Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting Juanzhen Sun NCAR, Boulder, Colorado Oct 25, 2011

Outline Introduction - Unique aspects of convective-scale DA - Overview of techniques Success and Issues Future challenges Oct 25, 2011 This talk is in the context of Warm-season QPF Radar observations NCAR experiences

What makes convective-scale DA different? Objective - QPF, high-impact weather nowcast/forecast - Forecast accuracy: county/city scale Predictability of high-impact weather systems - Rapid error growth - Small-scale with multiple scale interaction Observations - Limited high-resolution in-situ observations - Remote sensing: high resolution, but limited coverage, limited and indirect variables

Convective-scale DA strategies Place storms at right locations - Warm Start: Cloud analysis, latent heating insertion, saturation adjustment, updraft profiling Use frequent update - Sub-hourly; min window for 4DVAR - Take advantage of high temporal frequency obs. - Forced by predictability limitation Consider cloud-scale balance - Temporal derivative terms should not be neglected - Different balance from the large-scale Use different error statistics - Large-scale error statistics is not applicable - Research is still lacking

Overview of techniques Techniques based on reflectivity or precipitation - DFI, nudging, cloud analysis - Simple and efficient - No or limited multivariant balance 3D techniques assimilating both RV and RF from radar - 3DVAR - Efficient - Balance is mostly large-scale 4D techniques assimilating both RV and RF - 4DVAR, EnKF (and its variants) - Computationally expensive - Full model balance, but compromised in practice (limited ensemble members, limited assimilation window)

Latent Heat Nudging  ingest radar reflectivity observations (converted to QR/QS)  add tendency terms to model variables QR/QS and T based on the model state and observations  result in thermodynamic and microphysical adjustment Hydrometeor increment per Δt (dQR/dt) obs * Δt if QR mod 0 ΔQR =g *(QR obs - QR mod )if QR mod > QR obs 0otherwise Temperature increment ΔT = C LS /C PM * ΔQR where C LS is the latent heat of condensation (or fusion) C PM =C P *(1.+0.8*QV) is the specific heat for moist air Mei Xu

Impact of radar data LHN case observation no radarwith radar LHN analysis

1 h forecast Impact of radar data LHN case observation no radarwith radar LHN

2 h forecast Impact of radar data LHN case observation no radarwith radar LHN

3 h forecast Impact of radar data LHN case observation no radarwith radar LHN

Skills for June 11-17, 2009 Front Range Domain FSS Evaluation Analysis period Averaged over 24 forecasts

WRF 3DVAR Radar DA Reflectivity data assimilation - Assimilate rainwater - Cloud analysis (optional) - Assimilate saturation water vapor within cloud (optional) Control variables - stream function - unbalanced velocity potential - unbalanced temperature - unbalanced surface pressure - pseudo relative humidity Cost function For radar DA Hongli Wang

IHOP one-week runs NORD: Control with no radar DA RV: Assimilate radial velocity RF: Assimilate reflectivity RVRF: Assimilate both One-week FSS skill (5mm) RF RV 6-h Forecasts after four 3DVAR cycles Cycled 3DVAR Both

Beijing Results NORD: Control with no radar DA RV: Assimilate radial velocity RF: Assimilate reflectivity RVRF: Assimilate both FSS skill for four 2009 summer cases RV RF OBS No Radar RV RF 2-hour forecasts Shuiyong Fan

Diurnal variation of Radar DA impact 00Z 12Z Radar DA has longer positive impact for late evening initializations The positive impact only lasted 4 hours for morning initializations It suggests that the radar DA works more effectively for growing storms than dissipation storms Dashed lines: Warm start Solid lines: Cold start

An example of failed forecast Cold start analysis 3DVAR cycled analysis Cold start Cycled 3DVAR RF

From Sugimoto et al. (2009) Radial componentTangential component Can 3DVAR retrieve the tangential wind? Truth Analysis Radars with overlap Corr: Single radars Corr: 0.402

Study of a supercell storm using a 4DVAR system VDRAS Sun (2004) ObservationForecast Color contour: q r w w qvqv qvqv Radial velocity only Reflectivity only Observati on RF only RV only RV and RF Without radial velocity, the rain falls out quickly. Radial velocity assimilation results in slantwise updraft and moisture, but not the reflectivity assimilation Assimilating both RV and RF consistently outperforms RV or RF only Rainwater correlation

4DVAR systems: VDRAS and WRF 4DVAR VDRAS Developed for a cloud model Trajectory is modeled by the nonlinear model Full adjoint of the cloud model is used to calculate the gradient in the minimization Control variables are model prognostic variables WRF 4DVAR Developed for WRF model Trajectory is modeled by the tangent linear model of WRF with reduced physics Adjoint of the reduced tangent linear model Control variables follow those in WRF 3DVAR

Inserting VDRAS analysis into WRF inner domain VDRAS 3km 19 UTC 15 June 2002 WRF 9 km

Observation (061302) No VDRAS With VDRAS 2-h WRF forecasts valid at

Observation(061305) No VDRAS With VDRAS 5-h WRF forecasts valid at

WRF 4DVAR Radar Data Assimilation 4-hour forecasts from a case study (13 June 2002) OBS3DVAR 4D_RV4D_RF

ETS of 0-6 hour forecast 4D_RF 4D_RV 1 mm 5 mm 3DVAR

Observations Mem 1 control Mem 1 w/ radar assim 00Z01Z02Z WRF/DART EnKF Convective scale data assimilation Glen Romine

Future Challenges DA for nowcasting application requires different configurations - Frequent updating - Radar DA crucial for minimizing spinup time - Different background error statistics - Multiple pass for observations with different resolutions - Different DA schemes - Make better use of surface observations - Different physics options? Rapid cycling with/without radar DA can have negative impact on convective initiation - Will more frequent updating with radar DA help? - Diurnal variation of radar DA impact - The impact also depends on convection type

Opportunities and Challenges Radar DA still a great challenge - Reflectivity assimilation > Improve the accuracy of the latent heating and relative humidity specification in the simple techniques > Balance with dynamics > Error statistics - Radial velocity assimilation > Retrieval of the tangential component in 3DVAR > Clear air returns > Balance with thermodynamics and microphysics

Opportunities and Challenges Challenges for the 4D techniques - Computation cost - Large resource required for developing a full 4DVAR - Choice of control variables for the convective scale in 4DVAR - Sample issues and maintenance of ensemble spread for EnKF - Model errors

VDRAS radar data assimilation reveals how cold pools trigger storms UTC UTC Pert. Temp. (color) Shear vector (black arrow) Wind vector at km (brown arrow) Contour (35 dBZ reflectivity)