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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).

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indirect use of nwp in nowcasting

Indirect Use of NWP in Nowcasting

Yong Wang, ZAMG, Austria

WithcontributionfromBica, Meyer, Kann, Pistotnik, Xie etc.

nowcasting systems use nwp indirectly
Nowcasting systems use NWP indirectly

(Präsentation)

(Präsentation)

20.10.2014

Folie 2

(Pierce et al., 2004)

nowcasting systems use nwp indirectly1
Nowcasting systems use NWP indirectly

(Präsentation)

(Wilson et al., 2010)

nowcasting systems use nwp indirectly2
Nowcasting systems use NWP indirectly

(Präsentation)

(George Isaac, 2011)

slide5

IntegratedNowcastingthroughComprehensive Analysis

(Präsentation)

Radiosonde

Surfaceobservations

NWP forecasts

INCA

Data QC, Integration, optimisation

Satelliteobsercations

Geoinformation data

Radar observations

AnalysesandNowcasting

(INCA reference see Haiden et al., 2011)

slide6

INCA-CE ─ A Central European Nowcasting Initiative

(Präsentation)

  • 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
inca configuation and topography
INCA configuation and topography

(Präsentation)

Domain size

600 x 350 km

Elevation range

100 - 4000 m

Resolution

Horizontal: 1 km

Vertical: 150 m

Time: 15 min – 1h

Update frequency

5 min – 1h

Availability

+ 20 min … +30 min

inca uses nwp products
INCA uses NWP products

(Präsentation)

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).

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.

(Haiden et al., 2011)

slide9

Indirectuse of NWP in Nowcasting in:

(Präsentation)

  • Observation analysis
  • Blending
  • Nowcast, including advection, initiation, growth and

decay of convection

  • Nowcastproducts
  • Ensemble Nowcasting
  • Comparison: INCA (NWP based) – VERA (non-NWP)
observation analysis
Observation analysis

(Präsentation)

Short range NWP forecasts areusuallyused as

firstguess in theobservationanalysis in nowcasting

observation analysis in inca temperature
Observation analysis in INCA: Temperature

(Präsentation)

  • The analysis of temperature starts with an NWP short-range forecast as a first guess, which is then corrected based on observation–forecast differences.
  • Corrections to the first guess are computed based on the differences ΔTkbetween the observed and NWP temperatures at stationlocations.
  • Similar to Temperature , NWP forecasts areused as firstguess in humidity and wind analysis.

(Haiden et al., 2011)

blending
Blending

(Präsentation)

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.

overview of blending
Overview of blending

(Präsentation)

(Atencia and Germann, 2010)

overview of blending1
Overview of blending

(Präsentation)

blending in b08fdp
Blending in B08FDP

(Präsentation)

(B08FDP/RDP report, 2009)

blending in inca
Blending in INCA

(Präsentation)

  • 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 shownanybenefit.

Update frequency:

ECMWF 12 h (availableat +9 h)

ALARO5 6 h (availableat +5 h)

Nowcasting 5,15 min (availableat +20…25 min)

(Haiden et al., 2011)

blending in inca1
Blending in INCA

(Präsentation)

slide18

Nowcast in INCA: convection

(Präsentation)

  • „INCA convectiveNowcasting“:
  • Foreach „convectivegirdpoint“ (i.e., with CAPE
  • > 50 J/kg in a certainarea):
      • Initiation?
      • Growth?
      • Decay?

All theindexarecomputedfrom NWP products.

(Pistotnik et al., 2011)

slide19

Nowcast in INCA: verification

(Präsentation)

RMSE ofconvectiveNowcastwith ALADIN vs. RMSE oftranslation-Nowcast (all Termine, t0+3h)

Green: improvement by convective nowcast

slide20

Nowcast in INCA: verification

(Präsentation)

RMSE ofconvectiveNowcastwith AROME vs. RMSE oftranslation-Nowcast (all Termine, t0+3h)

Green: improvement by convective nowcast

slide22

Nowcast in INCA: temperature and humidity

(Präsentation)

  • 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, andviceversa.

(Haiden et al., 2011)

slide23

Nowacstproducts

(Präsentation)

Precip type

Snowfall

Snow/Rain mix

Rain

Freezing rain

Lightning rate

slide24

Nowcastproduct

(Präsentation)

  • Many nowcast products are diagnosed using nowcating forecasts
  • in conjunction with NWP products, which provide the estimate of
  • atmospheric 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)

short term ensemble prediction system nwp blend

Ensemble Nowcastingbased on det. NWP

Short Term Ensemble Prediction System- NWP blend

(Präsentation)

  • 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

Details in presentation of Peter Steinle

(Seed, 2011)

slide26

Ensemble Nowcastingbased on NWP EPS

(Präsentation)

(Kober et al., 2010)

en inca inca aladin laef
En-INCA: INCA + ALADIN-LAEF

(Präsentation)

En-INCA = INCA as control + downscaled spread (LAEF)

Experimental:

En-INCA = blending (INCA, ALADIN-det., LAEF)

= blending ( prob. convective nowcast, AROME, LAEF)

aladin laef
ALADIN-LAEF

(Präsentation)

LAEF: Limited Area Ensemble Forecasting

Atmosphere perturbation: Blending

ALADIN Bred + ECMWF EDA/SV

Surface perturbation: Non-Cycling surface Perturbation

Model perturbation: multi-physics

comparison inca and vera analysis
Comparison: INCA and VERA analysis

(Präsentation)

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.

inca vs vera
INCA vs. VERA

(Präsentation)

inca vs vera1
INCA vs. VERA

(Präsentation)

Weatherdependent!

conclusions
Conclusions

(Präsentation)

  • NWP is widely used in Nowcasting systems indiectly:
  • Observation analysis and nowcast products
  • Blending
  • Nowcast includingadvection, 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.