AEOLUS cal/val activities of interest to the Met Office

Transcription

1 AEOLUS cal/val activities of interest to the Met Office M. Forsythe, F. Marenco, P. Brown, G. Halloran, and D. Offiler Met Office, Exeter, United Kingdom

2 Validation of ADM-Aeolus Level 2 products by comparison with global NWP and in-situ flight data. Comparison with global NWP model shortperiod 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 Background: Analysis T+3 T+9 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 AMV: Atmospheric Motion Vector Add height option alongside pressure for some plot types Further bespoke changes as required

5 NWP Comparisons Some plot examples In all cases separate by Mie/Rayleigh Time Series Example: scatterometer on METOP -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

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 SAF: Satellite Applications Facility (EUMETSAT activity) 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

10 NWP Comparisons Analysis report - example STEP 1: Identify a feature of interest e.g. Slow bias in high level extratropics STEP 2: Use Hovmoeller plots to identify how persistent this feature is from day-today and to ID good case studies. Bias is not continuous through the month in extent or location > 400 hpa

11 NWP Comparisons Analysis report - example STEP 3 Plot raw data for some of these interesting cases (Renato Galante Negri)

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 assigned heights ( hpa). The observed winds are also more consistent with the model winds around 150 hpa. Slow bias probably linked to winds being assigned too low, possibly as multi-level cloud (see CALIPSO).

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 available through the NWP SAF website as part of CDOP-3 developments.

15 Airborne activities

16 FAAM BAe Atmospheric Research Aircraft Upward and Forward Video Cameras FAGE Inlet CVI on other side ADA on other side Air sample inlets BBRs SHIMS SWS on other side 5 port turbulence probe Total water probe JW Liq water Nevzerov total/liq water probe Rosemount temp probes Deimos or IR Camera TAFTS ARIES Cloud Physics Probes BBRs SHIMS MARSS Lidar Rearward and Downward Video Cameras Cloud Physics Probes Dropsonde on other side

17 Crew Scientists Length Wingspan Height Engines Max altitude Min altitude Range Cruise Altitude Typical endurance 2 pilots (1 cabin crew) 18 maximum 31m 26m 8.4m (to top of tail), 4.4m (top of fuselage) 4 Honeywell LF507-1H turbofans 35,000 ft 50ft (over sea) 3,700 km 27,000 ft 5.5 hours Min manoeuvring speed ms -1 (depending on payload) Payload FAAM BAe Atmospheric Research Aircraft 4,000 kg instrumentation

18 FAAM BAe Atmospheric Research Aircraft Instrumentation relevant for ADM cal/val 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 ( µm) Remote sensing of aerosols and clouds: backscatter lidar Vertical sounding of meteorological parameters: dropsondes

19 AIMSS and turbulence probes

20 Aerosols example: volcanic ash NEPH OPCs IN-SITU 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, /2011JD016688, Johnson, Turnbull, Brown, Burgess, Dorsey, Baran, Webster, Haywood, Cotton, Ulanowski, Hesse, Woolley, and Rosenberg, In situ observations of volcanic ash clouds from the FAAM aircraft during the eruption of Eyjafjallajökull in 2010, J. Geophys. Res. 117, /2011JD016760, 2012.

21 Lidar example: study on CALIPSO Aircraft lidar 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, , Level 1 data 20 September 2012 SAMBBA B737 (day time) Level 2 data CALIPSO aerosol subtype showing polluted dust (brown) whereas it is all smoke (black) Level 2 data

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, Dropsonde-derived transects of meteorological variables (64 min, 10 sondes)

23 ADM cal/val flight patterns 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, aerosolcloud discrimination, and aerosol classification. Effect of atmospheric heterogeneities on the representativity of wind retrievals.

24 Met Office ADM cal/val strategy using the research aircraft 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.

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 Dedicated campaign would need external funding

26 Questions?