Evaluation of the atmospheric water vapor content in the...
Transcript of Evaluation of the atmospheric water vapor content in the...
Evaluation of the atmospheric water vapor content in the regionalclimate model ALARO-0 using GNSS observations
Julie Berckmans1, Roeland Van Malderen1, Eric Pottiaux2, Rosa Pacione3
1 Royal Meteorological Insitute of Belgium, 2 Royal Observatory Belgium, 3 e-GEOS S.p.A. ASI/CGS Matera
17-19 May 2017
EUREF Symposium
Wroclaw
Outline
Introduction
Data
Methods
Results
Discussion and future research
J. Berckmans IWV validation Outline 2 / 17
Research topic
AimEvaluation of water vapor in regional climate models using observations
from GNSS
MotivationLack of validation by regional climate models, new reprocessed dataset
ready for climate studies
RelevanceQuality of regional climate model for climate projection
J. Berckmans IWV validation Introduction 3 / 17
Climate model
ALARO
−200
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Configuration of the ALADINmodel (v0)
Lateral boundary conditionsERA-Interim
Land surface model SURFEXDetails:Size: 149 x 149 grid points
Horizontal resolution: 20 km
Vertical 46 levels: from 17 m to 72 km
Lambert conformal projection
Radiation scheme ACRANEB
ALADIN International Team (1997), Gerard et al. (2009), DeTroch et al. (2013), Giot et al. (2016), Masson et al. (2003,Masson et al. (2013)
J. Berckmans IWV validation Data 4 / 17
GNSS Observations
EPN tropospheric product repro 2 1996-2014, selection criteria:
fit within domain
min. 10 years of data
min. 15 days per month
100 stations selected
Pacione et al. (2016)
J. Berckmans IWV validation Data 5 / 17
IWV calculation
ZTD observations to IWV
[Stull, 1995][Bevis et al., 1992]
=
[Askne and Nordius, 1987][Davis et al., 1985]
[Elgered et al., 1991]
Simplification for Tm: Hypsometric equation:
[Saastamoinen, 1972]
[Hogg et al., 1981]
Tm and Ps from ERA-Interim
J. Berckmans IWV validation Methods 6 / 17
IWV calculation
Model calculation of IWV
Horizontal interpolation: 4 nearest grid points (weighting: inversedistance)
Vertical linear interpolation based on Hagemann et al. (2003) butusing:
Pressure station level using barometric formulaT, Sfpres, H from modelStandard lapse rate for temperature -0.0065K/mVertical levels from lowest to +/- 20 km
Hagemann et al. (2003)
J. Berckmans IWV validation Methods 7 / 17
Model performance
Differences between models and observations
14 16 18 20
1416
1820
GNSS observations (kgm−²)
ER
AI /
ALA
RO
(kg
m−
2)
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ERAI−GNSS (0.977X+0.705, R²=0.988)
ALARO−GNSS (0.960X+0.617, R²=0.975)
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ERAIALARO 1:1 line
−6 −4 −2 0 2 4 6 8
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1.0
IWV (kgm−²)
Den
sity
ERAI−GNSS
median
−1 sigma +1 sigma
−6 −4 −2 0 2 4 6 8
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IWV (kgm−²)
Den
sity
ALARO−GNSS
median
−1 sigma +1 sigma
J. Berckmans IWV validation Results 8 / 17
Model performance
Distribution of all data Distribution of the 95th percentile
0 10 20 30 40
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IWV (kgm−²)
Den
sity
GNSSERAIALARO
25 30 35 40
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0.30
IWV (kgm−²)
Den
sity
GNSSERAIALARO
J. Berckmans IWV validation Results 9 / 17
Seasonal variability5
1015
2025
30
Month
IWV
(kg
m−
²)
J F M A M J J A S O N D
GNSSERAIALARO
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−2
−1
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Month
IWV
diff
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ce (
kgm
−²)
J F M A M J J A S O N D
● ERAI−GNSSALARO−GNSS
IWV bias
Overestimation ERAI, constant
Larger standard deviation insummer, both ERAI and ALARO
ALARO performs better than ERAI,except for July-August
Large underestimation ALARO inJuly-August
J. Berckmans IWV validation Results 10 / 17
Seasonal variability
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J F M A M J J A S O N D
−50
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Pre
cipi
tatio
n bi
as (
%)
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J F M A M J J A S O N D
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Tem
pera
ture
bia
s (°
C)
ALARO - E-OBS: precipitation bias
Good performance for May,June,Sep,Oct,Nov
Large neg. bias August
Large spread July
Large underestimation of precipitation inAugust+ Large underestimation of temperature= smaller moister holding capacity= explains negative IWV bias.
J. Berckmans IWV validation Results 11 / 17
Spatial variability
ALARO - GNSSLarge outlier = SJDV
Altitudinal difference (m)
Abs
olut
e IW
V d
iffer
ence
(kg
m−
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J. Berckmans IWV validation Results 12 / 17
Spatial variabilityBias [kgm-2] Stdev [kgm-2]
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−2
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ERAI ERAI
ALARO ALARO
J. Berckmans IWV validation Results 13 / 17
Discussion
Overestimation ERAI ≈ Lucas-Picher et al. (2013)
Larger standard deviation is expected with regional model comparedwith ERAI
Larger standard deviations in summer for both ALARO and ERAI
Underestimation of regional climate model in summer
Similar results as in ≈ Ning et al. (2013), but based on differentGNSS dataset and regional climate model
Relation precipitation and temperature model bias with IWV bias
Largest differences ALARO and ERAI in southern Europe = drymodel bias
Latitudinal dependence ≈ Pacione et al. (2016)
J. Berckmans IWV validation Conclusions 14 / 17
Future research
Closer look at spatial variability
Closer look at intra-month variability
Group stations based on similar characteristics
Diurnal cycle
...
J. Berckmans IWV validation Conclusions 15 / 17
References
Hagemann, S., Chen, C., Clark, D. B., Folwell, S., Gosling, S. N.,Haddeland, I., Hanasaki, N., Heinke, J., Ludwig, F., Voss, F., andWiltshire, A. J.: Climate change impact on available water resourcesobtained using multiple global climate and hydrology models, Earth Syst.Dynam., 4, 129-144, doi:10.5194/esd-4-129-2013, 2013.
Ning, T., Elgered, G., Willen, U., Johansson, J.M.: Evaluation ofthe atmospheric water vapor content in a regional climate model usingground-based GPS measurements, J. Geophys. Res., 118, 329-339, doi:10.1029/2012DJ018053, 2013.
Pacione, R., Araszkiewicz, A., Brockmann, E., and Dousa, J.:EPN-Repro2: A reference GNSS tropospheric data set over Europe, Atmos.Meas. Tech., 10, 1689-1705, doi:10.5194/amt-10-1689-2017, 2017.
J. Berckmans IWV validation Conclusions 16 / 17
Extra: precipitation bias summer
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J. Berckmans IWV validation Conclusions 17 / 17