Assimilating high horizontal resolution sea ice concentration data into the US Navy ’ s ice forecast systems : Arctic Cap Nowcast / Forecast System ( ACNFS ) and the Global Ocean Forecast System ( GOFS 3 . 1 )

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Introduction
Knowing the ice edge location is extremely important for safe navigation and effective execution of the US Navy's daily operational missions (US Department of Navy, 2014).Since comprehensive records began with the satellite era in 1979, summer Arctic sea ice extent has trended downward with a new record minimum of 3.41 Mkm 2 occurring in September 2012.This 2012 record low in sea ice extent, followed by an increase in extent during 2013 and 2014, indicate high year-to-year variability in the ice cover and also in the spatial distribution of the ice (i.e., where open water forms).In this rapidly changing Arctic environment, it is likely that Arctic shipping will increase over the next several years.This, in turn, will demand an increase in US military presence in the Arctic.As the US military presence increases in this region, it is imperative to provide as accurate a sea ice forecast as possible.Currently, the Navy uses the Arctic Cap Nowcast/Forecast System (ACNFS) to predict conditions in all ice-covered areas poleward of 40 • N, with a grid resolution of approximately 3.5 km at the North Pole (Fig. 1).ACNFS graphical products are publically available from www7320.nrlssc.navy.mil/hycomARC.Recently, the Global Ocean Forecast System (GOFS) 3.1 has been transitioned to the Naval Oceanographic Office (NAVOCEANO), and is in the final operational testing phase.When GOFS 3.1 becomes operational, it will replace ACNFS and provide a global sea ice prediction capability including both the Arctic and the Antarctic.Prior to 2 February 2015, the ice concentration fields from both ACNFS and GOFS 3.1 had been updated with satellite-derived ice concentrations at a gridded resolution of approximately 25 km using the US Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager/Sounder data (SSMIS).SSMIS does have higher spatial resolution (12.5 km gridded) for high frequency (85-91 GHz) channels.However, most algorithms require the lower resolution channels, limiting the gridded resolution to 25 km, with the effective resolution dependent on the frequency of each channel used in the algorithm.Higher horizontal resolution sea ice Introduction

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Full information derived from satellite observations was critically needed for existing high resolution ice models.The National Oceanic and Atmospheric Administration (NOAA) National Ice Center (NIC) is charged with forecasting the location of sea ice in the Arctic.In addition to satellite imagery, the NIC presently uses ACNFS output and in the near future (once declared operational) will use the GOFS 3.1 output, to improve the accuracy and resolution of the determined and forecast ice edge location.ACNFS consists of coupled sea ice and ocean models that forecast sea ice conditions in all ice covered regions of only the Northern Hemisphere, whereas GOFS 3.1 covers both hemispheres.ACNFS and GOFS 3.1 are based on the HYbrid Coordinate Ocean Model (HYCOM) (Metzger et al., 2015) coupled to the Los Alamos National Laboratory Community Ice CodE (CICE) (Hunke and Lipscomb, 2008), and they employ the Navy Coupled Ocean Data Assimilation (NCODA) system (Cummings and Smedstad, 2014).ACNFS has undergone validation by the Naval Research Laboratory (NRL) (Posey et al., 2010), has been declared operational (September 2013) and runs daily at NAVOCEANO.GOFS 3.1 was transitioned to NAVOCEANO on 26 September 2014 and is undergoing the final operational testing by NAVOCEANO and the NIC (Metzger et al., 2015).This new ice forecast system is expected to be declared operational by summer 2015.
With the importance of forecasting the ice edge in day-to-day operational mission support, ACNFS and GOFS 3.1 are updated daily with observed sea ice concentrations derived from the DMSP SSMIS.One major drawback in using SSMIS is its low spatial gridded resolution of 25 km, which is considerably coarser than the 3.5 km resolution of both ACNFS and GOFS 3.1.In 2012, a higher resolution ice concentration product (AMSR2) became available for daily initialization in order to improve the modeled forecast ice edge location.Also during 2012, the NIC recommended that a greater effort be undertaken to assimilate their analyzed data and other satellite sources into the Navy's models in order to improve the forecasted ice edge location, especially during the summer season.Recently, investigators at the National Snow and Ice Data Center (NSIDC), National Atmospheric and Space Administration (NASA), NIC, and Introduction

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Full NRL, developed an ice concentration product that uses the daily observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) as well as data from the new higher resolution AMSR2 passive microwave sensor.The resolution of this blended data product is 4 km; much closer to the resolution of Navy ice forecasting systems than the SSMIS data.This study examines the impact of assimilating this new, high resolution blended data into the both ACNFS and GOFS 3.1.

Passive microwave
Several methods have been developed to estimate sea ice concentration from passive microwave brightness temperatures, generally via empirically derived algorithms based on differences or ratios between the passive signatures of ice and open water at different microwave frequencies and polarizations (e.g.Comiso and Nishio, 2008;Markus and Cavalieri, 2000).Since 1979, these algorithms have been applied to a series of multi-channel microwave radiometers such as the SSMIS.
The Advanced Microwave Scanning Radiometer on the NASA Earth Observing System (EOS) Aqua platform (AMSR-E) operated from 2002 until the sensor ceased normal operations in October 2011.A follow-on sensor, AMSR2, was launched in May 2012 on the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission -Water (GCOM-W) platform.The higher gridded spatial resolution of these new instruments allows for a higher resolution sea ice concentration product (12.5 km for AMSR-E and 10 km for AMSR2 vs. 25 km for SSMIS).
Problems associated with the interpretation of sea ice signatures in passive microwave data during summer months have been well documented (e.g., Cavalieri et al., 1990;Gloersen et al., 1978;Campbell et al., 1980).Summer sea ice concentrations are more uncertain than winter concentrations because of the presence of moist snow, passive microwave products tend to underestimate the ice concentration within the pack ice, and may not detect ice at all in some cases, even when ice is present in concentrations considerably greater than 15 %.Broad expanses of ice at relatively low concentration often make up the marginal ice zone (MIZ), and passive microwave products often place the ice edge farther north than actuality, resulting in an underestimation of Arctic-wide ice extent relative to more accurate methods used in human-derived analyses.
The magnitude of this underestimation of sea ice extent can be seen in Fig. 2 during the time period of 25 July-28 August 2012.Sea ice extent from passive microwave data (Fetterer et al., 2002) is approximately 1 Mkm 2 less on 13 August 2014 than that obtained from a high resolution, human-analysis-based product.The difference between the two extent products gradually decreases by the end of August 2012.Differences can also occur in winter because passive microwave sensors may fail to detect thin ice, although underestimations of ice extent in winter tends to be much lower in magnitude than in summer.Some of these differences are due to the lower spatial resolution of passive microwave imagery, with SSMIS sensor footprints on the order of 40-70 km for some channels used in the sea ice algorithms.AMSR2 has much higher spatial resolution, but sensor footprints (on the order of 10-20 km) are still much larger than the IMS resolution.It should be noted also that the human-analysis-based product has limitations as well.IMS inputs can vary in quantity and quality depending on the satellite coverage causing potential inconsistency over time (Meier et al., 2015) and some subjectivity will be imposed on the product due to the use of human analysis.

Interactive Multisensor Snow and Ice Mapping System (IMS) and Multisensor Analyzed Sea Ice Extent (MASIE)
An operational ice analysis, the Interactive Multisensor Snow and Ice Mapping System (IMS) (Helfrich et al., 2007;National Ice Center, 2008), is produced by the NIC daily.
IMS is an ice and snow mask product where sea ice is indicated when ice concentration is estimated to be greater than 40 % and open water where ice concentration Introduction

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Full is estimated to be less than 40 %.Human analysis of all available imagery including visible/infrared (VIS/IR), synthetic aperture radar (SAR), scatterometer, and passive microwave yields a daily map of sea ice extent at 4 km spatial resolution, with a 40 % concentration threshold for the presence of ice within each 4 km cell.The IMS ice fields are repackaged into several user-friendly formats to create the Multisensor Analyzed Sea Ice Extent (MASIE) product available to the public from the NSIDC (NIC and NSIDC, 2010).Figure 3 is a sample of a daily MASIE product.
The IMS sea ice extent fields upon which MASIE and the blended product are based upon are developed from a largely manual synthesis of information from numerous data sources.IMS documentation (NIC, 2008) lists 28 potential sources for snow and ice information.Most, but not all, of these sources are from satellite sensors.The MASIE product documentation (NIC and NDISC, 2010) has additional information on how IMS fields are produced.
A particular day's ice map is partially the product of subjective interpretation and is not exactly reproducible.However; each day's ice and snow extent fields are produced with fixed standards and quantified by areal coverage with set metrics.This contrasts with the operational chart products, where the NIC analysts have more flexibility with which to meet changing user needs.
We base our assertion that the IMS/MASIE product is a more reliable indicator of the presence or absence of ice than AMSR2 data due to several factors.Primarily, the manual analysis of numerous data sources is more dependable than a passive microwave concentration product alone.There are also several situations when the passive microwave's signature are identical to that of sea ice (i.e., surface water on top of ice during the summer, thin ice and presence of wind/aerosols).In addition, NIC analysts have access to data sources that are of higher resolution than AMSR2 and lends a higher quality to the IMS/MASIE product.Meier et al. (2015), compare passive microwave-derived ice extent with ice extent from IMS/MAISE annually and seasonally.
In this study, the MASIE product was used in an ACNFS hindcast from July 2012-July 2013, while the IMS product was used in ACNFS and GOFS 3.1 hindcasts from Introduction

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Full June 2014-August 2014.As stated above, these two products (MASIE and IMS) are identical in data values but differ in format and location of the data source; MASIE is delivered from the NSIDC, while IMS comes from the NIC.On 2 February 2015, the IMS ice mask product was incorporated into the ACNFS and GOFS 3.1 operational jobstreams at NAVOCEANO.

Blended IMS/MASIE + AMSR2
Posey et al. ( 2011) showed improved results when assimilating high resolution AMSRE ice concentration field into the ACNFS.Follow on testing provided additional motivation to develop a concentration product that improves upon the use of passive microwave concentration alone by capitalizing on the manual analysis and multiple data sources that make the IMS/MASIE product.In 2012 AMSR2 ice concentration became available in real-time, and, along with the IMS/MAISE product, could be evaluated for daily initialization in order to improve the modeled forecast ice edge location, especially during the summer season.Both data products (AMSR2 and IMS/MASIE) are available (within 24 h) for assimilation in daily operational forecasting applications.
The blended product uses AMSR2 data interpolated to the IMS grid and is produced following these rules: if IMS/MASIE has no ice, set the ice concentration to 0, if IMS/MASIE indicates ice and AMSR2 has an ice concentration value > 70 % for that grid cell, then use the AMSR2 concentration value, else use 70 % as the minimum.
Figure 4 shows how ice extent from IMS/MASIE differs from that seen by AMSR2 for representative days in the winter and summer days.While both IMS and AMSR2 show ice over most of the Arctic, discrepancies are seen near the ice edge; in most cases IMS/MASIE indicates ice where AMSR2 does not.In winter this is likely due to thin ice that falls below the threshold of detectability by passive microwave sensors.In summer the cause is likely a combination of thin, small ice floes of ice, and surface melt.

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Full However, there are some regions, particularly during summer, where AMSR2 indicates ice but IMS/MASIE does not.This may be due to timing differences of the source imagery (i.e., sub-daily change in the ice cover), spatial resolution limitations of AMSR2, or limitations in the IMS/MASIE analysis.The IMS/MASIE ice mask has a 40 % ice concentration threshold, meaning the actual concentration within each ice cell can range from 40 to 100 %.The mid-point or median, 70 %, is used as a reasonable minimum ice concentration value in the blended product.
Figure 5 shows the final blended AMSR2 and IMS/MASIE ice concentration product during the winter (15 March 2014) and summer (15 September 2014) days of Fig. 4. The magenta MASIE only areas of Fig. 4 are assigned a value of 70 % (dark blue) in the blended ice concentration product.The homogenous expanses of ice at 70 % are more noticeable in the summer when the passive microwave underestimates the extent of ice over large areas.Also to note, the AMSR2 coastal spillover (shown in green in Fig. 4) is mitigated by the IMS/MASIE ice mask product.There are no ice concentration values between 0 and 70 % in the blended product.

ACNFS assimilating AMSR2 ice concentration and MASIE ice mask
ACNFS and GOFS 3.1 previously assimilated near real-time sea ice concentration derived from SSMIS each day and generated a daily initialization ice concentration field, although the methodology is slightly different in each system.SSMIS-derived ice concentration is directly inserted into both models along the MIZ, defined here as areas near the ice edge where ice concentration is less than 15 %.For ACNFS, the assimilation continues using a weighting technique of the SSMIS and model ice concentration in areas where ACNFS ice concentration is between 15 and 40 %; more weight is put on the model value (and less on SSMIS) as the model ice concentration approaches 40 %.No ice concentration is assimilated where the ACNFS ice concentration is above Introduction

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Full 40 %.Similar to ACNFS, GOFS 3.1 assimilates the SSMIS ice concentration near the ice edge but also further into the interior using the NCODA ice analysis ±10 % as a guide.For this study, ACNFS assimilated three difference sources of sea ice concentration for the time period July 2012 through July 2013: (1) SSMIS, (2) AMSR2 only and (3) blended IMS/MASIE + AMSR2.All three products used the same assimilation methodology to update the initial ACNFS ice concentration.The 6 h forecast ice edge derived from ACNFS hindcasts of sea ice concentration assimilating the three different products were compared to the ice edge obtained from the NIC at 00:00 LT.The NIC ice edge product is generated daily by an ice analyst for the full Arctic region using all available imagery.In this product (Fig. 6 -black contour), the presence of any known ice is used to determine a conservative edge location as this product is used for navigational purposes to avoid ice hazards.The resolution of the ice edge can shift based on the resolution of the data sources and the buffer between the ice on the imagery and the analyzed ice edge line.The IMS product (Fig. 6 -blue contour) is also generated by an ice analyst, but it is generated as a gridded field that may provide more spatial detail at smaller scales.The NIC ice edge product and IMS product are independently derived and typically apply differing data sources.Although the NIC ice edge is one of the products examined during the IMS ice analysis, the criteria for the IMS ice extent is different than the NIC ice edge; the NIC ice edge can only provide an ice limit, whereas IMS provides a 4 km estimate of areas with > 40 % ice cover.Over the last 10 years, the NIC ice edge has been used for model ice edge validation, and will continue as part of this study since the NIC ice edge is not assimilated into ACNFS or GOFS 3.1.
The daily mean distance between the independent daily observed NIC ice edge and derived model ice edges from all ACNFS hindcasts were compared during the 13 month time period.Model ice edge locations are defined as those grid points that exceed a certain threshold value for ice concentration and that also have a neighboring point that falls below that value.In this case a threshold of 5 % was used to determine the model ice edge.The distances between each NIC observed point and the near-

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Full est model-derived ice edge location were then calculated, from which a daily mean was computed for each model day.Six analysis regions in the Arctic were compared (Fig. 7).Table 1 contains the regional mean distance difference (km) between the NIC ice edge and ACNFS assimilating SSMIS, AMSR2 only, and the blended MASIE + AMSR2.The last row is the percent improvement in ACNFS assimilating the new products.During this 13 month time period, the mean distance between the ACNFS ice edge using the SSMIS as initialization and the NIC ice edge was 45 km for the full Arctic domain, compared to 32 km for the ACNFS ice edge initialized using AMSR2.This is a 29 % reduction in error by assimilating the higher resolution AMSR2 ice concentration compared to the SSMIS.ACNFS assimilating the blended (AMSR2 + MASIE) product showed a larger reduction in overall mean ice edge errors by 36 % compared to ACNFS assimilating SSMIS (29 vs. 45 km.The AMSR2 only assimilation could result in anomalous concentration values along the coastal boundaries (shown in Fig. 4).With the addition of the MASIE product, the AMSR2 coastal spillovers are reduced as shown in the ice edge errors (32 to 29 km for the full Arctic domain).
Seasonal sea ice location errors initialized from SSMIS, AMSR2 and the blended product were also examined for the same time period.During the winter time period (January-April), ice edge locations for the Arctic region were similar assimilating the different data products (29 km using SSMIS, 22 km using AMSR2 only and 20 km using the blended product).During the summer melt season (June-September), the errors were larger (75 km using SSMIS, 55 km using AMSR2 only and 33 km using the blended product).The reduction in ice edge error locations are greater during the summer period (August-September) as shown in Fig. 8 for the Bering/Chukchi/Beaufort Sea region.Assimilating the blended product into the ACNFS, especially during the summer, significantly reduced the ice edge errors and therefore improve the accuracy of the model ice edge location.

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ACNFS and GOFS 3.1 assimilating AMSR2 ice concentration and IMS ice mask
In order for the operational ACNFS and GOFS 3.1 to assimilate the AMSR2 and IMS data sources, these two products must be available daily in real-time at NAV-OCEANO.Since October 2014, NAVOCEANO has successfully implemented these real-time sources into the daily data stream.Rather than blending AMSR2 and IMS by regridding AMSR2 and assimilating its gridded blended field as was done in the earlier test; the decision was made to regrid IMS to the model grid in the operational jobstream.The initial data assimilation step is based on AMSR2 (and SSMIS in the operational jobstream) swath data and the model forecast as background.The resulting gridded ice concentration analysis is then blended with IMS extent on the model grid to form the ice concentration field assimilated into the model.
A second ACNFS hindcast and an original GOFS 3.1 hindcast were performed to test the accuracy of assimilating the real-time NAVOCEANO data feed.These ACNFS and GOFS 3.1 hindcasts were integrated from 1 June-31 August 2014 using the realtime NAVOCEANO feed.As in the earlier test, the same ice edge error analysis was performed.Two additional ACNFS simulations were run assimilating (1) AMSR2 + SS-MIS and (2) AMSR2 + SSMIS with IMS.These last 2 hindcasts measure the effect of keeping the current coarser SSMIS as an assimilation data source.The assimilation study for GOFS 3.1 included assimilating (1) AMSR2 with IMS and (2) AMSR2 + SSMIS with IMS.All results are shown in Table 2.
During this 3 month time period, the mean ice edge distance between the ACNFS ice edge using the SSMIS as initialization and the NIC ice edge was 61 km for the full Arctic, compared to 44 km for the ACNFS ice edge initialized using the AMSR2.This results is a 28 % reduction in error by assimilating the higher resolution AMSR2 ice concentration as compared to the SSMIS alone.Assimilating both AMSR2 and SSMIS ice concentrations into ACNFS lowered the mean ice edge error compared to assimilating SSMIS alone (on average 61 to 46 km), an overall improvement of 25 %.The Introduction

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Full NIC ice edge location and the ice edge obtained from ACNFS and GOFS 3.1 initialized using both SSMIS and AMSR2/IMS data sets was calculated.Daily analyses of the ice edge location in both studies indicated that ACNFS and GOFS 3.1 initialized using the both AMSR2 and SSMIS/IMS data sets have substantially lower ice edge errors than the ACNFS and GOFS 3.1 initialized using the coarser SSMIS data.ACNFS initialized using the blended AMSR2/IMS product improves the ACNFS predicted ice edge location by 56 %, while GOFS 3.1 showed an improvement of 62 %.This analysis has shown that assimilating a higher horizontal resolution, blended AMSR2/IMS ice concentration product yields a more accurate ice edge forecast.While including the SSMIS ice concentration field (AMSR2 + SSMIS along with IMS) did not reduce the ice edge error in ACNFS or GOFS 3.1, it could prove to be beneficial if AMSR2 data becomes unavailable.For operational forecasting, the current SSMIS ice concentration real-time data source will still be utilized in addition to the AMSR2 ice concentration and the IMS ice mask for daily use.On 2 February 2015, these two new data sources (AMSR2 and IMS) were added to the operational ACNFS and the pre-operational GOFS 3.1 jobstreams.Introduction

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Table 1 .
Regional mean distance differences (km) between the NIC ice edge and 6 h ACNFS forecasts initialized from SSMIS, AMSR2 only and blended AMSR2 + MASIE.Analysis is done for time period July 2012-July 2013.The bold numbers denote the smallest mean distance error between the assimilation test cases.The bottom row shows the total Arctic percent improvement from each ice forecasting system compared to using SSMIS assimilation alone.

Table 2 .
Regional mean distance differences (km) between the NIC ice edge and 6 h ACNFS or 12 h GOFS 3.1 forecasts initialized from various combinations of SSMIS, AMSR2 and IMS data for the time period June-August 2014.The bold numbers denote the smallest mean distance error between the assimilation test cases.The bottom row shows the total Arctic percent improvement from each ice forecasting system compared to using SSMIS assimilation alone.