Assimilation of Snow and Ice Data (Incomplete list)

Transcription

1 Assimilation of Snow and Ice Data (Incomplete list) Snow/ice Sea ice motion (sat): experimental, climate model Sea ice extent (sat): operational, U.S. Navy PIPs model; Canada; others? Sea ice concentration (sat): experimental forecast model Snow cover (sat + surf): operational, NOHRSC; Canada; others? Snow water equivalent (sat + surf): operational, NOHRSC Ice surface temperature (sat, surf): experimental, climate model Atmosphere Polar winds (sat): operational forecast models (6 worldwide) Bottom Line: Cryosphere data are underutilized!

2 Cryosphere Snow - Snow water equivalent, depth, extent, state, density, snowfall, solid precipitation, albedo - in-situ climate & synoptic (manual, auto), weather radar, remote sensing Lake and River Ice - FU/BU, thickness, snow on ice - in-situ (shore based), remote sensing Sea Ice - extent, concentration, open water, type, thickness, motion, icebergs, snow on ice - landfast (manual), ship-based & aerial reconnaissance, satellite & airborne reconnaissance Glaciers, Ice Caps, Ice sheets - mass balance (accumulation/ablation), thickness, area, length (geometry), firn temperature, snowline/equilibrium line, snow on ice - ground-based (in-situ), remote sensing Frozen Ground/Permafrost - soil temperature/thermal state, active layer thickness, borehole temperature, extent, snow cover - in-situ (manual, auto), remote sensing (new)

3 Assimilation of Sea Ice Motion (Courtesy of Todd E. Arbetter, NSIDC) Satellite-derived observations greatly increase inventory of ice motions: SMMR, SSM/I, AVHRR, RGPS, QuikScat, AMSR Much improved spatial and temporal coverage These observations can be used with data assimilation to enhance model simulations. Correct ice motion to observed values Reduce propagation of errors in calculated fields Identify errors in model parameterizations

4 Optimal Interpolation of Ice Motion Data Baseline (without Data Assimilation), model processes external forcing as usual, predicts ice motion due to externałinternal momentum balance With Data Assimilation, satellite and buoy-derived (observed) ice motions are blended with modeled (predicted) ice motions before advection step

5 Fractional Ice-Covered Area Basin Mean Baseline simulation: Strong interannual variability Less MY Ice in 1990s Assimilation simulation: Less MY Ice overall, Ridged ice has stronger seasonal amplititude More summertime melt each year Open Water First-Year Ice Multi-Year Ice Ridged Ice

6 Assimilation of Snow Data SNODAS at NOHRSC (U.S. NWS) SNODAS combines all available data, including NWP model output coupled with meteorological and snow observations, to generate a best estimate of gridded snow water equivalent in near real- time. SNOWDAS includes: 1. data ingest and downscaling procedures, 2. a spatially distributed energy-and-mass-balance snow model that is run once each day, for the previous 24-hour period and for a 12-hour forecast period, at high spatial (1 km) and temporal (1 hr) resolutions, and 3. data assimilation and updating procedures. The snow model is driven by downscaled analysis and forecast fields from a mesoscale, NWP model, surface weather observations, satellite-derived solar radiation data, and radar-derived precipitation data. It is updated with satellite and surface observations of snow extent, snow depth, and snow water equivalent.

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8 Effects of Temperature Using TOVS temperature quickens melt rate, decreases minimum ice area, and ice remains thinner in autumn than all-poles case Conversely, POLES temperatures result in higher minimum ice area, thicker ice in autumn than all-tovs case The swapping of temperature forcing make the summer and autumn results resemble their counterparts; hence, the ice model results are strongly dependent on these data POLES TOVS w/tovs w/poles

9 Atmospheric Forcing Small differences have big impacts The variability of atmospheric forcing from various sources (ECMWF, NCEP, observations, etc.) is large, particularly at high latitudes. A shift in a low pressure system by 100 km can result in significantly different predictions of the ice state. Location and timing of atmospheric features is crucial to accurate modeling of the sea ice cover on synoptic scales Wind Motion POLES TOVS Surface Air T Wind Div

10 MODIS Polar Winds 500 hpa geo potential Pre ops tests in 2002 North Atlantic Europe

11 MODIS winds filling observing system void Being used operationally since Jan 2003

12 Historical Polar Winds from AVHRR Objective: Develop a 20+ year dataset of wind vectors (speed, direction, height) in both polar regions from AVHRR GAC. The period of interest is 1980-present. Daily composite of wind vectors derived from NOAA-17 AVHRR GAC data on March 17, 2003 over Antarctica. The South Pole is at the center of the image. The background is the AVHRR 11 micron brightness temperature image. Wind vectors are grouped into three height categories (for illustration only): below 700 hpa (yellow), from 400 to 700 hpa (cyan), and above 400 hpa.

13 Satellite-derived Surface Broadband Albedo

14 New Product Potential for Reanalyses: Low-level Atmospheric Temperature Inversion Strength from HIRS Inversion strength from raobs vs the HIRS 2-channel retrieval (asterisks) and the TOVS Path-P temperature profile retrievals (diamonds).