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 © Crown copyright Met Office

Presentation Outline Overview of Met Office Current global NWP system (1) 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 © Crown copyright Met Office

Met Office Headquarters in Exeter (2) Met Office Headquarters in Exeter The UK’s National Weather and Climate Service

Basic facts Turnover ~£204m (approx 15% Commercial) Operating Profit (3) 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 © Crown copyright Met Office 4

(4) http:www.metoffice.gov.uk/services

NWP (Unified Model) hierarchy (5) NWP (Unified Model) hierarchy © Crown copyright Met Office

Deterministic model configurations (6) Deterministic model configurations © Crown copyright Met Office

(7) 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 EW @ 50°N) 25.1km 16.7km Physical resolution EW @ equator) 39.1km

(8) 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.

Surface weather impacts Improved near-surface winds (9) 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

Current assimilation of ocean surface wind data (10) 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

Data coverage in one 6-hour cycle (11) Data coverage in one 6-hour cycle

Daily monitoring of observed minus model background wind speed (12) Daily monitoring of observed minus model background wind speed

Longer-term monitoring (13) Longer-term monitoring

Planned research using SMOS surface wind speed data (14) 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

Planned research using SMOS surface wind speed data (15) 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

Planned research using SMOS surface wind speed data (16) 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

(17) Any Questions? © Crown copyright Met Office