A Combined Radar-Radiometer Approach to Estimate Rain Rate Profile and Underlying Surface Wind Speed over the Ocean Shannon Brown and Christopher Ruf University of Michigan 26 October 2004 College of Engineering Space Physics Research Laboratory Department of Atmospheric, Oceanic & Space Sciences

Brown and Ruf, 26 October of 19 Introduction Pacific Field Campaign –LRR-X 10.7 GHz radiometer –PR and 35.6 GHz Doppler radar Algorithm Overview Retrieval in stratiform rain –Effect of melting layer model

Brown and Ruf, 26 October of 19 LRR-X – Synthetic Thinned Aperture Radiometer Visible Camera LRR Point Reyes National Seashore, CA –DC-8 nadir video camera (left) –LRR-X TB image at 10.7 GHz, H-Pol (right) LRR-X Specifications – Synthetic aperture 1 meter 2 ; Cross-track imaging – Spatial 11 km altitude 381 x 466 m (nadir); 1079 x 629 m (45 o cross track) – NE  T of 0.3 K

Brown and Ruf, 26 October of 19 PR-2 – Dual Frequency Doppler Radar Operates at 13.4 and 35.6 GHz Scans cross-track to + 25 o 37 m vertical resolution 800 m horizontal resolution

Brown and Ruf, 26 October of 19 June 13, 2003 Pacific Field Campaign Visible IR Flight Path

Brown and Ruf, 26 October of 19 Algorithm Basis Physically based algorithm Easily adaptable to multi-instrument platforms Use radar to determine DSD –Iteratively solve for two parameters of Gamma DSD at each range gate –Determine RR(z) and W(z) from DSD(z) Use DSD and T B to determine wind speed –Determine absorption and extinction profile from DSD –Remove atmospheric component to determine surface emissivity

Brown and Ruf, 26 October of 19 Stratiform Retrievals Radiometric retrieval in light stratiform rain driven by absorption in the melting layer –Passive rain retrieval –Surface parameter retrieval 1500 m

Brown and Ruf, 26 October of 19 Stratiform Retrievals (Bottom left) Retrieved wind speed without Melting Layer (Bottom right) PR-2 retrieved rain rate

Brown and Ruf, 26 October of 19 Melting Model Analysis Choose melting layer model based on fit to PR-2 data Apply to radiometric retrieval Thermodynamic model from Mitra et al –Ventilation coefficient –Initial snow density –Electromagnetic model

Brown and Ruf, 26 October of 19 Electromagnetic Models Maxwell-Garnett Dielectric Model Water | { air inclusions in ice matrix} {ice inclusions water matrix} | air air | {ice inclusions water matrix} {air inclusions in ice matrix} | water Strongest Weakest absorption scattering Fabry-Szyrmer Core-Shell Meneghini and Liao

Brown and Ruf, 26 October of 19 Fitting Procedure Assume particle mass conservation Stationary assumption Lapse rate set to 7.7 K/km (from RaOb) RH assumed to be 100 %

Brown and Ruf, 26 October of 19 Fitting Procedure Analyzed ~ 100 profiles with basal reflectivities of 25 – 31 dBZ Base of Melting LayerReflectivity Peak in Melting Layer

Brown and Ruf, 26 October of 19 Fitting Procedure Estimate D 0 from dBZ m (13.4) using average N 0 Melting Layer Model (F m, ρ s, ε m ) N 0 init, D 0 init, μ Attenuation Correction τ melt (13.4), τ melt (35.6) Estimate N 0, D 0 dBZ(13.4), dBZ(35.6) Melting Layer Model (F m, ρ s, ε m ) N 0, D 0, μ

Brown and Ruf, 26 October of 19 Fitting Procedure 1. {ice inclusions water matrix} | air 2. Fabry-Szyrmer Core(1)-Shell(3) 3. air | {ice inclusions water matrix}

Brown and Ruf, 26 October of 19 Melting Model Analysis Dielectric Formula13.4 Peak Bias (dB) 35.6 Peak Bias (dB) 13.4 Width Dif. (dB) Fraction of Opacity Mean Wind Speed (1) Water | { air inclusions in ice matrix} (2) {ice inclusions water matrix} | air * (3) Fabry-Szyrmer Core- Shell * (4) air | {ice inclusions water matrix} (5) {air inclusions in ice matrix} | water *

Brown and Ruf, 26 October of 19 Melting Layer Combination of MG models fits PR-2 data well –FS core shell Snow density model – lowest retrieval error in snow layer produces best fit in melting layer –FS model most sensitive to snow density variations –~ 2K variation between different density models/ventilation coefficient

Brown and Ruf, 26 October of 19 Effect on Retrievals No Melting Layer FS core shell Retrieved Rain Rate

Brown and Ruf, 26 October of 19 Effect on Retrievals Addition of melting layer reduced the wind speed retrievals by 30 to 40 % Increased radar retrieved rain rates approximately 10 % Fraction of Atmospheric Brightness at 10.7 GHz due to melting layer (FS model)

Brown and Ruf, 26 October of 19 Conclusions Melting layer contributes the majority of the atmospheric absorption in the microwave Radiometric retrievals in stratiform rain require an accurate model for the melting layer Electromagnetic models which blend MG mixing formulas produce the best results FS core shell model fit PR-2 data well and produced reasonable wind speed retrievals

Brown and Ruf, 26 October of 19 Algorithm Basis Radar Data Invert Backscatter Equation to get DSD(z) Correct for attenuation Mie Theory DSD(z), T(z) Invert RTE to get Invert Surface Emissivity Model to get Wind Speed Radiometer Data Output RR(z), W(z), WSpd Ancillary Data (e.g. SST, m v, ρ v ) Brightband Detection get T(z) dBZ(f), V r, LDR DSD(z) WSpd