ProSensing (United States)

Abstract Cold-air outbreaks over high-latitude oceans typically include mixed-phase clouds and precipitation, in particular supercooled liquid clouds that support snow and graupel through ice growth processes. The partitioning of the total water into the liquid and ice phases impacts both weather and climate prediction, but accurate measurements on the phase partitioning remain difficult to acquire, especially near–real time. Here, we present a machine learning approach to retrieve liquid water path (LWP) using passive microwave measurements combined with vertically integrated radar reflectivities. The approach is an extension of Cadeddu et al. (2009), with the novel addition of radar reflectivity. The machine learning models are trained using the Passive and Active Microwave Radiative Transfer (PAMTRA) code applied to output from numerical simulations of three independent cold-air outbreaks sampled during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) campaign. Brightness temperatures corresponding to the four sidebands of an upward-looking G-band (183 GHz) vapor radiometer, along with the vertically integrated reflectivity from a zenith-pointing 95-GHz Wyoming Cloud Radar, are simulated from the perspective of a near-surface aircraft track. The radar reflectivity helps discriminate the snow contribution to the brightness temperatures. The machine learning models are thereafter tested on a simulation of an independent cold-air outbreak during COMBLE and against measurements from the U.S. Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. This machine learning approach is shown to provide robust, computationally efficient, near-real-time measurements of LWP and water vapor path during the Cold-Air Outbreak Experiment in the Sub-Arctic Region (CAESAR) campaign in February–April 2024. Significance Statement Precipitation from mixed-phase clouds over the high-latitude open waters impacts shipping, fishing, and coastal communities. Mixed-phase clouds are also a modeling challenge, inhibiting weather and climate prediction skill. More comprehensive measurements of the cloud liquid and ice water path occupying the same vertical column improve our understanding and ability to represent this challenging cloud type. Here, we propose a new retrieval to determine the liquid water path within air columns that also contain ice.

Abstract Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

Future synthetic aperture radar missions at L-band frequency, such as NASA-ISO NISAR and Copernicus ROSE-L, will provide relatively low frequency, deeply penetrating, snow and sea ice signals compared to traditional C-band and higher frequencies. A portable, sled- or tower-mounted L-band frequency radar scatterometer system designed to measure the radar backscatter characteristics of snow and sea ice in cold conditions in advance of these missions is described. The first polarimetric backscatter measurements collected using the system occurred during field campaigns in the Canadian Arctic in 2018, and the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic in 2019/20. Geophysical measurements of the snow-covered sea ice volume enable the signature controlling properties to be examined in the context of L-band SAR utilization.

The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.

The polarimetric L-band imaging synthetic aperture radar (PLIS) is a high spatial resolution (better than 6 m) airborne synthetic aperture radar system that has been dedicated to scientific research into civilian applications since 2010. The weight of PLIS is ~38 kg, allowing it to be installed aboard small low-cost aircraft, with two antennas used to measure the full backscatter matrix for a swath between 15° and 50° on each side of the flight direction. Calibration based on a total of 96 calibration points and a homogeneous forest during the two recent soil moisture active passive experiments (SMAPEx-4 and 5) showed an overall radiometric accuracy of 0.58 dB (root-mean-square error) over trihedral passive radar calibrators. Independent evaluation based on polarimetric active radar calibrators showed an amplitude imbalance of 0.17 dB with a standard deviation of 0.15 dB and a phase imbalance of 3.87° with a standard deviation of 2.86°. Two calibrated phased-array L-Band synthetic aperture radar-2 (PALSAR-2) images with different observation modes (ScanSAR and Stripmap) were compared with the calibrated PLIS images. The agreement between PALSAR-2 Stripmap and PLIS had a root mean square difference of 1.27 dB and a correlation coefficient of 0.87. Further comparisons over different landcover types confirmed that homogeneous forest and grassland areas constitute optimal targets for cross-validation and/or calibration.

Advances in millimeter wave solid-state power amplifier technology over the past decade have led to the development of a new generation of compact, lightweight cloud and precipitation radars. Solid-state power amplifiers (SSPAs) have lower peak power than traditional vacuum tube transmitters, but generally are capable of operating at much higher duty cycle. Taking advantage of the high duty cycle of SSPAs usually requires some signal processing and/or pulse compression technique to achieve similar sensitivity at comparable range resolution. FMCW (frequency modulated continuous wave) radars take full advantage of SSPAs capable of CW (continuous wave) operation, but the usual requirement for separate transmit and receive antennas is often impractical. Radars that combine a pulse compression waveform with a short pulse offset in frequency are now commonly used to operate at duty cycles as high as 10–20%. This paper reviews the latest solid-state millimeter wave radar designs, benefits, and limitations and describes operating modes and signal processing techniques that are especially well suited for single antenna operation. High PRF (pulse repetition frequency) operation using coherent power (single lag covariance) provides automatic noise cancellation without requiring explicit noise sampling. A novel phase coding technique employing Chu-codes with quadratically-increasing phase can be used to separate ambiguous signals when running at MHz-level PRFs. Weather data measured using the quadratic phase coding (QPC) technique with a solid-state Ka-band radar running at 3.1 MHz PRF is presented. This data was gathered at a transmitter duty cycle of approximately 50 percent and exhibits a 20 dB sensitivity improvement when compared to conventional short pulse data gathered at the same range resolution.

Abstract Detection of meteorological radar signals is often carried out using power averaging with noise subtraction either in the time domain or the spectral domain. This paper considers the relative signal processing gain of these two methods, showing a clear advantage for spectral-domain processing when normalized spectral width is less than ~0.1. A simple expression for the optimal discrete Fourier transform (DFT) length to maximize signal processing gain is presented that depends only on the normalized spectral width and the time-domain weighting function. The relative signal processing gain between noncoherent power averaging and spectral processing is found to depend on a variety of parameters, including the radar wavelength, spectral width, available observation time, and the false alarm rate. Expressions presented for the probability of detection for noncoherent and spectral-based processing also depend on these same parameters. Results of this analysis show that DFT-based processing can provide a substantial advantage in signal processing gain and probability of detection, especially when the normalized spectral width is small and when a large number of samples are available. Noncoherent power estimation can provide superior probability of detection when the normalized spectral width is greater than ~0.1, especially when the desired false alarm rate exceeds 10%.

Clouds are a critical component of the Earth's coupled water and energy cycles. Poor understanding of cloud–radiation–dynamics feedbacks results in large uncertainties in forecasting human-induced climate changes. Better understanding of cloud microphysical and dynamical processes is critical to improving cloud parameterizations in climate models as well as in cloud-resolving models. Airborne in situ and remote sensing can make critical contributions to progress. Here, a new integrated cloud observation capability developed for the University of Wyoming King Air is described. The suite of instruments includes the Wyoming Cloud Lidar, a 183- GHz microwave radiometer, the Wyoming Cloud Radar, and in situ probes. Combined use of these remote sensor measurements yields more complete descriptions of the vertical structure of cloud microphysical properties and of cloud-scale dynamics than that attainable through ground-based remote sensing or in situ sampling alone. Together with detailed in situ data on aerosols, hydrometeors, water vapor, thermodynamic, and air motion parameters, an advanced observational capability was created to study cloud-scale processes from a single aircraft. The Wyoming Airborne Integrated Cloud Observation (WAICO) experiment was conducted to demonstrate these new capabilities and examples are presented to illustrate the results obtained.

Coaxial connectors can be used either to probe the electrical properties of microwave circuits (e.g., microstrip, stripline) or as a permanent interface to the circuit. When used as a permanent interface, connectors can degrade circuit performance unless their electrical properties are characterized and accounted for during circuit design. Although it is more common to characterize microwave devices using scattering parameters (Collin, 1966) S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> , we have chosen to utilize impedance parameters since they simplify the derivation of equations presented in this article. The proposed measurement-based method for connector characterization is exact in the sense that its theoretical foundation uses no approximations or assumptions