised training, as

discussed above, the dBZ and R were estimated by

using the spectral moment as inputs from the training

and validation data sets. The estimated dBZ from the

training and validation data sets were further validated

with the observed dBZ. Pixel-to-pixel comparison of

the estimated and observed dBZ for training and

validation data sets are shown in Figs 5(a) and (b),

respectively. The root mean square error (rmse) value

for training data set with respect to observed values

was found to be 5.05 dBZ, with a bias of -0.02 dBZ

and correlation coefficient of 0.90 [Fig. 5(a)]. The

rmse value for validation data set was found to be

5.32 dBZ with a bias of 0.93 dBZ and CC of 0.86

[Fig. 5(b)]. Figures 6(a) and (b) show pixel-to-pixel

Fig. 5―Pixel-to-pixel comparison of estimated and observed radar reflectivity factor for (a) training data set and (b) validation data set

Fig. 6―Pixel-to-pixel comparison of estimated and observed rain intensity for (a) training data set and (b) validation data set

INDIAN J RADIO & SPACE PHYS, OCTOBER 2008

348

comparison of model estimated and observed R for

the training and validation data set. It was observed

that most of the time the peaks of the estimated rain

intensity were following the observed intensity,

though in some cases there were some differences in

magnitudes. The rmse values for training and

validation data set were found to be 5.32 mmh-1

and

7.74 mmh-1

, respectively. The correlation coefficients

for training and validation data set were found to be

0.95 and 0.78, respectively.

An independent validation of the model estimated

dBZ and R with the ground JWD observations was

carried out for a rain event on 22 June 2000. Further,

the model estimated parameters were also compared

with the results from the multivariable regression

method33

. This event consists of both convective and

stratiform rain. The details of this rain event are

provided in Figs 2(a)-(d). The temporal variation of

dBZ as estimated from MLP_ dBZ and multivariable

regression method, along with the observed values are

shown in Fig. 7(b). The rmse for the MLP_ dBZ

estimated values with respect to the observed values

was found to be 5.10 dBZ with a bias of -1.08 dBZ

and CC of 0.84. The rmse for the regression method

was found to be 5.44 dBZ and bias of 1.34 dBZ with a

CC of 0.69. It showed that the results obtained from

the MLP_dBZ model were reasonably improved

compared to that of regression model. Similarly, Fig.

7(a) shows the temporal variation of R as estimated

from MLP_R model and multivariable regression

model along with the observed values on this day.

With respect to observed values, the rmse for the

MLP _R estimated values was found to be 4.40

mmh-1

, with a bias and CC of 0.17 mmh-1

and 0.91,

respectively. In case of regression model, the rmse

value of 8.77 mmh-1

was found with a bias and CC of

3.21 and 0.33 mmh-1

, respectively. The observed and

MLP_R estimated total rain accumulation were found

to be ∼ 21 mm and ∼19 mm, respectively with an

error of ∼ 5.0 %. Here the rain accumulation from the

profiler measurement, i.e. MLP_R was

underestimated compared to rain accumulation from

JWD. Rajopadhya et al.34

, have reported an error,

between estimated and observed rain rate, of around

4% with a correlation coefficient of 0.76. In this

referred work the rain intensity was calculated from

the RDSD, which was estimated by fitting the Gamma

function to the Doppler spectra of UHF profiler at the

height of 1.9 km, by taking into account the vertical

clear air velocity.

Further, on 22 June 2000, the height profiles

of dBZ and R, as estimated by the proposed

Fig. 7―Temporal variation of estimated and observed (a) rain intensity (mmh-1) and (b) radar reflectivity factor (dBZ) [on 22 June 2000

obtained from regression and MLP method]

KONWAR et al.: RAIN PARAMETERS FROM WIND PROFILER USING MLP NETWORK MODEL

349

methodology are shown in Figs. 8(a)-(d) and 8(e)-(h),

respectively. The presented height profiles are at

different stages of convective and stratiform rain.

Figures 8(a) and (b) show the height profiles of dBZ

during the initial and mature stage of the convective

systems, respectively. During initial convective stage,

the reflectivity factor of around 32 dBZ were

observed up to ∼ 2.0 km, whereas during mature

stage the significant values of reflectivity factor of 40

- 50 dBZ were observed up to ∼ 4.5 km. Figures 8(c)

and (d) show the height profiles of dBZ during

different stages of stratiform rain. At the initial

stage of the stratiform rain, at 0154 hrs LT, the

presence of bright band is quite obvious at a height

of around ∼ 4.0 to 4.5 km by virtue of high value of

reflectivity gradient of ∼ 37 dBZ/km. At around 0216

hrs LT, at the later stage of the stratiform rain, the

bright band was not very prominent as concluded by

virtue of low value of reflectivity gradient of ∼ 12

dBZ/km at around same height [Fig. 8(d)].

Interestingly, the values of the estimated dBZ at the

lower heights are quite comparable with the JWD

observations at ground, which are provided at each

panel. Similarly, for the same day Figs 8(e) and (f)

show the model estimated height profiles of R during

the initial and mature stage of the convective systems,

respectively. During initial convective stage, the low

values of R of the order of 2-10 mm/h were observed,

whereas during the mature stage, high values of R, of

the order of 50-90 mm/h were observed up to ∼ 5 km.

Figures 8(g) and (h) show height profiles of R during

different stages of stratiform rain. During stratiform

situation, the overall values of estimated R were low,

i.e. of the order of 2-10 mmh-1

. At the bright band

level, there is an ambiguity of high values of R, which

require a correction for the realistic measurements of

these parameters at bright band level. During the

absence of strong bright band, this ambiguity is

reduced to certain extent [Fig. 8(h)]. Overall, the

values of the model estimated R at the lower heights

is reasonably comparable with the JWD observations

at ground, which are provided in each panel.

On 18 May 1999, a case study was carried out to

compare the height profiles of model estimated dBZ

and R with the Precipitation Radar (PR) observation

[on board of Tropical Rainfall Measuring Mission

(TRMM) satellite]. On this day, the TRMM satellite

passed nearly over the radar site during precipitating

time (TRMM orbit number 8463). For this

comparison, the 2A25 data product of PR was

utilized. The nearly coincident footprints of TRMM-

PR and L-band profiler were selected for analysis.

Fig. 8―Height profiles of MLP estimated-Z during (a), (b) convective and (c), (d) stratiform; and height profiles of R during

(e), (f) convective and (g), (h) stratiform, type of rain on 22 June 2000

INDIAN J RADIO & SPACE PHYS, OCTOBER 2008

350

The pixel resolution of PR is 16.0 km2 with a height

resolution of 250 km and a beam width of 1°. The

profiler and PR estimated height profiles of dBZ and

R are shown in Figs 9(a) and (b), respectively. As

these two systems have different height resolution,

therefore, the MLP and PR estimated rain parameters

are plotted at 150 and 250 m resolutions, respectively.

The important feature of this comparative study of

dBZ is the detection of a bright band at around

~ 3.7 km, by virtue of reflectivity gradient of 11

dBZ/ km signifying the presence of weak bright

band during stratiform precipitation [Fig. 9(a)].

Though the height profile of R from MLP_R is

comparable with PR measurements, it had slightly

underestimated rain rate compared to PR. For the

height profile of R, at the bright band level, the

ambiguity of rain rate estimation from MLP_R is

relatively less due to the presence of weak bright

band [Fig. 9(b)]. Though this comparison may not be

statistically very significant, overall good agreement

was found between the height profiles of model and

PR estimated dBZ and R.

5 Summary and discussions

Artificial Neural Network (ANN) based two MLP

models have been developed to estimate the dBZ and

R from the spectral moments of the L-band wind

profiler at NARL, Gadanki. The characteristics of the

spectral moments of the L-band wind profiler were

studied with respect to rain parameters. It was

observed that back scattered power P was most

closely related with these parameters by virtue of

higher value of exponent in the power law relations

between these two parameters. The fall velocity of

rain drops is most closely related with these

parameters in terms of high value of correlation

coefficient. The SNR parameter of the profiler,

though poorly correlated with rain parameters, was a

good indicator to represent rain or no-rain situations.

Overall good agreement between the estimated rain

parameters from the proposed methodology and the

JWD measured parameters were observed. It was also

found that the MLP model performed better compared

to parametric regression models. The estimated rain

parameters from the proposed methodology were also

able to show the general characteristics of the

convective and stratiform rain by virtue of its high

values of dBZ and R up to upper heights during

convective rain and the presence of bright band

during stratiform rain. It was noticed that during the

presence of strong bright band there is significant

error in the rain rate estimation at that height.

The ambiguity of the rainfall estimation at the

bright band is expected because different

microphysical processes dominate the precipitating

mechanisms. The MLPs estimated dBZ and R were

compared with the PR estimated height profiles over

Gadanki. Significantly the height profiles of dBZ

from both the systems have shown bright band

signature at around 3.7 km height. There are many

reasons to expect the discrepancy in the model

estimated and the observed rain parameters34

. There is

a limitation of sample volumes in the estimation of

height profiles of the rain parameters. In the present

study, the spectral moments at the lower most reliable

range bin (0.75 km) were trained with the JWD

Fig. 9―Comparison of height profiles of (a) Z; and (b) R (from PR observed and MLP estimated)

KONWAR et al.: RAIN PARAMETERS FROM WIND PROFILER USING MLP NETWORK MODEL

351

observations, which is essentially a point observation.

Now to further estimate the rain parameters at higher

range bins, which have increasingly different sample

volume, the errors in the estimation of height profiles

of these parameters are expected to increase. The

difference between the measurements from these two

systems could also be attributed to the ambient

atmospheric conditions such as humidity and

temperature, which determine the rate of evaporation

of the drops that fall from the retrieved heights to the

ground (JWD).

During thunderstorm or squall line seasons there

may be advection and convergence at the surface that

may have effect on the fall velocity of the

hydrometeor particles35

. It is important to mention

here that the TRMM ground validation field

campaigns36

were carried out by utilizing a standard

915 MHz profiler during Texas and Florida

Underflights Experiment (TEFLUN)-A and

TEFLUN-B in 1998. In that experiment the profiler

was calibrated with collocated RD-69 JWD at the

lowest range gates. In the present study, an L-band

wind profiler operated at 1.3 GHz has been utilized to

estimate the reflectivity and rain profiles with the help

of collocated RD-69 disdrometer. The study shows

that the MLP based techniques can be utilized to

estimate the rain parameters from the L-band wind

profilers with a reasonably good accuracy, though it

was originally developed to measure the clear air

velocity. A practical implication of the proposed

methodology is that it will contribute to the ongoing

effort to enhance the capability of the wind profiler

as a rain profiling system, when the clear air echoes

are masked by the precipitation echoes.

Acknowledgements Financial grant from Department of Space to carry

on this work [No. 10/4/362, under RESPOND

program] is gratefully acknowledged. Authors would

like to thank Director, NARL, Gadanki and

Coordinator SVU-UGC Centre for MST Radar

Applications for providing the necessary support to

conduct the experiment at radar site and subsequent

data analysis. The active support from the engineers at

NMRF during the experiments is thankfully

acknowledged. Authors are thankful to NASA-GSFC

for providing the TRMM data. Authors are thankful to

Kohima Science College authorities for providing

necessary facilities to carry out the research work.

Authors are grateful to the three anonymous reviewers

for their valuable comments and suggestions.

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