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Kwo-Sen Kuo
,
Eric A. Smith
,
Ziad Haddad
,
Eastwood Im
,
Toshio Iguchi
, and
Alberto Mugnai

Abstract

In developing the upcoming Global Precipitation Measurement (GPM) mission, a dual-frequency Ku–Ka-band radar system will be used to measure rainfall in such a fashion that the reflectivity ratio intrinsic to the measurement will be sensitive to underlying variations in the drop size distribution (DSD) of rain. This will enable improved techniques for retrieving rain rates, which are dependent upon several key properties of the DSD. This study examines this problem by considering a three-parameter set defined by liquid water content (W), DSD effective radius (r e ), and DSD effective variance (υ e ). Using radiative transfer simulations, this parameter set is shown to be related to a radar reflectivity factor and specific attenuation in such a fashion that details of the DSDs are immaterial under constant W, and thus effectively represent important variations in DSD that affect rain rate but with a minimal number of parameters. The analysis also examines the effectiveness of including some measure of mean Doppler fall velocity of raindrops ( υ ), given that the fundamental properties of a given precipitation situation are uniquely defined by a combination of a drop mass spectrum and drop vertical velocity spectrum. The results of this study have bearing on how future dual-frequency precipitation retrieval algorithms could be formulated to optimize the sensitivity to underlying DSD variability, a problem that has greatly upheld past progress in radar rain retrieval.

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Ziad S. Haddad
,
Jonathan P. Meagher
,
Stephen L. Durden
,
Eric A. Smith
, and
Eastwood Im

Abstract

The threat of flooding from landfalling tropical cyclones is a function of the local variation in rain rate and rain accumulation. To date, these have been inferred from single-frequency radar reflectivity measurements. However, the Tropical Rainfall Measuring Mission experience has confirmed that one of the main difficulties in retrieving rain profiles using a single-frequency radar is the unknown raindrop size distribution (DSD). A dual-frequency radar such as the one planned for the upcoming Global Precipitation Measurement (GPM) core satellite is expected to help sort out at least part of this DSD-induced ambiguity. However, the signature of precipitation at 14 GHz does not differ greatly from its signature at 35 GHz (the GPM radar frequencies). To determine the extent of the vertical variability of the DSD in tropical systems and to quantify the effectiveness of a dual-frequency radar in resolving this ambiguity, several different models of DSD shape are considered and used to estimate the rain-rate and mean-diameter profiles from the measurements made by Jet Propulsion Laboratory’s (JPL’s) airborne second generation precipitation radar (PR-2) over Hurricanes Gabrielle and Humberto during the Fourth Convection and Moisture Experiment (CAMEX-4) in September 2001. It turns out that the vertical structures of the rain profiles retrieved from the same measurements under different DSD assumptions are similar, but the profiles themselves are quantitatively significantly different.

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Eric A. Smith
,
Mickey M-K. Wai
,
Harry J. Cooper
,
Michael T. Rubes
, and
Ann Hsu

Abstract

Surface, aircraft, and satellite observations are analyzed for the 21-day 1989 intensive field campaign of the First ISLSCP Field Experiment (FIFE) to determine the effect of precipitation, vegetation, and soil moisture distributions on the thermal properties of the surface including the heat and moisture fluxes, and the corresponding response in the boundary-layer circulation. Mean and variance properties of the surface variables are first documented at various time and space scales. These calculations are designed to set the stage for Part II, a modeling study that will focus on how time–space dependent rainfall distribution influences the intensity of the feedback between a vegetated surface and the atmospheric boundary layer. Further analysis shows strongly demarked vegetation and soil moisture gradients extending across the FIFE experimental site that were developed and maintained by the antecedent and ongoing spatial distribution of rainfall over the region. These gradients are shown to have a pronounced influence on the thermodynamic properties of the surface. Furthermore, perturbation surface wind analysis suggests for both short-term steady-state conditions and long-term averaged conditions that the gradient pattern maintained a diurnally oscillating local direct circulation with perturbation vertical velocities of the same order as developing cumulus clouds. Dynamical and scaling considerations suggest that the embedded perturbation circulation is driven by surface heating/cooling gradients and terrain effects rather than the manifestation of an inertial oscillation. The implication is that at even relatively small scales <30 km), the differential evolution in vegetation density and soil moisture distribution over a relatively homogenous ecotone can give rise to preferential boundary-layer circulations capable of modifying local-scale horizontal and vertical motions.

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Giulia Panegrossi
,
Stefano Dietrich
,
Frank S. Marzano
,
Alberto Mugnai
,
Eric A. Smith
,
Xuwu Xiang
,
Gregory J. Tripoli
,
Pao K. Wang
, and
J. P. V. Poiares Baptista

Abstract

Precipitation estimation from passive microwave radiometry based on physically based profile retrieval algorithms must be aided by a microphysical generator providing structure information on the lower portions of the cloud, consistent with the upper-cloud structures that are sensed. One of the sources for this information is mesoscale model simulations involving explicit or parameterized microphysics. Such microphysical information can be then associated to brightness temperature signatures by using radiative transfer models, forming what are referred to as cloud–radiation databases. In this study cloud–radiation databases from three different storm simulations involving two different mesoscale models run at cloud scales are developed and analyzed. Each database relates a set of microphysical profile realizations describing the space–time properties of a given precipitating storm to multifrequency brightness temperatures associated to a measuring radiometer. In calculating the multifrequency signatures associated with the individual microphysical profiles over model space–time, the authors form what are called brightness temperature model manifolds. Their dimensionality is determined by the number of frequencies carried by the measuring radiometer. By then forming an analogous measurement manifold based on the actual radiometer observations, the radiative consistency between the model representation of a rain cloud and the measured representation are compared. In the analysis, the authors explore how various microphysical, macrophysical, and environmental factors affect the nature of the model manifolds, and how these factors produce or mitigate mismatch between the measurement and model manifolds. Various methods are examined that can be used to eliminate such mismatch. The various cloud–radiation databases are also used with a simplified profile retrieval algorithm to examine the sensitivity of the retrieved hydrometeor profiles and surface rainrates to the different microphysical, macrophysical, and environmental factors of the simulated storms. The results emphasize the need for physical retrieval algorithms to account for a number of these factors, thus preventing biased interpretation of the rain properties of precipitating storms, and minimizing rms uncertainties in the retrieved quantities.

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