AEOLUS cal/val activities of interest
to the Met Office
M. Forsythe, F. Marenco, P. Brown, G. Halloran, and D. Offiler
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
Comparison with global NWP
model short-period forecasts
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
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
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
NWP Comparisons
Some plot examples
Density
O HLOS wind vsB 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
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.
NWP Comparisons
Analysis report
Long experience analysing the AMVs – NWP SAF analysis reports produced
every 2 years – will start work on 7
thanalysis 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)
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
NWP Comparisons
Analysis report - example
STEP 3
Plot raw data for
some of these
interesting
cases
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
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.
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
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
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
• 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
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
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)
Dropsonde example:
extratropical cyclone Friedhelm
8 December 2011Vaughan et al, Cloud Banding and Winds in Intense European Cyclones: Results from the DIAMET Project, Bull. Amer. Meteor. Soc., in press, 2014.
• 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.
• 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
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