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Arctic observing system for regional NWP

Harald Schyberg (met.no), Frank Thomas Tveter (met.no) Roger Randriamampianina (met.no), Trygve Aspelien (met.no) ( h.schyberg@met.no). Arctic observing system for regional NWP. Outline. Introduction Observing system and impact studies

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Arctic observing system for regional NWP

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  1. Harald Schyberg (met.no), Frank Thomas Tveter (met.no)Roger Randriamampianina (met.no), Trygve Aspelien (met.no) (h.schyberg@met.no) Arctic observing system for regional NWP

  2. Outline • Introduction • Observing system and impact studies • Ongoing work on improving satellite data usage in high-latitude Results are from projects • EUCOS space-terrestrial network studies • Norwegian IPY-THORPEX project • DAMOCLES (EU FP7 project)

  3. Operational NWP LAM modeling – some characteristics Should add information to products from global modeling centres (ECMWF, …) • Higher resolution • More frequent data assimilation, shorter cutoff times (some satellite data types arrive late) Forecasts from a given analysis time available before those from global model

  4. Numerical Weather Prediciton in the Arctic NWP quality in general determined by: • Quality of model formulation • Physics of processes in the Arctic • Quality of boundary forcing • In particular lower boundary: Ice/snow/ocean: heat and moisture flux, momentum flux, radiative fluxes • For LAMs: Lateral boundaries • Quality of initial state estimate • As determined in data assimilation –issues of observation coverage/usage in the Arctic

  5. WMO workshop on impact of obs systems, 2008: • Synopsis from many studies using “observing system experiments” • Figure shows impact of observing systems in terms of gain in forecast range for “medium range” forecasts

  6. Regional experiments – do we get consistent results with this picture? Two approaches in anaysing data denial impact studies: Statistical, long term Case studies No weighting to favorhigh-impactweather Statistical significance?

  7. EUCOS impact studies Two questions in these “Space-terrestrial studies”: • In the presence of the full conventional observing system, what is the impact of adding the various satellites • In the presence of the full satellite observing system, what it the impact of the various components of the conventional observing system Met.no (and several other global and regional centres) participated in the second part funded by EUCOS

  8. EUCOS impact trials at met.no • Use of HIRLAM 3D-Var Winter period 2004-05 and summer period 2005

  9. Baseline scenario Add E-ASAPs Add AIREPs Control Scenario (all available in-situ observations) Results- All scenarios (more details later)

  10. Results from met.no HIRLAM OSEs inEUCOS terrestrial network study • Conventional observations have large positive impact in our system in the prescence of satellite data • Probably higher impact than in ECMWF system (we use less satellite obervation types) • TEMPs still dominating factor for analysis quality, wind more than temperature • Aircraft obs complement radiosondes (positive impact of adding aircraft in the presence of sondes) • Significant positive impact from E-ASAP network

  11. The Arctic observing system for regional NWP • An observation gap in the conventional observing network: • Lack of radiosondes • Lack of surface observations, but some buoys, more during IPY • The routinely available Arctic Ocean observing system consists basically of buoys and satellite data • AMSU, AIRS/IASI radiances and MODIS winds • Most of the satellite data are free atmosphere, surface/lower troposphere info lacking The total observing system in the Arctic Ocean: • Lacks surface/near surface observations • Relies on efficient use of satellite data in assimilation schemes • Use of satellite sounding data is less straightforward over sea ice and land than over ocean  Met.no focus on including IASI and on use of surface affected AMSU measurements

  12. Challenges for Arctic data assimilation:Radiosonde observation coverage • ff

  13. Challenges for Arctic data assimilation:aricraft obs coverage

  14. Challenges for Arctic data assimilation:SYNOP surface obs coverage

  15. Challenges for Arctic data assimilation:buoy surface obs coverage

  16. Coverage from some satellite observation types

  17. The Norwegian IPY-THORPEX project: Assimilation of extra campaign data and IASI observations(R.Randriamampianina, T. Aspelien) Observing system experiments during the Norwegian THORPEX campaign (several polar lows): • After implementing and optimizing IASI assimilation: What is the impact of assimilating IASI data(Norwegian version of HARMONIE NWP system) • What is the impact of assimilating campaign data:Extra radiosonde launches from Norwegian and Russian Arctic stations, Norwegian coast guard vessels, dropsondes, …(HARMONIE and LAMEPS)

  18. HARMONIE and its assimilation system (Hirlam Aladin Regional/Meso-scale Operational NWP In Europe) Model domain: rotated Lambert pr. Dx=dy= 11 km, 60 vertical levels up to 0.2 hPa • 3D-Var assimilation system • Use of conventional and satellite data • Obs operator for radiance data: RTTOV- 8.7

  19. Exploring the use of IASI during the Norwegian IPY-THORPEX campaign period IASI has 8461 channels, of which we extract 366 for potential use. Four experiments have been performed using 41 active channels Period: 2008022000 – 2008031712 (Warming period 5 days)

  20. Impact as function of forecast range over the period • Left effect of IASI with campaign data,right without campaign data • Verification against ECMWF analyses

  21. A forecast sensitivity measure to various observations during the campaign period:

  22. Polar low case 16-17 March 2008 IASI,Caobs No IASI,Caobs +24 hrs forecasts of pressure and precip No IASI,no Caobs IASI,no Caobs

  23. 3 March 2008: Probabilistic variables (Wind, + 51 hrs) wind > 15 m/s Green= With campaign Red=Without Campaign data

  24. Probabilistic variables (Precipitation + 51 hrs) Green: Regular + Campaign Obs rr > 1 mm/3hr Red: Regular Obs rr > 1 mm/3hr

  25. Developments towards using surface affected AMSU-A data over sea ice

  26. AMSU-A channels over sea ice We simulate observations from NWP fields using radiative transfer model RTTOV-8 (“B”) and compares against the real observations (“O”) When using fixed emissivity and NWP surface temperatures, typical O-B rms magnitudes over sea ice are: Ch 3 ~5K Ch 4 ~3K Ch 5 ~2K Ch 6-9 ~ 0.5K Previously ch 6-10 has been used over sea ice. Can we improve the use of ch 6-7 and add lower peaking channels? How to define emissivity and surface temperature?

  27. 3 issues in using surface-affected microwave sounding (AMSU-A) over sea ice • The sea ice emissivity • Accounting for penetration depth of the radiation and vertical temperature gradient within the sea ice • Getting a realistic (first guess of) surface temperature to RTTOV

  28. Emissivity: Help using data from Ocean and Sea ice SAF • High-latitude centre: Hosted by Met.no and DMI • Daily sea ice products: Concentration, edge, type etc on 10 km grid • Will also include info on sea ice microwave emissivity in future • See www.osi-saf.org and saf.met.no

  29. Emissivity (1) • Use daily OSISAF concentration chart to find near-100% ice covered area • In this area multi-year sea ice from OSISAF was used as predictor for sounding ch emissivity

  30. Emissivity (2) • An alternative approach is to estimate the emissivity from a window AMSU-A channel using simplified radiative transfer theory leading to: = (Tb obs – Tb sim(=0)) / (Tb sim (=1)-Tb sim(=0)) • One could then use the assumption that emissivity varies slowly with frequency and also apply it for other channels • Statistical analysis showed that using the MY ice map from SSM/I gave a better fit between modeled and observed data

  31. Handling of surface emitting temperature (figures from R.Tonboe) Variations in penetration depth, increasing temperature with ice depth: • The colder, the more misrepresentative is the surface temperature for the emitting layer

  32. Example: O-B statistics ch 4 using RTTOV-8 over sea ice Right panel: Departures as a function of ground temperature for first-year sea ice Left panel:The same for multi-year sea ice • The colder, the more misrepresentative is the surface temperature for the emitting layer • a) 0-10% multiyear b) 70-80% multiyear

  33. Implementation in variational data assimilation J(x) = ½ (x-xb)T B-1 (x-xb) + ½ (y-H(x))TO-1 (y-H(x)) Multiyear ice fraction based surface emissivity dependence included in the observation operator H(x) Variational bias correction: • A linear correction extension added to H(X) for handling dependence of emitting temperature on surface temperature • Control variable x extended with slope coefficient for this dependence • An optimal dependence on surface temperature is determined intrisically, constrained by other information available in the analysis

  34. Setup of NWP assimilation experiment over sea ice, using HIRLAM 4D-Var Reference experiment:no variational bias correction, constant sea ice emissivity,emitting temperature equal to surface temp,channels 6 and 7 Experiment 1:variational bias correction,channels 6 and 7 Experiment 2:variational bias correction,channels 5,6 and 7

  35. Impact study of changing the surface handling using channels 6 and 7 in HIRLAM 3D-Var • Small effects on”standard” surface verification averaged over the whole European network • Some positive effect seen on profile verification against radiosondes • Problem of lack of verifying obs near sea ice areas • Red: Fixed sea ice emissivity and surface temperature from HIRLAM • Blue: Emissivity estimation based on OSISAF products and surface temperature in the VarBC control variable

  36. Problem of modeling surface temperatures over sea ice: • Several present NWP system utilizes digital ice concentration maps from passive microwave (SSM/I, SSMIS, AMSR) for instance OSISAF • Difficult for low-resolution satellite data like SSM/I to detect small areas of open water within the sea ice • But the effect of such areas on surface fluxes very important • Present passive microwave concentration algorithms show small, but unrealistic, concentration fluctuations over closed sea ice • This also affects present assimilation of AMSU data

  37. Typical example: Fraction of ice used in operational HIRLAM NWP model (April 2010) • Ice concentrations above 95% set to 100%, • but still some unrealistic values remaining between 90 and 95%

  38. Example: Corresponding 2m temperature • Temperatures typically in the range from -10 to -30

  39. Example: Corresponding difference T (2m) – T (0m) • Erroneous small water areas creates large differences (and erroneously large heat fluxes) • Also a problem for use of satellite sounding data • No straightforward solution to this

  40. Summary • Gaps in the Arctic observation network: Particularly in lower troposphere • A potential for improvement with conventional observations even in precense of satellite. Profile info most valuable. • A good coverage of satellite data of the Arctic will be available for the foreseeable future:It is probably cost-effective to exploit the still unrealized potential there • Particular issues on surface description for satellite observation usage over sea ice

  41. Thank you …

  42. Backup slides follow

  43. Challenges for NWP in the Arctic application • Turbulence modeling • Stable boundary layer • Unstable boundary layer • Surface fluxes over sea ice • Heat • Radiative • Momentum • Routine observing system is basically satellite • Utilization of satellite data over sea ice:surface properties, cloud detection, moisture/cloud assimilation • Arctic cloud microphysics parametrization Leads, meltponds, albedo... Roughness

  44. Some main paths from observations to products for numerical atmospheric modeling Focus of this talk Arctic atmosphericobservations Process studies Data assimilation Improved NWPmodel parametrizations Reanalysis Analysis Modeling offuture climate Monitoring of past and present climate Short/medium range operational NWP

  45. Scatter in O-B can be further reduced by introducing surface temperature dependence Physical basis described by Mathew et al, 2008 Leads to empirical expression: Temitting = aT2m + b

  46. Can this be dealt with through linear correction (“bias correction”)? Linear dependence of observed brightness temperature on Ts But: • slope is of Tb vs Ts slightly dependent on ice characteristics such as type • need to simultaneously include multi-year ice fraction dependence Have chosen to implement this using variational bias correction

  47. Some targets for atmospheric process studies • Turbulence modeling • Stable boundary layer • Unstable boundary layer • Surface fluxes over sea iceLeads, meltponds, albedo... • Heat • Radiative • Momentum (Roughness) • Arctic cloud microphysics parametrization Should improve numerical modeling capability of the Arctic atmosphere NWP skills, reanalysis quality, climate modeling capability

  48. EUCOS Impact Scenarios at met.no with HIRLAM • Winter period: 14 December 2004 – 20 January 2005 • Summer period: 1 August – 31 August 2005 • 6 hours cycling, forecasts up to +48hrs from 00Z cycle stored • Baseline scenario: • All available satellite observations: • AMSU-A over ocean (EUMETSAT retransmission) • QuikScat winds (100 km product) • MSG cloud drift winds (AMV/SATOBs) • SYNOPS from GSN station list • Radiosondes from GUAN climate station network. • Bouys (no ship data)

  49. Role of the in situ observation network • Hard to see an increase in the Arctic conventional network for day-to-day NWP • Observation campaigns expected to be important • Process studies which feed back to model physics • Validation studies – identify NWP model problem areas • Validation/calibration of satellite observing system

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