Assimilating Reflectivity Observations Assimilating Reflectivity Observations of Convective Storms into of Convective Storms into

Convection-Permitting NWP ModelsConvection-Permitting NWP Models

David DowellDavid Dowell11, Chris Snyder, Chris Snyder22, Bill Skamarock, Bill Skamarock22

11 Cooperative Institute for Mesoscale Meteorological Studies, Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma, USANorman, Oklahoma, USA

22 National Center for Atmospheric Research, National Center for Atmospheric Research, Boulder, Colorado, USABoulder, Colorado, USA

Ensemble Kalman Filter (EnKF) Ensemble Kalman Filter (EnKF) Assimilation of Radar DataAssimilation of Radar Data

for Convective Storm Analysisfor Convective Storm Analysis Assimilation of Doppler velocity observationsAssimilation of Doppler velocity observations

– Snyder and Zhang 2003, Zhang et al. 2004, Snyder and Zhang 2003, Zhang et al. 2004, Dowell et al. 2004a, Caya et al. 2005, Tong and Xue 2005Dowell et al. 2004a, Caya et al. 2005, Tong and Xue 2005

Recently, assimilation of Recently, assimilation of reflectivity observations reflectivity observations (together with Doppler (together with Doppler velocity observations)velocity observations)– Dowell et al. 2004bDowell et al. 2004b– Tong and Xue 2005Tong and Xue 2005

Research QuestionsResearch Questions

1.1. For retrieving storm characteristics, what is the value of For retrieving storm characteristics, what is the value of assimilating reflectivity observations?assimilating reflectivity observations?

– Reflectivity observations only, and together with Doppler Reflectivity observations only, and together with Doppler velocity observationsvelocity observations

2.2. Is it necessary/advantageous to process in a special way Is it necessary/advantageous to process in a special way observations that indicate low values of reflectivity?observations that indicate low values of reflectivity?

3.3. Do conclusions drawn from “perfect-model” experiments Do conclusions drawn from “perfect-model” experiments change when model errors are significant?change when model errors are significant?

– Uncertainty in storm environmental conditionsUncertainty in storm environmental conditions– Model-physics errorsModel-physics errors

OSSE Design (part 1)OSSE Design (part 1)

NCOMMAS forecast modelNCOMMAS forecast model– Similar results obtained with WRFSimilar results obtained with WRF– Idealized base state (moderate CAPE, low CIN)Idealized base state (moderate CAPE, low CIN)– 4 hydrometeor types (rain, ice particles, snow, hail/graupel)4 hydrometeor types (rain, ice particles, snow, hail/graupel)– xx==yy=2000 m, =2000 m, zz=500 m=500 m– Same model used to produce reference simulation and to Same model used to produce reference simulation and to

assimilate synthetic observationsassimilate synthetic observations

Reference Simulation: SupercellReference Simulation: Supercell

30 min 60 min 90 min

reflectivity and wind at 2.25 km AGL

100 km

Reference Simulation: Squall LineReference Simulation: Squall Line

1 hour 2 hours 3 hours

reflectivity and wind at 2.75 km AGL

200 km

OSSE Design (continued)OSSE Design (continued)

Synthetic radar observationsSynthetic radar observations– Volumetric observations produced from reference Volumetric observations produced from reference

simulation every 5 minsimulation every 5 min– Smith et al. 1975 equations used to compute reflectivity Smith et al. 1975 equations used to compute reflectivity

corresponding to model state variablescorresponding to model state variables– ““Radar” observes Radar” observes uu component of wind in reference component of wind in reference

simulation, simulation, only where reflectivity > 15 dBZonly where reflectivity > 15 dBZ– Random errors added to truthRandom errors added to truth

errorerror = 2.0 dBZ for reflectivity = 2.0 dBZ for reflectivity

errorerror = 2.0 m s = 2.0 m s-1-1 for radial velocity for radial velocity

OSSE Design (continued)OSSE Design (continued) Initialization of 50-member ensembleInitialization of 50-member ensemble

– ““First echoes” initiated by warm bubbles in random First echoes” initiated by warm bubbles in random locations within a subdomain containing observed stormslocations within a subdomain containing observed storms

Ensemble Kalman filter Ensemble Kalman filter (square-root filter; Whitaker and Hamill (square-root filter; Whitaker and Hamill

2002)2002) data-assimilation schemedata-assimilation scheme

Supercell Assimilation ExperimentSupercell Assimilation Experiment

0

1

2

3

4

20 30 40 50 60 70 80 90 100

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity only

assimilate both

Temperature(K)

Vertical velocity(m/s)

RMS errors in rain region, EnKF analysis

0

1

2

3

20 30 40 50 60 70 80 90 100

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity only

assimilate both

Squall Line Assimilation ExperimentSquall Line Assimilation Experiment

0

1

2

3

4

20 40 60 80 100 120 140 160 180

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity only

assimilate both

Temperature(K)

Vertical velocity(m/s)

RMS errors in rain region, EnKF analysis

0

1

2

3

20 40 60 80 100 120 140 160 180

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity only

assimilate both

Suppressing Spurious Cells by Suppressing Spurious Cells by Assimilating Reflectivity ObservationsAssimilating Reflectivity Observations

Velocity observations are available in precipitation Velocity observations are available in precipitation regions only.regions only.

Reflectivity observations are available everywhere.Reflectivity observations are available everywhere.

Reflectivity and wind at 1.25 km AGL at 60 min (supercell)

Truth Ensemble Member 4(assimilation of radialvelocity only)

Ensemble Member 4(assimilation of reflectivityand radial velocity)

Low-Reflectivity ObservationsLow-Reflectivity Observations

Consider a simpler method for suppressing spurious Consider a simpler method for suppressing spurious cells by assimilating low-reflectivity observations:cells by assimilating low-reflectivity observations:

If both the observation and the ensemble mean If both the observation and the ensemble mean indicate low reflectivity, then only force outliers indicate low reflectivity, then only force outliers (ensemble members with anomalously high (ensemble members with anomalously high reflectivity) back toward the ensemble mean reflectivity) back toward the ensemble mean locally:locally:

Otherwise, assimilate the reflectivity observations Otherwise, assimilate the reflectivity observations (and velocity observations) with the standard (and velocity observations) with the standard EnKF method, as before.EnKF method, as before.

€

xia = xi

f +W x f − xif

( ) (all model fields, outlier members only)

Standard EnKF vs.Standard EnKF vs.EnKF with Simpler Spurious Cell SuppressionEnKF with Simpler Spurious Cell Suppression

0

1

2

3

4

20 30 40 50 60 70 80 90 100

Time (min)

standard EnKF

EnKF withsimplerspurious cellsuppression

Temperature(K)

Vertical velocity(m/s)

RMS errors in rain region, EnKF analysis

0

1

2

3

20 30 40 50 60 70 80 90 100

Time (min)

standard EnKF

EnKF withsimplerspurious cellsuppression

Issues ConcerningIssues ConcerningLow-Reflectivity ObservationsLow-Reflectivity Observations

Variations in reflectivity values below ~15 dBZ often Variations in reflectivity values below ~15 dBZ often represent phenomena not in the model (e.g., insects), so represent phenomena not in the model (e.g., insects), so it’s probably best to ignore these variations and treat all it’s probably best to ignore these variations and treat all low values in the same way.low values in the same way.

Forecast distributions in low-reflectivity regions are often Forecast distributions in low-reflectivity regions are often non-Gaussian (spurious storms in a few members, no non-Gaussian (spurious storms in a few members, no storm in most members), so Kalman analysis equation storm in most members), so Kalman analysis equation isn’t necessarily valid.isn’t necessarily valid.

In experiments to date, the simpler algorithm for In experiments to date, the simpler algorithm for suppressing spurious cells gives comparable results to the suppressing spurious cells gives comparable results to the standard EnKF, and computation is faster.standard EnKF, and computation is faster.

Additional Information from Reflectivity: Additional Information from Reflectivity: More Efficient Retrieval of Main SupercellMore Efficient Retrieval of Main Supercell

0

1

2

3

4

20 30 40 50 60 70 80 90 100

Time (min)

update allmodelvariables

updatehydrometeorsonly whenassimilatingreflectivity

Vertical velocity(m/s)

RMS errors in rain region, EnKF analysis

Experiment with Sounding ErrorsExperiment with Sounding Errors((errorerror = 2 m/s for = 2 m/s for uu and and vv, 1 K for , 1 K for TT and and TTdd))

0

1

2

3

4

20 30 40 50 60 70 80 90 100

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity onlyassimilate both

Temperature(K)

Vertical velocity(m/s)

RMS errors in rain region, EnKF analysis

0

1

2

3

20 30 40 50 60 70 80 90 100

Time (min)

assimilatereflectivityonlyassimilateDopplervelocity only

assimilateboth

Other Data-Assimilation ResearchOther Data-Assimilation ResearchSevere Weather Analysis and Prediction GroupSevere Weather Analysis and Prediction Group

at CIMMSat CIMMS(1)(1) and NSSL and NSSL(2)(2)

Radar-data quality control, error-covariance estimation, and Radar-data quality control, error-covariance estimation, and “3.5DVar” assimilation (Xu)“3.5DVar” assimilation (Xu)

EnKF radar-data assimilation and prediction for real-data EnKF radar-data assimilation and prediction for real-data cases: supercells and squall lines (Dowell, Coniglio, Wicker)cases: supercells and squall lines (Dowell, Coniglio, Wicker)

Mesoscale ensemble forecasting (initial-condition, boundary-Mesoscale ensemble forecasting (initial-condition, boundary-condition, and model-physics diversity) and EnKF assimilation condition, and model-physics diversity) and EnKF assimilation of surface data (Fujita, Stensrud, Dowell)of surface data (Fujita, Stensrud, Dowell)

Precipitation microphysics-scheme diversity for storm-scale Precipitation microphysics-scheme diversity for storm-scale data assimilation (Mansell, Wicker)data assimilation (Mansell, Wicker)

(1)(1) Cooperative Institute for Mesoscale Meteorological Studies, Norman, Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma, USAOklahoma, USA

(2)(2) National Severe Storms Laboratory, Norman, Oklahoma, USANational Severe Storms Laboratory, Norman, Oklahoma, USA

ConclusionsConclusions

Results of similar quality are obtained for multiple Results of similar quality are obtained for multiple convective modes: supercell and squall line.convective modes: supercell and squall line.

Both Doppler velocity and reflectivity observations Both Doppler velocity and reflectivity observations are useful for retrieving the atmospheric state on the are useful for retrieving the atmospheric state on the scale of convective storms:scale of convective storms:

– Velocity observations provide information about inner Velocity observations provide information about inner storm structure.storm structure.

– Reflectivity observations mainly provide information Reflectivity observations mainly provide information about where storms are and are not, but also lead to about where storms are and are not, but also lead to more efficient retrievals of inner storm structure.more efficient retrievals of inner storm structure.

Conclusions (continued)Conclusions (continued)

When assimilating When assimilating low-reflectivitylow-reflectivity observations, it observations, it seems to be more efficient to simply force outlier seems to be more efficient to simply force outlier members toward the ensemble mean locally rather members toward the ensemble mean locally rather than to use the standard EnKF.than to use the standard EnKF.

Typical errors in estimated environmental conditions Typical errors in estimated environmental conditions do not prevent good storm-scale analyses produced do not prevent good storm-scale analyses produced by assimilating radar data. However, precipitation by assimilating radar data. However, precipitation microphysics errors (not shown today) do pose a microphysics errors (not shown today) do pose a serious challenge.serious challenge.