Introduction to ACCESS NWP Chris Tingwell Bureau of

Introduction to ACCESS NWP Chris Tingwell Bureau of

Introduction to ACCESS NWP Chris Tingwell Bureau of Meteorology R&D Branch Earth Systems Modelling Program Data Assimilation Team ACCESS Users Training Workshop March 23rd 2016 NWP in a nutshell Boundary conditions F = ma Model NOW initial condition FUTURE forecast NWP in a nutshell Boundary conditions Land surface model, ancillaries UM

Model Data Assimilation OPS + VAR FUTURE forecast The initial condition: Data Assimilation The problem: The model state may consist of ~50 million grid points and 7 meteorological variables at each point - 350 million pieces of information but we may actually assimilate "only" 3 4 million individual observations, many of which are indirect (particularly in the case of satellite observations) What a data assimilation system has to do

Create initial condition for the model Also an accurate analysis of the atmosphere Only include features that are supported by the model Filter out small scale imbalances that lead to spurious gravity waves (initialisation). Use vast amount of data Account for differences in time, space, variable, scale and accuracy Data can be indirect and most data are indirect: satellite radiances, GPS signals, radar etc. Account for complex & non-linear relationship between observed and basic variables Use the previous forecast as "background"

Represents previous observations projected forward in time Important source of data, independent of current observations Necessary as number of grid values >> number of observations Data assimilation: 4dVAR model state Assimilation cycle repeats every 6 hours Jo first guess (previous forecast) analysis Jo Jb Jo Jo new forecast

(3 days/10 days) Jo the new initial condition is initialised (usually with a digital filter) observations 6 hours t-3 t0 00/06/12/18 UTC t+3 time Observation processing In-situ/ conventional, AMVs (GTS,

local) JULES VAR screen-level analysis nudging Soil moisture RTDB BUFR MARS Satellite (Exeter, local, AP-RARS) ODB OPS QC, thinning, bias correction

4D VAR IC Direct assimilation of radiances in the presence of all other observations. Observations used at correct observing time UM SST Sea-ice Six hour analysis forecast cycle FG OPS VAR

IC UM +6h forecast FG 6 hours later OPS VAR IC UM +6h FG 6 hours later

OPS VAR ACCESS-G3 ACCESS: APS1 APS2 Current operational system is APS1(APS2) APS = "Australian Parallel Suite as per Met Office PS "Parallel Suite" Currently upgrading APS1 to APS2 APS0 APS1 APS2 APS3 PS17 PS24 PS32 PS37 ACCESS: APS1 APS2

70 vertical levels Grid size (km) APS1 11 G 40 GE - R 12 TC 12 C

4 ACCESS: APS1 APS2 70 vertical levels Grid size (km) 12 APS1 APS2 G 40 25 GE - 60

R 12 12 TC 12 12 C 4 1.5 ACCESS: APS1 APS2 APS1 Surface: synops, ships, buoys Sondes, wind profilers Aircraft: AIREPS, AMDARS

Satellite observations (1) Wind: Scatterometer surface winds (ASCAT), AMVs from GEOS POES GNSS-RO: bending angle observations Satellite observations (1I): IR and MW radiances Platform Instrument NOAA-16 NOAA-17 NOAA-18 NOAA-19 MetOp-A AMSU-A/B + HIRS AMSU-B + HIRS AMSU-A/B + HIRS AMSU-A/B + HIRS AMSU-A/B HIRS IASI (138 channels) AIRS (48 channels) (old instrument)

EOS: Aqua & ACCESS: APS1 APS2 APS2 Surface: synops, ships, buoys Sondes, extra wind profilers Aircraft: AIREPS, AMDARS Satellite observations (1) Wind: Scatterometer surface winds (ASCAT), AMVs from GEOS POES GNSS-RO: bending angle observations Satellite observations (1I): IR and MW radiances reduced thinning Platform Instrument NOAA-18 NOAA-19 MetOp-A

AMSU-A/B AMSU-A/B AMSU-A/B + HIRS IASI (138 channels) AMSU-A/B + HIRS IASI (138 channels) AIRS (139 channels) CrIS (134 channels) ATMS Clear Sky Radiances / AMVs MetOp-B EOS: Aqua Suomi-NPP MTSAT-2 / Himawari-8 & Global Observation Coverage: APS2 ACCESS-G Forecast Sensitivity to Observations IASI (IR) SONDES AMSU-A (MW)

CrIS (IR) Aircraft Synop AIRS (IR) ATMS (MW) BUOYS NOAA AMVs ASCAT MSG AMVs AMSU-B PILOT (SONDE) WIND PROF. ESA AMVs JMA AMVs GPSRO SHIP HIRS MTSAT2 IR Long term Global Model Forecast Skill in the Australian Region ACCESS APS1 APS2

ACCESS-C in APS 3 and beyond ACCESS-C 1.5 km domains will have their own assimilation cycles for the first time Key systems for significant high impact weather forecasting Hourly Rapid Update Cycle (RUC) Assimilation of Radar data Local Himawari-8/9 image processing and AMV generation will be essential to meet the low latency (~ 30 min max.) required by the RUC cycle DRAFT ACCESS NWP Configurations APS-2 (Op: 2016) APS-3 (Op: Mid-2018) APS-4 (Op: End-2020) ACCESS-G

25km {4dV} 12km {Hy-4dV} 12km {Hy/En-4dV} ACCESS-R 12km {4dV} 8km 4.5km {Hy/En-4dV} ACCESS-TC 12km {4dV} 4.5km {Hy-4dV} 4.5km {Hy-4dV} ACCESS-GE

60km (lim) 30km 30km ACCESS-C 1.5km {FC} 1.5km {Hy-4dV} 1.5km {Hy/En-4dV} {Hy-4dV} ACCESS-CE - 2.2km (lim) 1.5km

ACCESS-X (on demand) - 1.5km {Hy-4dV} 1.5km {Hy/En-4dV} ACCESS-XE - - 1.5km Operational date = full system - Start rolling out operational systems about 12 months earlier The future: challenges and promises Development of higher resolution systems will be constrained by available R&D computing resources

initial development testing probably at lower resolution than target operational system New more stringent security model requires a much more formalised, structured transition process to operations managed by the Bureau's Information Systems & Services (ISS) division More rapid development cycles: MOSRS should enable ACCESS to stay more closely in sync with Met Office Parallel Suites challenge for collaboration partners to develop more interoperable NWP suites Thank you Chris Tingwell [email protected] Extra slides Land Data Assimilation System Imtiaz Dharssi, Vinodkumar

ASCAT SMOS JULES LSM Extended Kalman Filter Offline soil moisture analyses at 5 km horiz resolution. Initialise high resolution regional NWP systems. Used for fire danger warnings.

Used in research mode, in operational use ~2016. Built around the JULES land surface model (LSM). 25 Observation types and status: Better background errors: hybrid VAR Current ("climatological") "Hybrid" Assimilation in high resolution and convective scale NWP Major effort to provide mesoscale (~1.5 km) city-based assimilation and prediction systems Part of wider project to

significantly upgrade Bureaus generation and use of Radar data Also driven by very high priority given to improvement of severe weather and hydrological forecasting 3dVAR Rapid Update Cycle (RUC): 1hour assim window Plans include a relocatable system for severe weather 27 Radar assimilation (Doppler-winds, rain-rate via latent heat nudging)

SREP: Strategic Radar Enhancement Project Integration with other data including geostationary and polar-orbiter satellite radiances. High resolution ACCESS NWP represents the priority ACCESS application for Himawari-8 data: IR radiance assimilation to provide moisture information Cloud top pressure data to constrain model convection

Locally received and processed moisture sensitive microwave data (e.g. MHS) will also be assimilated Radar Data Assimilation Radar+Gauge 10min Accumulation Latent Heat Nudging & cloud T-1 T-0.5 T+0 T+0.5 Obs Analysis increments IAU

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