Flood Forecasting with Radar

Transcription

1 Flood Forecasting with Radar Miguel Angel Rico-Ramirez Encuentro Internacional de Manejo del Riesgo por Inundaciones, UNAM, 22 th Jan 2013

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3 Talk Outline Rainfall estimation with radars (QPE) Radar-based precipitation forecasting (QPF) Uncertainty in QPE & QPF Hydrological apps

4 Real-Time Flood Forecasting Rainfall (measured & forecasted) Hydrological Model Simulated/ Forecasted Flow Water level Flow, Water level

5 Real-Time Flood Forecasting

6 Rainfall measurement Radar Raingauge <1km 2 <5min ~200cm 2 integration in time (0.2mm tips)

7 Radar Rainfall Estimation Z h = ar b Z h = Radar Reflectivity R = Rainfall a, b = coefficients that depend on precipitation type and drop size distribution 3.6 Z D 6 N( D) dd R D N( D) dd UK Z 200R Harrison et al (2001) Met Apps 1.6 US Z 300R 1.4 Fulton et al (1998) Weather & Forecasting

8 H-polarization H and V-polarization Visual of conventional and dual-pol radar polarization states From NEXRAD Radar Operations Center

9 Radar Rainfall Estimation Z h = ar b Z h = Reflectivity R = Rainfall a, b = coefficients that depend on precipitation type and drop size distribution Polarimetric radar: Z h Z ZDR Z h v Magnitude measurements: Size of raindrops Z h Zv ZDR LDR hv Z v Phase measurements: dp Kdp Doppler measurements: V W Raindrops falling to the ground are distorted into oblate spheroids due to aerodynamic forces, being in average their larger dimensions horizontally oriented

10 Sources of uncertainty in radar rainfall Dry Moist

11 Height (km) Vertical Profile of Reflectivity (VPR) Bright band (melting ice) Snowflakes (above the bright band) Range Z R Raindrops (below the bright band)

12 Vertical Profile of Reflectivity Rico-Ramirez et al (2007), Met Apps

13 Real-time bright band correction Kitchen et al (1994) Rico-Ramirez et al (2005), Atmos Letters

14 Clutter signals in weather radars Clutter are those unwanted echoes on weather radar scans: Fixed ground echoes (predictable) Anomalous propagation echoes (unpredictable) Sea echoes (unpredictable) Birds, airplanes, ships (unpredictable) Wind Farms

15 Clutter and Anomalous Propagation

16 Precipitation and clutter

17 Normalised PDFs to classify clutter (ground & anaprop echoes) and precipitation clutter precipitation Radar measurement ),..., ( ) ( ) ( ),..., ( n n i i n x x P c x P c P x x c P Rico-Ramirez and Cluckie (2008), IEEE TGRS.

18 Clutter removal using a Bayes classifier Raw reflectivity Clutter Corrected Reflectivity Classification performance (CSI) = 78% (Zh), 93% (Zh, V), 98% (polarimetric radar) Rico-Ramirez and Cluckie (2008), IEEE TGRS.

19 Extreme Events severe attenuation Chenies radar Thurnham radar 20th July 2007

20 5 db (10 GHz) (5.5 GHz) 1 db PIA=0.1 db (3 GHz) 20 mm/h Uniform rain rate of 20 mm/h 10 km path Bringi et al

21 Attenuation Correction: 20 th July 2007 Raw reflectivity Attenuation-corrected reflectivity A h K dp

22 Attenuation correction Rico-Ramirez (2012) IEEE TGRS.

23 Attenuation correction results Errors: Rico-Ramirez (2012) IEEE TGRS.

24 Composite rainfall estimation R az Eq.1 b R a' Eq.2 b K dp ' R Eq.3 Z Z n dr Bringi et al (2011), J. of Hydrometeorology

25 Summer 2007 floods Total radar rain accumulation July 2007

26 Representativeness errors Fabry et al. (1994)

27 Variance between radar and gauges R r Radar rainfall (Att-uncorr) (Att-corr) 1.6 Z 200R var R r R g R g Gauge rainfall R a True Areal Rainfall var R g R a Bringi (2011), J. of Hydrometeorology

28 Areas where polarimetry can help Identification of non-meteorological echoes (Clutter & anomalous propagation) Attenuation correction Radar auto calibration Rainfall estimation (Zh, ZDR, KDP) Hydrometeor classification

29 Quantifying Radar Residual Errors However, despite significant progress to correct and adjust radar rainfall estimates, residual errors remain So, what can we do about it? We need to move towards a probabilistic approach: Evaluation of individual sources of error Computation of error covariance

30 Error Covariance E k xk ˆ 10log t, xk G t, xk / Rt, xk T C LL How to generate the perturbations? Finally, the perturbations are generated using: δ Ckk Var xk C, μ Ly kl t i t, i xk Cov, y N 0, I Taking into account the temporal correlation of the error: δ ' t, i Ly t, i a 1 δ ' t 1, i a2 δ ' t 2, i δ t, i μ v δ' t, i t, i Φ t, i 10log Rt, i t, i 10log δ xl Germann et al. (2009), QJRMS

31 Study Area >200 tbr EA raingauges UKMO 1km/5min Urban area: 15 flow monitors, 7 depth monitors 4-6 raingauges Additional raingauges from Bradford CC Data sets:

32 Mear error (G/R) in db

33 Mear error (G/R) in db

34 Temporal correlation of the error

35 Example of perturbations ( t,i ) t t+1 t

36 Example of radar ensembles ( t,i ) t t+1 t

37 How many ensembles?

38 Urban catchment model

39 Urban catchment, schematic Liguori et al (2012), Atmospheric Research

40 Ensemble flow simulations Average rain 10.8mm

41 Ensemble flow simulations Average rain 10.1mm

42 Ensemble flow simulations Average rain 53.3mm * Additional uncertainties are coming from the hydraulic model

43 Precipitation Forecasting Radar advection-based methods are known as radar nowcasts. They start with high initial skill, which decreases with forecasting lead time as growth/decays processes are not resolved. Numerical Weather Models (NWP) take into account growth/decay processes, but they have a lower skill at the beginning of the forecast. However, the skill remains more or less constant with forecasting lead time.

44 Forecast skill vs forecasting time Austin, et al., (1987) Golding, (1998) Lin et al., (2005)

45 Radar Scans T-2 T-1 T How to calculate: T+1, T+2,, T+24?

46 Radar Extrapolation Forecasting Methods Use of correlation techniques Tracking radar echoes by correlation (Rinehart & Garvey, (1995)) Tracking of centroids of rain cells Use of NWP advection techniques Spectral decomposition & optical flow (Bowler et al, 2004, J. Hydrology) Merged techniques (Nowcast+NWPs): STEPS (Short-term ensemble prediction system) (Bowler et al, 2006, QJRMS)

47 Nowcasting (Spectral decomposition & optical flow) RADAR RADAR FORECASTS

48 Nowcasting (Spectral decomposition & optical flow) Radar Radar Forecasts

49 Uncertainties in nowcasting Uncertainty in radar rainfall estimations Uncertainty in the nowcasting model: Tracking Growths & decays processes not taken into account

50 Probabilistic Nowcasts (example T+3h) DET E1 E2 E3 E4 E5

51 Numerical Weather Prediction Models RADAR NWP Forecasts Additional uncertainties come from NWP forecasts

52 Nowcasts+NWP Merging (e.g. STEPS) MERGED FORECAST

53 NOWCASTING MERGED Predictability of radar nowcasting & merged forecasts Forecast lead time Liguori & Rico-Ramirez (2012), Hydrological Processes

54 Probabilistic Flood Forecasting Source: EA Liguori & Rico-Ramirez (2013), Hydrological Processes

55 Concluding comments (QPE) Radar is the only way to estimate rainfall over a large area and in real-time Radar rainfall error increases with distance from the radar location, and in hilly areas Polarimetry can help to improve data quality (e.g. discriminate non-meteorological echoes, attenuation correction, rainfall estimation), but VPR will remain one of the largest sources of errors. Improving radar coverage & use of high-resolution radars can help. Real-time radar/gauge merging might help to reduce errors Radar rainfall errors can be modelled by using the error covariance matrix, but this has to be recomputed if new radar corrections are used.

56 Concluding comments (QPF) Forecast skill is a function of spatial scales (i.e. the smallest scales are less predictable) Forecast skill is a function of rainfall intensity (i.e. the most intensive rain cells are less predictable) There is more work to do to model uncertainties in nowcasting models by incorporating more meteorological knowledge There is more work to do to use ensemble radar rainfall forecasts in urban flood forecasting. The future is data assimilation.

57 Acknowledgements The Engineering and Physical Sciences Research Council, grant EP/I012222/1 UK Met Office, Environment Agency, Yorkshire Water Services LTD, Bradford City Council, British Atmospheric Data Centre National Centre for Atmospheric Research Thanks!