An FSL-RUC/RR Proposal for AoR Stan Benjamin Dezso Devenyi Steve Weygandt John M. Brown NOAA / FSL...
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Transcript of An FSL-RUC/RR Proposal for AoR Stan Benjamin Dezso Devenyi Steve Weygandt John M. Brown NOAA / FSL...
An FSL-RUC/RR Proposal for AoR
Stan BenjaminDezso DevenyiSteve WeygandtJohn M. Brown
NOAA / FSL
Help from NOHRSC/NWS – Chanhassen, MN - Tom Carroll, Don Cline, Greg Fall
USWRP AoR Workshop29 June 2004
2
Outline of proposal
Combined approach
- Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC) 13km RUC 10km RR)
- Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis)
- Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite
RUC analysis
1-2km downscaled grids
1-2km analysis
background
3
Advantages for FSL combined-approach AoR proposal
• Extension of existing and planned NCEP operational products• Much less expensive for computer power than full-model-downscaling• Can produce hourly AoRs within 30 min of valid time• Builds on ongoing work to assimilate full METAR/sfc obs
• incl. ceiling, cloud levels, visibility, current wx (dev RUC) • to be added to GSI for future Rapid Refresh and other NCEP models
• Builds on current hourly 1km CONUS downscaling from National Operational Hydrologic Remote Sensing Center (NOHRSC). Other downscaling methods (e.g., PRISM) also applicable.• Builds on collaborative GSI development with NCEP• Applicable to Eta/WRF-North American input as well as RUC/WRF-Rapid Refresh (use ensemble approach).
4
Outline of proposal
Combined approach (sequential 3 steps)
- Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC) 13km RUC 10km RR)
- Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis)
- Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite
RUC analysis
1-2km downscaled grids
1-2km analysis
NCEP model hierarchy – RUC (1h frequency) Eta (6h) Global (6h)
The 1-h Version of the Rapid Update Cycle at
NCEP
Figure 1. Data ingest, analysis, and forecast cycle for the Rap id Update Cycle (RUC) NWP Model.
12 hr. fcst
3 hr. fcst
00 01 02 03 04 05 06 07 08 09 10 11 12 (UTC)
Analysis times
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
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3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst
To 12Z12 hr. fcst
3 hr. fcst
00 01 02 03 04 05 06 07 08 09 10 11 12 (UTC)
Analysis times
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
3 hr. fcst
3 hr. fcst
12 hr. fcst
3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst
To 12Z12 hr. fcst
3 hr. fcst
00 01 02 03 04 05 06 07 08 09 10 11 12 (UTC)
Analysis times
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
3 hr. fcst
3 hr. fcst
12 hr. fcst
3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
3 hr. fcst
3 hr. fcst
12 hr. fcst
3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst
To 12Z12 hr. fcst
3 hr. fcst
00 01 02 03 04 05 06 07 08 09 10 11 12 (UTC)
Analysis times
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
3 hr. fcst
3 hr. fcst
12 hr. fcst
3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
3 hr. fcst
12 hr. fcst To 15Z
3 hr. fcst3 hr. fcst
12 hr. fcst To 18Z
3 hr. fcst
3 hr. fcst
12 hr. fcst
3 hr. fcst3 hr. fcst
To 21Z
1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr. 1 hr.
DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA DATA
fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst fcst
To 12Z
6
10km RUC9-h
forecast surface wind-
speed and barbs
overlaid on sfc reports
- valid 15z 28 Mar 02
Forecast max wind-speed
48 kts
7
Verify RUC sfc fcsts against all U.S. sfc obs
10-m wind speed
2-m temperature
SUM (Apr–Sep)
WIN (Oct–Dec)
Persist
Persist
0-h
1-h
3-h
6-h
9-h
12-h
Fcst Length
0-h
1-h
3-h
6-h
9-h
12-h
Fcst Length
RUC improves surface wind, temp skill down to 1-h fcst
Much better than 1-h, 3-h persistence forecasts
8
PBL-based METAR assimilation
Use METAR data through PBL depth from 1h fcstRUC oper analysis
18z 3 Apr 02IAD
x xxx
Effect of PBL-basedMETAR assimilation
9
Assimilation of surface cloud, visibility, current weather observations into RUC
Goal: Modify hydrometeor, RH fields to 1) force near match to current ceiling/vis obs when passed through ceiling/vis translation algorithms 2) improve short-range predictions
Running in real-time test since Oct 2003Clearing/building of RUC 3-d hydrometeor fieldsUse QC with GOES and radarPart of RUC cloud/precip analysis w/ GOES, radar, surface obs, background 1-h forecast
IFRLIFR
VFR CLR MVFR
10
Cloud ceiling (m)RUC – with and without METAR cloud assimilation
18z Obs17 Nov 2003
Diagnosed ceiling from RUC hydrometeors
Corresponding Ceiling height - meters
IFRLIFR VFR CLR MVFR
METAR Flight Rules
Oper RUC - w/o METAR cloud assim
With METAR cloud assim
11
17z 27 Jan 04 analysis –After assimilation ofMETAR cloud obs
Cloud water mixing ratio (qc),
Background – 1h fcst
12
Added assimilation of visibility obs - Feb 2004
• Use FG or BR reports from METARS • Only when
• Precip is not also reported• T-Td < 1K
• Build at lowest 2 levels in RUC (5 m, 20 m)
13
Characteristics of RUC analysis appropriate for AoR
• Hourly mesoscale analysis (digital filter essential)• Designed to fit observations (within expected error)
(incl. Sfc 2m temp (as ), dewpoint, altimeter, wind )• Consistent with full-physics 1-h forecast
(most important in physics – PBL, land-surface)
(real-time testing at FSL in RUC20 and RUC13)• Accounting for local PBL depth in assimilation of surface data• Accounting of land-water contrast • Assimilation of METAR cloud, vis, current wx• Assimilation of full mesonet obs• Assimilation of GPS PW, PBL profiler• QC criteria for mesonet different than METARs• Assimilation of hourly radar reflectivity/lightning and GOES cloud-top data into initial fields of 3-d hydrometeors (5 types)
14
Outline of proposal
Combined approach
- Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC) 13km RUC 10km RR)
- Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis)
- Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite
RUC analysis
1-2km downscaled grids
1-2km analysis
15
Interactive Snow information National and RegionalSnow Analyses
Airborne Gamma Snow Survey
The interactive website includes time series plots of modeled and observation data for stations, the ability to choose physical elements and shapefile overlays to display images, and create basin averaged text and map products.
The national and regional snow analyses provide daily comprehensive snow information for the coterminous United States. The products include daily regional maps, text summaries, and model analyses.
The airborne snow survey page includes current survey information, schedule of surveys, historical and current airborne gamma data, and background information for the Airbrone Gamma Snow Survey program.
The National Operational Hydrologic Remote Sensing Center (NOHRSC) - Chanhassen, MN
• provides remotely-sensed and modeled hydrology products for the coterminous U.S. and Alaska for the protection of life and property and the enhancement of the national economy.• produces snow data/products - airborne, satellite, and modeled snow data and products - used by NWS, other govt agencies, private sector, and public to support operational/ research hydro programs across nation. • produces snow products and information that include estimates of: snow water equivalent, snow depth, snow pack temperatures, snow sublimation, snow evaporation, estimates of blowing snow, modeled and observed snow information, airborne snow data, satellite snow cover, historic snow data, and time-series for selected modeled snow products.
16
NOHRSC Daily Snow AnalysisNOHRSC Daily Snow AnalysisNational Operational Hydrologic Remote Sensing Center – Chanhassen, Minnesota
http://www.nohrsc.nws.gov
17
NOHRSC Hourly analyses at 1 km 1000z 29 June 2004
2m temp, RH - RUCSnow precip, non-snow precip – RUC (later corrected w/ obs)Surface wind - RUCSolar radiation - GOES
Contour interval = 5K
18
NOHRSC Hourly analyses at 1 km 0600z 29 June 2004
2m temp, RH - RUCSnow precip, non-snow precip - RUCSurface wind - RUCSolar radiation - GOES
19
GeospatialRelationalDatabase
GeospatialRelationalDatabase
ProductGeneration
ProductGeneration
FieldOfficesField
Offices
NOHRSCSnow
Mapping
NOHRSCSnow
MappingTemperatureRelative Humidity
Wind SpeedSolar Radiation
Atmos. RadiationPrecipitationPrecipitation
Type
RUC 20km Hourly Input Gridded Data Downscaled to 1 km
RUC 20km Hourly Input Gridded Data Downscaled to 1 km
SoilsLand Use/Cover
Silvics
Static GriddedData (1 km)
Static GriddedData (1 km)
Snow Energy and Mass Balance Model
Snow Energy and Mass Balance Model
Blowing Snow ModelBlowing Snow Model
Radiative Transfer ModelRadiative Transfer Model
State Variables forMultiple Vertical Snow
and Soil Layers- Thickness- Density
- Temperature- Liquid Water
Content- Grain Size
- Melt- Sublimation
-Mass Transport
State Variables forMultiple Vertical Snow
and Soil Layers- Thickness- Density
- Temperature- Liquid Water
Content- Grain Size
- Melt- Sublimation
-Mass Transport
State Variables forMultiple Vertical
Snow & Soil Layers
NOHRSC SNODAS Snow ModelNOHRSC SNODAS Snow Model
20
Full-Res (Internet)Full-Res (Internet)
CONUS Hourly Mesoscale Input Data
RUC20 Analyses (20 km, 50 levels)RUC20 12-h Forecasts (20 km, 50 levels)
FSL RUC Analyses (20 km, 50 levels)FSL RUC 12-h Forecasts (20 km, 50 levels)
GOES Two-Stream Solar (0.5o)(Direct Beam and Diffuse)
FSL RUC20FSL RUC20
NCEP RUC20NCEP RUC20
NESDIS SOLARNESDIS SOLAR
Hourly Snow Model Forcing (1 km)
Surface, Spatially & Temporally Continuous
Air TemperatureRelative Humidity
Wind SpeedPrecipitation (Snow)
Precipitation (Non-Snow)Solar Radiation
Physically BasedDownscaling (1 km)
Physically BasedDownscaling (1 km)
Spatial/TemporalGap Filling
Spatial/TemporalGap Filling
Preprocessing: Forcing Data (RUC20)Preprocessing: Forcing Data (RUC20)
21
Downscaling: Solar RadiationDownscaling: Solar Radiation
GOES Two-Stream Solar Radiation0.5 degree Direct Beam and Diffuse IrradianceGOES Two-Stream Solar Radiation0.5 degree Direct Beam and Diffuse Irradiance
Terrain Cross-SectionTerrain Cross-Section
Direct Beam IrradianceDirect Beam Irradiance
Terrain Cross-SectionTerrain Cross-Section
Diffuse IrradianceDiffuse IrradianceSky-View FactorSky-View Factor
• Terrain ReflectionTerrain Reflection• Topographic ShadingTopographic Shading
• Sky-View FactorSky-View Factor• Incidence AnglesIncidence Angles
22
NRCS SNOTELSnow Water Equivalent
NRCS SNOTELSnow Water EquivalentP
oin
t
CADWRSnow Water Equivalent
CADWRSnow Water EquivalentP
oin
t
NOHRSC GOES/AVHRRSnow Cover
NOHRSC GOES/AVHRRSnow CoverG
rid
NOHRSC Airborne Gamma
Snow Water Equivalent
NOHRSC Airborne Gamma
Snow Water Equivalent
Are
a
NWS/CooperativeSnow Water Equivalent
Snow Depth
NWS/CooperativeSnow Water Equivalent
Snow DepthPoin
t SnowModelSnowModel
Gridded Data Sets
Auto QCAuto QC
PointData Sets
Preprocessing: Update DataPreprocessing: Update Data
23
NOHRSC data- BoulderSnowstorm in Colorado (18-19 March 2003)
RUC forcing
Observed
6 days
24
Wind speed downscaling --Use u* from model grid scale to calculate wind speed at 1km grid scale using 1km roughness length
Other improved downscaling? - PRISM - simple PBL/near-sfc wind models - …
Zo at 20km based on USGS 1km data
Enhancements needed forNOHRSC-like downscaling
25
Outline of proposal
Combined approach
- Step 1. Full model-based 1-h (or less) assimilation cycle at coarser resolution (e.g., 20km (current RUC) 13km RUC 10km RR)
- Step 2. Non-model downscaling using ~1-2km topography, land-use, roughness length, land/water (e.g., NOHRSC 1km snow analysis)
- Step 3. Analysis w/ high-resolution observations – Mesonet/METAR inc. cloud/vis.., radar, satellite
RUC analysis
1-2km downscaled grids
1-2km analysis
26
STEP 3: 1-2 km analysis w/ high-resolution observations
Background = 1-2km downscaled grids (from step 2).(Step 2 grids are downscaled from Step 1 grids)
Possible tools – all fast analysis steps on 1-2km scale• Barnes- or Bratseth-type analysis using innovations (high-res obs minus results of downscaling in step 2)
• Simple, fast• 2dVAR or 3dVAR of innovations, using wavelet or digital-filter modeled covariances. (Problem is mathematically better conditioned than standard 3dVAR, also parallelizable.)• Optimum interpolation (OI):
• Fast, reliable, easy to parallelize• Ensemble Kalman filters
• also applicable in 3-step method proposed here• RUC-like use of PBL height, cloud/radar/vis/current wx
• Note: RUC/GSI 3dvar also assimilate radial winds
27
Our position: 3-d model component necessary for AoR.But what is the trade-off?
• Only way to allow physical consistency in analysis fields for
• topography• land use (including land-water), land-sfc parameterization• boundary-layer, cloud physics, radiation, …• Essential to produce best possible skill at grid points between observations
• Problem with model component for AoR• Bias in favor of NDFD forecasts that are taken from same model as used in AoR. • Brad Colman (and others) goal: AoR should be independent as possible from any given model
• Our guarded hopes: 1) Steps 2 and 3 will provide independence from Step 1. 2) Step 1 can have multiple models.
28
Advantages for FSL combined-approach AoR proposal
• Extension of existing and planned NCEP operational products• Much less expensive for computer power than full-model-downscaling• Can produce hourly AoRs within 30 min of valid time• Build on ongoing work to assimilate full METAR/sfc obs
• incl. ceiling, cloud levels, visibility, current wx (dev RUC) • to be added to GSI for future Rapid Refresh and other NCEP models
• Build on current hourly 1km CONUS downscaling from National Operational Hydrologic Remote Sensing Center (NOHRSC). Other downscaling methods (e.g., PRISM) also applicable.• Builds on collaborative GSI development with NCEP• Applicable to Eta/WRF-North American input as well as RUC/WRF-Rapid Refresh (use ensemble approach).