Ensemble Data Assimilation at DWD

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Ensemble Data Assimilation at DWD

System and Selection of Research Projects

Andreas Rhodin, Ana Fernandez, Roland Potthast, Christoph Schraff, Hendrik Reich, Harald Anlauf,

Anne Walter, Alex Cress,

u.v.m

DWD, Germany & University of Reading, UK

Bonn Sept 2016

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Global NWP Modelling

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t v t v

z g z c

w w w t v

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c n z w v n v K t f

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ICON Model 13km + Nest over Europe

(6.5km)

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3

4. ICON Ensemble Datenassimilation

We are running ICON EDA in our Routine since Jan 2016

• 40 Members each with 40km global resolution and 20km NEST over Europe

• 1 deterministic 13km member

• EPS forecasts 40 Members 7 Days + 1 Deterministic

• Output for convective-scale EDA/EPS

• Hybrid System

Grafics by ICON EDA Head Dr. Andreas Rhodin, FE12

Operational since January 2016 : Rhodin, Fernandez, Cress, Anlauf, etc.

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Roland Potthast

ICON EnVar

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Roland Potthast

ICON EnVar

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Hybrid Methods: EnVAR Scores

ICON EDA

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Hybrid Methods: EnVAR Scores

ICON EDA

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Particle Filter

• Localized version of Particle Filter

 Classical Particle Filter PF and

 Localized Markov Chain Particle Filter LMCPF (See book of Nakamura and Potthast)

• Hybrid Ensemble Var Particle Filter

 Particle filter coupled with

 Variational Method (3D-VAR)

 Global NWP with ICON Model

 40 Particles 40km global resolution,

 Deterministic run 13km

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 You get a prior distribution p(x) by some prior ensemble

 Measurements define a data distribution p(y|x)

 Bayes theorem defines a posterior distribution by p(x|y) = c p(x) p(y|x)

The core game is

how to get an analysis ensemble from p(x|y).

Particle Filter

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PRIOR

DATA

Posterior

Analysis Ensemble

BAYES Data Assimilation

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 Following the LETKF philosophy

 Replacing the LETKF square root filter by a particle selection which works for non-Gaussian distributions

 Localizing the EDA part in Observation space

 Localizing the coupled variational part in state space.

 Using standard tools for spread control from EnKF, i.e.

multiplicative and additive covariance inflation, relaxation towards prior perturbations, …adaptively.

 Preventing particle filter collapse by a pseudo-random draw in each analysis step around the particles with non-zero weight.

Particle Filter Details

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Roland Potthast 2016

EnKF T on level 85

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PF T on level 85

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TEMP T 3h, 5 days

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Summary

• Implemented a localized particle filter for the ICON EDA global assimilation

• Implemented a hybrid EnVar Particle Filter for the deterministic run

• Testing the system in a case study

• In principle we see that the system is functioning

• The behaviour of the forecasts in the case study was useful

• The 3h o-f scores of the PF were worse than for the LETKF

• The 3h o-f scores of the Hybrid PF were better than for the EnVAR

• The forecasts scores were comparable between EnVar-LETKF and EnVar-PF, the comparison is not yet significant

• We need some adaptive spread control in our particle filter, this is ongoing work.

• Further studies and investigation of many details are ongoing.

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Convective Scale EDA

 Use an approprite fast update cycle (e.g. 1h)

 Deliver probabilistic (pdf) rather than deterministic forecast

 Need ensemble forecast and ensemble data assimilation system

http://opt-

prod.s3.amazonaws.com/traject/files/content_items/relateds/000/045/077/original/5272aa3d07121c9422d

6af52-convection_20in_20atmosphere.jpg?1446064102 Roland Potthast - September 2016

Convection-permitting NWP: Convection!

 Fast processes, a few hours is „long term“!

 Much uncertainty in processes, surface, physical parametrizations

 High-resolution data needed, indirect measurements, sparse data not resolving all processes

 Strong non-linearities in the processes

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Convective Scale EDA

Goal is the prediction of convection and subsequent precipitation, here model grid (left) and upscaled probability (right)

(Courtesy: FE15)

Upscaling/downscaling of statistics is non-trivial!

Roland Potthast - September 2016

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Convective Scale EDA

Design of a convective scale EDA System

(Image: A. Rhodin and C.

Schraff)

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Convective Scale EDA+EPS

Design of a convective scale EPS System 1

LBC + IC + Physics

ICON, IFS, GFS, GSM

perturb.

COSMO-DE EPS

Construction of atmospheric Probability Distribution by very different Perturbation Techniques

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Convective Scale EDA+EPS

Design of a convective scale EPS System 2

LBC + IC + Physics

ICON EPS

perturb.

COSMO-DE EPS

Construction of atmospheric Probability Distribution by very different Perturbation Techniques

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Convective Scale EDA

Snow Analysis

Deterministic analysis

The snow analysis for COSMO-DE deterministic runs every 6 hours using observations from snow depth, precipitation combined with 2m temperature, and weather observations ww to analyse snow depth.

Background field is the previous analysis.

Ensemble system

For the ensemble system no explicit snow depth perturbations are applied, differences result from free running snow variables for each member. The ensemble is adjusted after each deterministic

analysis to ensure the ensemble mean matches the deterministic analysis.

 Collocation Method with radial basis functions

= Cressman Method, Successive Correction

http://media.gettyimages.com/videos/high-angle- wide-shot-time-lapse-clouds-moving-across-the-snow- covered-video-id996-6?s=640x640

NOAA snow depth analysis previous day

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Convective Scale EDA

Sea Surface Temperature (SST)

Deterministic System

SST analysis for COSMO-DE deterministic runs daily at 0:00 UTC using background fields from ICON which are based on

NCEP input data. Sea ice is updated using the BSH ice mask.

Ensemble System

The SST analysis for the ensemble system is based on the analysis from COSMO-DE deterministic. Perturbations are

generated by a stochastic method with random perturbations and a localization based on Gaspari Cohn functions.

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Convective Scale EDA

Sea Surface Temperature (SST) and Soil Moisture (w_SO) Perturbations

 Random algorithm with two scales

 Surface temperature differences from soil moisture perturbations and model dynamics

Difference Member 3 – Mean (left) or Member 1 – Mean (right) of T_SO

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Convective Scale EDA

Sea Surface Temperature (SST) and Soil Moisture (w_SO) Perturbations

 Random algorithm with two scales

 Surface temperature differences from soil moisture perturbations and model dynamics

Difference Member 3 – Mean (left) or Member 1 – Mean (right) of W_SO

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 Hourly Analysis of Atmospheric Fields

 No Soil-Moisture Analysis, but hourly soil-

moisture perturbations (with spread control) and relaxation of soil moisture towards the

deterministic run

 Snow Analysis every 6 hours at 0, 6, 12, 18 UTC

 SST once per day at 0 UTC

EDA Component Schedule

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Distributions EPS Members

Histogram T50 Full temperature Distribution

Of COSMO Model, 1 time slice

Histogram ΔT50 With

subtraction of mean

for each point

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• Talagrand Rank Histogram

• Checks the distribution of

observations compared with the distribution of the

ensemble

Distributions EPS Members

T2m is

underdispersive

ensemble obs

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Verification Scores Survey

Upper Air Verification

Surface Verification

Precipitation Verification Satellite Data

Verification

 Scores

 Metrics

 Bias

 Field Properties

 Spectral Distributions

High Impact Weather Verification

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Verification Scores Survey 1

bias RMSE

Nudging + LHN vs. LETKF + LHN

T [K] RH

wind

[m/s]

bias RMSE RMSE RMSE

Verification of 6-h forecasts against radiosondes , 28 days (18.05. – 15.06. 2014)

Roland Potthast - September 2016 (Courtesy: C. Schraff

and H. Reich)

 LETKF: smaller wind errors, larger humidity errors

 LEKTF less able to correct (model) biases

 Temperature neutral

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Verification Scores Survey 2

 KENDA: neutral (similar results for convective period) reduction of variance [%] rmse

pressure [hPa]

KENDA-LETKF vs. nudging

rmse

(averaged over lead times &

initial times)

T

wind speed

wind direct.

Differences are RH

not significant Differences are

not significant

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Verification Scores Survey 3

pressure [hPa]

KENDA-LETKF vs. nudg./multi-model CRPS

initial times)

T

zonal wind

merid.

wind RH

(Courtesy: C. Schraff and H. Reich)

 KENDA: much better CRPS

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Verification Scores Survey 4

lead time [h]

KENDA-LETKF vs. nudg./multi-model

 KENDA: much better CRPS in all variables except surface pressure

CRPS

initial times)

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Verification Scores Survey 5

28 days

18.05. – 15.06.

2014

 with LHN: small difference in first 4 hours due to dominating

influence of LHN, thereafter, advantage of KENDA over nudging tends to be larger than without LHN

1 mm/h

0-UTC runs

12-UTC runs

0.1

mm/h

1-hrly precip

FSS ( 30 km)

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Verification Scores Survey 6

0-UTC runs

12-UTC runs

Brier skill score , 14 m/s spread / rmse

10-m wind gusts

 KENDA: better spread + skill + BSS (for 14 m/s + 18 m/s, due to improved reliability)

KENDA-LETKF nudg./multi-model

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Convective Scale EDA

• Observe!

RADAR Reflectivity

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GPS/GNSS Tomography

 GNSS (GPS) Slant Path Delay : humidity integrated over path

from ground station to GNSS (GPS) satellite, all weather obs

(45) GPS obs from 1 station / 9 satellites in 15 min.

 many stations  3-D information on humidity, but !

 at 5° (7°), path reaches height of 10 km at ~ 100 (80) km distance

 vert. + horiz. non-local obs (not point measurements)

Roland Potthast - September 2016 (Courtesy: M. Bender, A. Rhodin,

C. Schraff, R. Potthast)

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GPS/GNSS Tomography

Slant Total Delay :

humidity integrated over path from ground station to satellite

elevation angles 90° - 5

 vert. + horiz. non-local obs

 difficult to use in LETKF:

explicit localization (doing separate analysis at every analysis grid point, select only obs in vicinity and scale R^-1)

analysis grid points

used obs

discarded obs

non-local obs

(Courtesy: Michael Bender,

Rhodin, Schraff) Roland Potthast - September 2016

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GPS/GNSS Tomography

8 days

17. – 24.06.

2014

 spread reduced particularly in lower atmosphere

RH -TEMP T -AIREP wind -AIREP

spread LETKF settings:

• STD localised 1000 m above the GNSS station

• vertical localisation length : 125 hPa ≈ 1000 m (v_loc = 0.15)

• horizontal localisation length : 30 km (h_loc = 30)

(Courtesy: M. Bender, A. Rhodin,

C. Schraff, R. Potthast) Roland Potthast - September 2016

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GPS/GNSS Tomography

8 days

17. – 24.06.

2014

RH -TEMP T -AIREP wind -AIREP

std dev

 low level degraded

 upper levels improved

T –AIREP bias

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GPS/GNSS Tomography

1-hrly precip FSS ( 30 km )

8 days

17 – 24 May 2014

0.1 mm/h

 0.1 mm/h : slightly worse for 0-UTC runs, slightly better for 6-, 18-UTC runs CONV only

CONV + GNSS CONV + LHN

CONV + LHN + GNSS

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Convective Scale EDA

• Observe!

RADAR Reflectivity

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Convective Scale EDA

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• mature convection: precipitation

 radar: 3-dim. reflectivity

3-dim. radial velocity

 Therea Bick left  Axel Seiffert

Elisabeth Bauernschubert (DWD/IAFE), Virginia Poli (ARPAE): (1 week DA exp).

Assimilation of Radial Velocities

Based on the Ensemble Data Assimilation KENDA

(Courtesy: Bauernschubert, K. Stephan, C.

Schraff, H. Reich, R. Potthast) Roland Potthast - September 2016

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Convective Scale EDA

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Convective Scale EDA

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Convective Scale EDA

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Convective Scale EDA

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Convective Scale EDA

1-hrly precip

FSS ( 30 km ) 12-UTC forecast runs

2 mm/h 8 days

21 – 29 May 2014

0.1 mm/h

CONV only

CONV + RAD Vr RAD Vr only

preliminary tuning experiments (4 radars used)

 moderate sensitivity, optimal values: obs error 3 m/s (better than 5 m/s),

superobbing 10 km (5 km, 20 km), horizontal localisation 32 km (16 km)

 generally positive impact on first few hours of forecasts (upper-air + surface verif) CONV only

• only 1 radar used (Boostedt in Northern Germany)

• obs error 5 m/s, superobbing 10 km, h-loc 16 km

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Convective Scale EDA

1-hrly precip

FSS ( 30 km ) 12-UTC forecast runs

2 mm/h 8 days

21 – 29 May 2014

0.1 mm/h

CONV only

preliminary tuning experiments (4 radars used)

 moderate sensitivity, optimal values: obs error 3 m/s (better than 5 m/s),

superobbing 10 km (5 km, 20 km), horizontal localisation 32 km (16 km)

 generally positive impact on first few hours of forecasts (upper-air + surface verif) CONV only

• only 1 radar used (Boostedt in Northern Germany)

• obs error 5 m/s, superobbing 10 km, h-loc 16 km

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Convective Scale EDA

Convective Scale Data Assimilation is a key challenge for the upcoming years

We need Algorithms to deal with Uncertainty, Nonlinearity, Predictability Questions

We need many temporally and spatially high-resolution observations

We need to bring together process understanding and measurement data

Within an Integrated Forecasting System we merge Nowcasting and NWP

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Many Thanks!

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Spread EnKF T on level 85

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Spread PF T on level 85

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Ens 01-Mean, PF T 90

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Ens 01-Mean, EKF T 90

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Ensemble Data Assimilation at DWD