The impact of assimilating GPS-RO over Antarctica and the Southern Ocean

David H. Bromwich, Aaron B. Wilson, Lesheng Bai, Sheng-Hung Wang, Tae-Kwon Wee, Hailing Zhang, Zhiquan Liu, Bill Kuo, Shu-Ya Chen and Hui-Chuan Lin

PRESENTED BY: Tae-Kwon Wee

Outline1. Overview of the Global Positioning System (GPS)

Radio Occultation (RO) and Data Use in Numerical

Weather Prediction

2. Study Methodology (GPS-RO Assimilation, Study

Design)

3. GPS-RO Impact on Antarctic Surface Pressure in One

Year Reanalysis Mode Cycling Runs

4. Improved Modeling of the Development of Cyclones

over the Southern Ocean

5. Conclusions

• Stable - All weather

• High accuracy: < 0.1 K

• Global 3-D coverage: 40 km

to surface

• Vertical Resolution: ~100 m

in the lower troposphere

Overview of GPS RO and Motivation

• Independent height, pressure, and temperature data

• Low-power, Low-cost

• Accurate PSFC correction for Antarctica is needed for

the GRACE surface mass balance estimates.

Bending angle Pros:

- One-step-less processed than refractivity and is thus simpler in the observation error characteristics. Cons:

- Observation operator (Abel transform) requires an integration from TOA, but model’s top height is not always high enough. Accordingly, mismodeling can be significant at the upper part of model domain.

- Discrete Abel transform on model grid is subject to significant error because model’s vertical resolution is limited (especially, evaluation of vertical derivative of refractivity).

- Modeled impact parameter (x=nr) contains error arising from model’s error in the refractivity. This translates into an observation error and depreciates observation value.

RefractivityCons:

- Error specification and quality control are more challenging.- The inverse Abel transform (Abel inversion) in RO data processing collects bending angle error and

propagates them downward. The refractivity error is thus strongly correlated in vertical direction.Pros:

- Observation operator is simple and inexpensive.- Numerical error of discrete Abel Inversion is of no concern because the vertical resolution of observed

bending angle is much higher than model’s.- The Posterior Height Determination (Wee, 2017) reduces refractivity error substantially. PHD means

that the height of refractivity is undetermined until the refractivity itself is known via Abel inversion.- Location of observation does not depend on model’s state vector, in contrast to bending angle.

Bending angle vs. refractivity for DA

Motivation• Refractivity is easier to assimilate; but, uncontrolled downward propagation of bending

angle error in Abel Inversion is problematic. • Is it possible to obtain refractivity profile of improved quality (compared to Abel inversion)

by attenuating the bending angle error effectively?

Approach• Optimizes the bending angle in the observation space via a variational regularization.

Regularized parameter is refractivity in the impact parameter space. • Seeks for the refractivity profile that describes best noisy or error-possessing bending

angles by turning Abel transform into an optimization problem. • The regularization is conducted in the observation space and so the numerical error due to

the discretization of Abel transform is minor.• In the regularization, perfect reconstruction of bending angle in the impact parameter is the

sufficient condition for obtaining perfect refractivity. In DA, however, perfect fit to error-free bending angle does not yield perfect refractivity (DA cannot place bending angles at the right location because of model’s error in the background refractivity and the subsequent modeled impact parameter).

• Upholds all the benefits of refractivity assimilation

Refractivity via Variational Regularization

Refractivity error estimates• Estimated by applying Hollingsworth-Lönnberg method to closely located RO-RO soundings

• The method is based on the assumption is that forecast errors are spatially correlated, whereas observation errors are uncorrelated with themselves and with the forecast errors.

• ~ 1.6 M RO-RO pairs (April 2007 - April 2016) and operational ECMWF forecasts are used

• RO soundings are obtained from UCAR CDAAC, which are produced via Abel inversion

Caused by unsmooth transition from geometrical optics (> 20km) to wave optics (< 20km). Bending angles above and below 20 km differ significantly in the noise level.

Observation error is large in the lowest 1-2 km

Observation error is larger than forecast error in the polar stratosphere and lowest 1-2 kmThe question is whether the variational regularization is able to reduce the observation error there

Verification with dropsonde and radiosonde(Zhang et al., 2017)

Radiosondes ()

(Independent Concordiasi) dropsondes (+)

Month-long (OCT 2010) continuous DA experiments using WRF 3DVAR

6h forecasts during the whole month are compared to dropsonde and radiosonde temperatures (the bias shown is forecast minus sonde)

Exp. Description

NOGPS Assimilates NCEP’s operational data sets except for RO data

RT NOGPS + the near real-time RO data provided to NCEP back in 2010

PP Post-processed RO data by the COSMIC CDAAC with more recent algorithms

VR Refractivity is derived via the VariationalRegularization from PP bending angle

RT is processed by multiple RO processing centers and with different algorithms

PP is processed solely by the CDAAC and with up-to-date processing algorithms

VR is obtained from the same bending angle (PP) but using the variational regrularization instead of Abel inversion

One Year Reanalysis Mode Cycling RunsPeriod: Jan 2008 ~ May 2009

Full 6-hourly cycling run at Ohio Supercomputer Center

Polar WRF(V3.7.1), WRF-Var(V3.7.1) are used for the dataassimilations.

Two experiments are conducted:

With GPS RO: GTS+RAD+GPSWithout GPS RO: GTS+RAD

Performed twice: strong and weak nudging to ERA-Interim, only weak nudging results are presented here.

Polar WRF Configuration

• Polar WRF version 3.7.1 with fractional sea ice and SST from ERA-Interim

• 15 km horizontal resolution (721 x 721 mesh)

• 71 vertical levels with 10 hPa top and bottom model level at about 4 meters

• Lateral boundary conditions provided by ERA-Interim reanalysis (model level)

• Selected Physics Include:• Microphysics: Goddard• LW/SW Radiation: RRTMG• PBL: MYNN 2.5 TKE• Surface: Noah LSM• Kain-Fritsch convective scheme• Spectral nudging to ERA-Interim above

250 mb

ASR Preliminary Meeting Boulder, Colorado

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

MonthBackground

Error(gen_be)

Polar WRF6h Forecast

Update Lateral

Lower BCs

WRF-3DVar

Data Assimilation FlowchartWRF-3DVar POLAR-WRF

Performed in a 6-h interval

PREPBUFRdata

Lateral BCs Boundary

Lower BCs Boundary

ForecastAnalysis

OUTPUT

Analysisoutput

Forecastoutput

GPSROdata

Data for AssimilationERA-Interim reanalysis model level data

The T255 (0.7 degrees) horizontal resolution ERA-Interim reanalysissurface and upper air model level data are used to provide the initialand lateral boundary conditions as well as statistical backgrounderror.

PREPBUFR• Conventional observations: synoptic, metar, ship, buoy, radiosonde• Satellite observations (QuickSCAT, SSM/I – sea surface wind speed)• Satellite radiance data (AMSUA, MHS, HIRS3, HIRS4)

GPS (GPSRO)Refractivity is derived via Variational Regularization by Tae-Kwon Wee from post processed bending angle data from the COSMIC CDAAC

SST and sea ice dataBoth from ERA-Interim.

Atmospheric Observation data in 6-hour time window

Surface Pressure Compared to ERA-Interim (RMSE)Analysis Aug 2008 (Winter)

6-hour Forecast Aug 2008 (Winter)

GTS+RAD GTS+RAD+GPS [GTS+RAD+GPS] – [GTS+RAD]

[GTS+RAD+GPS] – [GTS+RAD]GTS+RAD+GPSGTS+RAD

Surface Pressure Compared to ERA-Interim (RMSE)Analysis Jan 2009 (Summer)

6-hour Forecast Jan 2009 (Summer)

GTS+RAD+GPS [GTS+RAD+GPS] – [GTS+RAD]

[GTS+RAD+GPS] – [GTS+RAD]GTS+RAD+GPSGTS+RAD

GTS+RAD

Analysis Surface Pressure Compared to ERA-InterimRMSE[GTS+RAD+GPS] –RMSE [GTS+RAD]

Months outlined in red may have some issues with GPS-RO retrievals

Analysis Surface Pressure Compared to Automatic Weather Station Observations

Diff = GPS - Ctrl

Absolute Bias Correlation RMSE # ofCtl (no GPS) GPS Diff Ctl (no GPS) GPS Diff Ctl (no GPS) GPS Diff Stat.

2008.06 2.0040 2.0015 -0.0025 0.9755 0.9755 0.0000 2.6539 2.6527 -0.0012 312008.07 1.9979 2.0021 0.0042 0.9884 0.9887 0.0004 2.7550 2.7415 -0.0135 282008.08 1.7050 1.7103 0.0052 0.9865 0.9868 0.0003 2.5621 2.5479 -0.0143 242008.09 1.8176 1.8132 -0.0044 0.9786 0.9792 0.0005 2.5605 2.5371 -0.0234 312008.10 1.6250 1.6416 0.0166 0.9838 0.9849 0.0011 2.0718 2.0583 -0.0135 302008.11 1.5151 1.5183 0.0032 0.9852 0.9854 0.0002 2.0252 2.0157 -0.0096 422008.12 1.6315 1.6306 -0.0009 0.9943 0.9946 0.0003 1.9490 1.9378 -0.0112 532009.01 1.6732 1.6732 0.0000 0.9799 0.9807 0.0008 2.0448 2.0322 -0.0126 552009.02 1.6306 1.6307 0.0001 0.9896 0.9900 0.0004 2.0132 2.0004 -0.0128 602009.03 1.8198 1.8251 0.0052 0.9836 0.9840 0.0004 2.3401 2.3316 -0.0084 582009.04 2.0607 2.0550 -0.0057 0.9861 0.9866 0.0005 2.5891 2.5661 -0.0229 572009.05 1.9594 1.9607 0.0014 0.9873 0.9879 0.0007 2.4714 2.4465 -0.0248 54AVG 1.7867 1.7885 0.0019 0.9849 0.9854 0.0005 2.3363 2.3223 -0.0140

Preliminary Analysis ResultsSurface Pressure at Antarctic AWS Locations

for Strong Grid Nudging Casenot the Weak Nudging Case Presented up to Now

Very small impact on bias and correlation but consistent small decrease in RMSE for strong nudging case.Expect much reduced bias (and RMSE) and increased correlation with GPS-RO assimilation for weak nudging case, not yet analyzed.

The Adélie Land Coastal Region of Antarctica

• Prominent cyclogenesis region in the Southern Hemisphere

• Adjacent to the continent’s most intense katabatic wind regime.

• AMPS data identifies two primary patterns of cyclogenesis. – Secondary development:

Enhanced Low-level cyclonic Vorticity and Baroclinicity

– Lee cyclogenesis: Low-level Warm Potential Temperature Anomaly.

Bromwich et al. (2011): Tellus A.

Improved Prediction of a RapidlyDeveloping Cyclone over the

Southern Ocean• A synoptic storm

that occurred on December 2007

• Use WRF with WRF 3D-VAR

• Two assimilation experiments– With GPS RO

refractivity (WG)– Without GPS RO

refractivity (NG)Chen et al (2014): Mon. Wea. Rev.

Track and Intensity Improved with Assimilation of GPS RO

• GPS RO improves intensity in the 3-5 day forecast (red circle)

• Large-scale circulations better predicted

• The GPS RO data inhibited the development of the coastal low along the Adélie Land coast, accurately simulating the observed processes of cyclogenesis.

• We have achieved our goal of < 1 hPa monthly mean bias in surface pressure for many stations in Antarctica. Several AWS locations have much larger biases that need investigation.

• Noticeable surface pressure RMSE improvements from GPS-RO assimilation over Antarctica and the Southern Ocean and in the favored southern ocean cyclogenesis region of Adélie Land. Greatest impact in winter, least in summer.

• Need to resolve uncertainties and complete analysis. Test the impact of using a more advanced data assimilation approach. We expect to derive surface pressure over Antarctica more accurately than ERA-Interim.

• Coverage of GPS-RO profiles in this region is less than desired. More profiles would mean greater benefit from GPS-RO assimilation.

Conclusions

Forecast Surface Pressure Compared to ERA-InterimRMSE[GTS+RAD+GPS] – RMSE [GTS+RAD]