An operational analysis system for the global diurnal cycle of sea surface temperature: implementation and validation

Abstract

An operational system for producing a global diurnally varying analysis of skin sea‐surface temperature (SST) has been developed at the Met Office. Skin SST is formulated as the sum of a foundation temperature, a warm‐layer temperature difference, and a thermal skin temperature difference. Foundation temperature is taken from the Operational Sea surface Temperature and sea Ice Analysis (OSTIA) system, while numerical models are used for the warm layer and thermal skin layer. Both the thermal skin layer and warm‐layer models are forced using outputs from the Met Office's numerical weather prediction system. Data assimilation is used to improve estimates of the warm layer, with observations coming from the geostationary SEVIRI and GOES‐W instruments, as well as the polar orbiting NOAA‐AVHRR sensors. The SST observations of these instruments are converted to observations of the warm‐layer temperature difference by subtracting an instrument‐specific foundation SST calculated using just night‐time data from that sensor. A quality control procedure removes warm‐layer observations where the satellite‐specific foundation estimates are less robust due to lack of data. Validation of the analysis system has been performed by comparing the model to the assimilated satellite data, and to independent near‐surface observations from Argo floats. The mean state of the system was assessed via a comparison to a climatology generated from drifting buoys. Results from the validation show that the system does a good job of replicating the climatology and that assimilation improves the analysis when assessed against Argo. However, the system has been found to underestimate the diurnal range of skin SST by approximately 0.1–0.3 °C on average. Data from the system are available free of charge from the Copernicus Marine Environment Monitoring Service.

Extended representation of wind–mass correlation by ensemble forecasting for data assimilation

“…where C is a regression coefficient depending on the vertical level and the latitude band (Song et al, 2017b). Each one-degree latitudinal band, the proxies for describing forecast errors are sampled to make an n × n vertical cross covariance where n is the number of vertical layers.…”

“…imposed on the initialization process to suppress an unwanted drift of the initial forecasts (Fisher, 2003;Song et al, 2017b).…”

“…The nonlinear balance equation (NLI) is an extended version of the wind-mass balance including the advection term (Charney, 1955). The advection terms of the horizontal wind are significant when the flow is strongly accelerated or curving; in the jet stream regions, the NLI demonstrated its superior ability relative to the REG in the previous studies (Fisher, 2003;Song et al, 2017b). However, the zonal wind in the tropical troposphere and the temperature in the Antarctic stratosphere were analysed rather in a wrong direction by the NLI scheme (Song et al, 2017b).…”

Impact of high‐resolution ocean–atmosphere coupling on fog formation over the North Sea

“…Representing the distribution and evolution of SST accurately as the lower boundary condition in global and regional-scale atmosphere model configurations has therefore been demonstrated to be important for a range of processes across a range of time-scales. In particular, representing the diurnal variation of SST (for example, Clayson and Bogdanoff, 2013;While et al, 2017) is known to play a key role in modifying surface fluxes (for example, Guemas et al, 2013;Weihs and Bourassa, 2014). A diverse range of large-scale atmospheric processes can be impacted, including storm-track location (Brayshaw et al, 2011;Woollings et al, 2012), frontal propagation (Parfitt et al, 2016;Passalacqua et al, 2016) and precipitation (Minobe et al, 2008), the evolution of the Madden-Julian Oscillation (Seo et al, 2014;DeMott et al, 2015;Stan et al, 2018), the El Niño-Southern Oscillation (ENSO: for example, Ham et al, 2010;Masson et al, 2012), and Indian (for example, Terray et al, 2012) and Australian monsoon systems (Wang and Zang, 2017).…”

Evaluation of NASA GEOS‐ADAS Modeled Diurnal Warming Through Comparisons to SEVIRI and AMSR2 SST Observations

“…This was attributed to the upper ocean vertical profile assumption embedded in the TBBJ10 model. Nevertheless, the TBBJ10 model is a significant step forward and has been integrated into European Center for Medium range Weather Forecasting (ECMWF; Takaya et al, ; TBBJ10; ZB05), the Met Office (While et al, ), Naval Research Lab (NRL; McLay et al, ), and NASA's Goddard Earth Observing System (GEOS)‐ Atmospheric Data Assimilation System (ADAS), Version 5 (GEOS‐5) (henceforth referred to as GEOS‐ADAS; Akella et al, ). Recently the GEOS‐ADAS has been modified to resolve the diurnal cycle in SST (details are given below).…”