Evaluation of Surface Conditions from Operational Forecasts Using in situ Saildrone Observations in the Pacific Arctic

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June – September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multi-model means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multi-model means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

Atmospheric Fronts Shaping the (Sub)Mesoscale SST‐Wind Coupling Over the Southern Ocean: Observational Case

“…Though the positive relationships between surface wind (air temperature, heat flux) and the SST perturbations can be further rectified potentially using mooring data, it is still remarkable that Saildrone observations can reveal such correlations. As Saildrone is becoming a routing tool for air‐sea observations (Gentemann et al., 2020; Sutton et al., 2020; Vazquez‐Cuervo et al., 2019; C. Zhang et al., 2022; D. Zhang et al., 2019), it can be used to study the global submesoscale air‐sea coupling in future.…”

Using age compositions derived from spatio-temporal models and acoustic data collected by uncrewed surface vessels to estimate Pacific hake (Merluccius productus) biomass-at-age

“…Uncrewed surface vessels (USVs) are particularly useful for missions of long duration in harsh environments (Liu et al, 2016;Mordy et al, 2017;Meinig et al, 2019), and are well suited to carry out survey operations in circumstances that would limit or prevent the operation of crewed surveys (e.g., De . Therefore, USVs have been used to understand physical oceanography (Wills et al, 2021;Nickford et al, 2022;Zhang et al, 2022), animal distribution and behavior (De Robertis et al, 2019b;Verfuss et al, 2019;Levine et al, 2021), and collect data in service of fishery resource survey programs (Chu et al, 2019;De Robertis et al, 2021;Sepp et al, 2022). Incorporating USVs into fishery-independent survey programs is of particular interest given their potential to increase the efficiency of ship-based survey effort and mitigate the effects of unexpected circumstances (e.g., funding shortfalls, vessel unavailability).…”