The US Navy s Current and Future Sea Ice Forecast Capabilities

Transcription

1 The US Navy s Current and Future Sea Ice Forecast Capabilities Pamela G. Posey, E. Joseph Metzger, Alan J. Wallcraft, Richard A. Allard, David A. Hebert, Ole Martin Smedstad, Julia Crout and Michael Phelps GODAE IV-TT Workshop Sept 2016, Montreal, Canada

2 Arctic Cap Nowcast/Forecasting System (ACNFS) ACNFS consists of 3 components: Ice Model: Community Ice CodE (CICE) Ocean Model: HYbrid Coordinate Ocean Model (HYCOM) Data assimilation: Navy Coupled Ocean Data Assimilation (NCODA) Prescribed atmospheric forcing from NAVy s Global Environmental Model (NAVGEM) Declared operational Sept 2013 Runs daily at the Naval Oceanographic Office (NAVOCEANO) ACNFS produces nowcast/7-day forecasts of ice concentration, ice thickness, ice drift, SST, SSS and ocean currents for the Northern Hemisphere Products pushed daily to the U.S. National Ice Center (NIC) and NOAA Daily graphics can be found: www7320.nrlssc.navy.mil/hycomarc Grid Resolution: ~3.5 km North Pole Black line is the independent ice edge location (NIC). Animation spans Aug Sept

3 Global Ocean Forecasting System (GOFS) 3.1 Metzger et al. (2014), Oceanography Ocean Model: HYbrid Coordinate Ocean Model (HYCOM) (DoD) 0.08 (~9 km near equator, ~7 km at mid-latitudes) 41 vertical hybrid layers Sea Ice Model: Community Ice CodE (CICE) (DOE) 0.08 (~3.5 km at North Pole) Ocean/Ice Data Assimilation: NCODA 3DVar 24 hour update window Cummings and Smedstad (2013) in Data Assim for Atmos, Ocean & Hydro Applications Pre-Operational 7 day forecasts Atmospheric boundary forcing supplied by NAVGEM Will replace ACNFS sic NAVGEM CICE HYCOM t/s/u/v NCODA 3

4 Global Ocean Forecasting System (GOFS) 3.1 Like ACNFS, GOFS 3.1 produces ice forecasts in the Northern Hemisphere and also has the added capability of forecasting ice conditions in the southern hemisphere. Daily graphics available: www7320.nrlssc.navy.mil/glbhycomcice1-12 GOFS 3.1 Ice Concentration Valid GOFS 3.1 Sea Surface Salinity Valid GOFS 3.1 Sea Surface Temp Valid

5 Observations used in the Navy s assimilation scheme As of Feb 2015: AMSR2 ice concentration and NIC s ice mask also used in operations Global Telecommunication System (GTS) collection and distribution of observations in real-time 5

6 Assimilating ice observations into systems Since the late 1990 s, DMSP SSMI and then SSMIS ice concentration (~25km) has been assimilated in the Navy s ice forecast systems. More recently, AMSR2 (~12.5km) has become available in real-time. Passive microwave sensors have a known problem with underestimating sea ice during the summer. Worked with NSIDC to develop technique to assimilate AMSR2 and NIC s Interactive Multisensor Snow and Ice Mapping System (IMS) ice mask (4km). Adding in AMSR2 and IMS, overall ice edge errors in the Arctic were reduced by 36% and 56% (year and summer, respectively). Findings documented: Posey et al., 2015, The Cryosphere Hebert et al., 2015, JGR-Oceans ACNFS Ice Concentration 15 Aug 2012 SSMIS only ACNFS Ice Concentration 15 Aug 2012 AMSR2 and IMS Implemented new real-time data feeds into operational ACNFS GODAE IV-TT 2016 and Workshop pre-operational GOFS 3.1 in Feb

7 Assimilating ice observations into systems Current operational jobstream: SSMIS and AMSR2 ice concentrations NCODA PREP and NCODA-QC ACNFS: Assimilates ice concentration (IC) observations (< 40%) along the MIZ GOFS 3.1: Assimilates all IC s observations. NCODA NCODA Analysis I M S Modified NCODA Analysis ACNFS and GOFS 3.1 IMS rules: IMS has no ice and analysis has ice, set IC = 0 IMS has ice and analysis < 70%, IC = 70% IMS has ice and analysis > 70%, use analysis *New data sources (red) were implemented Feb Passive microwave (sees through clouds): SSMIS (~25km) and AMSR2 (~12.5 km) 7

8 Assimilating ice observations into systems Proposed operational jobstream: SSMIS, AMSR2 and NPP VIIRS ice concentrations Update NCODA PREP and NCODA-QC to 1) ingest NPP VIIRS data and 2) use NIC s IMS ice mask for qc IMS rules: IMS has no ice and analysis has ice, set IC = 0 IMS has ice and analysis < 70%, IC = 70% IMS has ice and analysis > 70%, use analysis By the end of this FY16 project NCODA NCODA Analysis ACNFS and GOFS 3.1 Assimilate NPP VIIRS IMS moved to NCODA PREP ACNFS: Assimilates ice concentration (IC) observations (< 40%) along the MIZ GOFS 3.1: Assimilates all IC s observations. Passive microwave (sees through clouds): SSMIS (~25km) and AMSR2 (~12.5 km) Visible (does not see through clouds): VIIRS (750m) 8

9 Products used in Navy s special missions Used as guidance in Nov 2011 convoy 103 M gallons of fuel to Nome, Alaska Used in pre-flight planning for NASA Operation Ice Bridge missions Used in ONR field experiments: Marginal Ice Zone (2014) and Sea State (2015) Used in Navy s ICEX field work OIB plane - Fairbanks March 2013 CLUSTER LOCATIONS 9

10 Future Operational Products NEXT GENERATION: GOFS 3.5: 1/25⁰ global two-way coupled HYCOM-CICE modeling system with data assimilation including tides Resolution 1.75 km at the North Pole (double resolution of GOFS 3.1) Transition to NAVOCEANO scheduled for FY18 GOFS 3.5 Ice Concentration (%) 15 March 2014 GOFS 3.5 Ice Thickness (m) 15 March

11 Navy s Earth System Prediction Capability (ESPC) Atmospheric Model: NAVy s Global Environmental Model (NAVGEM) (DoD) Hogan et al. (2014), Oceanography T359 horizontal resolution (~37 km) 50 vertical levels Atmosphere Data Assimilation: NAVDAS-AR 4DVar 6 hour update window Rosmond and Xu (2005), Tellus Ocean Model: HYbrid Coordinate Ocean Model (HYCOM) (DoD) 0.08 (~9 km near equator, ~7 km at mid-latitudes) 41 vertical hybrid layers Sea Ice Model: Community Ice CodE (CICE) (DOE) 0.08 (~3.5 Same km at as North GOFS Pole) 3.1 Ocean/Ice Data Assimilation: NCODA 3DVar 24 hour update window Cummings and Smedstad (2013) in Data Assim for Atmos, Ocean & Hydro Applications Operational Capability: Initial Operational Capability in day deterministic high resolution forecast every day 30 to 45 day ensemble standard resolution forecasts Rapid Development: Wave Watch III CICE version 5 Aerosols Coupled Data Assimilation sic NAVDAS-AR t/p/u/v NAVGEM CICE HYCOM t/s/u/v NCODA 11

12 GOFS 3.1 Class 4 Ice Inter-comparison Following the Canadian CONCEPTS initiative to compare GODAE OceanView ice-concentration prediction with AMSR-2 ice concentration, we have: Created class 4 files Interpolated GOFS 3.1 to AMSR-2 locations GOFS 3.1 has 7 forecast days of global coverage (as opposed to class 4 standard 10 day forecasts) GOFS 3.1 products are output as instantaneous values for Z (24-h forecast intervals). To mimic the other Class 4 model average values, the GOFS Z and 24Z bounding times are averaged. Hindcast analysis is not readily available, so best_estimate is an average of the 00z nowcast for the start of the AMSR-2 day and the 00Z nowcast for the end of the AMSR-2 day AMSR-2 Ice Concentration Observations valid 22 Sept 2016 GOFS 3.1 Ice Concentration Best Estimate valid 22 Sept

13 GOFS 3.1 Class 4 Model Data Best_estimate Start of day End of day Observation Forecast GOFS 3.1 Class 4 files created for Aug 2016 Persistence Presentation Title 13

14 GOFS 3.1 Class 4 Model Data netcdf class4_ _nrl_gofs_3.1_aice.nc netcdf class4_ _nrl_gofs_3.1_aice { dimensions: numobs = ; numvars = 1 ; numdeps = 1 ; numfcsts = 10 ; variables: float longitude(numobs) ; longitude:long_name = "Longitudes" ; longitude:unit = "Degrees" ; short QC06(numobs, numvars, numdeps) ; float latitude(numobs) ; QC06:long_name = "QC number 6" ; latitude:long_name = "Latitudes" ; QC06:_FillValue = -99s ; latitude:unit = "Degrees" ; QC06:QC_flag = "1 if No sea water, else 0" ; float obs_time(numobs) ; short QC07(numobs, numvars, numdeps) ; obs_time:long_name = "data inversion time" ; QC07:long_name = "QC number 7" ; obs_time:units = "Seconds since :00:00 utc" QC07:_FillValue ; = -99s ; float observation(numobs, numvars, numdeps) ; QC07:QC_flag = "1 if climatological frequency of ice occurance is zero, otherwise 0" ; observation:long_name = "ice concentration float leadtime(numfcsts) from AMSR2" ; ; observation:unit = "None" ; observation:_fillvalue = f ; observation:maximun = 1.f ; observation:minimun = 0.f ; observation:retrieval = "Nasa Team2 algorithm" ; short QC02(numobs, numvars, numdeps) ; QC02:long_name = "QC number 2" ; QC02:_FillValue = -99s ; QC02:QC_flag = "1 if SST >4 degr, else 0" ; QC02:whereSST = "Sea Surface Temperature" ; short QC03(numobs, numvars, numdeps) ; QC03:long_name = "QC number 3" ; QC03:_FillValue = -99s ; QC03:QC_flag = "1 if SAT >0 degr, else 0" ; QC03:whereSAT = "Surface Air Temperature" ; short QC04(numobs, numvars, numdeps) //; global attributes: QC04:long_name = "QC number 4" ; QC04:_FillValue = -99s ; QC04:QC_flag = "1 if SAT >2 degr, else 0" ; short QC05(numobs, numvars, numdeps) ; QC05:long_name = "QC number 5" ; QC05:_FillValue = -99s ; QC05:QC_flag = "1 if SAT >4 degr, else 0" ; 00Z" ; } leadtime:long_name = "Model forecast day offset" ; leadtime:units = "Hours" ; float forecast(numobs, numvars, numfcsts, numdeps) ; forecast:long_name = "Model forecast counterpart of obs. value" ; forecast:time_forecast = "forecast daily mean at noon of every day, GMT 12:00" ; forecast:_fillvalue = f ; float persistence(numobs, numvars, numfcsts, numdeps) ; persistence:long_name = "Model persistence counterpart of obs. value" ; persistence:time_analyses = "analyses made at the end of every day, GMT 24:00" ; persistence:_fillvalue = f ; float best_estimate(numobs, numvars, numdeps) ; best_estimate:long_name = "Model best estimate counterpart of obs. value" ; best_estimate:time_analyses = "analyses made at the end of every day, GMT 24:00" ; best_estimate:_fillvalue = f ; :institution = "NRL" ; :contact = "pamela.posey@nrlssc.navy.mil" ; :configuration = ; :version = "3.1" ; :obs_type = "AMSR2 brightness temperature" ; :time_interp = "Average between 00Z and 24Z daily 00Z instantaneous fields" ; :creation_date = "06-Sep :30:27" ; :system = "GOFS" ; :best_estimate_description = "nowcast instantaneous fields at time 00Z and next day 14

15 GOFS 3.1 Class 4 Ice Concentration Verification NRL s preliminary Class 4 verification (18-26 August 2016) Order of magnitude verification for 9 day period Arctic region only ~ 3.3 M observations Yellow areas where AMSR-2 has values but GOFS 3.1 has none. ~ 1.75 M observations Created GOFS 3.1 Best_estimate Eliminated observations where there was no model coverage Concentration Total = sum of all ice concentrations at AMSR-2 locations (number of observations vary daily) Interior North American lakes,caspian Sea, interior lakes in Kazakhstan and Russia 15

16 GOFS 3.1 Class 4 Ice Concentration Verification GOFS 3.1 forecast verification vs AMSR-2 Per Obs For Forecast Concentration Total and RMS calculated for each day (18-26 Aug 2016) Forecast ( ) Persistence ( ) Observation ( ) * Mean values shown in black GOFS 3.1 RMS error vs AMSR-2 Per (dashed) For (solid) Initial questions to answer: Were predicted concentration totals in ball park of observations? Did GOFS 3.1 forecast error increase with forecast length? Were GOFS 3.1 forecasts an improvement over persistence? 16

17 GIOPS/GOFS 3.1 Class4 Ice Concentration GIOPS 2.1 forecast verification vs AMSR-2 GIOPS 2.1 RMS error vs AMSR-2 GOFS 3.1 RMS error vs AMSR-2 GOFS 3.1 forecast verification vs AMSR-2 Total concentration and RMS error of Model Forecast, Persistence vs Observations GIOPS and GOFS 3.1 showed similar 17 results

18 GIOPS/GOFS 3.1 Class 4 Ice Concentration Inter-comparison Best_estimate for 9 days (18-26 Aug 2016) for GIOPS 2.1 and GOFS day average Forecast and Persistence Totals 9-day average RMS-Error 18

19 Ongoing/Future Work As part of the US ESPC effort, currently working with Environment Canada to compare an expanded list of products (ice concentration, ice edge, thickness (or volume), ice drift, snow and ice surface temperature) from the global systems (GIOPS/GOFS 3.1). For the GODAE Class 4 effort: Routinely generate GOFS 3.1 Class 4 files Take part in the Inter-comparison study between models for both global and regional areas. Ice Concentration (%) for GIOPS 2.1 GOFS 3.1 Ice Thickness (m) for GIOPS 2.1 GOFS

20 Thanks y all! Merci beaucoup! 20