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Eric A. Smith and Throy D. Hollis

Abstract

Currently, satellite algorithms are the methodology showing most promise for obtaining more accurate global precipitation estimates. However, a general problem with satellite methods is that they do not measure precipitation directly, but through inversion of radiation–rain relationships. Because of this, procedures are needed to verify algorithm-generated results. The most common method of verifying satellite rain estimates is by direct comparison with ground truth data derived from measurements obtained by rain gauge networks, ground-based weather radar, or a combination of the two. However, these types of comparisons generally shed no light on the physical causes of the differences. Moreover, ground validation measurements often have uncertainty magnitudes on the order of or greater than the satellite algorithms, motivating the search for alternate approaches. The purpose of this research is to explore a new type of approach for evaluating and validating the level-2 Tropical Rainfall Measuring Mission (TRMM) facility rain profile algorithms. This is done by an algorithm-to-algorithm intercomparison analysis in the context of physical hypothesis testing.

TRMM was launched with the main purpose of measuring precipitation and the release of latent heat in the deep Tropics. Its rain instrument package includes the TRMM Microwave Imager (TMI), the Precipitation Radar (PR), and the Visible and Infrared Scanner (VIRS). These three instruments allow for the use of combined-instrument algorithms, theoretically compensating for some of the weaknesses of the single-instrument algorithms and resulting in more accurate estimates of rainfall. The focus of this research is on the performance of four level-2 TRMM facility algorithms producing rain profiles using the TMI and PR measurements with both single-instrument and combined-instrument methods.

Beginning with the four algorithms' strengths and weaknesses garnered from the physics used to develop the algorithms, seven hypotheses were formed detailing expected performance characteristics of the algorithms. Procedures were developed to test these hypotheses and then applied to 48 storms from all ocean basins within the tropical and subtropical zones over which TRMM coverage is available (∼35°N–35°S). The testing resulted in five hypotheses verified, one partially verified, and one inconclusive. These findings suggest that the four level-2 TRMM facility profile algorithms are performing in a manner consistent with the underlying physical limitations in the measurements (or, alternatively, the strengths of the physical assumptions), providing an independent measure of the level-2 algorithms' validity.

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Beat Schmid, Robert G. Ellingson, and Greg M. McFarquhar

water), effective droplet size, etc. Satellite calibration and validation, where the instruments were used to indirectly calibrate sensors on operational satellites as well as to validate retrieval algorithms for such derived quantities as flux divergence, cloud properties, and water vapor profiles. The following material summarizes the major achievements of the ARM-UAV program from several campaigns before the program was integrated with other aircraft activities to form the AAF. The reader may

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D. D. Turner, J. E. M. Goldsmith, and R. A. Ferrare

content in cirrus clouds, and the technique compared well with more traditional radar–lidar analysis techniques. The addition of the rotational Raman scattering channels provides an excellent way to get ambient temperature profiles in the same volume as the water vapor mixing ratio, and thus improve measurements such as relative humidity from CARL. However, these channels were only added to the narrow FOV detection system, and thus there were two challenges. The first challenge was that the algorithm

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Greg M. McFarquhar, Darrel Baumgardner, Aaron Bansemer, Steven J. Abel, Jonathan Crosier, Jeff French, Phil Rosenberg, Alexei Korolev, Alfons Schwarzoenboeck, Delphine Leroy, Junshik Um, Wei Wu, Andrew J. Heymsfield, Cynthia Twohy, Andrew Detwiler, Paul Field, Andrea Neumann, Richard Cotton, Duncan Axisa, and Jiayin Dong

the results of processing algorithms presented in this chapter. Multiple probes are needed to measure microphysical properties given the wide range of particle shapes, sizes, and concentrations that exist in nature. Thus, it is critical to understand the strengths, limitations, uncertainties, and caveats associated with the derivation of ice properties from different probes. Two other chapters in this monograph are dedicated to these issues. Baumgardner et al. (2017 , chapter 9) discusses

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W.-K. Tao, Y. N. Takayabu, S. Lang, S. Shige, W. Olson, A. Hou, G. Skofronick-Jackson, X. Jiang, C. Zhang, W. Lau, T. Krishnamurti, D. Waliser, M. Grecu, P. E. Ciesielski, R. H. Johnson, R. Houze, R. Kakar, K. Nakamura, S. Braun, S. Hagos, R. Oki, and A. Bhardwaj

CRMs. These CRM-simulated datasets are especially valuable for LH algorithm developers (see Tao et al. 1990 , 1993 , 2000 , 2006 , 2010 ; Shige et al. 2004 , 2007 , 2008 , 2009 ; Grecu and Olson 2006 ). The Madden–Julian oscillation (MJO) is one of the most prominent climate variability modes and exerts pronounced influences on global climate and weather systems (e.g., Zhang 2005 ; Lau and Waliser 2011 ). Current general circulation models (GCMs), however, exhibit rather limited

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I. Gultepe, A. J. Heymsfield, P. R. Field, and D. Axisa

, and the liquid water content. The bulk microphysics algorithms in cloud or forecast models, with varying degrees of complexity (one- or two-moment schemes), can be used to predict parameters related to cloud ice crystals, precipitating snow particles, graupel, and hail ( Morrison and Milbrandt 2011 ). Microphysical processes for converting between hydrometeor types are not well constrained. Figure 6-1 (from Tomita 2008) shows the major components of a six-class microphysical scheme used in

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Ismail Gultepe, Andrew J. Heymsfield, Martin Gallagher, Luisa Ickes, and Darrel Baumgardner

visibility sensors, can be used to improve the prediction and monitoring of ice fog micro and macrophysical properties. Passive, low-earth-orbit and geostationary satellite measurements have been used for monitoring ice fog in the absence of higher-level cloud layers ( Pavolonis 2010 , Calvert and Pavolonis 2011 ; Gultepe et al. 2015 ). Calvert and Pavolonis (2011) describe the physical basis of the fog/low cloud detection algorithm for the Advanced Baseline Imager (ABI), flown on the GOES-R series

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Ken'ichi Okamoto

Abstract

The Tropical Rainfall Measuring Mission (TRMM) satellite carried aboard the world's first spaceborne precipitation radar (PR). This paper describes a short history of the TRMM PR. It describes the Communications Research Laboratory's (CRL's) airborne dual-frequency rain radar/radiometer system, some results of the airborne experiments, and considerations of system design and system parameters of the PR. It also describes data processing and analysis algorithms for the PR, and examples of PR rain measurements.

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Frédéric Fabry and R. Jeffrey Keeler

Abstract

The design and implementation of signal-processing algorithms are specialized trades of radar meteorology practiced by a small group of experts and poorly understood by most other radar data users. Yet signal processing is the essential first step of radar data processing, and the skill with which it is done determines the type and quality of data that will be available to radar meteorologists. Like many other facets of radar meteorology, it is undergoing a rapid evolution as computing capabilities expand exponentially. In this chapter, an overview of the current state and evolution of signal processing for the nonspecialist is provided. To achieve this, the nature and the properties of the radar signal itself is first described, as it determines the type and quality of the information that can be obtained. After these foundations are laid, the current state of signal processing on operational radars and then some of the latest developments that may shape the future are described.

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Frank D. Marks Jr.

Abstract

Radar played an important role in studies of tropical cyclones since it was developed in the 1940s. In the last 15 years, technological improvements such as the U.S. National Oceanic and Atmospheric Administration (NOAA) WP-3D tail airborne Doppler radar, the operational Weather Service Radar 1988-Doppler (WSR-88D) radar network, portable Doppler radars, and the first spaceborne radar system on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (NASA TRMM) satellite have produced a new generation of tropical cyclone data whose analysis has given scientists an unprecedented opportunity to document the dynamics and rainfall of tropical cyclones, and has led to improved understanding of these devastating storms.

The NOAA WP-3D airborne Doppler datasets led to improved understanding of the symmetric vortex and the major asymmetries. The addition of a second airborne Doppler radar on the other WP-3D enabled true dual-Doppler analyses and the ability to study the temporal evolution of the Kinematic structure over 3–6 h. The advent of the WSR-88D Doppler radar network, and the construction of portable Doppler radars that can be moved to a location near tropical cyclone landfall, has also generated new and unique datasets enabling improved understanding of 1) severe weather events associated with landfalling tropical cyclones, 2) boundary layer wind structure as the storm moves from over the sea to over land, and 3) spatial and temporal changes in the storm rain distribution. The WP-3D airborne Doppler and WSR-88D data have also been instrumental in developing a suite of operational single Doppler radar algorithms to objectively analyze a tropical cyclone's wind field by determining the storm location and defining the primary, secondary, and major asymmetric circulations. These algorithms are used operationally on the WP-3D aircraft and on the ground at NOAA's Tropical Prediction Center/National Hurricane Center.

The WSR-88D rainfall data, together with new satellite microwave passive and active sensors on the NASA TRMM satellite, are proving useful in studies of the temporal and spatial variability of rain in tropical cyclones. The instantaneous satellite snapshots provide rain estimates to improve our understanding of tropical cyclone rain distributions globally, providing estimates from one instrument and common algorithms in each basin, while the WSR-88D provides high-temporal-resolution rain estimates (1 h), to improve our understanding of the temporal variability of the rain as the storm makes landfall.

While these new datasets have led to improved understanding, they have also led to a number of new challenges that the radar meteorology community must face by transferring the understanding gained into new applications and improved numerical weather prediction. These challenges will drive our science well into the next century.

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