Comparison of Atmospheric Refractivity Estimation Methods and Their Influence on Radar Propagation Predictions

Abstract

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.

Refractivity Inversions From Point‐To‐Point X‐Band Radar Propagation Measurements

“…which are known to have significant effects on PL (Cherrett, 2015;Hitney & Vieth, 1990;Pastore et al, 2021). Neutral/stable regimes generally correspond to higher duct heights from the inversion solution, which is consistent with previous studies showing EDH increases in stable environments (Cherrett, 2015;Zhang et al, 2017).…”

“…The most striking differences occur with respect to the mixed layer slope (e.g., Figures 9A, 9I or 9L); however, it has been shown that differences in the mixed layer slope do not affect propagation as significantly as other refractive parameters in the near surface (Lentini & Hackett, 2015;Matsko & Hackett, 2019;Pastore et al, 2021;Penton & Hackett, 2018), resulting in a relative insensitivity of the GA optimization to this parameter. Much more important is the duct height and the shape below the duct height (Cherrett, 2015;Pastore et al, 2021). In many cases, EDHs from the inversions are similar to other estimates 1).…”

“…COAMPS is initialized with boundary conditions from the Navy Global Environment Models (NAVGEM) for the atmosphere (Hogan et al., 2014) and the Global Hybrid Ocean Coordinate Model (HYCOM) for ocean conditions (Halliwell et al., 1998). Meteorological forecasts were generated hourly from a 6‐hr initialization cycle throughout the duration of CASPER‐East (Pastore et al., 2021; Ulate et al., 2019). In the lowest kilometer of the atmosphere, the average vertical resolution of the model is ∼32 m with 70 terrain following levels; this resolution is unsatisfactory for resolving evaporation ducts thus, COAMPS is blended with the Navy Atmospheric Surface Layer Model (NAVSLaM; Frederickson, 2016) increasing resolution to a decimeter in the lowest 100 m enabling resolution of evaporation ducts (Pastore et al., 2021).…”

“…where, a is the radius of the earth, z is height above sea level, e is partial water vapor pressure (millibars) that is computed from q as well as T for estimating the saturation vapor pressure (Buck, 1981), and p is pressure in hPa. Surface refractivity (M 0 ) is determined by the surface values of q and T profiles after extrapolation to the sea surface (Pastore et al, 2021;Wang et al, 2018). MAPS refractivity profiles are measured within ±12 hr of RF measurements.…”

“…Variations in location and time of the MAPS data relative to the RF measurements is due to poor weather/sea state conditions and limited to daylight hours (Wang et al, 2018); MAPS profile times and locations can be seen in Figure 1 and Table 1. Further details on the deployment of MAPS and justification for the seventh order polynomial fit can be found in Alappattu et al (2016), Rainer (2016), Wang et al (2018), and Pastore et al (2021). Garratt, 1994;Fairall et al, 1996;Fairall et al, 2003).…”