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

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.

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

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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.

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NOHRSC Daily Snow AnalysisNOHRSC Daily Snow AnalysisNational Operational Hydrologic Remote Sensing Center – Chanhassen, Minnesota

http://www.nohrsc.nws.gov

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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

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NOHRSC Hourly analyses at 1 km 0600z 29 June 2004

2m temp, RH - RUCSnow precip, non-snow precip - RUCSurface wind - RUCSolar radiation - GOES

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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

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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)

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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).