1. Plans for Met Office contribution to SMOS+STORM Evolution
James Cotton & Pete Francis, Satellite Applications, Met Office, Exeter
SMOS+STORM Evolution KO, 2nd April 2014
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2. Presentation Outline Overview of Met Office Current global NWP system
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Presentation Outline
Overview of Met Office
Current global NWP system
Current assimilation of ocean surface wind vectors
Planned research using SMOS-HWS ocean surface wind speed data
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3. Met Office Headquarters in Exeter
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Met Office Headquarters in Exeter
The UK’s National Weather and Climate Service

4. Basic facts Turnover ~£204m (approx 15% Commercial) Operating Profit
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Basic facts
Turnover
~£204m (approx 15% Commercial)
Operating Profit
~£10m
Tangible Assets
~£117m
People
~1889 Staff (1842 FTE)
~500 in Science Directorate
Locations
~50 manned locations
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6. NWP (Unified Model) hierarchy
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NWP (Unified Model) hierarchy
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7. Deterministic model configurations
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Deterministic model configurations
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Model changes: resolution Horizontal resolution increase in deterministic GM
Resolution of 4D-Var inner loop also increased
N216 (≈60km) → N320 (≈40km)
Horizontal resolution (N number)
N512 (New Dynamics)
N768 (ENDGame)
Gridpoints (EW x NS)
1024 x 769
1536 x 1152
Physical resolution (NS)
26.1km
17.4km
Physical resolution 50°N)
25.1km
16.7km
Physical resolution equator)
39.1km

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Model changes: resolution Horizontal resolution increase in deterministic GM
N512 (≈25km) New Dynamics N768 (≈17km) ENDGame
A picture comparing the grids between the two models.

10. Surface weather impacts Improved near-surface winds
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Surface weather impacts Improved near-surface winds
N512 GA3.1
N768 GA6.1
Winds are also improved, which shows up in verification.
More frontal structure (as with rain/PMSL)
Improved biases over land (5A GWD scheme)
Occasionally start to resolve valley flows

11. Current assimilation of ocean surface wind data
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Current assimilation of ocean surface wind data
Metop-A and Metop-B ASCAT (C-band) vectors from EUMETSAT/KNMI (25km sampling)
Oceansat-2 OSCAT (Ku-band) vectors from EUMETSAT/KNMI (50km sampling)
Coriolis Windsat (passive microwave radiometer) vectors from NRL (12.5km sampling) 11

12. Data coverage in one 6-hour cycle
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Data coverage in one 6-hour cycle

13. Daily monitoring of observed minus model background wind speed
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Daily monitoring of observed minus model background wind speed

14. Longer-term monitoring
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Longer-term monitoring

15. Planned research using SMOS surface wind speed data
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Planned research using SMOS surface wind speed data
1. Statistical analysis
Primarily through comparison of the SMOS-HWS wind speed data with short range forecasts of 10m winds from Global Model background to generate observed minus background values (O-B)
The SMOS wind speeds and O-B values will also be compared with collocated scatterometer surface wind measurements from the ASCAT, OSCAT and WindSat instruments
The statistical analysis should ideally cover a period of several months and should span tropical and extra-tropical seasons
Development of a suitable quality control (QC) methodology for use within the Met Office Observation Processing System (OPS), using the supplied QC flags to screen for potentially contaminated observations
Investigation of biases and possible need for bias correction
(SMOS-HWS wind speed data will be processed in the OPS by employing code originally developed for the quality control of wind speed observations from the Special Sensor Microwave/Imager (SSM/I). However, some code development will be necessary to adapt the existing system for use with SMOS wind speed data.) 15

16. Planned research using SMOS surface wind speed data
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Planned research using SMOS surface wind speed data
2. Assimilation
Assimilation experiments will be performed to demonstrate the impact of SMOS wind speed observations on Met Office analyses and forecasts.
Should cover two seasons of at least 6 weeks in length, e.g. a North Atlantic / Pacific tropical cyclone season and a winter extra-tropical season. Season-long experiments will help replicate the new observing system's impact were it to be used operationally.
An accurate specification of the SMOS observation error will be important to assimilate the data in a near-optimal way.
The impact of assimilating SMOS-HWS wind speeds will be demonstrated by diagnosing changes to the mean global atmospheric analyses e.g. low- level wind field, pressure at mean sea level (PMSL), etc. Forecast verification will show how changes in the analysis as a result of assimilating SMOS wind speed observations affect global model forecasts out to lead times of T+144 hours. 16

17. Planned research using SMOS surface wind speed data
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Planned research using SMOS surface wind speed data
3. Tropical cyclone verification
For the tropical storm season, the time period will be chosen to encompass enough storms in order to verify the mean impact on tropical cyclone forecast skill across the whole season. The following measures can be used:
Track forecast error
Track forecast skill against CLIPER (climatology & persistence)
Frequency of superior performance (for track) i.e. summing up the number of forecasts when the trial error was lower
Mean change in intensity as measured by 850mb relative vorticity, 10m wind and central pressure.
Mean absolute error of 10m wind and central pressure
Intensity tendency skill score (ability to correctly predict strengthening or weakening) - separate strengthening and weakening scores can also be calculated. 17

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Any Questions?
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