Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval

Ground-based remote sensing of atmospheric parameters is often limited to single station observations by vertical profiles at a certain geographic location. This is a limiting factor for investigating gravity wave dynamics as the spatial information is often missing, e.g., horizontal wavelength, propagation direction or intrinsic frequency. In this study, we present a new retrieval algorithm for multistatic meteor radar networks to obtain tomographic 3-D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) and called 3D-Var, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3D-Var retrieval is demonstrated using two meteor radar networks: the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). The optimal estimation implementation provide statistically sound solutions and diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the rotational k−3 and divergent k-5/3 mode at scales of 80–120 km. In addition, we performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3D-Var algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to central Norway.

Three-way Calibration Checks Using Ground-Based, Ship-Based and Spaceborne Radars

This study uses weather radar observations collected from Research Vessel Investigator to evaluate the Australian weather radar network calibration monitoring technique that uses spaceborne radar observations from the NASA Global Precipitation Mission (GPM). Quantitative operational applications such as rainfall and hail nowcasting require a calibration accuracy of 1 dB for radars of the Australian network covering capital cities. Seven ground-based radars along the coast and the ship-based OceanPOL radar are first calibrated independently using GPM radar overpasses over a 3-month period. The calibration difference between the OceanPOL radar and each of the 7 operational radars is then estimated using collocated, gridded, radar observations to evaluate the accuracy of the GPM technique. For all seven radars the calibration difference with the ship radar lies within ±0.5 dB, therefore fulfilling the 1 dB requirement. This result validates the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks (provided that the spaceborne radar maintains a high calibration accuracy). The analysis of the day-to-day and hourly variability of calibration differences between the OceanPOL and Darwin (Berrimah) radars also demonstrates that quantitative comparisons of gridded radar observations can accurately track daily and hourly calibration differences between pairs of operational radars with overlapping coverage (daily and hourly standard deviations of ~ 0.3 dB and ~ 1 dB, respectively).

Sequential Monte Carlo methods for multi-sensor tracking with applications to radar systems

The aim of this thesis is to explore specific sequential Monte Carlo (SMC) methods and their application to the unique demands of radar and bearing only tracking systems. Asynchronous radar networks are of special interest and a novel algorithm, the multiple imputation particle filter (MIPF), is formulated to perform data fusion and estimation using asynchronous observations. Convergence analysis is carried out to show that the algorithm will converge to the optimal filter. Simulations are performed to demonstrate the effectiveness of this filter. Next, the problem of multi-sensor bearing only tracking is tackled. A particle based tracking algorithm is derived and a new filter initialization scheme is introduced for the specific task of multi-sensor bearing only tracking. Simulated data is used to study the efficiency and performance of the initialization scheme.

Sequential Monte Carlo methods for multi-sensor tracking with applications to radar systems

The aim of this thesis is to explore specific sequential Monte Carlo (SMC) methods and their application to the unique demands of radar and bearing only tracking systems. Asynchronous radar networks are of special interest and a novel algorithm, the multiple imputation particle filter (MIPF), is formulated to perform data fusion and estimation using asynchronous observations. Convergence analysis is carried out to show that the algorithm will converge to the optimal filter. Simulations are performed to demonstrate the effectiveness of this filter. Next, the problem of multi-sensor bearing only tracking is tackled. A particle based tracking algorithm is derived and a new filter initialization scheme is introduced for the specific task of multi-sensor bearing only tracking. Simulated data is used to study the efficiency and performance of the initialization scheme.

Atmospheric tomography using the Nordic Meteor Radar Clusterand Chilean Observation Network De Meteor Radars: networkdetails and 3DVAR retrieval

Ground-based remote sensing of atmospheric parameters is often limited to single station observations of vertical profiles at a certain geographic location. This can be a limiting factor to investigating gravity wave dynamics. In this study we present a new retrieval algorithm for multi-static meteor radar networks to obtain tomographic 3D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) called 3DVAR, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3DVAR retrieval is demonstrated using two meteor radar networks, the Nordic Meteor Radar Cluster and the Chilean Observation Network De MeteOr Radars (CONDOR). The optimal estimation implementation provides a statistically sound solution and additional diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the vortical κ−3 and divergent κ−5/3 mode at scales of 80–120 km. In addition, we have performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3DVAR algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to mid-Norway.