Arctic observing system for regional NWP

Arctic observing system for regional NWP

Harald Schyberg (met.no), Frank Thomas Tveter (met.no)Roger Randriamampianina (met.no), Trygve Aspelien

(met.no)([email protected])

Norwegian Meteorological Institute met.no

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)

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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

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Numerical Weather Prediciton in the Arctic

NWP quality in general determined by:1. Quality of model formulation

– Physics of processes in the Arctic

2. Quality of boundary forcing– In particular lower boundary:

Ice/snow/ocean: heat and moisture flux, momentum flux, radiative fluxes

– For LAMs: Lateral boundaries

3. Quality of initial state estimate• As determined in data assimilation –

issues of observation coverage/usage in the Arctic

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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

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Regional experiments – do we get consistent results with this picture?

Two approaches in anaysing data denial impact studies:

Statistical, long term Case studiesNo weighting to favor high-impact weather

Statistical significance?

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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

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EUCOS impact trials at met.no

• Use of HIRLAM 3D-Var Winter period 2004-05 and summer period 2005

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Results- All scenarios (more details later)

Control Scenario(all available in-situ observations)

Baselinescenario

Add E-ASAPs

Add AIREPs

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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

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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 lackingThe 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

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Challenges for Arctic data assimilation:Radiosonde observation coverage

• ff

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Challenges for Arctic data assimilation:aricraft obs coverage

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Challenges for Arctic data assimilation:SYNOP surface obs coverage

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Challenges for Arctic data assimilation:buoy surface obs coverage

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Coverage from some satellite observation types

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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)

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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

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Run with IASI data Run without IASI Run with campaign data THCL1 THCL2

Run without campaign data THCL3 THCL4

IASI has 8461 channels, of which we extract 366 for potential use.

Four experiments have been performed using 41 active channelsPeriod: 2008022000 – 2008031712

(Warming period 5 days)

Exploring the use of IASI during the Norwegian IPY-THORPEX campaign period

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Impact as function of forecast range over the period

• Left effect of IASI with campaign data,right without campaign data

• Verification against ECMWF analyses

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

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Polar low case 16-17 March 2008+24 hrs forecasts of pressure and precip

IASI,Caobs

No IASI,Caobs

No IASI,no CaobsIASI,

no Caobs

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3 March 2008: Probabilistic variables (Wind, + 51 hrs)

wind > 15 m/s

Green=

With campaign

Red=Without

Campaign data

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Probabilistic variables (Precipitation + 51 hrs)

Green:

Regular + Campaign Obs

rr > 1 mm/3hr

Red:

Regular Obs rr > 1 mm/3hr

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

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AMSU-A channels over sea iceWe 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?

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3 issues in using surface-affected microwave sounding (AMSU-A) over sea ice

1. The sea ice emissivity2. Accounting for penetration depth

of the radiation and vertical temperature gradient within the sea ice

3. Getting a realistic (first guess of) surface temperature to RTTOV

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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

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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

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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

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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

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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

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Implementation in variational data assimilation

J(x) = ½ (x-xb)T B-1 (x-xb) + ½ (y-H(x))T O-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

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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

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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

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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

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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%

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Example: Corresponding 2m temperature

• Temperatures typically in the range from -10 to -30

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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

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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

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Thank you …

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Backup slides follow

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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

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Arctic atmosphericobservations

Some main paths from observations to products for numerical atmospheric modeling

Process studies Data assimilation

Improved NWPmodel parametrizations

Reanalysis Analysis

Modeling offuture climate

Monitoring of past and present climate

Short/medium range operational NWP

Focus of this talk

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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

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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 dependenceHave chosen to implement this using

variational bias correction

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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

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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)

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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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Some more conclusions

• Many recent studies shows clear positive impact of adding sounding data and improving use of satellite data: A potential for improving forecasting and reanalysis capability

• A main contributor to the Arctic observing system for data assimilation will be satellite data

• Surface observations missing in the Arctic• Improvements in accounting for the sea ice

contribution to satellite measurements• Improvements in describing boundary layer

processes over sea ice• Future progress in Arctic atmospheric modeling will

take place on the interface between expertise on sea ice, data assimilation and atmospheric physical processes