An Overview of the NCEP Eta Model 14 December 1999 COMET/UCAR SOO Symposium on NWP EDAS slides by Eric Rogers Presented by Thomas Black EMC/Mesoscale Modeling Branch Outline Brief model description Eta Data Assimilation System Physics Examples of products/statistics Future Domain Semi-staggered Arakawa E grid 32km horizontal resolution 45 vertical eta layers Silhouette step topography Eta Domains Past & Present 32km Domain Topography w/ Water Points 32km CONUS Sigma and Eta Coordinates MSL P ground At point P: P is small ground Eta Coordinate Mean Sea Level P=P MSL Reference heights and temperatures taken from the standard atmosphere Z= Z REF LM =1 LM-1 LM-2 at P = LM-2 P SMSL Z=0 LM-3 P= LM-2 P MSL P= LM-3 P MSL P= LM-1 P MSL at P = LM-1 P SMSL Z= Z REF Eta Model 45-Layer Distribution 1000 hPa 850 hPa 700 hPa 500 hPa 250 hPa 25 hPa 27 hPa The Semi-Staggered E Grid H V H V H V H V H V H V H V H V H V H V H V H V H d H mass point V velocity point d constant transformed latitude constant transformed longitude GOAL : PRODUCE BEST POSSIBLE INITIAL CONDITIONS FOR ETA MODEL FORECAST* KEY COMPONENTS - State of the art analysis (variational) - Consistency between assimilating and forecast model (resolution, physics, dynamics) - Intelligent selection and use of observations * NOT necessarily the same as fitting all the observations exactly What is 3D-VAR? An analysis technique that attempts to minimize analysis error Takes background (forecast) and observation error into account Variational method allows use of non- traditional data sources, such as GOES precipitable water ETA 3DVAR ANALYSIS Loosely patterned after NCEP global SSI analysis Analysis variables: - Stream function - Potential function - Temperature - Specific humidity - Surface pressure- Geopotential height More adaptable than OI for using new data types (e.g., NEXRAD radial velocities used in Eta-10 runs during 1996 Olympics) (Parrish et al NWP Preprint Volume) 3D-VAR vs. OI EDAS Original Configuration Eta-48 fcst 00Z/12Z Eta-29 fcst 03Z/15Z WHY DO CYCLING? Initial conditions more consistent with forecast model Less spinup of divergence, cloud, precipitation, and TKE More accurate representation of soil moisture Observations Used By ETA 3DVAR Upper air data - Rawinsonde height/temperature/wind/moisture - Dropwindsondes - Wind Profilers - NESDIS thickness retrievals from polar orbiting satellites (oceans only) - VAD winds from NEXRAD - Aircraft (conventional and ACARS) winds/temps - Satellite cloud drift winds - SSM/I and GOES precipitable water retrievals - Synthetic tropical cyclone data Surface data - Surface land wind/temperature/moisture - Ships and buoys - SSM/I oceanic surface winds DATA QUALITY CONTROL CQC: Complex QC of raob height/temps (baseline, hydrostatic, lapse rate, radiation correction, etc.) ACQC : Quality control of conventional aircraft data (remove duplicates, track checks, create superobs) 3DVAR : Analysis performs gross check vs. first guess: - Temperature : +/- 15 o C - Wind :+/- 25 ms -1 - RH : +/- 90% - Precipitable water : +/- 12 g/kg - Height : +/- 100 m SDMEDIT : NCEP Senior Duty Meteorologist can flag all or parts of suspect raobs Use of Surface Data: Eta OI vs. Eta 3DVAR Eta OI Analysis Eta 3DVAR Analysis New 3DVAR tested in July 1998 and showed improved fit to surface and raobs (especially moisture) Re-tuned 3DVAR implemented on 3 November 1998 We thought everything was OK.. BUT.. Solid = Eta Short Dash = NGM Long Dash = AVN/MRF 24-H ACCUMULATED PRECIPITATION EQUITABLE THREAT SCORES: ALL FCSTS 12/1/97 - 2/28/9812/1/98 - 2/28/ % drop in Eta skill between and Persistent synoptic error in Eta-32 during winter of 98-99: weaker and faster Eastern Pacific troughs/cyclones than observed Example : 48-h Forecasts valid 1200 UTC 17 March 1999 PROBLEM 1: November 98 change degraded mass/wind balance in 3DVAR If mass / wind balance well- behaved, positive height correction is coincident with center of anticyclonic wind correction 850 mb ANL-GUESS height/wind 80KM EDAS valid 00Z 3/15/99 Note 10 degree longitude displacement between centers of wind and height correction Problem is most severe in regions and at analysis times without widespread raob data but with large amounts of wind or mass only data (e.g., satellite winds) SOLUTION : Improve geostrophic coupling of mass/wind analysis corrections in 3DVAR Operational 3DVAR analysisModified 3DVAR analysis Note: Improved height/wind coupling near Aleutians PROBLEM 2: Horizontal/vertical correlations too narrow : observation had VERY limited impact on analysis away from its level One observation test : Insert one height observation 10 m greater than first guess at 200, 500, 900 mb and measure impact in horizontal/vertical Operational 32-km 3DVARModified 32-km 3DVAR 200 mb 900 mb Performance of new 3DVAR : 3 December 1998 to 16 January 1999 test at 80 km resolution 24-h accumulated precipitation threat scores: All forecasts Dashed = Modified 3DVAR Solid = Operational 3DVAR Equitable Threat Score Threshold (in) Split Explicit Integration: Dynamics Fundamental prognostic variables T, u, v, q, P sfc, TKE, cloud water/ice Inertial gravity wave adjustment forward-backward scheme ( t=90s) Vertical advection Euler-backward scheme centered in space piecewise linear for q Split Explicit Integration: Dynamics Horizontal advection modified Euler-backward scheme Janjic advection in space conservative, (nearly) shape-preserving scheme for H 2 0 upstream advection near boundaries Split Explicit Integration: Physics Betts-Miller-Janjic convection Mellor-Yamada level 2.5 turbulent exchange GFDL radiation explicit cloud water/ice prediction 4-layer NOAH land surface package 2 horizontal diffusion One-way Boundary Conditions 3-hour tendencies 6 hour old AVN forecast used for 00Z, 12Z, and 18Z runs (and now 06Z) 3 hour old for 03Z run (moved to 06Z) Runstream Schematic of Eta Model Integration gridscale cloud gridscale precip convection turbulence horizontal advection vertical advection inertial grv wave adjustment timestep t = 90 s Radiative temperature tendency updates Shortwave: 40 timesteps (1 hour) Longwave: 80 timesteps (2 hours) The Betts-Miller-Janji Convection Scheme in the Eta Model References: Betts, 1986 (QJRMS) Betts and Miller, 1986 (QJRMS) Janji , 1994 (MWR) Deep (precipitating) convection Temperature reference profile Moisture reference profile Convective adjustment Modification for precipitation efficiency Shallow (non-precipitating) convection Temperature reference profile Moisture reference profile Find the Deep Convective Clouds 1. For all parcels within 130 mb of the ground find P sat and ES 2. At each point, select parcel with the maximum ES 3. Given P sat, choose cloud base as the model level just below it 4. Adjust cloud base if needed: (a) at least 25 mb above middle of lowest layer (b) at least one model layer above lowest layer 5. Compute T mad above cloud base using ES and P in lookup tables 6. Set cloud top at highest level where T mad 450 mb) (b) reset top to level where maximum (RH)/ p occurs (c) at least two model layers deep Construction of Temperature Reference Profile For Shallow Convection cloud top REFERENCE TEMPERATURE cloud base Mixing Line Correct T ref assuming (c p T p) = 0 Shallow Convection Moisture profile calculation Forces a net positive entropy change Turbulent Exchange References: Mellor and Yamada, 1974 (J. Atmos. Sci.) Mellor and Yamada, 1982 (Rev. Geo. Space Sci.) Janji , 1994 (Mon. Wea. Rev.) Vertical advection occurs through transport Turbulent vertical diffusion of variable A is given by: Fundamental task is to determine exchange coefficient K A by resolvable vertical motion Turbulent diffusion Turbulent diffusion occurs through transport by subgrid scale turbulent eddies Modes of Turbulent Exchange SURFACE free atmosphere surface layer A LM -1 A LM A z0 LM-1 LM z0z0 Exchange in the Free Atmosphere Use second order closure scheme of Mellor-Yamada Level 2.5 Exchange coefficients for heat and momentum given by: l is the mixing length Q 2 /2 is the turbulent kinetic energy (TKE) S H, S M are quantities determined from MY level 2.5 TKE TKE is a fully prognostic variable needed to compute exchange coefficients in the free atmosphere The predictive relationship for TKE is: A variety of approaches have been used to solve the production/dissipation tendency, generally with imposed limits on Q P s + P b - prod/dissp Eta Model technique: Cast in terms of ( l / Q) Write in finite difference form and solve for ( l / Q) Place physical constraints on l and not on Q Use new Q to compute new K H and K M Exchange in the Surface Layer Use similarity theory between the surface and the middle of the lowest layer Vertical change of variable A is described by: S * = F/u * where F is flux, u * is friction velocity F is a prescribed function (empirical) = z/L where L is Monin-Obukov length For neutral static stability or small z, 1 integration of A / z yields log profile k is the von Karman constant (~ 0.4) Note: L is a function of heat and momentum fluxes and thus of the exchange coefficients Take u * and L from the previous timestep then iterate: Replace F with the standard relationship: Integrate A / z for the general case between two levels: where F is an integral function of F Solve for K A : K A L K A L K A new surface exchange coefficients Considerable application of theory and computation is given to determining values needed at the lower boundary Cloud Top Pressure Cloud Top Temperature Cloud Base Height 850mb Cloud Water (kg/kg) 850mb Cloud Ice (kg/kg) Winter Precipitation Type Area T w > -4 C < 3000 deg m? Coldest T in a saturated layer < 269K? Area sfc based T w < 0 C < deg m? OR Net area with respect to 0 C < deg m and sfc based T w > 0 C < 50 deg m? Lowest level T > 0 C ? snow freezing rain ice pellets rain Y N Y N Y N N Y Example of precip type Eta/AVN/NGM Equitable Threat Scores 1 Jan - 31 Oct 1999 Impact of Eta degradation AVN Vorticity Output Eta 32 Vorticity Ouput : Coarse Eta 32 Vorticity Output: Fine 00-h Eta 32 Vorticity Output: Fine Subsets (tiles) of 32km grid Future Directions of Eta Effort with the Class VIII hours for on-time runs 12km/60layer resolution Expanded domain Cloud and precipitation assimilation Microphysics 4D-VAR Short range ensembles Nonhydrostatic model Cloud Data Assimilation (Zhao et al, 1998, 12th NWP, Phoenix, AZ) Data sources real-time Neph Analyses (USAFGWC) hourly radar/gauge observations Cloud Data Assimilation Rainfall Data Assimilation During the 12h pre- forecast assimilation period at each timestep compare the model predicted rainfall to observed Adjust the models latent heating profile accordingly (Carr and Baldwin, 1991) c 22km Domain / Topography 22km CONUS Topography w/ Water Points Workstation Version Pontiac, MI running 10km including lake temps from GLERL other changes to the model NSSL/SPC running Kain-Fritsch version Code available from NCEP: contact Matt Pyle