Top Banner
EnKF Assimilation of Simulated HIWRAP Radial Velocity Data Jason Sippel and Scott Braun - NASAs GSFC Yonghui Weng and Fuqing Zhang - PSU
16

EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Feb 24, 2016

Download

Documents

Akira

EnKF Assimilation of Simulated HIWRAP Radial Velocity Data. Jason Sippel and Scott Braun - NASAs GSFC Yonghui Weng and Fuqing Zhang - PSU. Background. Motivation: NASA’s HS3 Campaign. Global Hawk flown for 2012-2014 hurricane seasons 26-h flight time; 19-km altitude - PowerPoint PPT Presentation
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Jason Sippel and Scott Braun - NASAs GSFC

Yonghui Weng and Fuqing Zhang - PSU

Page 2: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Global Hawk flown for 2012-2014 hurricane seasons– 26-h flight time; 19-km altitude– ‘Over-storm’ payload:

• HIWRAP Doppler radar (Ku & Ka)• HAMSR sounder (Tb for qv & T above

precip)• HIRAD radiometer (sfc. winds)

– ‘Environmental’ payload:• Dropsondes (u, v, qv, T)• S-HIS sounder (clear-air radiances for

qv & T)• Cloud Physics Lidar (aerosols)• (maybe) TWILITE Doppler Lidar

Motivation: NASA’s HS3 CampaignBackground

Global Hawk at NASA’s Dryden hangar

Page 3: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Motivation: Past WRF-EnKF success• Same ensemble Kalman filter

(EnKF) setup as used for:– WSR-88D Vr assimilation in

Hurricane Humberto (Zhang et al. 2009, MWR)

– P3 Vr assimilation in Hurricane Katrina (Weng and Zhang 2011, MWR)

– Penn. State University HFIP real-time WRF/EnKF

• Proven successful, even with difficult storms

Background

Humberto: Obs vs. EnKF analysis

Page 4: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Objectives1. Generate a 48-h ensemble without data assimilation

2. Select ‘truth’ realizations for simulated data experiments

3. Assimilate simulated HIWRAP observations with an ensemble Kalman filter (EnKF)

4. Assess quality of analyses and forecasts

Page 5: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

WRF-EnKF system• EnKF from Zhang et al. (2009)

• WRF-ARW V3.1.1, 27/9/3 km

• 30-member ensemble, IC/BCs from WRF-VAR + GFS

• Ensemble integrated 12 h to generate mesoscale covariance

• WSM-6 mp for assimilation; GSFC for truth (model error)

Methods

Model domains

Page 6: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Selecting ‘truth’ realizations

Realizations selected to test EnKF performance in face of:

• Small error of the priorHow much improvement does the EnKF offer when the forecast is already pretty good? (NODA1)

• Large error of the priorHow well can the EnKF correct when the truth is unlike most of the prior? (NODA2)

Methods

‘Truth’ realizations and NODA forecasts

Page 7: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

‘Truth’ simulation flight tracks• Instantaneous scans every ~28

km; observation cones slightly overlap at surface

• Data grouped into 1-h flight segments from same output time; ~1900 obs/hr

• Add 3 m/s random error, only assimilate when attenuated dBZ > 10

Methods

50°

~ 3 km

~ 3 km

19.0 km

Observations every ~3 km on cone surface

Page 8: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: CTRL analyses evolutionOSSE Results

• Intensity metrics: Generally small corrections due to small initial error in NODA

• Position: more noticeable correction, particularly for CTRL2

Page 9: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: Analysis error reductionOSSE Results

• EnKF reduce RM-DTE > 80% after 13 cycles in both cases [DTE = 0.5 × (u`u` + v`v` + Cp/Tr × T`T`), prime is difference from truth]

• CTRL2 has larger and more widespread error reduction than CTRL1

Comparison of RM-DTE differences

Page 10: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: Deterministic forecastsOSSE Results

• Forecast error is reduced relative to NODA in both cases, particularly from 36-48 h

• NODA2 needs more time to produce better analyses (i.e., that produce ‘good’ forecasts)

Page 11: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: Ensemble forecastsOSSE Results

Similar to deterministic forecast results…

Note huge benefit of cycling

Page 12: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Truth

13 HIWRAP cycles12 HIWRAP cycles

+ 1 HIWRAP/HIRAD cycle

Benefit of multiple data sources

Improved analysis of inner-core winds and wavenumber 1 asymmetry with complement of simulated HIRAD data

OSSE Results

Page 13: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

SummaryHIWRAP data appears to be useful for EnKF analyses and subsequent forecasts of a hurricane

• Strong analysis error reduction, particularly for a poor first guess

• Notable forecast improvements after just one assimilation cycle in CTRL1

• A longer assimilation window (i.e., Global Hawk time scale) appears to benefit forecast more when the first guess is poor

• Future work will assess impact of multiple platforms at once as well as multiple instruments (e.g., HIRAD and dropsondes)

Page 14: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: Re-examining flight tracks

Compare two different flight track philosophies:

• CTRL: 360-km legs that approximately span entire precipitation region

• VLEG: Single 360-km pattern followed by two complete patterns with 180-km legs, then repeat (i.e., focus more on center)

Methods (again)

Page 15: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: CTRL/VLEG comparisonOSSE Results

• Average CTRL RM-DTE near center is ~3-3.5 m/s

• VLEG systematically reduces near-center error more but error farther out less

Page 16: EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

Results: CTRL/VLEG comparisonOSSE Results

• All CTRL forecasts better than NODA• Differences between CTRL and VLEG forecasts on par

with differences as a result of obs error (i.e., AERR)