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(1)

AEOLUS cal/val activities of interest

to the Met Office

M. Forsythe, F. Marenco, P. Brown, G. Halloran, and D. Offiler

(2)

Validation of ADM-Aeolus Level 2 products

by comparison with global NWP and in-situ

flight data.

• Comparison with global NWP model

short-period forecasts

• Research flights with the FAAM Bae-146

aircraft

(3)

Comparison with global NWP

model short-period forecasts

(4)

NWP Comparisons (Monitoring)

Introduction

Compare ADM to Met Office global model background (short

period forecasts collocated to observation location and time)

• Established approach for monitoring data quality

• Best guess of state of atmosphere – available everywhere so can… • … validate all observations…

• … enabling stable comparison statistics in short period of time and covering full range of geographic and atmospheric conditions

Plan to adapt established wind monitoring/analysis system

used for AMVs and scatterometer winds to….

• Use HLOS winds

• Add height option alongside pressure for some plot types • Further bespoke changes as required

Background:

• Analysis

• T+3

• T+9

AMV:

Atmospheric

Motion Vector

(5)

NWP Comparisons

Some plot examples

Time Series

-Bias

-Standard deviation -RMS difference

-Number of observations -Mean O HLOS wind -Mean B HLOS wind

-Mean time delay in receipt

Hovmoeller

•vs Height •vs Latitude

(for a large area, e.g. hemisphere, tropics, etc.) -Bias -Standard deviation -RMS difference -Number

-Mean O HLOS wind -Mean B HLOS wind

In all cases separate by

Mie/Rayleigh

Example: scatterometer

(6)

NWP Comparisons

Some plot examples

Map

-Bias

-Standard deviation -RMS difference -Number

-Mean O HLOS wind -Mean B HLOS wind

Zonal

(for a given time, typically 1 month)

-Bias

-Standard deviation -RMS difference -Number

-Mean O HLOS wind -Mean B HLOS wind

(7)

NWP Comparisons

Some plot examples

Density

O HLOS wind vs

B HLOS wind

(typ. 1 month of observations)

Line plots

-Vs pressure -Vs oberror -Can be extended...

e.g. We could use to evaluate how well Aeolus error estimates agree with O-B statistics

Meteosat-7 IR October 2008 All latitude bands

(8)

NWP Comparisons

Some plot examples

Collocation

Observations vs. observations

We have capability to compare satellite to satellite etc – could extend to compare different observation types e.g. sondes, aircraft (if time allows)

Visualisation

Case studies for highlighted problems

We also plot raw data to investigate interesting cases

For Aeolus – extend to plot as profiles similar to ECMWF example below – could also plot co-located profiles from sondes/aircraft.

(9)

NWP Comparisons

Analysis report

Long experience analysing the AMVs – NWP SAF analysis reports produced

every 2 years – will start work on 7

th

analysis soon.

Main focus is a record of features observed in the monitoring

We could just provide a list……

But ideally we want to understand the features so we can:

identify improvements to the derivation of wind

identify improvements for quality control

improve our approach to assimilating the HLOS data (e.g. improved

observation errors and observation operator)

Therefore carry out bespoke follow-up investigations, often using case studies.

Propose to produce a similar style report for Aeolus HLOS winds –

in order to do this

well we need to better understand likely error sources in the data – need

information from ESA and ADM-Aeolus team.

e.g. a nice summary of ADM errors, with links to detailed information

SAF:

Satellite Applications Facility

(EUMETSAT activity)

(10)

NWP Comparisons

Analysis report - example

STEP 1:

Identify a feature of interest

e.g.

Slow bias in high level

extra-tropics

STEP 2:

Use Hovmoeller plots to identify how

persistent this feature is from

day-to-day and to ID good case studies.

Bias is not continuous through the

month in extent or location

(11)

NWP Comparisons

Analysis report - example

STEP 3

Plot raw data for

some of these

interesting

cases

(12)

NWP Comparisons

Analysis report - example

STEP 4

Make use of other information to better understand possible cause of bias.

In this case CALIPSO shows cloud top at ~150 hPa, much higher than the AMV

(13)

NWP Comparisons

Specific studies

Alongside this more general feature-based approach we also intend to carry

out some specific studies

e.g. Static and slowly varying bias over the orbit due to limitations of zero

wind calibration and assessment of slope errors with wind speed.

Any systematic biases (particularly with Mie) in regions of strong wind shear

due to thick range bins

<inhomogeneity>

We would benefit from wider discussion and input to agree the most sensible

list of specific studies.

As before – it is critical for this that we better understand the likely

sources of error in the data.

(14)

Looking further ahead

Assimilation trials and routine monitoring

Assimilation trials

When we have completed an analysis and as long as the data is of sufficient

quality we intend to trial for assimilation in the Met Office global model and

assess where the data provides most benefit.

An analysis of verification results will be produced.

Routine monitoring

Will continue for the life-time of the mission. Proposal to make

widely

(15)
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FAAM BAe–146–301

Atmospheric Research Aircraft

5 port turbulence probe Total water probe JW Liq water Nevzerov total/liq water probe Rosemount temp probes

Air sample inlets FAGE Inlet

CVI on other side

Deimos or IR Camera

TAFTS ARIES

Cloud Physics Probes

Cloud Physics Probes MARSS SWS on other side BBRs Lidar SHIMS

Upward and Forward Video Cameras BBRs SHIMS Rearward and Downward Video ADA on other side Dropsonde on other side

(17)

Crew

2 pilots (1 cabin crew)

Scientists

18 maximum

Length

31m

Wingspan

26m

Height

8.4m (to top of tail), 4.4m (top of fuselage)

Engines

4 Honeywell LF507-1H turbofans

Max altitude

35,000 ft

Min altitude

50ft (over sea)

Range

3,700 km

Cruise Altitude

27,000 ft

Typical endurance

5.5 hours

Min manoeuvring speed

90 - 115 ms

-1

(depending on payload)

Payload

4,000 kg instrumentation

FAAM BAe–146–301

(18)

• In situ 3-D winds: Turbulence probe (32 Hz, ±0.3 m/s)

• In situ 3-D winds: AIMSS probe (20 Hz, ±0.5 m/s)

• In situ aerosols: 3-wavelength nephelometer (1 Hz)

• In situ aerosols: optical particle counters (0.3-50 µm)

• Remote sensing of aerosols and clouds: backscatter lidar

• Vertical sounding of meteorological parameters: dropsondes

FAAM BAe–146–301

Atmospheric Research Aircraft

(19)
(20)

Aerosols example: volcanic ash

Turnbull, Johnson, Marenco, Haywood, Minikin, Weinzierl, Schlager, Schumann, Leadbetter, and Woolley, A case study of observations of volcanic ash from the Eyjafjallajökull eruption: 1. In situ airborne observations, J. Geophys Res. 117, 10.1029/2011JD016688, 2012.

Johnson, Turnbull, Brown, Burgess, Dorsey, Baran, Webster, Haywood, Cotton, Ulanowski, Hesse, Woolley,

IN-SITU

(21)

Level 1 data

Level 2 data

Level 2 data

Aircraft lidar

Lidar example: study on CALIPSO

20 September 2012

SAMBBA – B737

(day time)

Marenco, Amiridis, Marinou, Tsekeri, and Pelon, Airborne verification of CALIPSO products over the Amazon: a case study of daytime observations in a complex atmospheric scene, Atmos. Chem. Phys.

14, 11871–11881, 2014.

CALIPSO aerosol subtype

showing “polluted dust”

(brown) whereas it is all

smoke (black)

(22)

Dropsonde example:

extratropical cyclone Friedhelm

8 December 2011

Vaughan et al, Cloud Banding and Winds in Intense European Cyclones: Results from the DIAMET Project, Bull. Amer. Meteor. Soc., in press, 2014.

(23)

• In situ: direct comparison of wind at different altitudes,

coordinated with the footprint and resolution of ADM.

Average wind and quantification of variability; issues of

scale.

• High-level flights: direct comparison of wind and aerosol

profiles sampled using dropsondes and lidar.

• Studies on scene classification: backscatter lidar and in

situ aerosol probes: layer detection algorithms,

aerosol-cloud discrimination, and aerosol classification.

• Effect of atmospheric heterogeneities on the

representativity of wind retrievals.

(24)

• Regions accessible from the UK

• Ad hoc flights embedded in planned campaigns (e.g.

India and Namibia in 2016, Indonesia 2017, etc.)

• Coordinated flights with DLR Falcon 20 carrying A2D

• Possibility to perform a dedicated cal/val campaign in a

location to be defined, contingent to finding external

funding.

• Schedule and number of flights TBD: has to fit with the

schedule of the FAAM aircraft; will have to be planned

when launch date is certain.

Met Office ADM cal/val strategy

using the research aircraft

(25)

Summary

• ADM – represents an improvement in which we

have a large interest

• NWP approach:

Initial phase monitoring

Analysis

Specific studies

Assimilation trials

Routine monitoring for mission lifetime

• Airborne research

Add cal/val flights to existing campaigns

Coordinated flights with A2D

Budget is planned yearly – quantity of research flying is TBD

(26)

References

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