09 March 2005

‘Radar studies on Precipitation, IITM, Pune …madhuchandra r.k

Welcome to

Radar Studies on Precipitation

MADHU CHANDRA R K, IITM, Pune

Importance & Relevance

Background

Technological advances

Figure 3.3. Height profiles of (a) potential temperature () (circled line) and Relative Humidity (RH) (solid line) (b) vertical wind (w) (circled line) and Richardson number (Ri) (solid line) (c) log (circled line) and log Cn

2 (solid line) and (d) wind shear (circled line) and wind speed (Vh)(solid line). All parameters as set (a, b, c and d) associated with particular radiosonde lunch observations correspond to 27 July 1999 at 1630 LT (top panels) and 10 August 1999 at 1800 LT (bottom panels), respectively. The arrow on θ-profile shows the top of the boundary layer.

Figure 3.4. HTI sections of diurnal plots of (a) refractive index structure parameter (b) turbulent energy dissipation rate (c) corrected velocity variance (d) wind speed (e) wind shears and (f) vertical wind observed on 27 July 1999. Star marks show the radiosonde estimated boundary layer top.

Electromagnetic Spectrum

Residence time

Typical Rain Drop

R = 1000 m or 1 mm; N= 0.01 cm-3; V = 650 cm s-1

Large AerosolR = 5 m

N = 10 cm-3

V = 3 m hr-1

Small AerosolR = 0.5 mN = 10 cm-3

V = 0.3 m hr-1

Residence time ~ 10 days Residence time ~ 23 days

Residence time ~ few minutes

Alt

itud

e (m

)

1000

2000

3000

4000

5000

Surface32C

22C

12C

-3C

2C

-8C

Condensation level

Clouds are visible aggregates of minute water droplets or

tiny crystals of ice.

Clouds are classified on the basis of their appearance and

height.

There are three basic cloud types. Cirrus, Stratus, and

Cumulus.

Cirrus clouds are high, white and thin. The cumulus consists

of individual cloud masses with large vertical extent.

Stratus clouds are sheets or layers that cover much or all of

the sky.

Collision-Coalescence

Cloud Droplet Growth

Conventional boarder line between cloud drops

& rain dropsR=100 m

V = 70 cm s-1

Typical Condensation Nucleus (CN)R = 0.1 - 1mN = 1000 cm-3

V = 0.001 mm s-1

Typical Cloud DropR=10 m

N = 300 - 500 cm-3

V = 1 cm s-1

Larger Cloud DropR=50 m

N = 10 cm-3

V = 27 cm s-1

Cloud Drops and Rain Drops

Typical Rain Drop

R = 1000 m or 1 mm; N= 0.01 cm-3; V = 650 cm s-1

Absorption spectra of atmospheric gases

CH4

CO2

N2O

H2O

O2 & O3

atmosphere

AB

SO

RPTIV

ITY

WAVELENGTH (micrometers)IR Windows

InfraredVisible

UV

Figure 5.1. Gadanki four-year rain statistics, like rain accumulation, number of rain events in each year with number of rainy days are presented.

Figure 5.2. Monthly rain accumulation information for the years 1998-2001 is shown in panels (a-d) respectively. Gray color columns represent the accumulation contribution from heavy rain. Panel (e) shows the percentage of heavy rain (h.r) contribution to the annual rain accumulation during 1998 to 2001.

Figure 5.3. Average diurnal variation of precipitation from August to November.

MST Radar Antenna Array

Disdrometer

ORG

LAWP

System (s) used

Parameters Specification

Sampling area (cm2 : 50 Dimensions (cm) : 10 x 10 x 17 Range of drop diameter (mm) : 0.3 to 5 Accuracy of measured diameter : 5% No. of drop size classes : 20Time resolution (min.) : 1

Table 2.1. LAWP and MST system and the experimental specifications used for the present study._____________________________________________________________________PARAMETER SPECIFICATIONS LAWP MST Radar_____________________________________________________________________Location Gadanki (13.5N, 79.2E)Antenna (Phased Array) 3.8m X 3.8m 130m X 130mFrequency (f) 1357.5 MHz 53 MHz Radar wave length (lR ) 0.22 m 5.66 m Transmitted peak power (Pt) 1000 Watts 2.5 X 106 WattsEffective aperture (Ae) 10 m2 104 m2Beam width 4 3No. of beams* 3(E15, Zenith & N15) 3(E10, Zenith & N10)Gain (G) 33 dB 36 dBReceiver band width (BN) 1.58 x 106 Hz 1.7 x 106 HzReceiver path loss (ar) 4 dB 4.4 dBTransmitter path loss (at) 0.5 dB 2.65 dBCosmic temperature (Tc ) 10 K 6000 KReceiver temperature (Tr) 107 K 607 KMaximum duty ratio 0.05 0.025Pulse width 1 sec 1 µsecInter Pulse Period (IPP) 70 msec 1 msecRange resolution (Δr) 150 m 150 mCoherent Integration (NC) 100 128No. of FFT points 128 128Observational window: Lowest range bin (km) 0.3 1.5Highest range bin (km) 9.25 14.25 Incoherent integration 100 1Beam Dwell time (sec.) 93 17_____________________________________________________________________

Figure 2.6b. Representative LAWP observed vertical beam Doppler spectra during convective (top) at 0009 LT, and stratiform (bottom) at 0536 LT on precipitation conditions with their respective moments (a) signal power (b) Doppler shift (c) spectral width and (d) SNR observed on 01 August 1998.

Bright band

Figure 5.7. (a) Isometric plot of LAWP Doppler radar spectra for zenith beam during stratiform rain (b) Isometric plot of LAWP Doppler radar spectra for zenith beam during convective rain.

Figure 5.8. Vertical profiles of the range corrected SNR during the presence of bright- band in stratiform rain observed on 01 August 1998.

Received Signal

Ranging(Sampling)

Coherent integration(Time-Domain Averaging)

Spectral analysis(Fast Fourier Transform)

Incoherent Integration(Spectral Averaging)

Spectral Cleaning

Noise Level Estimation

Spectral moments viz.,Echo Power, Doppler Shift

and Spectral Width

Wind vectors (U, V, & W)

Figure 2.5a. Flow chart followed of data processing sequence.

D.C. Removal

Figure 2.5b. Example of a Doppler velocity spectrum observed by the LAWP at 3.45 km at 0227 LT on 02 August 1999. The beam direction is East 15º. Dotted line shows the result of least square fitting to Gaussian function. The gray filled area is the signal power (S) and the hatched area under dashed line is noise power (N) .

Rayleigh scattering theory can be used to approximate the characteristics of signals reflected back to the radar from precipitation particles (rain, snow, graupel and hail), when the diameter D of the scatterer is small compared to the radar wavelength. The volume reflectivity in this case is given by

= 5 |K|2 Z -4 where Z = NiDi

6 is the reflectivity factor in mm6 m-3 and is often expressed by radar meteorologists in the form dBZ equal to 10 log10(Z). The summation is over all drop sizes D, with N, the number of drops in a unit volume. |K|2 is a function of the complex index of refraction of the target equal to 0.93 for liquid water and 0.18 for ice particles. For Rayleigh scattering from spherical particles (i.e., when the diameter D of the particle is small in comparison with the radar wavelength), the backscattering cross-section of a single particle is

6UnitVolume

24

5

D|K|

|K|2 = |(m2-1)/(m2+2)|2

Precipitation studies (Rain Drop Size

Distribution)

The objectives of this study are……

To see the suitability of two DSD models namely exponential and lognormal model with different combination of moments for observing the characteristic features of tropical rain.

To classify stratiform and convective precipitation with the help of LAWP spectra and to study the characteristics of bulk rain integral and lognormal model parameters with respect to stratiform and convective precipitation.

To study the natural variation of DSD with respect to mass weighted mean diameter Dm during stratiform and convective precipitation.

Tropical precipitation systems using DSD & Doppler spectra

RAIN DROP SIZE DISTRIBUTION (DSD) MODELS

Estimation of rain from radars and satellites requires the knowledge of DSD. The DSD is the most versatile rain parameter, by which one can derive many rain integral parameters such as rainfall intensity, liquid water content, radar reflectivity factor, optical extinction, and etc., The exponential and lognormal model parameters can be derived by moment regression method. The moment equation is given by following expression [Ulbrich, 1983],

where N(D) is the number density of drops, D is the drop diameter, dD is the increment in drop diameter, Indexed p indicate the respective moment of the drop spectra and ap is a constant for the corresponding pth moment.

0

ppp dDN(D)Da M

The exponential model is expressed by the following equation [Waldvogel, 1974].

N(D)= N0 exp(-D) where N0 = 446.43 W (W /Z) 1.333 and = 6.1196 (W /Z) 0.333

Here N0 (m-3 mm-1) and (mm-1) are the coefficient and slope of DSD curve respectively. To estimate N0 and , liquid water content W (mm3 m-3) and radar reflectivity factor Z (mm6 m-3) are calculated for each observational DSD spectrum of one-minute duration.

The model equation for the lognormal distribution is expressed by the following expression [Ajayi and Olsen, 1985]

N (D)= (Nt /. (2) .Di) exp [-1/2{(lnDi - ) / } 2]

where Nt (m-3) is total number of drops, (mm) is mean of lnD and is standard deviation of the drop spectra which can be expressed as

Nt = ao (R) bo ; = A + B ln (R); 2 = A + B ln (R)

In the above equation R (mm h-1) is rainfall intensity used as the input parameter, and Di is the drop diameter for ith channel of disdrometer. A, B, A and B are the coefficients of the above equations. These coefficients can be calculated by the moment regression for the combinations of two sets of moments namely (2,3) and (5,6).

Figure 5.5. Comparison of the model DSD with the observed distribution at different rainfall intensity. Figure 5.6. Comparison of chi square error of observed and model for (a) rainfall rate, for exponential and lognormal distribution (moments 2,3) & moments (5,6) and (b) radar reflectivity factor, for exponential and lognormal distribution for (moments 2,3) & moments (5,6) (c) Comparison of chi square error of ORG observations and model values of rainfall intensity for exponential and lognormal with moments (5,6).

Figure 5.9. Scatter plots of (a) M2 Vs R (b) M3 Vs R (c) M4 Vs R (d) M6 Vs R during stratiform rain and (e) M2 Vs R (f) M3 Vs R (g) M4 Vs R (h) M6 Vs R during convective rain.

Parameter Type Relation Correl. coeff.

M2-R StratiformConvective

M2 = 146.8 R1.08

M2 = 50.8 R1.07

0.9690.960

M3-R StratiformConvective

M3 = 136.6 R1.11

M3 = 55.0 R1.22

0.9900.995

M4-R StratiformConvective

M4 = 166.9 R1.23

M4 = 61.1 R1.42

0.9990.997

M6-R StratiformConvective

M6 = 405.5 R1.61

M6 = 80.8 R1.93

0.9850.968

Figure 5.14. Scatter plots of (a) Nt Vs R (b) Vs R and (c)2 Vs R, for Stratiform and Convective rain.

Figure 5.15. Scatter plot of (a) dBZ Vs Dm (b) R Vs Dm, during stratiform and convective rain.

Parameter Type Relation Correl. coeff

Z-Dm StratiformConvective

Z = 118 Dm 2.80

Z = 3633Dm2.50

0.890.87

R-Dm Stratiform Convective

Dm = 1.36 R0.15

Dm = 1.27 R0.16

0.840.81

During the convective center the value of µ is greater than zero towards positive side. During dissipation stage and stratiform type its value is less than zero. For the same rain rate, in the range of 0.1 to 7.0 mm h -1, the stratiform precipitation has higher value of µ, compared to convective precipitation. However, N t values for stratiform precipitation system is lower compared to convective precipitation system.

During high convective precipitation the values of Dm ≥ 2.0 mm and for stratiform precipitation the average value of Dm ~ 1.5 mm are observed. It is observed that Dm plays an important role in determining the radar reflectivity factor dBZ. Rain fall estimation is very sensitive to the measurements of Dm

Figure 5.10. HTI plot of back scattered signal to noise ratio during stratiform rain on 01 August 1998.

Figure 5.12. HTI plot of back scattered signal to noise ratio during convective type of rain on 22-23 June 2000.

Precipitation studies (Rain Drop Size

Distribution)

The evaporation/precipitation cycle of water has a definite effect on the vertical and horizontal transport of energy in the atmosphere. The spatial and temporal information of precipitation helps in understanding and characterization of the precipitation systems. Potential precipitation parameters like Drop Size Distribution (DSD) can be used to characterize the precipitation. The spatial and temporal distribution of precipitation can be better understood with the availability of height profiles of rain Drop Size Distribution.

The purpose of the present work is

To retrieve reliable rain DSD height profile using simultaneous observations of UHF and VHF vertically pointing Doppler radar spectra and understanding the height distribution of rain rate and liquid water content with respect to the precipitation systems viz., convective and stratiform.

It has observed that lognormal distribution is characterizing Gadanki rain distribution better than exponential distribution. Therefore, the lognormal distribution fit is used for retrieval of DSD from radar Doppler spectra.

Estimation of DSD from VHR/UHF Radar Doppler spectra

The backscattered power from the hydrometeors depends upon the size, phase, and number density of the hydrometeors in the pulse volume. The radar reflectivity factor Z due to the hydrometeors varies as the sixth power of the diameter of the hydrometeors summed over all the hydrometeors per unit volume and is expressed as [Doviak and Zrnic ,1993]

Rain DSD retrieval: Methodology

Z =

0

6dDN(D)D

The lognormal distribution form of N(D) is as follows [Ajayi and Olsen, 1985; Feingold and Levin, 1986]

N (D)= (Nt / (2) Di) exp [-1/2{(lnDi - ) / } 2]

3.10

w65.9ln667.1)w(D

= (ρ/ρo) 0.45

dDD)(wN(D)D6

R0

3

0

3w dD N(D)D6

W

Figure 6.6. UHF radar zenith beam Doppler spectrums observed during 2233 LT on 22 June 2000 and 0158 LT on 23 June 2000.

[d] [e][f]

[a][b] [c]

Figure 6.4. Height profile of DSD obtained from representative convective rain Doppler spectra observed during at 2233 LT on 22 June 2000. Height profile extends from 1.8 km to 5.1 km with height resolution 300 m.Each panel consists of observed DSD with and without vertical air velocity corrections shown as bold solid and dotted line respectively. Lognormal least square fit to corrected DSD spectra shown in grey line. Rain rate and Liquid water content estimated from lognormal distribution is given for each height.

Figure 6.8. Height profile of DSD obtained from representative stratiform rain Doppler spectra observed during at 0158 LT on 23 June 2000. Height profile extends from 1.5 km to 3.6 km with height resolution 300 m.

Figure 6.9. Estimated height profile of rain rate (R) (black dotted line) and liquid water content (W) (red solid line) from (a and c) convective Doppler spectra and (b and d) stratiform Doppler spectra.

Figure 6.2 (a) Rain rates and (b) Liquid water content estimated from radar Doppler spectrums observed at a time interval of every ~10 minutes from 2215 to 0230 LT on 22-23 June 2000.

Gadanki rain observational statistics show that the average annual rainfall accumulation is ~ 810 mm. About 66% of annual rain accumulation is due to heavy rain though their occurrence is about 31 %. The rain occurrence rate is more during August to November and it is noticed that their frequency is maximum between 1400 LT and 0600 LT during the day.

It is found that lognormal model distribution better characterizes the Gadanki rain in comparison to exponential model. The lognormal model parameter shows good correlation with rainfall rate. This parameter shows positive values for convective rain and negative for stratiform rain. By using the various power law relation between rain rate and DSD moments (rain integral parameters), median volume diameter (Dm) and parameter all together can be used to classify the precipitation systems as convective or stratiform.

LAWP vertical beam Doppler spectra shows clear distinguishable features for stratiform and convective precipitation conditions. It is observed that during stratiform precipitation condition, there is strong SNR gradient of order 5 – 15 dB observed at radar bright band level and the Doppler shifts are observed to be around ~ 60 – 70 Hz below the bright band level. During convective precipitation conditions, there is no bright band feature but strong SNR values are found even up to the height of 9 km with Doppler shifts around ~ 60 – 125 Hz. Therefore, these distinct futures of LAWP spectra make feasible to distinguish between convective and stratiform precipitation systems.

The LAWP spectral observations enable us to study the height variation of DSD during various precipitation events. The DSD height profile gives an interesting view of vertical structure of precipitation systems. This height profile of DSD also gives an indication of heights where the precipitation is triggered. These studies can thus help in understanding the microphysical properties of clouds.

It is observed that the estimated rain rate and liquid water content height profiles, from radar retrieved DSD height profiles, for convective and stratiform rain are different. Moreover, it is possible to determine different stages of the convective precipitation system from the continuous time height profiles of DSD.

Gross Conclusions

Future motivation

Challenges 4 u(s)

LAWP is unable to probe the first few hundred meter of the atmosphere, i.e. 500 m, from the Earth’s surface. This lower height region which plays a vital role on the structure and dynamics of boundary layer. So, it is important to probe this region using SODAR and tower with fast responding sensors, together with LAWP observations for better understanding the boundary layer structure, turbulent fluxes and convective plumes characteristics. This is particularly important for the studies of nocturnal boundary layer and boundary layer related wave studies such as mountain waves.

It is observed that during monsoon season, like LLJ, there exists Tropical Easterly Jet (TEJ) near tropopause height (~ 16 km). The relation between LLJ and TEJ can be studied as a coupled entity to understand the wind systems during monsoon season.

The occurrence of occasional peculiar high reflectivity in LAWP observations during nocturnal time, i.e., sunset to sunrise, can be examined in detail to identify the source mechanism associated with such peculiar echoes.

During various precipitation events such as SW, NE monsoon systems and convection events Doppler spectra from the LAWP observations can be used to retrieve DSD height profiles. This will help in understanding the cloud microphysical properties associated with various tropical precipitating systems.

Simultaneous height profiles of both DSD and meteorological parameters from GPS sonde can be used to estimate the latent heat profiles associated with various tropical precipitation systems.

Challenges 2 U(s)

Thank you 4 ur co-operation & patience

Thank

you

4 ur

c ooperat ion & pat ience

Thanks 4 ur c peration

& patience

________Ji Hindu

Coming together is a beginning

Keeping together is progress

Working together is success

________Henry Ford

National MST Radar Facility, Gadanki

(13.5oN, 79.2oE)