Use of SSM/I ice concentration data in the ECMWF SST analysis

Fernández, Pedro Riesgo; Kelly, Graeme; Saunders, Roger

doi:10.1017/s1350482798000942

Cited by 1 publication

(2 citation statements)

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“…A probability of error is then calculated based on the difference between the observation and background data. The sea ice concentration climatology is obtained from the European Centre for Medium Weather Forecast (ECMWF) climatology formed from years 1979 to 1996 [ Fernandez et al ., ], while ocean climatologies are obtained from Generalized Digital Environmental Model (GDEM) [ Carnes et al ., ]. If the observation probability of error reaches a specified threshold (usually 5%), the data are not assimilated.…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

“…A probability of error is then calculated based on the difference between the observation and background data. The sea ice concentration climatology is obtained from the European Centre for Medium Weather Forecast (ECMWF) climatology formed from years 1979 to 1996 [Fernandez et al, 1998] threshold (usually 5%), the data are not assimilated. Second, the QC'd data are then used in the 3DVAR method to create an analysis field based on the observed and background data and the error covariance.…”

Section: Data Assimilationmentioning

confidence: 99%

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Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

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Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

“…A probability of error is then calculated based on the difference between the observation and background data. The sea ice concentration climatology is obtained from the European Centre for Medium Weather Forecast (ECMWF) climatology formed from years 1979 to 1996 [Fernandez et al, 1998] threshold (usually 5%), the data are not assimilated. Second, the QC'd data are then used in the 3DVAR method to create an analysis field based on the observed and background data and the error covariance.…”

Section: Data Assimilationmentioning

confidence: 99%