Environmental predictions in the marine atmospheric surface layer (MASL) are imperative to optimize X‐band radar system performance in marine environments. Evaporation ducts (ED) lead to anomalous propagation where characterization of EDs in the MASL occurs primarily through two methods: in‐situ measurements and numerical modeling. This study investigates the differences in co‐located and synchronous refractivity estimations from the CASPER‐East campaign. Propagation predictions are generated for refractive profiles from in‐situ measurements, Monin‐Obukov boundary layer similarity theory, and numerical weather prediction forecasts. Variations in evaporation duct height (EDH) are found to be a primary driver of differences in propagation between the estimated refractivity profiles, where location of the EDH relative to the transmitter changes the sensitivity of propagation predictions to EDH estimates. Differences in propagation are large when EDH estimates span the transmitter height and the lowest EDH across the methods is small, regardless of how much variation there is in EDH estimates. When the lowest EDH is small and EDH estimates span the transmitter height there are differences in physical regimes causing large propagation discrepancies–for example, leakage into versus trapping within the duct. Variation in EDH between the methods is greatest in stable environments. M‐deficit and curvature of the refractive profiles also influence propagation specifically in scenarios when EDH spans the transmitter. When all EDHs are below the transmitter, EDH variance is the primary contributor to propagation variance, but M‐deficit and profile curvature variance play a secondary role. M‐deficits and curvature between the methods agree most often during periods of atmospheric stability.

Dynamic refractive environments within the marine atmospheric boundary layer (MABL) pose difficulties in the prediction of X‐band radar wave propagation due to natural phenomena such as evaporation ducts (ED). This study utilizes a unique data set collected during the Coupled Air‐Sea Processes and Electromagnetic Ducting Research (CASPER)‐East field campaign, including multiple refractivity estimation methods and twelve point‐to‐point (PTP) electromagnetic datasets, to assess the efficacy of PTP inversion techniques for remote sensing of atmospheric refractivity within the MABL. Comparison of refractivity between the inverse and other refractivity methods show reasonable evaporation duct height estimates by the inversion, and inverse‐based propagation predictions are also shown to be more accurate than propagation based on other refractivity prediction methods: numerical weather prediction, theory, and in situ atmospheric measurements. These results propose the effectiveness of a PTP metaheuristic radar inversion to remotely sense refractive environments from radar propagation measurements in stable and unstable atmospheric conditions.

This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island Field Experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground-truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared to this “ground-truth” refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the “ground-truth” parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.