Development of a variational data assimilation system for the diurnal cycle of sea surface temperature

While, James; Martin, Matthew J.

doi:10.1002/jgrc.20215

Cited by 5 publications

(5 citation statements)

References 37 publications

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“…Nonetheless, Pimentel et al (2008) does report that the method reduces the discrepancy between assimilated observations and modelled SST. A more sophisticated 4D‐Var methodology, which explicitly accounts for model‐ and observation‐error variances, is presented in While and Martin (2014) (WH14 hereafter). Like Pimentel et al (2008), WH14 modifies the applied wind and heat fluxes in order to move a model of the diurnal warm layer closer to the observations.…”

Section: Introductionmentioning

confidence: 99%

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

While

Mao

Martin

et al. 2017

Quart J Royal Meteoro Soc

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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.

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Section: Introductionmentioning

confidence: 99%

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

While

Mao

Martin

et al. 2017

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

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“…org/10.48670/moi-00167, accessed on 5 September 2022). It is an hourly mean skin SST at 0.25°× 0.25°horizontal resolution, analyzed by the UK Met Office, using in-situ and satellite data from infra-red radiometers (While and Martin, 2013). The skin SST is the temperature measured by satellite infra-red radiometers and can experience a large diurnal cycle.…”

Section: Ostia Skin Sstmentioning

confidence: 99%

A Local Analytical Optimal Nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Wang

Ali

Wang

2023

Preprint

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Abstract. A Local Analytical Optimal Nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM Version 2.2.98) and the Los Alamos multi-category sea ice model (CICE Version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (model minus observation) is designed to be proportionally distributed to the multiple sea ice categories. A twin experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4 and neXtSIM which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. The LAON also produces a continuous evolution of sub-daily SIC, which avoids abrupt jumps often seen in other assimilated products. This efficient and accurate method is promising for data assimilation in global and high-resolution models.

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“…ZB05 and TBBJ10 used the three‐band absorption profile of Soloviev () to obtain the penetrating short‐wave radiation, SW PEN given by

\frac{S W_{PEN} (z)}{S W_{net}^{normalS}} = \sum_{i = 1}^{N = 3} a_{i} \exp (- z b_{i}),

where z = d ; the coefficients a i and b i are as in ZB05. A modified version of the nine‐band model of Paulson and Simpson () was used by Gentemann et al () and While and Martin (). Besides the obvious differences in the number of terms ( N = 3 or 9) and values of coefficients ( a i , b i ), the nine‐band model differs from the three‐banded model because it also includes contribution from the solar zenith angle in b i (Gentemann et al , ).…”

Section: Skin Sst Model In the Geos‐agcmmentioning

confidence: 99%

“…In the context of data assimilation (DA), While and Martin () tested a prototype system for producing near‐real‐time global analysis of diurnal SST using the Takaya et al () (hereafter TBBJ10) model. They sampled a TBBJ10 model‐generated trajectory to obtain synthetic observations of a diurnally varying skin SST.…”

Section: Introductionmentioning

confidence: 99%

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system

Akella

Todling

Suárez

2017

Quart J Royal Meteoro Soc

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The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modeling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld, in-situ buoy measurement of near-surface SST. Evaluation of forecast skill scores show marginal to neutral benefit from the modified Ts.

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Development of a variational data assimilation system for the diurnal cycle of sea surface temperature

Cited by 5 publications

References 37 publications

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

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

A Local Analytical Optimal Nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system

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