water vapor estimates using simultaneous s and ka band radar measurements scott ellis, jothiram...

Water vapor estimates using simultaneous S and Ka band

radar measurements

Scott Ellis, Jothiram VivekanandanNCAR, Boulder CO, USA

Background

• NCAR S-Pol radar upgraded with simultaneous S- and Ka-band measurement capability– Matched beam widths– Matched range gates

S-band antenna

Ka-band antenna

• For Rayleigh scatterers reflectivity differences can be related to attenuation by liquid and gas

Objectives• Retrieve path-integrated humidity

– Differential gaseous absorption

– Compare reflectivity at nearest edge of cloud

– Create profile by plotting mid-point of path integrated estimates

Range

Hei

ght

Range resolved cloud liquid

Path integrated water vapor profiles

++

+

• Retrieve range-resolved liquid water content (LWC) and median volume diameter (MVD) through clouds– Differential absorption through

clouds

+

+

Method

• Remove non-meteorological targets• Determine where Rayleigh scattering

approximation is valid at Ka-band– Use S-band dual-pol measurements

• < 1 mm drops– Estimate D0 from S-band Z and ZDR– D0 must be < 0.5 mm

• Z at S-band < 20 dBZ• ZDR < 0.4 dB

– Produce Rayleigh mask

• Estimate attenuations– Liquid– gaseous

Blue = Mie Brown = Rayleigh

Example PPI plot of Rayleigh mask of cloud field during RICO

Distance from radar (km)

Method: Humidity retrieval

• Run radiation model many times varying T, P and specific humidity (SH, g m-3)

• Compute polynomial fit of SH to attenuation

Spe

cifi

c hu

mid

ity

(g m

-3)

1-way atm attenuation (dB km-1)

SH = 201.40A3 – 209.60A2 + 120.55A – 2.25

Where SH is specific humidity (g m-3) and A is gaseous attenuation (dB km-1)

Method: Liquid retrieval

• Liquid water attenuation at Ka-band (Aka) linearly related to LWC (g m-3) – No dependence on drop size distribution

– Small temperature correction (CT)

• MVD (mm) retrieved from LWC and reflectivity (Ze, mm6 m-3)

LWC = 0.74*Aka*CT

MVD3 = 2.16 x 10-4*Ze/LWC

Results from RICO

+ radar retrieval – primary ray + Radar retrieval – secondary ray - Sounding

Without dry layer

With dry layer

RMS difference: sounding (g m-3)

0.85 1.40

Results from RICO

+ radar retrieval – primary ray + Radar retrieval – secondary ray - Sounding - Average sounding

Average sounding computed at height H as the average specific humidity from 0 to 2*H

Results from RICO

+ primary ray + secondary ray - Sounding - Average sounding

Sounding average humidity for secondary ray

Results from RICO

+ radar retrieval – primary ray + Radar retrieval – secondary ray - Sounding

RMS difference: sounding (g m-3)

0.75

LWC and MVD retrievalsS-band reflectivity (dBZ) with C-130 track displaying LWC (g m-3) collected from RICO 12 January, 2005

C-130

LWC and MVD retrievals

S-band reflectivity (dBZ) LWC (g m-3)

Distance from radar (km) Distance from radar (km)

C-130

LWC and MVD retrievals

Outliers due to attenuation underestimates

MVD (mm) LWC (g m-3)

Distance from radar (km) Distance from radar (km)

Future work -- planned• Automate algorithms• Further verification

– Use aircraft/radar matching technique for LWC

• Algorithm refinements– Humidity

• Compute humidity – attenuation relationships for several layers and use most appropriate one

• Compute optimal humidity profiles using primary and secondary rays

– LWC/MVD• Improve attenuation estimation to remove outliers

• Account for increased attenuation in non-Rayleigh rain

Future work -- planned

• Compare dual-wavelength humidity with refractivity, GPS, water vapor DIAL, radiometer… during REFRACTT 06 and COPS 07

• Combine humidity retrieval with near-surface refractive index humidity for radar based 3-D moisture field

• Detection/quantification of super-cooled liquid water– Microphysics

– Aviation safety

Future work -- desired

• Partner with international community to verify satellite derived and model microphysical products– Cloudsat

• Cloud fraction

• LWC

• Ice content

– NASA GPM

• Partner with data assimilation community

Thank you.

Questions?

Discussion

• Possible to obtain accurate path-integrated water vapor estimates in boundary layer

• Both horizontal and vertical distributions can be obtained

• Depends on cloud distribution

• Path integration limits resolution

• Not automated yet

• Can be automated

Sources of error

• Failure to exclude contamination

• Errors in humidity- and liquid- attenuation relationship

• Radar calibration errors

• Modification of humidity by environmental conditions by clouds; e.g. moist, cool outflows

Sources of error• Radar calibration

– Errors in reflectivity differences

Errors in attenuation and humidity resulting from errors in dBZ differences (S – Ka)

Method: potential primary rays

S-band reflectivity (dBZ)

Ka-band reflectivity (dBZ)

Method: creating secondary rays method 1



Method: creating secondary rays method 2 (not implemented yet)



water vapor estimates using simultaneous s and ka band radar measurements scott ellis, jothiram...

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