Optimisation of Aeolus Sampling

Optimisation of Aeolus Sampling

[email protected] 1 _{Gert-Jan Marseille} 1_{, Jos de Kloe} 1

Harald Schyberg 2 _{Linda Megner} 3 _{Heiner Körnch} 3

1 _{KNMI, NL} 2 _Met.no 3 _{MISU/SMHI, SE}

Vertical and Horizontal Aeolus

Measurement Positioning (VHAMP)

 Maximum exploitation of wind observations in NWP

 Establish optimal ADM-Aeolus observation size and quality

to maximize mission impact

 Simulate such ADM-Aeolus observations and investigate

impact using

Hi-Res radiosondes, CALIPSO, LES & NWP inputs, … (KNMI Aeolus data base)

Simple theoretical data assimlation tool Ensemble Data Assimilation System

 Review of Mission Requirements Document in light of

ADM-Aeolus operation concept changes Vertical (range gate positioning)

Horizontal (heterogeneous aggregation in 2D plane) Calibration, QC, accuracy, precision, biases, error correlation

Some Sampling Scenarios . . .

Aeolus vertical

 24 bins of 250m to 2km depth  May be changed 8x per orbit  Recommendations for Aeolus

integration and bin positioning?

 Impact assessment in optically

heterogenous atmosphere, i.e., with clouds

 Input for the Mission

Requirements Document (MRD)

 Input for the L2B processing

and NWP data assimilation strategy

 PBL cross calibration  Ground calibration

Aeolus in the atmosphere

 Rayleigh clear, Mie cloudy => Complementarity Rayleigh and Mie

 Clear area >> Cloudy area => Rayleigh is critical for ADM-Aeolus  Many variable/mixed scenes => R and M signal aggregation and QC?

Rayleigh HLOS

Mie HLOS

30

18

S E N E S E N E



One simulated orbit



LIPAS

Hi-res radiosonde work

•

Radiosondes provide high-resolution vertical

variability

•

Houchi et al. (2010) studied wind and shear

•

Extended now with cloud vertical variability

(and aerosol)

•

Radiosondes also provide T and p

Hi-res radiosonde shear

Collocation data base Winds agree very well Shear in ECMWF model 2-3 times lower Tropical tropopause strongly variable Effect of shear on Aeolus? Houchi et al. 2010 RAOB ECMWF ECMWF RAOB

Cloud layer statistics

 1/3 of the cloud

layers are thinner than 400m

 Such layers cause

non-uniform backscatter and extinction  Mean height of backscatter particles will be uncertain

 Wind and wind

shear will be biased

Centre-of-Gravity (COG)

Simulation

Analytical calculation

(no T,p dependence)

Using LIPAS and (T,p)

from radiosondes

COG

w(z)

is the signal

strength inside the Aeolus bin as a

function of altitude

z

Particle free bin – analytical

Rayleigh height assignment

error is height dependent

Typical atmosphere

Stratosphere, 2 km

Rayleigh bin, wind-shear 0.01 s-1

H=40 m ~ 0.4 ms-1 _bias Extreme: 0.05 s-1 _shear and ~ 2.0 ms-1 _bias

Biases exceed mission

requirement in more extreme scenes (tropopause jet

stream, PBL) if height assignment error is not corrected

2 km

1.5 km

1 km

height assignment error as function of Rayleigh channel bin size

(T,p) from radiosonde database

1 year, station De Bilt => (T,p) => _m(z) => w(z) => COG Height assignment errors are slightly larger than from analytical

expressions

Not very sensitive to T,p errors and predictable

analytical

radiosonde (T,P)

mean stddev

Use AUXMET to correct for Rayleigh channel height assignment errors in L2B optical properties code

RMSE wind error (systematic)

Rayleigh HLOS insensitive to z _c can be obtained from optics

Mie HLOS sensitive to unknown z

Mie

Rayleigh

cloud layer

z



bin

Mie wind performance is severely degraded in clouds

ECMWF B error – mid-latitudes

Horizontal analysis

 Single obs. Experiment  Over the English channel  500 hPa analysis increment

Courtesy: Andras Horanyi (ECMWF)

Background error length scale ~ 400 km

Aeolus burst-mode observation

separated by 200 km (< B length scale) Not fully independent information, some redundancy

Aeolus continuous mode observation separated by 86 km

ADM-Aeolus Cal/Val Workshop, Feb 2015

B-matrix formulation for operational global and mesoscale models

Daley (1991) definition of B length scales:

Global (ECMWF; EnDA) and mesoscale (HARMONIE; NCEP method) model Observation-model intercomparison

(o-b) statistics: COV(o-b) = HBHT ₊ R; i.e., the sum of (i) Background error and (ii) Instrument and wind representativeness error

How to separate B and R? Used

Desroziers et al. (2005)

Application to ASCAT, aircraft

observations, ECMWF and HIRLAM

Background error length scales

ASCAT + HIRLAM 150 km

Effective resolution UTLS

500 km Mode-S ECMWF ECMWF model starts to loose variance > 500 km scales

Model does not

show a k-5/3 spectrum, i.e., turbulence spectrum AMDAR/ACARS/AIREP (ODB)/Mode-S

Representativeness error

• From ODB ECMWF T1279

0.8 m/s on ASCAT 12.5 km wind Upper troposphere: 2.1 m/s on aircraft components

Along-track accumulation reduces the representativeness error

Accumulation length of observations such that the resulting spectrum matches the model spectrum:

(1) Upper troposphere: aircraft

accumulation along 100-150 km track (2) Ocean surface: ASCAT accumulation

along 85-100 km track

 Aeolus representativeness error negligible for ~ 100 km along-track accumulation

Log variance vs wave number

Aeolus simulation

LIPAS (

Li

dar

P

erformance

A

nalysis

S

ystem)

Heritage since 1999 and has evolved with mission updates

Input: KNMI atmospheric database of CALIPSO backscatter /

extinction and ECMWF/UKMO dynamics

Marseille et al., 2011

CALIPSO

UKMO

30 km

LIPAS HLOS wind statistics

1000 150

0

LIPAS QC: SNR too low

BM 110 mJ, 50 km CM 110 mJ, 85 km CM 80 mJ , 85 km CM 80 mJ, 250 km

mission requirement 110  80 mJ _reduced

Rayleigh coverage

Added value NWP by Aeolus

Theoretical tool

Based on theoretical equations data assimilation

No competitive observations Limited to analysis quality, no forecast projection

EDA – Ensemble Data Assimilation

Experiments in operational ECMWF system

Aeolus in competition with other observing systems

Ensemble spread is a measure of impact

Compare forecast spread for different sets of observations

Less spread means better forecast Does Aeolus reduce forecast

spread?

x_a= x_b+W(y-Hx_b)

A= (I-WH)B(I-WH)T + WOWT

Impact: tr(A)/tr(B)

Norwegian Meteorological Institute met.no

1D theoretical tool

• Usual meteorological analysis equations

• Fully solved

• 1D = horizontal (in VHAMP)

• Horizontal characteristics from ECMWF

and HARMONIE model and (LIPAS) Aeolus

observations

• Introduction of bias, correlation,

averaging, thinning and misspecification

Norwegian Meteorological Institute met.no

Correlated representativeness error

• 60N background statistics, continous 80 mJ, 500hPa (LIPAS Rayleigh channel mean obs error), 2/3 B bandwidth

• Representativness error variances based on

assuming global model effective resolution 7*x (112 km; Skamarock) • Triangular O correlation

structure with half basis of 112 km

• Much lower analysis quality

• Optimal accumulation length is now about the effective model resolution

Conclusions theoretical tool

 Impact increases substantially from Burst Mode (BM)  Cont.

Mode (CM)

 Impact reduces substantially at 80 mJ (CM 80 mJ ~ BM 110 mJ)  Aeolus impact appears maximum around 250 hPa

 Aeolus impact is maximum in the Tropics

 Impact is maximized for ~85 km accumulation length

Latitudinal dependence

Conclusions theoretical tool

Observation error correlation up to 0.1-0.15 is not detrimental

Correlation 0.1 corresponds to an increase of random error of 0.2 m/s Correlation 0.38 corresponds to an increase of random error of 0.7 m/s

Biases > 0.5 m/s are detrimental

Negative impact for biases exceeding 1 m/s

Impact of mis-specified B-matrix is substantial

ESA VHAMP, TN8

Conclusions EDA experiments

EDA experiments conducted :

No sondes; to assess radiosonde impact as reference for Aeolus BM, 110 mJ, 50 km accumulation CM, 110 mJ, 85 km accumulation CM, 80 mJ, 85 accumulation

CM 80 mJ, 250 km accumulation CM 80 mJ, 85 km accumulation; 1-year mismatch; to test impact of erroneous observations

 Aeolus impact comparable to

radiosonde

- above 24 km Aeolus quality

reduces

- Below 10 km, Aeolus impact larger  Impact all Aeolus scenarios very

similar (large-scale impact)

100 hPa

500 hPa

Conclusions EDA experiments

 Maximum Aeolus

impact in the tropics and in the UTLS

 In agreement with

theoretical tool

Conclusions EDA experiments

85 km

85 km

Impact at 500 hPa

 Impact reduces

when going from 110 mJ  80 mJ , but not dramatic However, assumed

Perfect calibration No instrument

biases

No laser jitter

Conclusions

 Issues of instrument wind calibration, zonal wind variability climate,

atmospheric heterogeneity, expected beneficial impact, and data assimilation method are all at interplay

 The vertical bin sizes should be at least 1 km for the Rayleigh channel

in the lower troposphere increasing to 2 km in the stratosphere to obtain accuracy over a ~100km horizontal context

 It is advantageous to change the Mie vertical sampling along track, i.e., positioning top Mie bins around 11 km over the Poles to up to 18 km in the tropics to better sample tropical cirrus and obtain maximum NWP benefit

 Along track accumulation in the range 85-100 km for global NWP, but

continuous mode allows context-sensitive aggregation, esp. for Mie

 Wind biases should be below 0.5 m/s for a successful mission  Observation error correlations should stay below 0.15

 Zero wind calibration on ground targets is probably favorable with the

Mie channel (unfavourable for Rayleigh), but calibration procedures need further evaluation (cause detrimental vertically correlated error)

 Assimilation of wind shear causes the loss of information on observed

deep vertical structures and is not a good alternative for lack of absolute calibration

 Recommend advanced NWP monitoring to obtain instrument biases

and consistency with the Global Observing System

Conclusion Vertical Sampling

 Rayleigh bin size is driven by the mission HLOS wind quality

requirements (1-2 km)

 Increasing the Mie channel sampling in heterogeneous

atmospheric regions reduces height assignment errors of both Mie and Rayleigh winds near the tropopause and jet stream and provides NWP impact (EnDA, theoretical tool)

 Zero wind calibration on ground targets is probably favorable

with the Mie channel (unfavourable for Rayleigh)

 Issues of instrument wind calibration, zonal wind variability

climate, atmospheric heterogeneity, expected beneficial impact, and data assimilation method are all at interplay

 An advanced ADM-Aeolus vertical sampling scenario takes

account of climate regions and ground calibration opportunities and has been proposed in the VAMP project

 Assimilation of wind shear causes the loss of information on

observed deep vertical structures and is not a good alternative for lack of absolute calibration

VHAMP method

Establish the ability to exploit wind observations in

weather models

Establish optimal ADM-Aeolus observation size and

quality to maximize mission impact

Simulate such ADM-Aeolus observations and

investigate impact using

Simple theoretical data assimlation tool

Ensemble Data Assimilation System

Review of MRD in light of ADM-Aeolus operation

concept changes