Deutscher Wetterdienst
Slide 1 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
ABL Measurements at MOL-RAO
Serving Numerical Weather Prediction
Frank Beyrich with contributions from
C. Becker, B. van Kesteren, J.-P. Leps, E. Päschke, S. H. Richter, U. Rummel, G. Vogel, U. Weisensee et al.
Deutscher Wetterdienst
Slide 2 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
The Atmospheric Boundary Layer and the „Lindenberg Column“
1. Introduction
ABL = base of the column
high temporal variability
large horizontal variability
strong vertical gradients
interaction with land surface
Deutscher Wetterdienst
Slide 3 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Bild: http://www.ph.unito.it/
Flow: friction, chanelling, whirls Energy: radiation turbulent transport Water: precipitation, evaporation, run-off Trace Substances: emission, deposition Soil processes: heat / water transfer & storage Vegetation processes: transpiration, gas exchange, assimilation
1. Introduction
Land Surface Processes
Deutscher Wetterdienst
Slide 4 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
state parameters of the air (temperature,
humidity, wind, pressure, tke)
process parameters in the air (precipitation, evaporation, momentum and energy fluxes <radiation, sensible heat, latent heat>, tke)
soil state parameters (temperature, water content)
vegetation parameters (LAI, stomatal resistance, surface temperature)
water state and process variables (temperature, waves)
What do We Need to Measure?
2. ABL Measurements at MOL-RAO
… in the soil, at the surface, up to ~ 3 km … … above different surfaces …
Deutscher Wetterdienst
Slide 5 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Air state variables
Radiation
Soil
parameters
Turbulence
Precipitation
Sensors
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 6 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
The Falkenberg Boundary Layer Field Site (GM Falkenberg)
Sites (I)
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 7 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Photo: DWD-MOL2 (G. Hollaz, 2003)
Forest station Kehrigk
Photo: DWD-MOL2 (J.-P. Leps, 2003)
Photo: DWD-MOL2 (F. Beyrich, 2003)
Sites (II)
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 8 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Remote sensing/ airborne systems
Complex Measurement Systems (III)
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 9 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Operation Times
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 10 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Co
ntr
ol
Sit
e
Op
era
tio
n
• Site selection and characterization
• Sensor selection and characterization
• Station set-up and validation
• Maintenance regime, (re-)calibrations
• Data transmission and storage (incl. back-up)
• Quicklook / visual data control
• Automatic data control
• Manual data control
• Data base (measured values + quality flag)
• Data user / -analysis feedback
• optional re-calculation / revision of QC and flagging User
QA / QC Measures
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 11 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Redundant Sensor Systems
Quantity Standard Measurement Redundant Determination Realization
air temperature Pt-100 (HMP45) Pt-100 (Frankenberger) Y
air humidity capacitive (HMP45) psychrometer (Frankenberger) Y
wind speed cup anemometer (sonic anemometer) (Y)
wind direction wind vane (sonic anemometer) (Y)
air pressure piezo-resistive - N
precipitation weighing tipping bucket / Hellmann Y
snow depth manual reading sonic ranging Y
short wave radiation thermopile (CM 24) photo diode (PAR) (Y)
long wave radiation thermopile (PIR) pyro-electric (KT 15) Y
soil temperature Pt-100 Pt-100 (2nd profile) N
soil moisture TDR (Trime EZ) Lumbricus / gravimetry (monitoring) Y
soil heat flux thermopile (flux plate) (soil temp. profile) (Y)
turb. Momentum flux sonic anemometer wind speed profile Y
turb. sensible heat flux sonic anemometer temperature profile, scintillometer Y
turb. latent heat flux sonic anemometer + IR-hygrometer air humidity profile Y
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 12 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Results of QA/QC Activities:
Data Availability and Data Quality
2. ABL Measurements at MOL-RAO
Deutscher Wetterdienst
Slide 13 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
micrometeorol. / flux measurements
Scintillometry
Foto: DWD-MOL (D. Dauß)
Foto: DWD-MOL (G. Hollaz)
Ground based remote sensing
Airborne measurements Field Experiment: LITFASS-2003
3. Heterogeneous Land Surface
Deutscher Wetterdienst
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-200
-100
0
100
200
300
400
500
600
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
UTC
Late
nt
Hea
t F
lux
in
W/m
2
HV Forest N4 Grass
N2 Grass FS Water
SS Water A1 Rye
A5 Rye A3 Barley
A8 Triticale A2 Rape
A7 Rape A9 Rape
A4 Maize A6 Maize
Surface Fluxes over Different Land Use Types
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-100
0
100
200
300
400
500
600
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
UTC
Sen
sib
le H
ea
t F
lux i
n W
/m2
• large differences between
forest, farmland, water
• factor 2-3 for farmland
• var (H) > var (LE)
3. Heterogeneous Land Surface
Deutscher Wetterdienst
Slide 15 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Strategies to Determine Area-Averaged Fluxes
(from Measurements)
Suitable (weighted)
averaging of local
measurements
Aircraft
measurements
Scintillometry Budget
methods
i
Xii
Xp
FpF )(/~ 2
LzCF XXX XwFX
~
t
h
dt
XdFX ;~
© J. Bange (TU Braunschweig)
3. Heterogeneous Land Surface
Deutscher Wetterdienst
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-200
-100
0
100
200
300
400
500
600
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
UTC
Sen
sib
le H
ea
t F
lux i
n W
/m2
Forest Mean
Forest Helipod
Forest LAS
Farmland Mean
Farmland Helipod
Farmland LAS/MWS
25.05.2003
LITFASS-2003: Area-Averaged Fluxes
3. Heterogeneous Land Surface
Deutscher Wetterdienst
Slide 17 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Aspects
4. Contributions to NWP
operational diagnostics long-term validation cluster analysis / conditional validation data sets case studies parametrization development external (land surface) parameters spatial variability
© Mike Ek
Deutscher Wetterdienst
Slide 18 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Operational Diagnostics: Time Series
4. Contributions to NWP
Deutscher Wetterdienst
Slide 19 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Operational Diagnostics: Mean Monthly Diurnal Cycles
4. Contributions to NWP
Deutscher Wetterdienst
Slide 20 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
4. Contributions to NWP
Longterm Validation
Deutscher Wetterdienst
Slide 21 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
4. Contributions to NWP
Conditional Validation: Profiles for VDI-3783/8
Deutscher Wetterdienst
Slide 22 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
4. Contributions to NWP
Case Studies: Solar Eclipse March 2015
Deutscher Wetterdienst
Slide 24 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
4. Contributions to NWP
Parametrization Studies: Soil Moisture in TERRA
Improved simulation of soil water content through • consideration of bare soil evaporation • modified root density distribution with depth • use of vegetation parameters (LAI, plcov) from satellite data • modified deep soil water exchange • Consideration of enlarged infiltration
Deutscher Wetterdienst
Slide 25 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
4. Contributions to NWP
Data Sets: The SRNW Data Pool
Deutscher Wetterdienst
Slide 27 of 27 GRUAN-ICM10 @ MOL-RAO, 26.4.2018
Thank you for your attention!