Assimilating Reflectivity and Doppler Velocity Observations of Convective

Storms into Storm-Scale NWP Models

David DowellCooperative Institute for Mesoscale Meteorological Studies

Norman, Oklahoma

and

Challenges of Storm-Scale Data Assimilation

Acknowledgments

Jeff Anderson Bill SkamarockAlain Caya Chris SnyderMike Coniglio Dave StensrudMike French Lou WickerTed Mansell Qin Xu

Also, thank you Fuqing and Chris,for organizing the workshop!

Challenges ofStorm-Scale Data Assimilation

Isolated, small, and unbalanced phenomena

Multiple scales Influence of larger scale systems on

storm initiation and evolution Larger scale response to heating

and cooling by deep, moist convection

Trier et al. 2000

Challenges ofStorm-Scale Data Assimilation

Unobserved fields Observations

• Doppler velocity -- scatterer motion toward or away from radar

• Reflectivity -- measure of hydrometeor size and number concentration

Model fields• Velocity, pressure, temperature, mixing coefficient, water-vapor

mixing ratio, cloud-water mixing ratio, hydrometeor mixing ratios (rain and ice)

Assimilation focused on retrieving unobserved fields

Relatively little information available for diagnosing errors in analyses

Challenges ofStorm-Scale Data Assimilation

Sensitivity to precipitation microphysics parameterization Heating and cooling by condensation and evaporation

strongly affect later storm evolution. There is a general feeling that the current “standard”

scheme (Lin et al. 1983; “LFO”) too quickly produces cold pools that are too strong.

Markowski

et al. 2002

Storm-Scale EnKF Assimilation of Doppler Radar Observations

Simulated data, perfect model Snyder and Zhang 2003 -- Doppler velocity Zhang et al. 2004 -- velocity, surface obs. Caya et al. 2005 -- velocity and reflectivity Tong and Xue 2005 -- velocity and reflectivity Xue et al. 2006 -- velocity and reflectivity

Real data Dowell et al. 2004a -- velocity and reflectivity Dowell et al. 2004b -- velocity and reflectivity

Simulated data, imperfect model Some results shown today

Observing System Simulation Experiments:Supercell and Squall Line

NCOMMAS reference simulations Weisman sounding with half-circle hodograph (supercell) or

straight-line hodograph (squall line) x=2 km, z=0.5 km LFO/Gilmore precipitation microphysics

• Cloud water, rain, ice crystals, snow, hail/graupel

supercell squallline

100 km 200 km

Observing System Simulation Experiments(OSSEs)

50-member ensemble with randomness in initial conditions Warm bubbles added in random locations to initiate storms

Sensitivity of assimilation results to observation type Reflectivity only Doppler velocity only Both reflectivity and Doppler velocity

Impact of model error on assimilation results (later presentations) Perfect model

• Microphysics scheme same in both reference simulation and assimilation Imperfect model

• Different microphysics schemes in reference simulation and assimilation

Synthetic Observations:Effective Reflectivity Factor (“Reflectivity”)

For typically assumed n0value ("Marshall - Palmer"),

Zrain 3.63109 qr 1.75.

Assume inv. exponential drop - size distribution in model :

n D n0 exp D .

For spherical raindrops, Zrain n D D6dD0

.

Reflectivity computations for ice species are more

complicated and less certain.

Synthetic Observations:Reflectivity

For point values of fields in ref. sim., compute sum of reflectivity for each hydrometeor category (Smith et al. 1975, Ferrier 1994)

Convert to logarithmic scale.

• Standard meteorological units (dBZ)

• Appropriate scale for expressing random error (assume 2 dBZ here) Produce volumetric observations every 5 min.

ZdB 10logZe

Ze Zrain Zhail Zsnow Z ice crystals

Zhail = ..., Zsnow = ..., Zice crystals = ...

Zrain 3.63109 qr 1.75

Synthetic Observations:Doppler Velocity

“Radar” observes u wind component in reference simulation, only where reflectivity > 15 dBZ (no “clear air” data).

Add random errors with 2.0 m s-1 standard deviation.

Produce volumetric observations every 5 min.

Supercell Perfect-Model Experiments:Assimilate ZdB only, Vr only, or both

assimilation begins at 1200 s

Squall Line Perfect-Model Experiments:Assimilate ZdB only, Vr only, or both

Conclusions fromPerfect-Model Experiments

Comparable results are obtained for the supercell and squall line.

Assimilating both reflectivity and Doppler velocity observations is better than assimilating one or the other.

Assimilating only reflectivity observations is better than assimilating only Doppler velocity observations. Spurious cells develop in velocity-only assimilation Reflectivity-only assimilation produces storms in correct

locations, and since model is perfect, these storms develop the correct characteristics

Experiences with Real Data

Single-radar assimilation 8 May 2003 Oklahoma City, OK supercell 15 May 2003 Shamrock, TX supercell (M. French) 11 June 2003 southwest OK multicell (M. Coniglio)

NCOMMAS LFO precipitation microphysics (cloud, rain, snow, ice crystals,

hail/graupel) Homogeneous environment

Ensemble size ~50 Variability from sounding perturbations and from randomness in

locations of warm bubbles that initiate storms

8 May 2003 Oklahoma City Storm:Single-Radar Assimilation vs. Independent

Dual-Doppler Analysis

image provided by Arthur Witt

Dual-Doppler (WSR-88D andTDWR) Analysis

Single-Doppler (KOUN) Assimilation

Reflectivity and Wind at 300 m AGL

Evidence of Precipitation Microphysics Errors

In these real data experiments, ensemble spread in reflectivity is too small (RMS innovation >> spread).

How do the observed and simulated reflectivities typically disagree? (What are the spatial patterns in bias?)

prior reflectivity innovation and ensemble spread (8 May 2003)

dBZ

innovation

ensemble spread

Time-Height Diagrams:Prior Innovations for Reflectivity

8 May 2003 supercell 15 May 2003 supercell(M.French)

11 June 2003 multicell(M. Coniglio)

Mean of (ob. - prior ensemble mean) reflectivity, in dBZ

Reflectivity computation for hail/graupel category Assume hail/graupel has a dry surface above the melting level

and a wet surface below the melting level. Assume all hail/graupel is dry.

Intercept parameter for rain category n0=8106 m-4 (default -- Marshall-Palmer)

n0=8105 m-4 (fewer drops, larger mean size)

8 May 2003 Supercell:Sensitivity to Precipitation Microphysics

8 May 2003 Supercell:Sensitivity to Precipitation Microphysics

LFO, dry hail/graupelfewer and larger raindrops,

dry hail/graupelLFO, wet hail/graupel

Mean of (ob. - prior ensemble mean) reflectivity, in dBZ

8 May 2003 Supercell:Sensitivity to Precipitation Microphysics

Pert. Temperature at 250 m AGL (after assimilation for 85 min)

LFO, dry hail/graupel fewer and larger raindrops,dry hail/graupel

Problems Encountered withReal-Data Experiments

Precipitation microphysics errors (LFO) Analyses are sensitive to choices for microphysics

parameters. Reflectivity statistics indicate problems.

• Although peak reflectivity is generally overpredicted by the model, areal coverage of moderate reflectivity is generally underpredicted.

• Observation operator that assumes all hail/graupel below melting level is wet produces reflectivity that is too high.

• Observation bias could be a problem, too, but it is probably less significant than other problems.

We need a flexible precipitation microphysics scheme that can be corrected for bias.

Problems Encountered withReal-Data Experiments (not shown)

Sensitivity of forecasts to virtually all aspects of model and assimilation Precipitation microphysics Observation resolution Grid resolution Sounding estimate …

Spurious temperature perturbations produced by assimilating “clear air” Doppler velocity data

Verification

Cause for Optimism -- Research

Encouraging EnKF wind analyses obtained from single-Doppler observations

Recent interest in improving precipitation microphysics parameterizations (Univ. of Illinois, NSSL, OU)

VORTEX2 (proposed for 2008 and 2009) Mobile radars, dual-polarization Unmanned aerial vehicles (UAVs) Precipitation microphysics probes High-density surface observations Assess model errors on storm scale?

Cause for Optimism -- Operations

Dual-polarization upgrade to WSR-88D (2009?) It seems unrealistic to expect the filter to retrieve several

microphysical parameters from just reflectivity and velocity obs. Polarimetric radar measurements provide additional information

about hydrometeor shape and size distribution.

Improved mesoscale observations (surface mesonets, satellite)

Ryzhkovet al. 2005

Current Work as NSSL(to be presented at the workshop)

Mesoscale ensemble forecasting and data assimilaton (Fujita/Stensrud)

Radar data assimilation in numerical cloud models with homogeneous environments (Dowell, Wicker, Coniglio)

Precipitation microphysics sensitivity studies (Mansell, Wicker)

Radar data quality control and compression (Xu)