All-Sky Microwave Radiative Transfer Modeling for DA: Advancing the CRTM

to Microphysics-Consistent Cloud Optical Properties

JSDSA Satellite Data Assimilation Summer Colloquium5 August 2015

Scott Sieron (email: sbs5130@psu.edu)Advisor: Fuqing Zhang (Penn State)

Major Collaborators: Eugene Clothiaux (Penn State), Lu Yinghui

Self Introduction

2009 – 2013• B.S. + M.S. Meteorology, The Pennsylvania State University

2013 – present • PhD Dept. of Meteorology, The Pennsylvania State University• Ongoing project began summer 2014

Quick Aside:M.S. Project, CloudSat Hurricane Overpasses

• Cloud top height of eyewall and near-storm convection to diagnose cyclone intensity (Wong and Emanuel 2007)• Publication did not include

CloudSat: thesis concluded that the vertical cross-section was insufficient sampling

Sieron, S. B., F. Zhang, and K. A. Emanuel (2013), Feasibility of tropical cyclone intensity estimation using satellite-borne radiometer measurements: An observing system simulation experiment, Geophys. Res. Lett., 40, 5332–5336, doi:10.1002/grl.50973.

CloudSat overpass through eye and eyewall of Typhoon Dolphin. Image courtesy nasa.gov

Background:Microwave

• Imaging channels: small clear-air opacity, signal dominated by surface and hydrometeors• At higher frequencies (>~50 GHz),

• Water surface has high emissivity (high TB in clear air)

• Hydrometeor impact dominated by snow/graupel/hail scattering

• At lower frequencies (<~50 GHz),• Water surface has low emissivity (low TB in

clear air) in H-polarization• Hydrometeor impact dominated by rain

absorption/emission, may be augmented by ice scattering

Primary O2 Absorption Bands Primary H2O Absorption Band

Temperature Sounding Channels Moisture Sounding Channels

Imaging ChannelsImaging Channel

Clear-Air Atmospheric Opacity vs. MW Freq. (AMSU channels demarked)

COLD WARM270250230210190

Hurricane Karl 09/17/10 0113Z(SSMI/S image courtesy NRL)

COLD WARM260240220200180160

Hurricane Karl 09/17/10 0113Z(SSMI/S image courtesy NRL)

High-mid frequency (91 GHz)Low-mid frequency (37 GHz)

Background:Data Assimilation of Microwave Radiances

• Global DA uses sounding channels: informative of vertical profile of temperature and moisture in clear (and cloudy) sky• There is potential for value in regional-scale (hurricane) DA of

precipitation information from imaging channels• Can our observation operator (Community Radiative Transfer

Model, CRTM) represent the radiance impacts of the hydrometeors with sufficient accuracy for DA?• Want to avoid (a high magnitude of) bias correction• If not, then could the process be beneficial to the CRTM and the

forecast model?

About Clouds and Precipitation in CRTM• CRTM clouds are specified by • hydrometeor type (cloud water, cloud ice, rain, snow, graupel, hail)• Radiative properties are calculated for spheres; snow and graupel are

represented as “soft spheres” with densities < 917 kg/m3

• amount of hydrometeor (vertically-integrated mass per volume)• size of hydrometeors (effective radius)

• Radiative properties are contained in lookup tables• Have dimensions of cloud effective radius and (for liquid) layer

temperature

Microwave and Precipitation

• Hydrometeor size is very important in microwave:• When [particle radius] < ~1/6 wavelength,

scattering increases by ~[particle mass]2

• Rayleigh scattering of a homogenous sphere• Considering spherical solid particle of ever-

increasing size: scattering per mass growth slows, oscillates, then declines• Mie scattering of a homogenous sphere

• These MW wavelengths are only several millimeters• Largest precipitation particles exceed ~1 mm

radius and are removed from well-behaved scattering regime

Mass extinction (thick solid), scattering (dashed) and absorption (thin solid) coefficients (m2 g-1) of solid ice spheres as a function of radius for three imaging channels. Wavelength and 1/6-wavelength demarked.

Microphysics Scheme Details, ExampleWSM6 Graupel • Exponential PSD:

• Based on Houze et al. (1979)•

• Soft sphere, ρg = 500 kg m-3

• Look-up table dimensions and bounds (2): ρaqg, [frequency]

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

-0.2

-1.66533453693773E-16

0.2

0.4

0.6

0.8

1

1.2

Exponential DistributionMean of Distribution

Testing the CRTM, All-sky Microwave DA –WRF Simulations• Hurricane Karl, initialized at 21Z 16 Sept. from EnKF analysis

after assimilating airborne Doppler radar radial velocities• Same as Masashi’s experiments• WRF version 3.6.1 (Skamarock et al. 2008)• PSU WRF-EnKF: Zhang et al. (2009); Weng and Zhang (2012) • Ensemble size: 60

• WSM6 microphysics (5 species, 1 moment)• 3 hour forecast

260240220200180160 270250230210190

37 GHz89 GHz

Observations (SSMI/S)

260240220200180160 27025023021019036.5 GHz 89 GHz

WSM6, Particle Size Distribution Means as CRTM Cloud Effective Radii

WSM6 Scheme

Note: CRTM assumes cloud ice is sufficiently small so as to not scatter, which is an invalid assumption for the sizes seen here

Mean particle radius (microns)

WSM6, Specified and Uniform CRTM Radii

260240220200180160 27025023021019036.5 GHz 89 GHz

Cloud: 15 μm Rain: 500 μm Ice: 50 μm Snow: 1000 μm Graupel: 1000 μm

Testing the CRTM, All-sky Microwave DA – First Attempts• Pre-specified radii: unacceptable

• Relatively ad-hoc• Simply not representing enough physics to be comfortable for DA

• Mean radius: too warm, too little scattering • Mean particle radius of a cloud < effective scattering particle radius of a cloud because

scattering is dominated by the large particles• Mean of appropriately transformed distribution could produce better results,

but…• It often exceeds the CRTM lookup table effective radius dimension (1500 μm)• At these wavelengths, the D6 scattering relationship often breaks down for large

particles, so using this transform will lead to over-estimated scattering

Testing the CRTM, All-sky Microwave DA – Next Efforts• Create new cloud optical property lookup tables• Model properties of single particles as specified by MP scheme

• Maxwell-Garnett mixing formula for ice dielectric constants (Turner et al., in prep)• Product of the Henyey-Greenstein and Rayleigh scattering phase functions, and

Legendre coefficients thereof, as specified by Liu and Weng (2006)• Calculate per-mass optical properties of clouds constructed with particle

size distribution as specified by MP scheme• (We allow for scattering by cloud ice)

• MP scheme will be perfectly and natively interfaced with CRTM• (Though both the MP schemes and CRTM remain a source of error/bias)

Testing the CRTM, All-sky Microwave DA – Next Efforts• Build lookup tables for multiple MP schemes:• WSM6 (Dudhia et al. 2008)• Goddard (Lang et al. 2007)• Morrison (Bryan and Morrison 2012)

•Modify CRTM source codes accordingly• Redo WRF simulation with these MP schemes, compare:• hydrometeor concentration and particle sizes• resulting forward CRTM simulations• Using 16+2 streams at all locations (removing effective radius broke the Mie

parameter stream determination method)

260240220200180160 27025023021019036.5 GHz 89 GHz

WSM6, Particle Size Distribution Means as CRTM Cloud Effective Radii

260240220200180160 27025023021019036.5 GHz 89 GHz

WSM6, Particle Size Distribution Means as CRTM Cloud Effective Radii

WSM6, New Look-up Tables

260240220200180160 27025023021019089 GHz36.5 GHz

Goddard, New Look-up Tables

260240220200180160 27025023021019089 GHz36.5 GHz

Morrison, New Look-up Tables

260240220200180160 27025023021019089 GHz36.5 GHz

Results and Discussion

• Scheme-specified cloud optical properties: too cold, too much scattering• Consistent with many studies involving radar, and passive microwave using the simpler

Goddard-SDSU radiative transfer solver [Zupanski et al. 2011; Zhang et al. 2013; Han et al. 2013; Chambon et al. 2014]• Conclusion: too much or too big of snow and/or graupel in upper troposphere

• Using fewer than 16+2 streams in CRTM leads to not-as-cold brightness temperatures• Simulations with only rain + cloud water (emitters) are very similar

• Goddard has most snow and graupel, also has substantial cloud ice scattering• Morrison is heavier on snow, lighter on graupel• WSM6 is lighter on snow, heavier on graupel

• Graupel stays near convective cells, creates very cold splotches• Snow spreads out

Future

• Certain: I’m continuing PhD work on this project• Uncertain: What work to be done and when

• Comparing to Goddard-SDSU• Refining these modifications, working in CRTM repository

• Stream number estimation• Revamp data structures, scheme selection interface• Tangent linear, adjoint, K-matrix ? Waiting for better microphysics scheme (Goddard 2-moment)• Working toward improved microphysics scheme ?

• Ensemble parameter estimation• Bias correction• OSSE*

• *though as long as this bias is present, such experiments will yield results of substantially limited value

ReferencesChambon, P., S. Q. Zhang, A. Y. Hou, M. Zupanski, and S.

Cheung, 2014: Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system. Quart. Jour. Roy. Meteor. Soc., 140, 1219–1235.

Han, M., S. A. Braun, T. Matsui, and C. R. Williams, 2013: Evaluation of cloud microphysics schemes in simulations of a winter storm using radar and radiometer measurements. J. Geophys. Res. Atmos., 118, 1401–1419.

Liu, Q., and F. Weng, 2006: Advanced doubling-adding method for radiative transfer in planetary atmospheres. J. Atmos. Sci., 63, 3459‒3465.

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF version 3. NCAR Technical Note 475, http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.

Weng, Y., and F. Zhang, 2012: Assimilating Airborne Doppler Radar Observations with an Ensemble Kalman Filter for Convection-permitting Hurricane Initialization and Prediction: Katrina (2005). Mon. Wea. Rev., 140, 841-859.

Wong, V., and K. A. Emanuel, 2007: Use of cloud radars and radiometers for tropical cyclone intensity estimation, Geophys. Res. Lett., 34, L12811, doi:10.1029/2007GL029960.

Zhang, S. Q., M. Zupanski, A. Y. Hou, X. Lin, and S. H. Cheung, 2013: Assimilation of Precipitation-Affected Radiances in a Cloud-Resolving WRF Ensemble Data Assimilation System. Mon. Wea. Rev.,141, 754–772.

Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter. Mon. Wea. Rev., 137, 2105-2125.

Zupanski, D., S. Q. Zhang, M. Zupanski, A. Y. Hou, and S. H. Cheung, 2011: A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations. J. Hydrometeor., 12, 118–134.

Extra Slides

Cloud IceGoddard

Morrison

WSM610.65-H 18.7-H 23.8-V

36.5-H 89.0-H 165.5-H

WSM6, New Look-up TablesCoarsened to 15x15 km

260240220200180160 27025023021019089 GHz36.5 GHz

WSM6, New Look-up Tables

260240220200180160 27025023021019089 GHz36.5 GHz