We present here the development and initial results of a low latency GNSS-R flood detection and visualisation framework, the Flood Assessment Spatial Triage. This uses airborne GNSS-Reflectometry data which are routinely collected as part of the Rongowai mission, hosted on an Air New Zealand Q300 domestic aircraft. The framework is able to detect flooding with low latency after the aircraft lands, through rapid processing of the Level-0 data which combines reflected GNSS signals with terrain data. These may be useful for flood preparedness and response, and may additionally help to guide the tasking of imaging assets.

Synspective, a leading Japanese space tech company, is building a constellation of Synthetic Aperture Radar (SAR) satellites. This paper presents an overview of the state of the constellation as of mid-2023, focusing on key aspects such as radar payload calibration, validation through sea wind retrieval, and the implementation of an automatic quality control system. The paper highlights the calibration procedures employed by Synspective, emphasizing their importance in enabling effective cross-satellite and cross-constellation image analysis. Furthermore, it demonstrates how SAR data can be utilized to retrieve sea wind information and validate the accuracy of calibration parameters. Given the substantial volume of images generated by the constellation, Synspective has implemented an automatic quality control system. This system is seamlessly integrated with the SAR-focusing software, allowing for efficient and reliable monitoring of data quality throughout the satellite’s lifetime to ensure the delivery of high-quality SAR imagery and data products.

We have launched our first demonstrative satellite – StriX-$\alpha$– at the end of 2020, are already about to launch our second satellite in the February 2022 and will go on to make a six-satellite constellation by 2023. Our goal is to build a constellation – StriX – consisting of 30 satellites. The name StriX was given based on the scientific name for the genus of owls – an allusion to its ability to ‘see’ This paper shows our latest status of our small SAR satellites and the evaluation results of our SAR images. We confirmed the image quality of the first satellite is pretty good as expected.

Spaced-antenna (SA) retrievals are a potential means of higher spatial-resolution wind-field retrievals than currently available single weather radar techniques. Relevant literature to date has focused on theoretical, numerical or engineering aspects. In a stratiform precipitation event case study with a median spectrum width of 0.53 m/s, we evaluate SA retrievals. The X-band active phased-array spaced antenna retrievals exhibited better measurement fidelity relative to simulated performance of the S-band national weather radar testbed (NWRT). This allowed for the first interpretation and evaluation of SA retrievals on precipitation echoes. SA measurements were confirmed to be in quadrature with radial velocity estimates. This signature is qualitatively consistent with simulation predictions for a spatially invariant wind field, where the radial velocity maximum corresponds to a null in the SA retrieval. Finally, SA retrievals were in agreement with reference wind field proxies derived using Doppler beam swinging (DBS) and velocity azimuth display (VAD) techniques. Among the SA techniques implemented, the time-domain Gaussian slope at zero lag (G-SZL) algorithm was found to perform better than the frequency domain full spectral analysis (FSA) algorithm. This is because the parametric nature of the G-SZL algorithm resulted in better immunity to correlation coefficient estimation errors.

Spaced antenna (SA) radars retrieve wind-fields by tracking resolution volume sized bins of scatterers as they advect between two physically displaced antennas. To date, SA methods have been applied for profiling the ionosphere and precipitation free atmosphere. The primary technological difficulty in applying these methods to probe precipitation at microwave frequencies is the requirement for such a short antenna separation that a significant overlap in apertures is necessary. In this article, we synthesize overlapped apertures by segmenting active phased-arrays into subarrays that are multiplexed in time. Antenna pattern measurements are then employed to evaluate beamforming errors on a family of implementations. Based on measurements and Monte Carlo simulations, we find that highly overlapping apertures are most immune to beam squint errors. This is because element-level phase errors are retained on the synthesized SAs and differential beam squint errors are minimized. Finally, we demonstrate a novel method to measure relative phase center displacement between SAs that obviates the need for near-field antenna measurements.

A dual-polarized X-band solid-state 1-D electronic scanning “phase-tilt” weather radar (PTWR) and its scanning geometry are presented. In this architecture, the true elevation angle decreases from the nominal array tilt angle and the true azimuth angle increases from the requested azimuth as one scans off boresight. Additionally, this scanning geometry induces a canting angle effect, which can be significant especially at higher elevation tilts. The predictions of potential biases in selected polarimetric variables due to this effect are derived. For elevation angles below 10°, where polarimetric measurements are of most value, predicted biases are negligible. The PTWR was deployed in Arlington, TX, USA, for an eight-week period during Spring 2014 collecting data on a number of weather events. The direct proximity (250 m away) of a mechanically scanning magnetron-based radar, employing a dual-polarized parabolic antenna allowed for a qualitative and quantitative data comparison. We find the differences in the observations made by the two radar systems are not attributable to the aforementioned biases, but appear primarily due to differences in sampling volumes of the two radars. We also find that at high-elevation tilts, the coupling of the true elevation angle with the array-relative azimuth scan angle complicates the interpretation of features at a constant altitude such as the melting layer.

During the Spring of 2014 a Phase-Tilt Weather Radar (PTWR) developed by the University of Massachusetts was deployed within the Dallas-Fort Worth Urban Testbed operated by the CASA Engineering Research Center. During an eight-week period it observed several weather events including severe thunderstorms and stratiform rain. Observations by the phased-array radar were compared to a nearby mechanically-scanned dual-polarized radar employing a parabolic dish with the objective of assessing the relative severity of biases in polarimetric observations by the PTWR. Such biases are due to polarization-basis rotation due to scanning along a tilted axis. It is expected these would be negligible except possibly at large (> 10°) elevation angles. At lower elevation angles where near-direct comparisons were possible, differences in observations were not uniquely attributable to this souce of error.

A microwave backscattering model for ground-based remote sensing of precipitation is developed and used to analyze back scattering measurements from snowfall and rain type precipitation. Backscattering from entire precipitation region is calculated by solving vector radiative transfer (VRT) equations. Geophysical parameters for rain or snow are determined by considering altitude and latent range. VRT equations are solved numerically by using matrix doubling method to take into account multiple-scattering effects. Results from model analyses agree well with measured radar data.