towards seamless mesoscale prediction of the land … · b g v e air z q w q k ... • various...

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


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.


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


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


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.


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.


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.


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


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.


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


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


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


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)

23

Thermodynamic Profiling During HOPE, Jülich, 2013


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.


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.


3D 80-cm scanner ofIPM DIAL system


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!


Only by coupling of end‐user models, reasonable decisions can be made and feedbackscan be taken into account.

Hohenheim Integrated Land System Model: Collaborative Unit of 12 Research Institutes

towards seamless mesoscale prediction of the land … · b g v e air z q w q k ... • various...

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