Remote Sensing of Precipitation from Airborne and Spaceborne Radar

Abstract

Weather radar measurements from airborne or satellite platforms can be an effective remote sensing tool for examining the three-dimensional structures of clouds and precipitation. This chapter describes some fundamental properties of radar measurements and their dependence on the particle size distribution (PSD) and radar frequency. The inverse problem of solving for the vertical profile of PSD from a profile of measured reflectivity is stated as an optimal estimation problem for single-and multi-frequency measurements. Phenomena that can change the measured reflectivity Zm from its intrinsic value Ze, namely attenuation, non-uniform beam filling, and multiple scattering, are described and mitigation of these effects in the context of the optimal estimation framework is discussed. Finally, some techniques involving the use of passive microwave measurements to further constrain the retrieval of the PSD are presented. [[H1]] IntroductionThe ability of weather radar to measure the location and intensity of precipitation was rapidly realized in the late 1940s following World War II. However, ground-based radars are limited in their ability to directly detect precipitation close to the ground far from the radar site due to ground clutter, refraction of the radar beam, and the curvature of the earth. Beam blockage by terrain also poses problems for radar coverage in mountainous areas. Coverage over oceans and other remote areas, where maintaining a ground radar would be difficult and costly, is also impractical, yet the precipitation that falls in these regions has important impacts on the global https://ntrs.nasa.gov/search.jsp?R=20180000191 2018-05-12T19:37:19+00:00Z atmospheric circulations via latent heating (e.g., Hoskins and Karoly, 1981, Hartmann et al., 1984, Matthews et al., 2004 and can have a profound influence on weather patterns thousands of kilometers away. Likewise, knowledge of precipitation over land, particularly in the form of snow, is a crucial component of the mass balance equation for glaciers and ice sheets, which must be properly characterized for realistic climate simulations (Shepherd et al., 2012).Airborne radar systems can provide high sensitivity and finely resolved vertical profiles to characterize precipitation microphysics for the benefit of model parameterizations and process understanding (e.g., Reinhardt et al, 2010, Heymsfield et al., 2013, Rauber et al., 2016.However, in order to achieve true global coverage, it had been proposed from nearly the beginning of the space age to put a weather radar in space (Kreigler and Kawitz, 1960), and efforts to do so began in earnest in the late 1970s and 1980s with the planning of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al., 1987, Okamoto et al., 1988 increased accuracy, sensitivity, and extension to higher latitudes. Both the TRMM PR and GPM DPR were intended not only to estimate precipitation directly from the radar data but also to construct a database of precipitation profiles to unify precipitation retri...

Simulation Study of the Applicability of the “Slice” Approach to Assessing the Water Content in Clouds from the Radar Return Signal

“…The measurement is based on the fact that the average power of the return signal from hydrometeors containing in the radar volume (a fictitious cylindrical volume inside the cloud at a given distance R that is bounded by the main lobe of the antenna pattern and half the spatial extent of the probe pulse) is equal to the sum of elementary return powers from these hydrometeors, e.g., [1]. Several factors can contribute to inaccuracies in the estimation of precipitation and water content of clouds by the radar method (e.g., [2]). In addition to them is a phenomenon of partially coherent backscattering due to the shape of the probing pulse, resonant or Bragg scattering and clustering scattering (e.g., [3][4][5]).…”

Evaluation of Higher‐Order Quadrature Schemes in Improving Computational Efficiency for Orientation‐Averaged Single‐Scattering Properties of Nonspherical Ice Particles

“…The canonical approach in physical precipitation retrieval has been (a) using the single‐scattering properties (SSPs) derived from ensembles of plausible hydrometeors in radiative transfer models (RTMs) for forward calculations to simulate instrument responses, and (b) matching observed instrument responses with simulated ones to arrive at the retrieved particle ensemble properties (Ding et al., 2016; Haddad et al., 2017; Kuo et al., 2016). Consistent physical estimates so retrieved from these microwave observations thus require SSPs of realistic precipitation particles covering the natural ranges of morphologies and compositions (Haddad et al., 1997; Kneifel et al., 2018; Munchak, 2018; Olson et al., 2016). Therefore, the uncertainty resulting from the assumptions of particle geometries or from their SSP calculations constitutes an upstream source of retrieval uncertainties, which is likely to propagate through the retrieval process and cause irreconcilable errors downstream.…”