The statistical properties of turbulence at upper levels in the atmosphere [upper troposphere and lower stratosphere (UTLS)] are still not well known, partly because of the lack of adequate routine observations. This is despite the obvious benefit that such observations would have for alerting aircraft of potentially hazardous conditions, either in real time or for route planning. To address this deficiency, a research project sponsored by the Federal Aviation Administration has developed a software package that automatically estimates and reports atmospheric turbulence intensity levels (as EDR ≡ ε1/3, where ε is the energy or eddy dissipation rate). The package has been tested and evaluated on commercial aircraft. The amount of turbulence data gathered from these in situ reports is unprecedented. As of January 2014, there are ~200 aircraft outfitted with this system, contributing to over 137 million archived records of EDR values through 2013, most of which were taken at cruise levels of commercial aircraft, that is, in the UTLS. In this paper, techniques used for estimating EDR are outlined and comparisons with pilot reports from the same or nearby aircraft are presented. These reports allow calibration of EDR in terms of traditionally reported intensity categories (“light,” “moderate,” or “severe”). The results of some statistical analyses of EDR values are also presented. These analyses are restricted to the United States for now, but, as this program is expanded to international carriers, such data will begin to become available over other areas of the globe.

Real-time ground-clutter identification and subsequent filtering of clutter-contaminated data is addressed in this two-part paper. Part I focuses on the identification, modeling, and simulation of S-band ground-clutter echo. A new clutter identification parameter, clutter phase alignment (CPA), is presented. CPA is a measure primarily of the phase variability of the in-phase and quadrature-phase time series samples for a given radar resolution volume. CPA is also a function of amplitude variability of the time series. It is shown that CPA is an excellent discriminator of ground clutter versus precipitation echoes. A typically used weather model, time series simulator is shown to inadequately describe experimentally observed CPA. Thus, a new technique for the simulation of ground-clutter echo is developed that better predicts the experimentally observed CPA. Experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG), and NCAR’s S-band dual-polarization Doppler radar (S-Pol) are used to illustrate CPA. In Part II, CPA is used in a fuzzy logic algorithm for improved clutter identification.

In this two-part paper the biases of polarimetric variables from simultaneous horizontally and vertically transmitted (SHV) data are investigated. Here, in Part I, a radar-scattering model is developed and antenna polarization errors are investigated and estimated. In Part II, experimental data from the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-Pol) and the National Severe Storms Laboratory polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar, KOUN, are used to illustrate biases in differential reflectivity (Zdr). The biases in the SHV polarimetric variables are caused by cross coupling of the horizontally (H) and vertically (V) polarized signals. The cross coupling is caused by the following two primary sources: 1) the nonzero mean canting angle of the propagation medium and 2) antenna polarization errors. The biases are strong functions of the differential propagation phase (ϕdp) and the phase difference between the H and V transmitted field components. The radar-scattering model developed here allows for the evaluation of biases caused by cross coupling as a function of ϕdp, with the transmission phase difference as a parameter. Also, antenna polarization errors are estimated using solar scan measurements in combination with estimates of the radar system’s linear depolarization ratio (LDR) measurement limit. Plots are given that show expected biases in SHV Zdr for various values of the LDR system’s limit.

In this second article in a two-part work, the biases of weather radar polarimetric variables from simultaneous horizontally and vertically transmit (SHV) data are investigated. The biases are caused by cross coupling of the simultaneously transmitted vertical (V) and horizontal (H) electric fields. There are two primary causes of cross coupling: 1) the nonzero mean canting angle of the propagation medium (e.g., canted ice crystals) and 2) antenna polarization errors. Given herein are experimental data illustrating both bias sources. In Part I, a model is developed and used to quantify cross coupling and its impact on polarization measurements.

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