Presentation is loading. Please wait.

Presentation is loading. Please wait.

1 Polar WRF Workshop – 3 November 2011 Arctic System Reanalysis: Land Surface Parameter Assimilation and Model Updates Michael Barlage Research Applications.

Similar presentations


Presentation on theme: "1 Polar WRF Workshop – 3 November 2011 Arctic System Reanalysis: Land Surface Parameter Assimilation and Model Updates Michael Barlage Research Applications."— Presentation transcript:

1 1 Polar WRF Workshop – 3 November 2011 Arctic System Reanalysis: Land Surface Parameter Assimilation and Model Updates Michael Barlage Research Applications Laboratory (RAL) National Center for Atmospheric Research Research funded by NSF (ARC )

2 2 −Land surface state spin-up: more consistent initialization, less time for soil states in lower boundary to equilibrate −Changes to model structure: add more and deeper soil layers, zero-flux lower boundary condition −Land surface parameter and state assimilation: snow cover and snow depth, albedo, and green vegetation fraction inserted into model daily/weekly Enhancements/Additions to WRF within ASR

3 3 −Why is this necessary? −Land surface models have their own climatology −Soil layers depths between models may be inconsistent −Vegetation types, soil types, terrain, etc. are likely different between models −Land surface equilibrium can take over one year for 4-layer soil with 2 meter depth −Five year spin-up for 10-layer with 8.5 meter depth Land Surface State Spin-up

4 4 −Use High Resolution Land Data Assimilation System (HRLDAS) with atmospheric forcing from reanalysis −HRLDAS: uses WRF model grid and static fields (land cover, soil type, parameter tables) to run an offline version of the Noah LSM −Use 6-hourly reanalysis output (precipitation, wind, temperature, pressure, humidity, shortwave and longwave radiation) from ERA-40 (1980 – 1999) and JRA-25 (2000 – 2009) −Spatially interpolate forcing fields to WRF grid and adjust temperature for terrain height differences between reanalysis and WRF −Use hourly timestep by linearly interpolating all but solar radiation; the total 24hr radiation is fit to a daily zenith angle curve −Advantages are that initial fields (especially soil ice/moisture/temperature): −are already on the WRF grid −are consistent with terrain, land cover and soil types/levels −are consistent with WRF land model Land Surface State Spin-up

5 5 −August 2008 volumetric soil moisture in top and bottom layer for ERA-I initialization (black) and HRLDAS multi- year simulation (red) −Region average near 64N, 158E (NE Siberia) −Land models have their own climatology −HRLDAS soil moisture is more likely to be in equilibrium for WRF cold start −Especially important for cycling runs Land Surface State Spin-up 1.5m 5cm

6 6 Comparison of HRLDAS Initial Soil Temperature cm cm

7 7 −The default WRF model uses the Noah land surface model with four soil layers that have nodes at 0.05m, 0.25m, 0.7m, and 1.5m and a fixed deep soil (8m/25m) temperature −It has been suggested that the fixed deep soil temperature is likely too low over much of the Arctic because it is based on annual mean air temperature −Within the ASR WRF model, the Noah LSM is modified to have 10 soil layers and a free, zero-flux lower boundary condition (3 subroutine + namelist changes) −The 10 soil layers have interfaces at 0.05m, 0.15m, 0.25m, 0.4m, 0.65m, 1.05m, 1.7m, 2.75m, 4.45m and 7.2m −For example, below is the 60-70N average bottom 10-layer T vs 4-layer 8m fixed T Changes to Land Model Structure 4-layer fixed 8m T 10-layer 7.2m T

8 8 Changes to Land Model Structure −Difference between lowest layer (7.2m) temperature [K] after a 28 year simulation and the assumed 8m deep soil temperature in standard WRF −Most of the Arctic region is much warmer in the 10-layer zero-flux model −Implications for soil temperature/moisture related processes, e.g., permafrost prediction

9 9 Alaska Measuring Stations 1km 2 measurement grid with 121 points 100m apart

10 10 HRLDAS Simulation Specifics 27-year ( ) point simulations over CALM measurement sites Forcing data: ERA-40 ( ); JRA-25 ( ) layers_control = (/0.05,0.25,0.70,1.5/) layers_zeroflux = layers_control layers_stagger = (/0.05,0.15,0.25,0.40,0.65,1.05,1.70,2.75,4.45,7.20, \ 11.65,18.85/) layers_constant = (/0.05,0.25,0.70,1.5,2.5,3.5,4.5,5.5,6.5,7.5, \ 8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5/) layers_highres = (/0.01,0.03,0.05,0.07,0.09,0.11,0.13,0.15,0.17,0.19, \ 0.21,0.23,0.25,0.27,0.29,0.31,0.33,0.35,0.37,0.39, \ 0.425,0.475,0.525,0.575,0.625,0.675,0.75,0.85,0.95,1.1, \ 1.3,1.5,1.7,1.9,2.25,2.75,3.25,3.75,4.25,4.75/) layers_organic = layers_highres Organic layer (peat) in the top 12cm

11 11 Point Simulation: Active Layer Thickness Black: control Blue: zeroflux Red: stagger Green: constant Orange: highres Brown-ish: organic Active Layer Thickness[cm]

12 12 Point Simulation: Active Layer Thickness Black: control Blue: zeroflux Red: stagger Green: constant Orange: highres Brown-ish: organic Active Layer Thickness[cm]

13 13 Point Simulation: Temperature Profiles Black: control Blue: zeroflux Red: stagger Green: constant Orange: highres Brown-ish: organic July January

14 14 Point Simulation: Temperature Profiles Black: control Blue: zeroflux Red: stagger Green: constant Orange: highres Brown-ish: organic July January

15 15 Point Simulation: Snow Depth Black: control Blue: zeroflux Red: stagger Green: constant Orange: highres Brown-ish: organic Snow depth much too low Snow Depth [cm]

16 16 Point Simulation: Temperature Profiles July January Black: highres Blue: organic Red: organic_2x Green: organic_4x Artificially add precipitation to get deeper snow

17 17 Point Simulation: Snow Depth Black: highres Blue: organic Red: snow z 0 Green: herb. tundra Changing z o over snow covered tundra brings model in line with observations Snow Depth [cm]

18 18 Point Simulation: Temperature Profiles July January Black: organic Blue: snow z o Red: herb. tundra

19 19 Point Simulation: Active Layer Thickness control_bias = 57.0 highres_bias = 40.7 snowz0_bias = 7.1 Black: highres Blue: organic Red: snow z 0 Green: herb. tundra Active Layer Thickness[cm]

20 20 Slope-Aspect Adjustment for ASR Domain Tested slope and aspect adjustment based on terrain Bin results based on cardinal directions: North (-45°- 45°), etc. Results are consistent with terrain structures in domain

21 21 Slope-Aspect Adjustment for ASR Domain However, if slopes < 1° are masked, the resulting locations where slope-aspect adjustment would make a difference are minimal 15km grid is too coarse to necessitate adjustment

22 22 Data assimilation - infrastructure added to HRLDAS/WRF(+WRF-Var) to include: - IMS snow cover: daily, 2004 to current at 4km; pre-2004 at 24km; this product is used operationally at NCEP - SNODEP snow depth: daily, obs/model product; on GFS T382 (~30km) grid; used as guidance to put snow where IMS says snow exists - MODIS albedo: 8-day 0.05º global; available from Feb 2000; also use MODIS snow cover and cloud cover - NESDIS vegetation fraction: weekly, 0.144º global; transitioning to use in NCEP operations - MODIS daily albedo over Greenland: ~1km, available over MODIS period - Greenland terrain provided by Ohio State Products are assimilated into the wrfinput file at 00Z of each cycle Assimilation Products

23 23 Product created in near real-time by NESDIS/STAR Based on smoothed AVHRR NDVI product to remove satellite drift and sensor degradation GVF(t) = (NDVI(t) – NDVI min )/(NDVI max – NDVI min ) Available as a 7-day product from 1984 to present Very similar procedure to existing WRF climatological vegetation so use product directly after interpolation to WRF grid Assimilation Procedure: Green Vegetation Fraction Vegetation Fraction on 0.144° global grid Use WPS to reproject to WRF grid Create minimum and maximum file Interpolate 7-day product to daily

24 24 Product Comparison: Green Vegetation Fraction Qualitative comparison to Drought Monitor August 24, 2004July 18, : largest “D2” area 2006: not significant statewide but dry in eastern Alaska 2009: small spike in “D2” but all concentrated on southern coast; east has no drought 2000 GVF Timeseries for east-central Alaska

25 25 Albedo highly dependent on snow so how to use MODIS albedo to be consistent with current model state Assimilation Procedure: MODIS Albedo MODIS 8-day albedo on 0.05° grid MODIS 8-day TERRA and AQUA snow cover MODIS 8-day T/A cloud cover Create a snow-free ( 70%) climatological dataset (cloud <50%) Starting with climatology move forward in time replacing with current snow-free or snow- covered albedo (cloud < 80%); repeat backward in time Use WPS to reproject MODIS snow-free and snow-covered albedo to WRF grid 1 2 2

26 26 Data Generation Procedure: MODIS Albedo −Develop snow-covered and snow-free albedo based on MODIS albedo and snow cover products MODIS Terra/ Aqua snow cover MODIS albedo and running min/max

27 27 Use IMS daily snow cover to determine snow coverage and SNODEP daily snow depth as guidance for quantity Assimilation Procedure: Snow IMS daily 4km/24km snow cover Air Force SNODEP 32km snow depth Use WPS to reproject to WRF grid 1.If IMS < 5%, remove snow if present 2.If IMS > 40% and SNODEP > 200% model snow or < 50% model snow, use existing model snow density to increase/decrease model snow by half observation increment 3.If IMS > 40%, don’t let SWE go below 5mm independent of SNODEP Run both products through a 5-day median smoother to remove snow “flashing” Use WPS to reproject to WRF grid

28 28 −Seven-month HRLDAS run with land data assimilation −Region near 69N, 155W (North Slope) −Model albedo agrees better with MODIS albedo −SNODEP snow is inconsistent with IMS snow cover in June −Report snow increments so users can recreate model snow MODIS Albedo Datasets Snow Depth Results Albedo Time series Snow cover and depth

29 29 Saw some questionable albedo variations in the standard MODIS albedo product over Greenland High summer albedo and relative low winter albedo is opposite time variation than expected Greenland MODIS albedo winter summer

30 30 A new daily MODIS-based albedo dataset was provided by Ohio State with higher resolution compared to current MODIS albedo datasets New Greenland MODIS albedo

31 31 New MODIS albedo dataset (red) shows a more realistic annual cycle than the original dataset This dataset is assimilated as the snow covered albedo over Greenland only Greenland MODIS albedo

32 32 A new terrain dataset was provided by Ohio State with higher resolution laser altimeter data (Bamber et al 2001) and accuracy compared to standard WRF geogrid data Note the non-zero terrain outside of Greenland Two likely causes: different reference geoids and ocean height differences WRF uses sphere-based definition of sea level Land terrain also not based from sea level (sphere) How to use this? New Greenland Terrain

33 33 Final product New Greenland Terrain

34 34 Difference between new and default Points surrounding Summit (3216m) New Greenland Terrain

35 35 Summary −Land surface state spin-up: use 20+ years of reanalysis data to make land states more consistent with model, land cover, terrain, and soil type −Changes to model structure −use 10 soil layers instead of the default 4 layers −soil layers go down to ~7m instead of 1.5m −zero-flux lower boundary condition to improve on fixed lower temperature −Land surface parameter and state assimilation −snow cover (satellite) and snow depth (in situ/model) −albedo (MODIS satellite) −green vegetation fraction (AVHRR satellite) −parameters/states updated daily/weekly

36 36 Test Simulation −WRF-3DVAR simulation −6 hour cycling −3 hour obs time window −January 2007 −60km −Physics options −Morrison MP −MYNN −Grell 3D −Noah LSM −Land surface parameter and state assimilation −snow cover and snow depth −Albedo max/min (MODIS satellite) −green vegetation fraction −Observations −METAR T 2m −SYNOP T 2m

37 37 Comparison to SYNOP 2-meter Temperature n= n= n= n= n= n= n= n= n= n= n= n=5 Net positive results: Improved bias in 32 of 48 region/times 00Z 12Z


Download ppt "1 Polar WRF Workshop – 3 November 2011 Arctic System Reanalysis: Land Surface Parameter Assimilation and Model Updates Michael Barlage Research Applications."

Similar presentations


Ads by Google