indirect use of nwp in nowcasting
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Indirect Use of NWP in Nowcasting. Yong Wang, ZAMG, Austria. With contribution from Bica , Meyer, Kann, Pistotnik , Xie etc. Nowcasting systems use NWP indirectly. (Präsentation). 20.10.2014. Folie 2. (Pierce et al., 2004). Nowcasting systems use NWP indirectly. (Wilson et al., 2010). - PowerPoint PPT PresentationTRANSCRIPT
Zentralanstalt für Meteorologie und Geodynamik
Indirect Use of NWP in Nowcasting
Yong Wang, ZAMG, Austria
With contribution from Bica, Meyer, Kann, Pistotnik, Xie etc.
Zentralanstalt für Meteorologie und Geodynamik
20.04.23(Präsentation)
Folie 220.04.23(Präsentation)
Folie 2Nowcasting systems use NWP indirectly
(Pierce et al., 2004)
Zentralanstalt für Meteorologie und Geodynamik
20.04.23(Präsentation)
Folie 3Nowcasting systems use NWP indirectly
(Wilson et al., 2010)
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20.04.23(Präsentation)
Folie 4Nowcasting systems use NWP indirectly
Model Name Organization Country Spatial Resolution
Temporal Resolution Available
Times of Day Run (UTC)
Length of Forecst (hours)
General Description
ABOMLAM1km Environment Canada Canada 1 Km 15 min Every 15 min Max 6 h
Adaptive Blending of Observation and Models
using GEM LAM1k
ABOMREG Environment Canada Canada 15 km 15 min Every 15 min Max 6 h
Adaptive Blending of Observation and Models
using GEM Regional
INTW Environment Canada Canada 1 and 15
km 15 min Every 15 min Max 6 hINTegrated Weighted
Model using LAM1k, GEM Regional and Observations
INCA ZAMG Austria 1 km 1 hour Every hour 18 hours
Downscaled ECMWF forecasts as a first guess and applies corrections according to the latest
observation.
(George Isaac, 2011)
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20.04.23(Präsentation)
Folie 5Integrated Nowcasting through Comprehensive Analysis
Analyses and Nowcasting
Surface observations
Satellite obsercations
Geoinformation data
NWP forecasts
INCAData QC, Integration,
optimisation
Radar observations
Radiosonde
(INCA reference see Haiden et al., 2011)
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20.04.23(Präsentation)
Folie 6INCA-CE ─ A Central European Nowcasting Initiative
• EU funded Nowcasting project• 16 partners from 8 European countries
• Hydro-Met services• Research institutions• Public authorities
• Project budget: 4.7 million US$• Project duration: Apr 2010 – Sep 2013
• ZAMG leading• www.inca-ce.eu
• Application orineted nowcasting R&D, rapid INCA, user oriented nowcast product/grafics
• Nowcasting application in crisis managment and risk prevention in civil protection, operational Hydrology and road management
• Nowcasting based transnational warning strategy
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20.04.23(Präsentation)
Folie 7
Domain size600 x 350 km
Elevation range100 - 4000 m
ResolutionHorizontal: 1 kmVertical: 150 mTime: 15 min – 1h
Update frequency5 min – 1h
Availability+ 20 min … +30 min
INCA configuation and topography
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INCA uses NWP products 20.04.23(Präsentation)
Folie 8
There is limited interdependency between the fields. In the nowcasting of temperature the cloudiness analysis and nowcast are taken into account. The surface cooling caused by convective cells due to the evaporation of precipitation enters the analysis and nowcasting of temperature.
Derived fields include convective parameters such as the lifted condensation level (LCL), or CAPE. Snowfall line and ground temperature are computed for nowcasts of precipitation type (snow, rain, snow–rainmix, freezing rain).
(Haiden et al., 2011)
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20.04.23(Präsentation)
Folie 9
Observation analysis
Blending
Nowcast, including advection, initiation, growth and decay of convection
Nowcast products
Ensemble Nowcasting
Comparison: INCA (NWP based) – VERA (non-NWP)
Indirect use of NWP in Nowcasting in:
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Observation analysis 20.04.23(Präsentation)
Folie 10
Short range NWP forecasts are usually used as first guess in the observation analysis in nowcasting
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20.04.23(Präsentation)
Folie 11
The analysis of temperature starts with an NWP short-range forecast as a first guess, which is then corrected based on observation–forecast differences.
Observation analysis in INCA: Temperature
Corrections to the first guess are computed based on the differences ΔTk between the observed and NWP temperatures at station locations.
Similar to Temperature , NWP forecasts are used as first guess in humidity and wind analysis.
(Haiden et al., 2011)
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Blending 20.04.23(Präsentation)
Folie 12
)()()()()( ,,,, NWPjjNWPnowcastiinowcastblend
The blended forecast is calculated as the weighted sum of the extrapolation and NWP. The forecast values are combined using a time-varying weighting function which is derived from the measured performances.
To choose an appropriate quality measure is crucial. The weighting method can be linear, exponential, or the introduction of stochastic noise.
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20.04.23(Präsentation)
Folie 13Overview of blending
(Atencia and Germann, 2010)
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20.04.23(Präsentation)
Folie 14Overview of blending
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20.04.23(Präsentation)
Folie 15Blending in B08FDP
(B08FDP/RDP report, 2009)
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20.04.23(Präsentation)
Folie 16Blending in INCA
To obtain a continuous sequence of forecast fields, a transition from the extrapolation forecast to the NWP forecast is constructed through a prescribed weighting function that gives full weight to the extrapolation forecast during the first 2 h and decreases linearly to zero at 6 h.
Attempts to improve upon the fixed weighting by making the time scale of the transition dependent on the magnitudes of NWP and nowcasting errors has as yet not shown any benefit.
Update frequency:ECMWF 12 h (available at +9 h)ALARO5 6 h (available at +5 h)Nowcasting 5,15 min (available at +20…25
min)(Haiden et al., 2011)
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20.04.23(Präsentation)
Folie 17Blending in INCA
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20.04.23(Präsentation)
Folie 18Nowcast in INCA: convection
„INCA convective Nowcasting“:
For each „convective girdpoint“ (i.e., with CAPE > 50 J/kg in a certain area):
• Initiation?
• Growth?
• Decay?
KTT
kgJCIN
VIS
sMOCON
kgJCAPE
trig 2
200
5.0
/102
1006
KTT
kgJCIN
hmmRR
sMOCON
kgJCAPE
trig 2
200
2.0
/102
506
hmmRR
sMOCON
kgJCAPECAPE
0.0
/0
100,min0
All the index are computed
from NWP products.
(Pistotnik et al., 2011)
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20.04.23(Präsentation)
Folie 19Nowcast in INCA: verification
RMSE of convective Nowcast with ALADIN vs. RMSE of translation-Nowcast (all Termine, t0+3h)
Green: improvement by convective nowcast
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20.04.23(Präsentation)
Folie 20Nowcast in INCA: verification
RMSE of convective Nowcast with AROME vs. RMSE of translation-Nowcast (all Termine, t0+3h)
Green: improvement by convective nowcast
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20.04.23(Präsentation)
Folie 21Nowcast in INCA: verification
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20.04.23(Präsentation)
Folie 22Nowcast in INCA: temperature and humidity
In the case of temperature and humidity, Lagrangian persistence explains only a small part of the total temporal variation, and variations due to the diurnal cycle become dominant.
The temperature nowcast is based on the trend given by the NWP model and computed for each grid point from a recursive relationship.
TINCA(t0) temperature at the analysis time
Thus, the INCA temperature nowcast is the latest analyzed temperature plus the temperature change predicted by the NWP model, multiplied by fT.
This factor is parameterized as a function of the cloudiness forecast error of the NWP model.
If the NWP model underestimates the cloudiness compared to the INCA cloudiness analysis and nowcast, it will tend to overpredict temperature changes, and vice versa.
(Haiden et al., 2011)
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20.04.23(Präsentation)
Folie 23Nowacst products
Precip typeSnowfallSnow/Rain mixRainFreezing rain
Lightning rate
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20.04.23(Präsentation)
Folie 24Nowcast product
Many nowcast products are diagnosed using nowcating forecasts in conjunction with NWP products, which provide the estimate ofatmospheric structure:
• Visibility: liquid water content, aerosol content
• Lightning rate: updraught velocity in convective clouds
• Precipiatation type: snowfall line, 3D T and Q, cloud information
• Icing potential: T and wind
(Golding, 1998; Haiden et al., 2011)
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20.04.23(Präsentation)
Folie 25Ensemble Nowcasting based on det. NWP
Short Term Ensemble Prediction System- NWP blend
Decompose NWP into a cascade Decompose the rainfall field into a cascade Use radar field to estimate stochastic model parameters Calculate the skill of the NWP at each level in the cascade using the
correlation between NWP and radar Blend each level in the radar & NWP cascades using weights that are a
function of the forecast error at that scale and lead time For each forecast
Add noise component to the deterministic blend, the weight of the noise is calculated using the skill of the blended forecast
Combine the cascade levels to form a forecast
(Seed, 2011)
Details in presentation of Peter Steinle
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20.04.23(Präsentation)
Folie 26Ensemble Nowcasting based on NWP EPS
(Kober et al., 2010)
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En-INCA: INCA + ALADIN-LAEF 20.04.23(Präsentation)
Folie 27
Experimental:
En-INCA = blending (INCA, ALADIN-det., LAEF) = blending ( prob. convective nowcast, AROME, LAEF)
En-INCA = INCA as control + downscaled spread (LAEF)
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20.04.23(Präsentation)
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Ensemble Size 16 +1
horizontal resolution
18 km
Vertical resolution
37 levels
Runs/day 2 (00,12UTC)
Forecast range 60h
Time step 720s
Coupling-model ECMWF EDA/SV EPS
Coupling-update
6h
LAEF: Limited Area Ensemble Forecasting
Atmosphere perturbation: Blending ALADIN Bred + ECMWF EDA/SV
Surface perturbation: Non-Cycling surface Perturbation
Model perturbation: multi-physics
ALADIN-LAEF
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20.04.23(Präsentation)
Folie 29Comparison: INCA and VERA analysis
There are wo Nowcasting systems in Vienna:
VERA (Vienna Enhanced Resolution Analysis, Steinacker et al. 2006) is NWP independent and based on variational principle applied to higher-order spatial derivatives. It uses a fingerprint technique to integrate conceptual / climatological information, or upscaled radar data.
INCA relies on NWP model products and remote sensing data to interpolate between observations.
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INCA vs. VERA 20.04.23(Präsentation)
Folie 30
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20.04.23(Präsentation)
Folie 31INCA vs. VERA
Weather dependent!
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Conclusions 20.04.23(Präsentation)
Folie 32
NWP is widely used in Nowcasting systems indiectly:
• Observation analysis and nowcast products• Blending• Nowcast including advection, initiation, growth and decay of convection• Ensemble Nowcasting
Progress in NWP in the last years, e.g. advanced data assimilation technique, comprehensive model physics and cloud resolving model; assimilation of very dense observations in time and space, like radar, GPS etc., there will be more and more use of NWP directly and indirectly in Nowcasting.
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20.04.23(Präsentation)
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Thanks!