Recent Data Assimilation Activities at Environment Canada€¦ · · 2014-08-11Recent Data...

Recent Data Assimilation Activities at Environment Canada

Mark BuehnerData Assimilation and Satellite Meteorology Research

• Major upgrade to global and regional deterministic prediction systems (now in parallel run)

• Sea ice data assimilation

– August-11-14

Upgrade to deterministic DA systemsIn a Parallel Run since June 2014, changes are mostly to DA:• 4D-EnVar replaces 4D-Var• Model and Analysis grids:

• Analysis increment: 50km instead of 100km grid spacing• Unchanged for background and analysis

• Satellite radiance observations:• Improved satellite radiance bias correction scheme: rely more on obs• Additional AIRS/IASI channels assimilated, now 142 each • Upgrade RTTOV8 to RTTOV10• Modified obs error stddev for all radiance observations [f x stddev(O-P)]

• Improved treatment of radiosonde (4D) and aircraft observations• Assimilation of ground-based GPS data over North America• Use of new global sea ice concentration analysis (3D-Var)• 4D-IAU (instead of DFI) and recycling of physics variables (GDPS only)• Use of Maestro sequencer for R/D/O

– August-11-14

2014 implementation: Increasing role of global ensembles

Global EnKF

Global ensemble forecast (GEPS)

Global deterministic

forecast (GDPS)

GlobalEnVar

Background error

covariances

2013-2017: Toward a Reorganization of the NWP Suites at Environment Canada

Regionalensemble forecast (REPS)

Regionaldeterministic

forecast (RDPS)

Regional EnVar

global system regional system

– August-11-14

Forecast Results: GDPS 4.0.0 vs GDPS 3.0.0 Verification vs. ERA-Interim analyses – 24h, Feb-March, 2011

Northern extra-tropics

U

GZ

RH

T

U

GZ

RH

T

Tropics

– August-11-14

Conclusions

GDPS 4.0.0 vs GDPS 3.0.0:• Proposed vs. existing system overall improvement to

forecasts• Relative improvements larger at short lead times than at

day 5• Improved surface scores, especially temperature during

winter snow density fix• Above ~20hPa, scores from EnVar alone similar to 3D-

Var, worse than 4D-Var (benefit from raising EnKF model top to 0.1hPa?)

• Compensated by assimilation of 4D radiosonde obs that gives improvements up to 10hPa

– August-11-14

Sea Ice Data Assimilation Project

• Collaboration between Meteorological Research and the Canadian Ice Service (CIS)

• Goal is to produce automated analyses of sea-ice conditions for the operational needs of the Meteorological Service of Canada:

• enhanced ability of CIS to deliver operational sea-ice products for marine navigation over larger area than currently possible (including new arctic METAREAs)

• improved NWP by supplying new sea-ice analyses for atmospheric models and for initializing coupled ice-ocean-atmosphere forecasts

• Assimilate numerous observation types using 3D-Var

CIS daily ice chartsSSM/I ice concentration

– August-11-14

Regional Ice Prediction System

• RIPS 2.0:– Additional assimilated observations:

▪ SSM/IS (DMSP16-17-18)▪ ASCAT

– measure of anisotropy of 3 look angles related to ice concentration through linear forward model

– no data rejected based on air temp, instead rejected when wind speed is low

– A new analysis-error stddev field and “correction” procedure for grid points with high uncertainty

– Sea ice model CICE used to produce short-term forecasts up to 48 hours

– August-11-14

North of 65°NTotal impact of all changes over 2010

Additional assimilated observations: SSMIS and ASCAT

Use of interpolated/extrapolated values to correct analysis where analysis error is high

Large positive impact when verified against U.S. ice center’s IMS manual ice extent analysis

– August-11-14

Future Outlook

• Work towards improving Regional Ice Prediction System and Global Ice Analysis System:

– assimilate AMSR-2 retrieved ice concentration when available– assimilate higher-resolution remote sensing data (AVHRR, VIIRS

and eventually SAR)– assimilate data related to ice thickness: starting with passive

microwave (AMSR-2 and SMOS) and nighttime thermal infrared (AVHRR and VIIRS)

• Use of numerical sea-ice model within data-assimilation cycle on an expanded domain covering entire Arctic ocean

• Expand 3D-Var to ensemble of 4D-EnVar assimilation cycles to capture complex error covariances

– August-11-14

Ensemble assimilation (with perturbed observations)

Ensemble forecasts

Analyses Backgrounds

Ensemble analyses Ensemble B

Observations RObs,

Ensemble A

Model: Perturbations to SST, Atmospheric and oceanic forcing, model parameters, etc.

Observations: Perturbations to observed ice concentration values consistent with expected observation error covariances

– August-11-14

Comparing to CIS daily ice charts: 9 July 2011 (N.B. only over limited areas!)CIS daily ice chart ice concentration Mean of the ensemble of backgrounds

RMSE of the ensemble mean Ensemble spread

50% 35%

– August-11-14

Conclusions• Initial ensemble strategy appears to give reasonable

relationship between spread and error, however…• Effect of model bias difficult to capture with ensemble

spread:– fast ice (ice stuck to land) not sufficiently represented in

model: ice incorrectly moves away from coast in all members– errors in SST near the ice edge: ice incorrectly melts or forms

in all members

• Now performing tests in which some members have dynamics or thermodynamics completely deactivated

• Also testing simple procedures to correct SST based on ice information

– August-11-14

Additional slides follow

– August-11-14

EnVar: a replacement for 4D-Var

Overall, global EnVar analysis (~10 min) is ~6X faster than 4D-Var (~1 hr) on half as many cpus (320 vs 640), even though much higher resolution increments (50km vs 100km)Identical configuration of EnVar used for both global and regional systems (unified deterministic analysis)For practical and scientific reasons, decision made to replace 4D-Var with EnVar in GDPS/RDPS, if results at least as good as current 4D-VarFuture improvements to EnKF will benefit deterministic forecasts incentive to increase overall effort on EnKF development

– August-11-14

Current operational systems

Global EnKF

Global ensemble forecasts (GEPS)

Global deterministic

forecast (GDPS)

Global4D-Var

2013-2017: Toward a Reorganization of the NWP Suites at Environment Canada

Regionalensemble forecasts (REPS)

Regional deterministic

forecast (RDPS)

Regional 4D-Var

global system regional system

– August-11-14

Forecast Results: GDPS 4.0.0 vs GDPS 3.0.0 Verification vs. (1D) Radiosondes – 72h, Northern extra-tropics

Feb-March, 2011

U

GZ

|U|

T

U

GZ

|U|

T

July-August, 2011

T-Td T-Td

– August-11-14

New satellite radiance bias correction approachCurrent approach uses background state as reference state for bias correction, i.e. background state assumed unbiasedNew approach: Perform an extra 3D-Var analysis that assimilates only: radiosonde, GPS-RO, aircraft, AMV, surface obs, scatterometerThis analysis is ONLY used as the reference state for computing the bias correction (instead of the background state)Allows conventional data and GPS-RO (instead of the forecast model) to have the last word for establishing the reference state for bias correction

24h global forecast vs ERA-interim

New approach (uses 3D-Var analysis)Old approach (uses background state)

GZ T

– August-11-14

• Increased volume of data: selection of observations according to model levels.

• Revised observation error statistics.

• Horizontal drift of radiosonde balloon and acquisition time taken into account in both data assimilation and verification systems 4D.

• Bias correction scheme for aircraft temperature reports.

• Bias correction scheme for radiosonde temperature and humidity under development.

OperationalProposed

for bothRadiosonde &

Aircraft

Upgrades to Processing and Assimilation of Radiosonde and Aircraft Data(Laroche and Sarrazin, W&F, 2013)

– August-11-14

cm-1 Spectral bands2008

4DVar2011

4DVar2014

EnVar

AIRS IASI AIRS IASI AIRS IASI

650-770

T sounding (peak higher than 80mb) 0 0 0 0 0 0

T sounding (80mb < peak < 150mb) 0 0 0 0 28 30

T sounding (peak lower than 150mb) 20 0 20 43 34 45

770-980 Surface, low peaking T, RH sounding 6 0 6 19 12 19

980-1070 Ozone sounding 0 0 0 0 0 0

1070-1310 Surface and cloud properties 4 0 4 0 8 6

1310-2035 Water vapor & temperature sounding 33 0 33 0 32 33

2035-2175 CO column amount 0 0 0 0 0 0

2175-2250 Temperature sounding (N2O band) 9 0 9 0 13 9

2250-2420 Temperature sounding (CO2 Band) 15 0 15 0 15 0

2420-2700 Surface and cloud properties 0 0 0 0 0 0

Total volume assimilated (Million/day)

870.7

00

870.7

620.5

1421.8

1422.0

Revision of AIRS/IASI channel selection

– August-11-14

• Data available every 30 minutes. Network uses mostly existing (NGS geodesy) GPS site infrastructure with some additional sites installed and maintained by NOAA. GPS MET from nearby SYNO/METAR at ~50% of sites.

• Like NOAA wind profiler network, still a “demonstration” network although GPS PW data are assimilated operationally in NCEP regional models. FSL plans to transfer network to OPS.

• More GPS sites in Canada could be added (with assistance from Environment Canada).

Assimilation of GB-GPS Data over North America(Stephen Macpherson)

GPS Receiver

zenithmapping function

– August-11-14

• The analysis increment computed by EnVar (δ) is applied as δ/N at each of N steps during the assimilation window

• The sequential increment periods are a “continuous integration”, allowing for selective recycling to dramatically reduce spin-up

Incremental Analysis Updates and Recycling in a “Continuous Integration”

– August-11-14

Reduction of Model Spin-Up as a Result of IAU and Recycling

Globally-averaged 6-h precipitation (top; total and convective), and 0-hour energy spectra comparing IAU (red) and DF-based (blue) cycles.

• Both physics (precipitation; top-left) and dynamics (energy; bottom-left) spin-up are significantly reduced in the proposed configuration (IAU and recycling)

• This change in cycling yields an improved semi-diurnal tide signal (below) as noted in tropical scores

• Response to modifications should be more predictable with a spun up state

Difference of 1000 mb heights in 24h forecast.

Total GDPS 4.0.0Total GDPS 3.0.0Conv. GDPS 4.0.0Conv. GDPS 3.0.0

IAU CycleDF Cycle

IAU Cycle – DF Cycle

– August-11-14

Regional Ice Prediction System: RIPS

• Main use: provide input for generation of CIS operational products (both manual and automated)

• Four analyses per day of ice concentration at 5 km resolution on rotated lat-lon grid

• Domain chosen to include new METAREAs and meet the needs of North American Ice Service (USA/Canada)

• Background state is previous analysis, persisted in time (no ice model)

• Also compute simple measure of uncertainty at each grid point

– August-11-14

Assimilated data: Typical data coverage

CIS daily ice charts

SSM/I ice concentration AMSR-E ice concentration

CIS image analyses

No longer available since

late 2011

– August-11-14

Change in mean Proportion Correct Total: exp.4 – exp.1

Impact of all changes (2010)

– August-11-14

Example of the results: background ensemble ice concentration variances

Ice concentration (ensemble mean) Background ensemble variances

10 Jul, 2011

10 Mar, 2011

– August-11-14

RIPS May 06, 201018:00 UTC

RIPS 2.0 analysis vs. AVHRR observationsA potential for higher resolution

AVHRR May 06, 201017:47 UTC

– August-11-14

Characteristic Values (CV) of sea ice and open water for various regions

AVHRR Ch1, June 2010 AVHRR Ch2, June 2010

Ice

Open Water

Ch1 and Ch2 reflectance are well separated for ice and open water.

– August-11-14

Sea Ice ensemble-DA• Goal is to:

– provide an estimate of the uncertainty in a deterministic forecast– provide a range of possible forecast scenarios– improve how observations are assimilated by using improved

background-error statistics obtained from ensembles• Uncertainty in forecasts results from many poorly known

sources in the forecast model and data assimilation system:– imperfect model dynamics and physics– limited spatial and temporal model resolution– imperfect observations that are assimilated– limited spatial and temporal coverage of assimilated observations– imperfect estimates of observation and background uncertainty

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