1Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Volker Wulfmeyer, Kirsten Warrach‐Sagi, Thomas Schwitalla, Hans‐Stefan Bauer, Andreas Behrendt, Florian Späth, and Josipa Milovac
Institute of Physics and Meteorology (IPM), University of Hohenheim (UHOH) Stuttgart, Germany
Acknowledgement:• WWRP WG on Mesoscale Weather Forecasting Research• WCRP CORDEX‐Europe• DFG Collaborative Research Units 1695, 1598, TR32• HD(CP)2 and its experiment HOPE with KIT• Météo‐France, DWD, JMA• NSSL, NCAR, NOAA• Water‐Earth System Science Competence Center• Environmental Research Center, Leipzig
Towards Seamless Mesoscale Prediction of the Land System for Europe
222Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Outline
1) The state of regional climate modeling2) Soil‐vegetation‐atmosphere (SVA) feedback3) Surface fluxes4) WRF‐NOAH‐MP model system5) Data assimilation studies for quantitative
precipitation estimation (QPE) and forecasting (QPF)6) Field studies of SVA feedback7) Theses
3
Kotlarski et al. GMD 2014, for the analyses of droughts see Vautard et al. Clim. Dynam. 2013
-5 -4 -3 -2 -1 -0.5 0.5 1 2 3 4 5 [°C]
Summer temperature bias (1989-2008)
1) The State of Regional Climate Modeling: CORDEX‐Europe Ensemble (0.11, 1989‐2008)
UHOH WRF‐NOAH
Bias correction hopeless without downscaling to about 3 km (Ehret et al. EES 2012). CP‐resolution promising but too dry (Warrach-Sagi et al. Clim. Dynam. 2013, Prein et al. Clim. Dynam. 2013). The parameterization chain in the SVA system needs to be improved.
444Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
airvgbsatWV eTerCzqKqw
)('' ,,*1
Horizontal wind
Soil and canopy water content evolution
Moisture tendency equationAtmospheric
stability
Entrainment flux
Canopy and aerodyn. resistance, photosynthesis
Latent heat flux profiles
Flux divergence
Energy balance closure (EBC)
ziz
0v qw
Temperature profile Free atmosphere
Interfacial layer (IL)
Mixed layer (ML)
Surface layer (SL)
z
0T
z
0soilq
2) Understanding SVA Feedback. Example: Latent Heat
'''' qwz
qVqVtq
Soil and canopy temperature
ABL
5
3) Energy Balance Closure and Soil Moisture Observedand Modeled Within and DFG FOR 1695 (see www.wess.info and klimawandel.uni‐hohenheim.de)
KraichgauNellingenStuttgart• Complete data set for forcing
of land‐surface models since 2009 using three EBC stations.
• Investigations of soil properties and vegetation processes such as root water uptake(Wizemann et al. MetZet 2014).
Water & Earth System Science Competence ClusterWESS
Accurate soil texture/hydraulics and vegetation dynamics have to be incorporated for realistic simulations of soil moisture and surface fluxes (Wöhling et al. IAHS 2012, Gayler et al. EES 2013, Gayler et al. WRR 2014).
CERES SPASSSUCROS GECROSCLM
F
F LH
F
F F F F
6
Convection‐permitting (CP) resolution: 0.037(4 km), selected periods, 2007, dry spells
CORDEX – Europe: 0.11 (12 km),1989‐2009, 2009‐2030
3 km 1 km 330 m 110 m
4) WRF‐NOAH‐MP Grey Zone Model Setup for Forecasting, Reanalyses, Dynamic Downscaling, and Projections
WRF‐NOAH‐MP model:• New soil texture from HWSD• IGBP‐MODIS/Corine land‐use data set • Incorporation of dynamic crops (albedo, LAI, etc.) (Gayler et al. WRR 2014)
• Separate EBC for soil and canopy• Various combinations of model physics (e.g., YSU turbulence, Morrison cloud microphysics)
• VAR and EnKF data assimilation• Coupling with and calibration using agricultural, hydrological, and matter transport models ongoing (Samaniego et al. AGU 2012)
(e.g., Schwitalla et al. QJRMS 2011, Greve et al. JAMC 2013, Wulfmeyer et al. JAMC 2013, Wulfmeyer and Schwitalla Meteorol. Z. 2014, Branch et al. HESS 2014)
7
Yellow: Upper air soundingsBlack: GPS Zenith Total Delay (ZTD)Dark blue: atmospheric motion vectorsLight Blue: metar airportGreen: Ship reportsWhite: synopRed: aircraft amdar, acars, airep
1‐km domain, 200 km x 200 km
Wideumont
Luxem‐bourg
Belgium
Germany
Neuheilen‐bach
5) Obs. for QPE‐QPF for (www.caos‐project.de)
3.6‐km domain
• Huge gaps in T and WV profiles important constraint for radar impact• Thermodyn. environment not reflected by radar data‐> Important error source!
Black diamonds: Radar sitesThermodyn. profiler sites (not assimilated):Yellow triangles: NDACC profiler stationsBlue diamonds: EARLINET stationsGreen squares: ObservatoriesRed circles: Microwave radiometers
Attertcatchment
8
European Radar 3DVAR 1‐h RUC QPE Study within
Reflectivity , dbZ
First trans‐boundary radar DA study using European network and WRF‐NOAH‐MP shows a positive impact. QPE studies extremely useful for investigating radar DA performance and analysis increments (Schwitalla and Wulfmeyer MetZet 2014, Bauer et al. Tellus 2014, submitted, Bauer/Schwitalla, poster this conference).New polarization radar forward operator developed (Poster Bakhshaii, this conference, Kawabata et al. ERAD 2014).
WRF CONTROL
DWD RADOLAN RX
26. Sept. 2012, 02 UTC
WRF 3DVAR WRF 1‐h forecast
WRF 3DVAR
999Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
6) IPM 3D Temp. and Water‐Vapor Raman Lidar
Temperature profile22.09.2013 14:04 UTC
Lidar, 20 min, 150 mRadiosounding
Optimized for daytime performance with resolution of turbulence. Scanning measurements are also possible.
Behrendt et al. AO 2004, Radlach et al. ACP 2008, Behrendt et al. QJRMS 2011
1 min, 150 m
101010Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
IPM 3D Water‐Vapor Differential Absorption Lidar (DIAL)
80‐cm scanner
DIAL: 20 min, 130‐300 m
Wagner et al. AO 2011, 2013
High accuracy and resolution of IPM DIAL system. Turbulenceresolved during daytime and 3D scanning capability.
111111Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
CASE A) Hum. Dependence on ABL Schemes: TR32, Sept. 8, 2009
Warrach-Sagi et al. HPCSE 2013, see also poster Milovac et al. GEWEX 2014
2‐km WRF‐NOAH: DIALYSUQNSEMYNN 2.5MYJACM2
2‐km WRF‐NOAH: YSU‐NOAHMPYSU‐NOAHMYNN‐NOAHMPMYNN‐NOAH
ABL too deep in all cases. Variability of the order of 1 g/m3. Strong sensitivity on both LS‐schemes and ABL parameterizations.
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B) ABL Profiling During HOPE, April 24, 2013, 11‐12 UTC
Unique information concerning turbulent transport andentrainment processes provided by lidar synergy. Measurementsstrongly deviate from grey zone (LES) run forced by WRF RUC.
Behrendt et al. ACP 2014 submitted
13
,
T ,
Scanning Doppler
lidar
Virtual meteorological tower
Soil moisture network
Mesoscale vortex
ABL top
Surface energy balance measurements
LAI, albedo
Hyper‐ and multi‐spectral
obs.
UAV
6) Currently Ongoing: Surface Atmospheric‐Boundary‐Layer Exchange (SABLE) Campaign
VScanning Doppler,
WV, and T lidar
′′′
′′
∗
,
New generation of SVA experiments in preparation at different sites such asSGP. Now it is possible to measure LS and entrainment fluxes simultaneously.
141414Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Example of SABLE Results
Water‐vapor scans available with unprecedented resolution and accuracy covering the surface and interfacial layers, simultaneously. Some clouds can be penetrated.
t = 10s, R = 285m
15
Research Vision: Consistent, Seamless Land System Model
WRF
Terrestrial data
Init., boundaries (ECMWF, CMIP5)
Simulation
Data assimilation
Verification
Observations 2
NOAH-MP LSM
Analysis, calibration
Downscaling (hindcast, projection)
Consistent forcing of flow and transport models such as mHM, OGS, Hydro
Forecast, Reanalyses
Observations 1
Improvement
Processstudies
Seamless means:• Throughout forecast
ranges• All components of the
water cycle includingrunoff and groundwater
• Throughout spatialand temporal scales
161616Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
ThesesThree prerequisites have to be fulfilled to realize this vision:• Thorough verification of models with respect to dynamics and high-resolution
thermodynamic profiles in the lower troposphere. • Improved process understanding of SVA feedback in the land system. This is the
way to cure parameterization errors instead of band-aid approaches.• Convective-scale data assimilation with observations of clear-air dynamics and
thermodynamic profiles resolving the structure of the ABL.
Three prerequisites have to be fulfilled to realize this vision:• Thorough verification of models with respect to dynamics and high-resolution
thermodynamic profiles in the lower troposphere. • Improved process understanding of SVA feedback in the land system. This is the
way to cure parameterization errors instead of band-aid approaches.• Convective-scale data assimilation with observations of clear-air dynamics and
thermodynamic profiles resolving the structure of the ABL.
Seamless prediction needs seamless observations (!) • of thermodynamics and dynamics on the mesoscale, • which must resolve the vertical structure of the atmosphere in the lower
troposphere,• with well-defined, high vertical and temporal resolution as well as accuracy,• to be obtained routinely in networks with a wide coverage.This gap cannot be closed by space borne remote sensing.A new generation of sensors is waiting for their commercialization. The tide is high to launch a comprehensive development and deployment program.
Seamless prediction needs seamless observations (!) • of thermodynamics and dynamics on the mesoscale, • which must resolve the vertical structure of the atmosphere in the lower
troposphere,• with well-defined, high vertical and temporal resolution as well as accuracy,• to be obtained routinely in networks with a wide coverage.This gap cannot be closed by space borne remote sensing.A new generation of sensors is waiting for their commercialization. The tide is high to launch a comprehensive development and deployment program.
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CASE 2) High Definitions Clouds and Precipitation for Climate Prediction Observational Prototype Experiment (HOPE), Jülich, 2013
Macke et al. AMS 2014, http://hdcp2.zmaw.de/HOPE.2306.0.html, Späth et al. ACP 2014 submitted
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Den
sity
% (m
m/d
ay) 100
1
0.01
0.0001
0 50 100Precipitation [mm/day]
Summer
DWD-ObsWRF-UHOHERA-Interim
Ivanov et al. EGU 2014
Bias correction of precipitation is hopeless without downscaling to about 3 km (Ehret et al. EES 2012). CP‐resolution promising but too dry (also Prein et al. Clim. Dynam. 2013) and needs further improvement of parameterization chain.
37 km12 kmCORDEX
3 km, convection-permitting (CP)
Warrach-Sagi et al. Clim. Dynam. 2013
The State of Regional Climate Modeling: CORDEX Ensemble Member WRF‐UHOH (0.11) over Germany
191919Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Vautard et al. Clim. Dyn. 2013
Extreme temperature variability represented reasonably well but still underestimated. Deficiencies over parts of Europe related to incorrect simulation of precipitation.Model performance and resolution must further be improved.
(0.11, 12 km)
CORDEX‐Europe Verification of Extreme Values
20
3) Surface Fluxes Observed and Modeled Withinand DFG FOR 1695
(see www.wess.info and klimawandel.uni‐hohenheim.de)
Kraichgau NellingenStuttgart
• Complete data set for forcing of land‐surface models since 2009.
• Excellent flux verification data using three EBC stations.
• Investigations of vegetation processes such as root water uptake and soil properties.
00:00 04:00 08:00 12:00 16:00 20:00 24:00
-600-500-400-300-200-100
0100200300400
Net radiation Sensible heat flux Latent heat flux Ground heat flux Residual
Ener
gy F
lux
(W/m
2 )
Hour, MEZ
Mean diurnal cycle of energy balance componentsand energy balance closure (EBC) in June 2009 forwinter wheat in Nellingen.
Water & Earth System Science Competence ClusterWESS
Water & Earth System Science Competence ClusterWESSWESS
Multi-objective parameter estimation using data from EC-stations and TDR-probes:Comparison of Pareto-fronts of the Community-Land-Model (CLM) and 4 crop models with different degrees of structural complexity (variation of thesame number of model parameters, irrespective of model complexity)
Which degree of detail is needed in a land-surface scheme to simulate simultaneously soil moisture () and latent heat flux (LHF) at plot scale?
CERES SPASSSUCROS GECROSCLM
F
F LH
F
structural complexity of models
Complex models SPASS and GECROS provide improved simulations of soil properties and root water uptake (Wöhling et al. IAHS 2012, Gayler et al. EES 2013).
F F F F
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5) IPM Integrated Forecast System for QPE and QPF
Forward OperatorsForward Operators
Boundaries (deterministic or ensemble prediction system)
VAR or EnKF providing state vector Xa and analysis error
covariance A
WRF‐VarWRF‐VarForecast or reanalysis ensemble, update of B
Forward OperatorsModel observation forward operators
H(X)
Convection permitting WRF‐NOAH‐MP model simulations of Xb with error covariance B
Observation vector Y with error covariance R
Data of meteorological services and
research institutes
Verification and Calibration
High frequency
Rapid Update Cycle (RUC)
Soil‐land‐surface obs.:‐ Streamflow ‐ In‐situ soil and veg.‐ Hyperspectral obs.
Atmospheric obs.:‐ In‐situ‐ GPS ZTD and STD‐ AMV‐ Radar Doppler and reflectivity‐ Lidar‐ Polarimetric radar‐ Passive rem. sens.
(Streamflow DA: Warrach-Sagi and Wulfmeyer GMD 2012)
242424Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
IPM 3D Temperature and Water‐Vapor Raman Lidar
Behrendt et al. AO 2004Radlach et al. ACP 2008Behrendt et al. QJRMS 2011
Flashlamp‐pumped, tripled Nd:YAG laser with 10 W average power in the UV.
25Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Temperature Rotational Raman Lidar (RRL)Inelastic Raman scattering intensities are Boltzmann distributed. Derivation and error analysis:
)(ln42)(
2
2
RQcabbaRT
Calibration with radiosoundings necessary, furtherdetails see our pubs.
,21
2
2
2
2
1
1 11
RRRRRR
RR
RR
RRT NN
QQT
IIQ
QT
Thus, the statistical errorscales according to tRT
11
and the vertical error correlationcorresponds to R.
26Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
3D 80-cm scanner ofIPM DIAL system
27Aug. 20, 2014 World Weather Open Science Conference, Montreal, Canada
Water‐Vapor Differential Absorption Lidar (DIAL)Methodology and error analysis, DIAL derives the absolute humidity:
)()(ln
21)(
RIRI
dRdRN
off
onWV
Wulfmeyer andWalther AO 2001a,b
22
22
off
off
on
on
WV
WV
IIN
I
RIon Ioff
Layer of the gasto be detected, e.g., water vapor
off
on
II
off
on
IIln
gasgasgas
off
on
N
II
dRd
ln
21
Very demanding with respect to lasertransmitter spectrum.
10 km, US standard atm.
1.8 GHzFWHM
No calibrationrequired!