SMHI activities on Data Assimilation for Numerical Weather Prediction

Transcription

1 SMHI activities on Data Assimilation for Numerical Weather Prediction ECMWF visit to SMHI, 4-5 December, 2017 Magnus Lindskog and colleagues

2 Structure Introduction Observation usage Monitoring Methodologies Re-analyses Future

3 Introduction What we are looking for: Data assimilation Best possible estimate of the atmospheric and surface states by utilizing observations and a forecast model. Aim: Better km-scale weather forecasts by improved initial states. A consistent archive of re-analyses for historical periods.

4 Introduction ALADIN/HIRLAM Collaboration MetCoOp HARMONIE-AROME version of ALADIN-HIRLAM NWP system Co-operation on limited-area operational modelling between Sweden, Norway and Finland. International/national networks and partners ECMWF, EWGLAM, WMO, Copernicus, COST, Universities,.

5 Observation usage MetCoOp Observation Usage Upper-Air (3D-Var) Conventional observation types (SYNOP, SHIP, Buoys, airep, radiosondes, PILOT). Satellite based information from AMSU-A, AMSU-B/MHS, IASI, ASCAT. Radar reflectivities from Swedish, Norwegian, Finish, Danish and Estonian radars (German radars to be introduced). Zenith total delays from GNSS (GPS) receiver stations (Nordic network of receiver stations and METO+ROBH). SEVIRI-radiances and satellite based cloud products. Radar radial winds, Mode-S, AMV, Aeolus,.. Surface (OI) MetCoOp model domain. RH2m, T2m from SYNOP stations Snow from SYNOP stations SST and SICE from NEMO Satellite based surface observations.

6 Observation usage Radar reflectivity OPERA volume data is used operationally by MetCoOp Quality controlled using the BALTRAD/OPERA toolbox Preprocessing of radar data Data sanity check Clean up bator (HDF5 reader) where many country specific things are taken A check for overlapping or too low elev. is made to avoid obs. from the same volume of air Super observation construction Publication Ridal, M., and M. Dahlbom, 2017: Assimilation of multinational radar reflectivity data in a mesoscale model: A proof of concept. J. Appl. Meteor. Climatol. doi: /jamc-d , 56, from Estonia

7 Observation usage GNSS Zenith Total Delay GNSS ZTD from NGAA Nordic Processing Centre and sites from METO and ROBH are assimilated operationally in MetCoOp. MetCoOp Operational GNSS stations from NGAA ROBH and METO processing centres (before and after spatial thinning) Lindskog, M., Ridal, M., Thprsteinsson, S., Ning, T.: Data assimilation of GNSS zenith total delays from a Nordic processing centre. Atmos. Chem. Phys., 17, 1 16,

8 Observation usage Initialisation with MSG based cloud product Case study: +0h and +3h forecast of total cloud cover compared with MSG based cloud product fc obs fc fc obs

9 Observation usage Processing and conversion of ASCAT derived soil moisture product Illustration for ASCAT METOP-A at UTC Derived Product (%) (in top layer) Ascatmin Ascatmax *(Ascatraw-Ascatmin) Transformed Product (m3/m3 Model uppermost layer) Modmin Modmax

10 Observation usage Observation operator for Radiances AMSR2 level 1C, 6.9 GHz H brightness temperatures V brightness temperatures Observed radiances model counterparts derived with observation operator

11 Monitoring Example: Observation Monitoring of Data Assimilation System and DFS, mean analysis increments,..

12 Methodologies Variational data assimilation Find the most likely initial state, x, by minimizing penalty function: J(x)=J B +J o (+J ls )=(x-x B ) T B -1 (x-x b )+(y-hx)r -1 (y-hx)+(j ls )+ where x x y B R H B ( u v T q ln ) T p s Background field Observations Background field covariances Observation error covariances Observation operator J ls,.. Large scale error constraint,.. Recent publication (2017)

13 Methodologies Multi-incremental 4D-Var

14 Methodologies Ensemble of forecasts Hybrid variational/ensemble data assimilation Ensemble of analyses Data Assimilation and Perturbation J(δx VAR,α)=β VAR J BVAR (δx VAR )+β ENS J BENS (α )+J o

15 Methodologies LETKF in collaboration with Pau Escriba (AEMET) Aim: 1) Integrate into HARMONIE 4DVAR system => 2) try in HARMONIE Hybrid 4DVAR => 3) possible ingredient in HARMONIE 4DEnVAR

16 Methodologies Surface data assimilation 1. Introduction of horizontally varying background error statistics. Impact of one single SYNOP Relative Humidity observation at the 2 meters height unit (%). The observation is located close to the west coast of France and the observed relative humidity is approximately 15 % less than the corresponding model value. Horizontally varying background error statistics Present Improved a 85 km L 195 km (vertical correlation scale ~500m) 2. Flow dependent Kalman filter based techniques and use of ensembles. 3. Coupling between NWP surface/upper-air and NWP surface/hydrology.

17 Re-analyses European reanalysis a consistent weather archive for Europe UERRA: 50+ years with ALADIN-HIRLAM km horizontal resolution Archived in MARS at ECMWF 30 h forecasts 00 UTC and 12 UTC 'PRECISE ; continuation for Coperrnicus C3S First a continuation of UERRA Then Higher resolution (~5 km) More observation types (satellite) Uncertainty quantification Operational system CARRA : Arctic domain MET Norway leads Copernicus C3S Climate Data Store

18 Re-analyses (R) Correlated obs. errors allows efficiently extract information from small-scales

19 Re-analyses in preparation

20 Future work Introduction of more wind observations and clouds Flow dependent data assimilation techniques Couplings Extending the data assimilation monitoring system Re-analyses