GEWEX GRP10/2011Ⓒ ECMWF Role of products:Imperatives 1, 2, 5 Coherent, consistent, data products (closure) Long time series Order: reprocessed / recalibrated

GEWEX GRP 10/2011 ECMWFⒸ

Role of products: Imperatives 1, 2, 5• Coherent, consistent, data products (closure)• Long time series• Order: reprocessed / recalibrated L1-products first (to be coordinated with other

int’l efforts), then L2 products• GRP’s role for defining product error metrics (error modelling templates)

Role of simulators: Imperatives 3, 6• Support comparison in observation space (using fact that L1-observations are

accurate and do not use a priori constraints)• Bridge to data assimilation• Needs more education of users (implementation, interpretation)• Needs decision on how deeply involved GRP becomes w/r/t RT-models

Role of models: Imperatives 2, 3, 4, 5, 6• Define priorities for model evaluation and parameterization development• Support physical consistency (and support advanced diagnostics)• See role of models/data assimilation systems as valuable diagnostic and tool for

defining priorities!

SSG comments – post Seattle


SSG comments• Response to SSG AIs and JSC comments is being produced – role of GRP needs to be

understood by SSG+• Interaction with other WMO bodies should be coordinated by administration• Will approach WGNE for their view

• Level of maturity of most products very high / substantial level of 3rd party funding been spent should be appreciated: the objective of creating long-term datasets leads to compromises (in observational as well as modelled products - bias correction), requirement to start with consistent L1 products is fundamental

• Role of GRP in defining assessment ingredients/metrics important (tbd)• Flux data sets particularly interesting from NWP perspective• Flux evaluations also produced very informative co-assessment of observations along

with models and sensitivity studies (e.g. same forcing, different flux param.)• Strategy for advanced diagnostics?• GRP role w/r/t RT-models, simulators?


Model verification → improved parameterizationsObservational requirements:

• moisture profiles (UTLS)• moisture convergence (particularly over land)• ice clouds• mixed-phase clouds• boundary layer clouds• diurnal cycle of convection• aerosols• soil moisture (profile)• land surface fluxes (turbulent, radiation)• snow cover, water equivalent, albedo

Observation types for model development:• high vertical resolution (water vapour, clouds)• good spatial coverage (everything)• uncertainty specification if derived product

→ Global NWP model physics (/dynamics) need to perform well:• for medium-range forecast• within data assimilation system• for Ensemble Prediction System (EPS) extended to monthly forecasts (with ocean)• for Seasonal Prediction System• for longer scales (climate) also ocean, aerosols, stratosphere etc.


• Satellite data based climatologies• Water vapour: SSM/I, TMI, MLS• Clouds: SSM/I, TMI, Cloudsat/Calipso, (ISCCP)• Precipitation: GPCP, TRMM• Snow: AVHRR/SSM/I, MODIS• Soil moisture: SMOS, ASCAT• Radiation/energy: CERES, COADS

• Satellite orbit data• Clouds : Cloudsat/Calipso (, all observations used in DA)

• Site observations• ARM, operational networks, field campaigns, other sites

• NWP (re)analyses• ERA-40, ERA-Interim: NWP-analyses incl. data used in DA system

→ evaluate mean model state (climate*)→ improve physical parameterizations

⇒ better parameterizations of model state do not necessarily mean better forecast skill, but are crucial for improving skill consistently

*http://www.ecmwf.int/products/forecasts/d/inspect/catalog/research/physics_clim/climate/clim2000

Datasets used for model verification

GEWEX GRP 10/2011 ECMWFⒸSlide 5

Parameters to constrain:• temperature• wind• water vapour• snow• surface properties (albedo, vegetation)• soil moisture• cloud• precipitation

Observation types for data assimilation:• satellite radiometer radiances• satellite radar/lidar reflectivities/backscatter x-sections

→ most radiance data is available from operational instruments→ radar/lidar data is only available from few experimental missions→ Requirements:

• continuity of existing system• high-vertical resolution observations of water vapour (limb, active), over land• wind observations (with accuracy better than 1 m/s)• soil moisture data not yet optimal (ASCAT, SMOS) but promising

Keeping in mind that data must be available in near-real-time (~3-hour delay for global NWP)

Data assimilation→ improved initial conditions


Data Assimilation:• Satellite orbit data

• Temperature: AMSU-A, IASI, AIRS, HIRS, GPSRO• Water vapour: AMSU-B/MHS, SSM/I, TMI, AMSR-E, AIRS, IASI, HIRS• Wind: GEO/LEO-AMV• Clouds: SSM/I, TMI, AMSR-E, AIRS, IASI• Precipitation: SSM/I, TMI, AMSR-E• Snow: AVHRR/SSM/I• Soil moisture: ASCAT/SMOS

• Conventional• Temperature: Radisondes, dropsondes, aircraft• Water vapour: Radiosondes, dropsondes• Wind: Radiosondes, profilers

→ produce physically consistent analyses to initialize forecast model

⇒ hydrological parameters are not the drivers for forecast performance ⇒ more complex processes (clouds/precipitation) require good parameterizations to translate observational information into better forecast skill

⇒ 95% of data is assimilated as level-1 product (errors, biases, efficiency, compatibility)

Model verification/development requirements are different from data assimilation requirements!

Datasets used for data assimilation


ANFC 9h FC 48h FC 96h

Observation – minus – Model: Temperature

Metop-A AMSU-A NHstd. dev.

R/S T NHstd. dev. bias

R/S T Trstd. dev. bias

COSMIC-1 φ NHstd. dev. bias

COSMIC-1 φ Trstd. dev. bias


Metop-A MHS SHstd. dev.

DMSP F-14 SSM/I SHstd. dev.

R/S q TRstd. dev. bias

R/S q NHstd. dev. bias

R/S RH NHstd. dev. bias

Observation – minus – Model: MoistureANFC 9h FC 48h FC 96h


12-year climatology (ECMWF vs TRMM)Rain intensity LST of rain maximum

3-hour difference of convective maximum over tropical land surfaces

Too intense monsoon

Model Model

TRMM TRMM

(Data courtesy Y. Takayabu)

Examples where modelling/assimilation needs GRP: Precipitation


Comparison of monthly averaged rainfall with combined rain gauge and satellite products (GPCP)

Reanalysis estimates of rainfall over ocean are still problematic

Results over land are much better



Trenberth et al. 2011 – water cycle


Trenberth et al. 2011 – energy cycle


WATER VAPOUR

CLOUDLiquid/Ice

PRECIP Rain/Snow

Evaporation

Autoconversion

Evaporation

Condensation

CLOUD FRACTION CLOUD

FRACTION

Old Cloud Scheme New Cloud Scheme (since 11/2010)

• 2 prognostic cloud variables + w.v.• Ice/water diagnostic fn(temperature)• Diagnostic precipitation

• 5 prognostic cloud variables + water vapour• Ice and water now independent• More physically based, greater realism• Significant change to degrees of freedom• Change to water cycle balances in the model• More than double the lines of “cloud” code!

Examples where modelling/assimilation needs GRP: Clouds/radiation


Tem

pe

ratu

re

Ice Water Content (g m-3)

-80

-60

-40

-20

0106 105 104 103 102 101 100

-80

-60

-40

-20

0

-80

-60

-40

-20

0106 105 104 103 102 101 100 106 105 104 103 102 101 100

Ice Water Content (g m-3) Ice Water Content (g m-3)

CloudSat/CALIPSO observations

ECMWF old scheme without snow

ECMWF new scheme with snow

New scheme with prognostic ice and snow allows much higher ice water contents (seen by the radiation scheme)

Relative frequency of occurrence of ice/snow for NH mid-latitudes in June 2006: ECMWF model vs. Cloudsat/Calipso retrievals



Wind

Surface Precipitation Orography

July 2007 case study (36 hour accumulation)

5 mm/36hr

50 mm/36hr

Surface Precip. Difference

1 year average

“Prognostic snow” minus “Diagnostic snow”

5 mm/36hr

- 5 mm/36hrSurface Precip Difference

“Prognostic snow” minus “Diagnostic snow”

- 5 mm/36hr

5 mm/36hr



Side effect No. 1

RTTOV-9 µφTime series of fit between upper tropospheric MHS and model radiances

Systematic difference between radiosonde and model specific humidity (kg/kg; NH 01/2011)

Next model

Current model


Old Cloud Scheme New Cloud Scheme

T2m

TCLW

Side effect No. 2


Ceilometer observations Sodankyla/Finland:

Old Cloud Scheme New Cloud scheme Revised Cloud Scheme

Side effect No. 2


xxxx

Observation: Grassland Model: Crops



Observation: Evergreen needle leaf Model: 70% crops, 30% Interrupted forest



Observation: Woody savannas Model: 30% tall grass, 70% interrupted forest



Aerosol optical depth Accumulated rainfall

Merapi eruption (Indonesia, Nov. 2010)

Relative change of LCC Relative change of total precipitation

Impact of precipitation on aerosols

Impact of aerosols on clouds and precipitation

Examples where modelling/assimilation needs GRP: Aerosols/precipitation


ERA sampled as CRUTEM3 (Brohan et al., 2006) ERA over land, not sampled

12m running averages for globe12m running averages for globe

Examples where GRP needs modelling/assimilation: T2m anomalies


TRMM 3B42 CMORPH NRLBLND PERSIANN ECMWF

2005-10 times series of mean rainfall over Southern England

2005-10 mean product-radar rainfall correlation

0.0 0.2 0.4 0.6 0.8 1.0(Courtesy C. Kidd)

Examples where GRP needs modelling/assimilation: Precipitation


Ch 2

Ch 3

Ch 4

Recorded on-board warm target temperature changes due to orbital drift for NOAA-14 (Grody et al. 2004)

Examples where GRP needs modelling/assimilation: L1 biases


Atmospheric reanalysis: ERA-Interim

ECMWF forecasts: 1980 – 2010

Changes in skill are due to:

• improvements in modellingand data assimilation

• evolution of the observing system• atmospheric predictability

ERA-Interim: 1979 – 2010• uses a 2006 forecast system• ERA-40 used a 2001 system

• re-forecasts more uniform quality• improvements in modelling and data assimilation outweigh improvements in the observing system


Observations used in ERA-Interim: Instruments

Radiances from satellites

Ozone from satellites

Backscatter, GPSRO, AMVs from satellites

Sondes, profilers, stations, ships, buoys, aircraft


How accurate are trend estimates from reanalysis?

Global mean temperatures, for MSU-equivalent vertical averages:

ERA-InterimRadiosondes only (corrected)MSU only, from RSS


Observation Counts in ERA-Interim

Documents

GEWEX GRP10/2011Ⓒ ECMWF Role of products:Imperatives 1, 2, 5 Coherent, consistent, data products (closure) Long time series Order: reprocessed / recalibrated