Ensemble Data Assimilation at DWD - University of Bonn...Ensemble Data Assimilation at DWD System and Selection of Research Projects Andreas Rhodin, Ana Fernandez, Roland Potthast,

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

Global NWP Modelling

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ICON Model 13km + Nest over Europe (6.5km)

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.

Roland Potthast

ICON EnVar

Roland Potthast

ICON EnVar

Hybrid Methods: EnVAR Scores

ICON EDA

Hybrid Methods: EnVAR Scores

ICON EDA

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

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

PRIOR

DATA

Posterior

Analysis

Ensemble

BAYES Data Assimilation

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

Roland Potthast 2016

EnKF T on level 85


PF T on level 85






TEMP T 3h, 5 days






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.

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/5272aa3d07121c9422d6af52-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


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


Design of a convective scale EDA System

(Image: A. Rhodin and C. Schraff)


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


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



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 Roland Potthast - September 2016


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.


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 Roland Potthast - September 2016

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 Roland Potthast - September 2016

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


Distributions EPS Members

Histogram T50 Full temperature Distribution Of COSMO Model, 1 time slice

Histogram ΔT50 With subtraction of mean for each point


• Talagrand Rank Histogram

• Checks the distribution of observations compared with the distribution of the ensemble

Distributions EPS Members

T2m is underdispersive

ensemble

obs

Verification Scores Survey

Upper Air Verification

Surface Verification

Precipitation Verification

Satellite Data Verification

Scores Metrics Bias Field Properties Spectral Distributions


High Impact Weather Verification

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)


(Courtesy: C. Schraff and H. Reich)

LETKF: smaller wind errors, larger humidity errors

LEKTF less able to correct (model) biases

Temperature neutral


KENDA: neutral (similar results for convective period)

reduction of variance [%] rmse

pre

ssu

re [h

Pa

]

KENDA-LETKF vs. nudging rmse

(averaged over

lead times &

initial times) T

wind speed

wind direct.

RH Differences are not significant Differences are

not significant

Verification Scores Survey 3 p

ressure

[hP

a]

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

(averaged over

lead times &

initial times) T

zonal wind

merid. wind

RH



KENDA: much

better CRPS

Verification Scores Survey 4 le

ad

tim

e [h

]

KENDA-LETKF vs. nudg./multi-model


KENDA: much better

CRPS in all variables

except surface pressure


CRPS

(averaged over

lead times &

initial times)



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 )



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




• Observe!

RADAR Reflectivity

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)


(Courtesy: M. Bender, A. Rhodin, C. Schraff, R. Potthast)

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

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

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


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




• Observe!

RADAR Reflectivity


51

• 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










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)


• only 1 radar used (Boostedt in Northern Germany)

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



1-hrly precip

FSS ( 30 km ) 12-UTC forecast runs

2 mm/h

8 days 21 – 29 May 2014

0.1 mm/h


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)


• only 1 radar used (Boostedt in Northern Germany)

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



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


Many Thanks!

Spread EnKF T on level 85

Spread PF T on level 85


Ens 01-Mean, PF T 90


Ens 01-Mean, EKF T 90


Ens 01- Ens 01, PF1 vs PF2 T 90

Documents

Ensemble Data Assimilation at DWD - University of Bonn...Ensemble Data Assimilation at DWD System and Selection of Research Projects Andreas Rhodin, Ana Fernandez, Roland Potthast,