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Water vapor estimates using simultaneous S and Ka band radar measurements Scott Ellis, Jothiram Vivekanandan NCAR, Boulder CO, USA.

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Presentation on theme: "Water vapor estimates using simultaneous S and Ka band radar measurements Scott Ellis, Jothiram Vivekanandan NCAR, Boulder CO, USA."— Presentation transcript:

1 Water vapor estimates using simultaneous S and Ka band radar measurements Scott Ellis, Jothiram Vivekanandan NCAR, Boulder CO, USA

2 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

3 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 Height 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 + +

4 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 D 0 from S-band Z and ZDR –D 0 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)

5 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 Specific humidity (g m-3) 1-way atm attenuation (dB km-1) SH = A 3 – A A – 2.25 Where SH is specific humidity (g m -3 ) and A is gaseous attenuation (dB km -1 )

6 Method: Liquid retrieval Liquid water attenuation at Ka-band (A ka ) linearly related to LWC (g m -3 ) –No dependence on drop size distribution –Small temperature correction (C T ) MVD (mm) retrieved from LWC and reflectivity (Z e, mm 6 m -3 ) LWC = 0.74*A ka *C T MVD 3 = 2.16 x *Z e /LWC

7 Results from RICO + radar retrieval – primary ray + Radar retrieval – secondary ray - Sounding Without dry layer With dry layer RMS difference: sounding (g m -3 )

8 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

9 Results from RICO + primary ray + secondary ray - Sounding - Average sounding Sounding average humidity for secondary ray

10 Results from RICO + radar retrieval – primary ray + Radar retrieval – secondary ray - Sounding RMS difference: sounding (g m -3 ) 0.75

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

12 LWC and MVD retrievals S-band reflectivity (dBZ) LWC (g m -3 ) Distance from radar (km) C-130

13 LWC and MVD retrievals Outliers due to attenuation underestimates MVD (mm) LWC (g m -3 ) Distance from radar (km)

14 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

15 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

16 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

17 Thank you. Questions?

18 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

19 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

20 Sources of error Radar calibration –Errors in reflectivity differences Errors in attenuation and humidity resulting from errors in dBZ differences (S – Ka)

21 Method: potential primary rays S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

22 Method: creating secondary rays method 1 S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

23 Method: creating secondary rays method 1 S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

24 Method: creating secondary rays method 1 S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

25 Method: creating secondary rays method 1 S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

26 Method: creating secondary rays method 2 (not implemented yet) S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

27 Method: creating secondary rays method 2 (not implemented yet) S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)

28 Method: creating secondary rays method 2 (not implemented yet) S-band reflectivity (dBZ) Ka-band reflectivity (dBZ)


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