Combined Cloud–Microwave Radiative Transfer Modeling of Stratiform Rainfall

Peter Bauer German Remote Sensing Data Center, German Aerospace Center, Cologne, Germany

Search for other papers by Peter Bauer in
Current site
Google Scholar
PubMed
Close
,
A. Khain Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Search for other papers by A. Khain in
Current site
Google Scholar
PubMed
Close
,
A. Pokrovsky Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Search for other papers by A. Pokrovsky in
Current site
Google Scholar
PubMed
Close
,
R. Meneghini NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by R. Meneghini in
Current site
Google Scholar
PubMed
Close
,
C. Kummerow NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by C. Kummerow in
Current site
Google Scholar
PubMed
Close
,
F. Marzano Dipartimento di Ingegneria Elettrica, Universita dell Aquila, L’Aquila, Italy

Search for other papers by F. Marzano in
Current site
Google Scholar
PubMed
Close
, and
J. P. V. Poiares Baptista Electromagnetics Division, European Space Agency/ESTEC, Noordwijk, Netherlands

Search for other papers by J. P. V. Poiares Baptista in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The simulation of explicit particle spectra during cloud evolution by a two-dimensional spectral cloud model was used to investigate the response of microwave radiative transfer to particle spectra development with special focus on the radiative effects of melting particles below the freezing level. For this purpose, 1) a particle-melting model was implemented with increased vertical resolution; 2) several models of the dielectric permittivity for melting particles were compared; 3) the dependence on size–density distributions was evaluated; and 4) the influence on the results by the replacement of explicit by parameterized particle spectra was tested.

Radiative transfer simulations over ocean background at frequencies between 10.7 and 85.5 GHz showed a considerable increase in brightness temperatures (TB) once melting particles were included. The amounts were strongly dependent on the implemented permittivity model, the number concentrations of large frozen particles right above the freezing level, and the local cloud conditions. Assuming a random mixture of air, ice, and meltwater in the particle, TBs increased by up to 30 K (at 37.0 GHz) in the stratiform cloud portion for nadir view. If the meltwater was taken to reside at the particle boundaries, unrealistic TB changes were produced at all frequencies. This led to the conclusion that for large tenuous snowflakes the random-mixture model seems most appropriate, while for small and dense particles a nonuniform water distribution may be realistic. The net melting effect on simulated TBs, however, depended strongly on attenuation by supercooled liquid water above the freezing level, which generally suppressed the signal at 85.5 GHz. Over land background, changes in TB due to melting particles remained below 8 K, which would be difficult to identify compared to variations in surface emission and cloud profile heterogeneity.

Replacement of the explicit particle spectra for rain, snow, and graupel by parameterized spectra (here, in exponential form with a fixed intercept) produced reductions of the melting signature by up to 40% over ocean. It was found that exponential size distribution formulas tended to underestimate number concentrations of large particles and overestimated those of small particles at those cloud levels where sufficient particle sedimentation leads to collection, aggregation, and evaporation, respectively. Consequently, the strongest differences between explicit and parameterized spectra occurred right above the freezing level for snow and graupel, and close to the surface for rain. Radiometrically, this resulted in an underestimation of scattering above the freezing level and an underestimation of emission by melting particles below the freezing level as well as by rain toward the surface. In the stratiform region, the net effect was a reduction of the melting signature; however, TB’s were still up to 15 K higher than from the no-melting case for the random-mixture permittivity model.

Corresponding author address: Dr. Peter Bauer, German Remote Sensing Data Center, German Aerospace Center, Linder Höhe, 51147 Köln, Germany.

Email: peter.bauer@dlr.de

Abstract

The simulation of explicit particle spectra during cloud evolution by a two-dimensional spectral cloud model was used to investigate the response of microwave radiative transfer to particle spectra development with special focus on the radiative effects of melting particles below the freezing level. For this purpose, 1) a particle-melting model was implemented with increased vertical resolution; 2) several models of the dielectric permittivity for melting particles were compared; 3) the dependence on size–density distributions was evaluated; and 4) the influence on the results by the replacement of explicit by parameterized particle spectra was tested.

Radiative transfer simulations over ocean background at frequencies between 10.7 and 85.5 GHz showed a considerable increase in brightness temperatures (TB) once melting particles were included. The amounts were strongly dependent on the implemented permittivity model, the number concentrations of large frozen particles right above the freezing level, and the local cloud conditions. Assuming a random mixture of air, ice, and meltwater in the particle, TBs increased by up to 30 K (at 37.0 GHz) in the stratiform cloud portion for nadir view. If the meltwater was taken to reside at the particle boundaries, unrealistic TB changes were produced at all frequencies. This led to the conclusion that for large tenuous snowflakes the random-mixture model seems most appropriate, while for small and dense particles a nonuniform water distribution may be realistic. The net melting effect on simulated TBs, however, depended strongly on attenuation by supercooled liquid water above the freezing level, which generally suppressed the signal at 85.5 GHz. Over land background, changes in TB due to melting particles remained below 8 K, which would be difficult to identify compared to variations in surface emission and cloud profile heterogeneity.

Replacement of the explicit particle spectra for rain, snow, and graupel by parameterized spectra (here, in exponential form with a fixed intercept) produced reductions of the melting signature by up to 40% over ocean. It was found that exponential size distribution formulas tended to underestimate number concentrations of large particles and overestimated those of small particles at those cloud levels where sufficient particle sedimentation leads to collection, aggregation, and evaporation, respectively. Consequently, the strongest differences between explicit and parameterized spectra occurred right above the freezing level for snow and graupel, and close to the surface for rain. Radiometrically, this resulted in an underestimation of scattering above the freezing level and an underestimation of emission by melting particles below the freezing level as well as by rain toward the surface. In the stratiform region, the net effect was a reduction of the melting signature; however, TB’s were still up to 15 K higher than from the no-melting case for the random-mixture permittivity model.

Corresponding author address: Dr. Peter Bauer, German Remote Sensing Data Center, German Aerospace Center, Linder Höhe, 51147 Köln, Germany.

Email: peter.bauer@dlr.de

Save
  • Bauer, P., and N. C. Grody, 1995: The potential of the combination of SSM/I and SSM/T2 data to improve the identification precipitation and snowcover. IEEE Trans. Geosci. Remote Sens.,33, 252–261.

  • ——, L. Schanz, and L. Roberti, 1998: Correction of three-dimensional effects for passive microwave remote sensing of convective clouds. J. Appl. Meteor.,37, 1619–1632.

  • ——, J. P. V. Poiares Baptista, and M. de Iulis, 1999: On the effect of the melting layer on the microwave emission of clouds over the ocean. J. Atmos. Sci.,56, 852–867.

  • Berry, E. X, and R. L. Reinhardt, 1974: An analysis of cloud drop growth by collection. Part I: Double distributions. J. Atmos. Sci.,31, 1814–1824.

  • Bohren, C. F., and L. J. Battan, 1982: Radar backscattering of microwaves by spongy ice spheres. J. Atmos. Sci.,39, 2623–2628.

  • Dissanayake, A. W., and N. J. McEwan, 1978: Radar and attenuation properties and bright band. IEE Conf. Publ. 169-2, Institution of Electrical Engineers, Stevenage, United Kingdom, 328 pp.

  • Khain, A. P., and I. Sednev, 1995: Simulation of hydrometeor size spectra evolution by water–water, ice–water and ice–ice interactions. Atmos. Res.,36, 107–138.

  • ——, and ——, 1996: Simulation of precipitation formation in the Eastern Mediterranean coastal zone using a spectral microphysics cloud ensemble model. Atmos. Res.,43, 77–110.

  • ——, ——, and V. Khvorostyanov, 1996: Simulation of coastal circulation in the eastern Mediterranean using a spectral microphysics cloud ensemble model. J. Climate,9, 3298–3316.

  • Klaassen, W., 1988: Radar observations and simulation of the melting layer of precipitation. J. Atmos. Sci.,45, 3741–3753.

  • Knight, C. A., 1979: Observations of the morphology of melting snow. J. Atmos. Sci.,36, 1123–1130.

  • Kummerow, C., 1993: On the accuracy of the Eddington approximation for radiative transfer at microwave frequencies. J. Geophys. Res.,98, 2757–2765.

  • Liebe, H. J., P. Rosenkranz, and G. A. Hufford, 1992: Atmospheric 60 GHz oxygen spectrum: New laboratory measurements and line parameters. J. Quant. Spectrosc. Radiat. Transfer,48, 629–643.

  • Magono, C., and T. Nakamura, 1965: Aerodynamic studies of falling snowflakes. J. Meteor. Soc. Japan,43, 139–147.

  • Meneghini, R., and L. Liao, 1996: Comparisons of cross sections for melting hydrometeors as derived from dielectric mixing formulas and a numerical method. J. Appl. Meteor.,35, 1658–1670.

  • ——, and ——, 1998: Effective dielectric constants of melting hydrometeors and their use in radar bright-band models. Proc. Int. Geoscience and Remote Sensing Symp., Seattle, WA, IEEE, 147–149.

  • ——, and ——, 2000: Effective dielectric constants of mixed-phase hydrometeors. J. Atmos. Oceanic Technol., in press.

  • ——, H. Kumagai, J. R. Wang, T. Iguchi, and T. Kozu., 1997: Microphysical retrievals over stratiform rain using measurements from an airborne dual-wavelength radar-radiometer. IEEE Trans. Geosci. Remote Sens.,35, 487–506.

  • Mitra, S. K., O. Vohl, M. Ahr, and H. R. Pruppacher, 1990: A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles. IV: Experiment and theory for snow flakes. J. Atmos. Sci.,47, 584–591.

  • Rutledge, S. A., and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: A model for the “seeder-feeder” process in warm-frontal rainbands. J. Atmos. Sci.,40, 1185–1206.

  • Schluessel, P., and H. Luthardt, 1991: Surface wind speeds over the North Sea from Special Sensor Microwave/Imager observations. J. Geophys. Res.,96, 4845–4854.

  • Schols, J. L., J. A. Weinman, R. E. Stewart, and R. P. Lawson, 1995:The retrieval of dry and wet snow distributions from SSM/I measurements and MM5 forecast results. Proc. Int. Geoscience and Remote Sensing Symp., Florence, Italy, IEEE, 887–889.

  • ——, ——, G. D. Alexander, R. E. Stewart, L. J. Angus, and A. C. L. Lee, 1999: Microwave properties of frozen precipitation around a North Atlantic cyclone. J. Appl. Meteor.,38, 29–43.

  • Vivekanandan, J., J. Turk, G. L. Stephens, and V. N. Bringi, 1990: Microwave radiative transfer studies using combined radar and radiometer measurements during COHMEX. J. Appl. Meteor.,29, 561–585.

  • Wexler, R., 1995: The melting layer. Blue Hill Observatory and Harvard University Meteorological Radar Studies No. 3., 29 pp. [Available from Harvard University, Blue Hill Observatory, Milton, MA 02186.].

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 193 33 1
PDF Downloads 51 18 0