Results From the FSL Regional Lidar

OSSE•NOAA/FSL

- Steve Weygandt- Stan Benjamin- Steve Koch- Tom Schlatter- Adrian Marroquin- John Smart

• NOAA/NWS/NCEP - Michiko Masutani

• NOAA/ETL - Mike Hardesty- Barry Rye- Aniceto Belmonte

• NCAR- Dale Barker- Qinghong Zhang

Goals of lidar Observing System Simulation Experiments

• Assess forecast impact of wind observations from a Doppler lidar flown aboard a polar orbiting satellite

• Evaluate efficacy of different lidar scan strategies

• Refine processing/assimilation techniques for space-based Doppler lidar wind obs

Motivation for Regional OSSE

•Regional models behave very differently than global models

- Typically run over data-rich regions

- Utilize high-frequency assimilation

- Better resolve local forcing from surface features

- Influenced by obs assimilation and boundary conditions

•Potential impact of lidar obs for mesoscale systems not well unknown

•Spatial variability of mesoscale impact over CONUS not well known

4

Relationship between Global and Regional OSSEs

ECMWF Global Nature Run

MRF Global OSSE

Experiment

MM5 Regional Nature Run

RUC Regional OSSE

Experiment

Global

Regional

Nature Run

OSSE Experiment

Simulated Observations

Boundary Conditions

Boundary Conditions

Simulated Observations

Global Verification

Regional Verification

Griddedoutput

Griddedoutput

Griddedoutput

Griddedoutput

• Model Physics

• Kain-Fritsch cu. param., Schultz microphysics

• Burk-Thompson PBL, RUC Land Surface Model

• RRTM radiation formulation

• Model Grid

• 10-km horizontal grid spacing (740 x 520)

• 43 sigma levels (top = 20 km, const. spacing ~800 m)

• Model Data

• Initial, boundary data from ECMWF nature run

MM5 REGIONAL NATURE RUN

• Model Physics

• Grell ensemble cu. param., Reisner microphysics

• Burk-Thompson PBL, RUC Land Surface Model

• Dudhia radiation formulation

• Model Grid

• 40-km horizontal grid spacing (151 x 113)

• 50 hybrid levels (top = 450 K, variable spacing)

• Model Data

• Initial, boundary data from MRF global experiments

• Simulated observations from MM5 nature run

RUC REGIONAL ASSIMILATION RUN

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Regional Assimilation Configuration• RUC 3DVAR (Zunb,u, v, v, lnQ)

• 3-h update cycle (previous RUC fcst)

• 6-h BC updates (matched MRF expt.)

• Start RUC 00z 13 Feb (MRF 1st guess)

• 48-h fcst (00z,12z); 18-h fcst (06z,18z)

• Grid verification to MM5 (15 Feb – 20 Feb)

11 12 13 14 15 16 17 18 19 20 21 22

MM5

Feb

RUC

Verification

15 16 17 18 19 20 21

EC

RUC analyzes horizontal wind: ()sincosHrvuvφφ=+

LIDAR FORWARD MODEL GEOMETRY

Lidar LOS wind components:

Assume vertical velocity small compared to horizontal velocity:

()sinsincoscosLOSvuvwηφφη=+−

sinHrLOSvvη=

(schematic only, actual model includes earth curvature)

Lidar Simulation Details

• Direct detection (molecular) Doppler lidar

• Low satellite orbit (450 km)

• 20-W trans., 355 nm, 1-m telescope, 5-sec avg.

• Cloud effects from model ice and liquid water - water clouds opaque

- ice clouds use backscatter, extinction from model ice water

• “ideal” lidar for initial study (match global strategy)

• 8 “stares” per sec, 5 shots per stare (over 35 km)

• Dual-look scanning of the same region

10

-4 -3 -2 -1 0 1 2 3

-6

-5

-4

-3

-2

-1

0L

atit

ude

[deg

rees

]

Longitude [degrees]

1

11

3

9

2

12

4

10

14

86

165

1513

7

1

2

3

4

5

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7

8

9

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15

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8-POINT SCANNadir Angle: 45°

Azimuth Angles: 36.65°, 136.07°, 19.18°, 153.53°, -26.47°, -160.82°, -43.94°, -143.36°

Forward

Aft

11

-4 -3 -2 -1 0 1 2 3-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0L

atit

ude

[deg

rees

]

Longitude [degrees]

8-POINT SCAN Nadir Angle: 45°

Shot Pattern

Forward

Aft

Forward

Aft

Forward

Aft

12

LOCATION OF NS LIDAR PASSES100 |

90 |

110 |

130 |

120 |

80 |

70 |

60 |

0132z

0000z

0304z0435z

Longitude

Observation Error Standard DeviationsObservation Type Error Std. Dev

Rawinsonde H 6.5 - 26.0 mRawinsonde T 0.4 KRawinsonde ln Qv 0.1 - 0.2Rawinsonde U,V 0.8 - 1.4 m/s

ACARS T 1.0 K ACARS U,V 1.5 m/s

Profiler U,V 1.0 m/sVAD U,V 1.0 m/sSurface U,V 0.5 m/s

Lidar Vr 1.0 m/s

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OSSE OB DATA COUNTS

Ob type Variables 12z 15zRaob (Z,T,Q,U,V) 3700 0Prof/VAD (U,V) 2600 2600ACARS (T,U,V) 1200 1300METAR/Buoy (T,Q,U,V) 1600 1600

Lidar (Vr) 1500 1500

Approximate no. of obs data points

• Lidar adds ~8% more wind obs at raob init times (00z, 12z)

• Lidar adds ~14% more wind obs at non raob init times (06z, 18z)

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LIDAR EXPERIMENTS

MRF Expt.Expt. Boundary ObservationsName Conditions Assimilated

StdNone standard obs noneStdStd standard obs standard

StdNoAcstandard obs standard - aircraft

StdLid standard obs standard + ideal lidar

LidNone standard + lidar obs noneLidStd standard + lidar obs standard

LidLid standard + lidar obs standard + ideal lidar

LidLidr standard + lidar obs standard + realistic lidar

(CNTL)

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• Realistic error growth with standard obs assimilation• Errors slightly larger for non-raob initialization times

Non-raobinit time(06z,18z)

Raob init time(00z,12z)

No obs (BC only)

Std obs

Regional OSSE Calibration

How well does the simulated data impact

(OSSE) match real data impact (OSE) ?

• Compare ACARS obs denial for:

- Real data OSE: 4-16 Feb 2001

- Simulated data OSSE: 15-20 Feb 1993

• Many differences between OSSE and OSE

- Different weather

- Different observation errors

- Different number ACARS obs

- Different verification (raob vs. grid)

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• Denying ACARS degrades both OSSE & OSE fcsts• Greatest impact at upper-levels for both OSSE, OSE

Verified against raobs with unknown errors

Real Data

4-16 Feb 2001

15-20 Feb 1993

Simulated Data

Verified against MM5nature run (no errors)

06z, 18z init 06z, 18z init

StdStdStdNoAc

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• Fcst degradation from

denying ACARS similar for OSE, OSSE

Normalize Errors

CNTLerror – EXPerror

CNTLerror

POS = fcst betterNEG = fcst worse

“% Improvement”=

- (% change error)=

20

• Lidar obs assimilation reduces forecast error• Error reduction greater for non-raob init times• Error reduction decreases with forecast length

Non-raobinit time(06z,18z)

Raobinit time(00z,12z)

Std obs

Std obs+lidar Err sd=1 m/s

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• ~5% improvement in 6-h forecast from lidar obs• Additional improvement likely from use of boundary conditions which contain lidar obs

Non-raobinit time(06z,18z)

Raobinit time(00z,12z)

Std obs+lidar Err sd=1 m/s

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• Lidar obs reduce forecast error at all levels• Reduction greater for non-raob init times

Raob init time (00z,12z)

Non-raob init time (06z,18z)

23

• Lidar obs improve fcst more at non-raob init times• Lidar obs improvement greatest aloft

Results Summary

•Forecast error characteristics for regional assimilation system appear realistic

•ACARS denial comparison against real-data OSE yields good agreement

•Regional assimilation of lidar obs produces modest short-term wind fcst improvement

•Lidar obs-related fcst improvement slight for other variables

Regional OSSE Issues

•Observation impact strongly dependent on observation error specification

- Representative errors

- Systematic errors

•Need realistic model depiction of clouds for realistic lidar obs simulation

•Drift of regional NR from global NR complicates interpretation of BC impacts

Ongoing Work

•Regional expts. using MRF boundary conditions containing lidar obs

- Relative forecast improvement from BCs vs. assimilation

- Effect of BCs on regional assimilation impact

•Regional expts. using more realistic lidar obs

•Evaluate lidar ob impact for specific cases

•Examine spatial variability of lidar ob impact