WRF Verification Toolkit Workshop, Boulder, February 2007 Spatial verification of NWP model fields Beth Ebert BMRC, Australia

WRF Verification Toolkit Workshop, Boulder, February New approaches are needed to quantitatively evaluate high resolution model output

WRF Verification Toolkit Workshop, Boulder, February What modelers want Diagnostic information  What scales are well represented by the model?  How realistic are forecast features / structures?  How realistic are distributions of intensities / values?  What are the sources of error? How can I improve the model? It's not so easy! How can I score?

WRF Verification Toolkit Workshop, Boulder, February Spatial forecasts Spatial verification techniques aim to: account for field spatial structure provide information on error in physical terms account for uncertainties in timing and location Weather variables defined over spatial domains have coherent spatial structure and features (intrinsic spatial correlation)

WRF Verification Toolkit Workshop, Boulder, February Recent research in spatial verification Scale decomposition methods  measure scale-dependent error Fuzzy (neighborhood) verification methods  give credit to "close" forecasts Object-oriented methods  evaluate attributes of identifiable features Field verification  evaluate phase errors

WRF Verification Toolkit Workshop, Boulder, February Scale decomposition methods  scale-dependent error

WRF Verification Toolkit Workshop, Boulder, February ECMWF Analysis36-h Forecast (CCM-2) 500 mb GZ, 9 Dec 1992, 12:00 UTC, N. America Wavelet scale components Briggs and Levine (1997)

Skill threshold (mm/h) scale (km) 0 1/16 ¼ ½ Intensity-scale verification technique Casati et al. (2004) Measures the skill as function of intensity and spatial scale of the error 1.Intensity: threshold  Categorical approach 2.Scale: 2D Wavelets decomposition of binary images 3.For each threshold and scale: skill score associated to the MSE of binary images = Heidke Skill Score Intense storm displaced threshold = 1mm/h

WRF Verification Toolkit Workshop, Boulder, February Multiscale statistical properties Harris et al. (2001) Does a model produce the observed precipitation scale- dependent variability, i.e. does it look like real rain? Compare multi-scale statistics for model and radar data Power spectrumStructure functionMoment scaling

WRF Verification Toolkit Workshop, Boulder, February Fuzzy (multi-scale) verification methods  give credit to "close" forecasts

WRF Verification Toolkit Workshop, Boulder, February Why is it called "fuzzy"? observationforecastobservationforecast Squint your eyes! "Fuzzy" verification methods Don't require an exact match between forecasts and observations  Unpredictable scales  Uncertainty in observations Look in a space / time neighborhood around the point of interest  Evaluate using categorical, continuous, probabilistic scores / methods t t + 1 t - 1 Forecast value Frequency

WRF Verification Toolkit Workshop, Boulder, February "Fuzzy" verification methods Treatment of forecast data within a window:  Mean value (upscaling)  Occurrence of event* somewhere in window  Frequency of event in window  probability  Distribution of values within window May apply to observations as well as forecasts (neighborhood observation-neighborhood forecast approach) * Event defined here as a value exceeding a given threshold, for example, rain exceeding 1 mm/hr

WRF Verification Toolkit Workshop, Boulder, February Sydney "high probability of some heavy rain near Sydney", not "62 mm of rain will fall in Sydney" single threshold EPS ROC Spatial multi-event contingency table Atger (2001) Vary decision thresholds: magnitude (ex: 1 mm h -1 to 20 mm h -1 ) distance from point of interest (ex: within 10 km,...., within 100 km) timing (ex: within 1 h,..., within 12 h) anything else that may be important in interpreting the forecast Forecasters mentally "calibrate" the deterministic forecast according to how close the forecast is to the place / time / magnitude of interest. Very close  high probability Not very close  low probability

WRF Verification Toolkit Workshop, Boulder, February Fractions skill score Roberts (2005) We want to know  How forecast skill varies with neighbourhood size.  The smallest neighbourhood size that can be can be used to give sufficiently accurate forecasts.  Does higher resolution provide more accurate forecasts on scales of interest (e.g. river catchments) Compare forecast fractions with observed fractions (radar) in a probabilistic way over different sized neighbourhoods observedforecast

WRF Verification Toolkit Workshop, Boulder, February Fractions skill score Roberts (2005)

WRF Verification Toolkit Workshop, Boulder, February Decision models *NO-NF = neighborhood observation-neighborhood forecast, SO-NF = single observation-neighborhood forecast

WRF Verification Toolkit Workshop, Boulder, February Fuzzy verification framework good performance poor performance

WRF Verification Toolkit Workshop, Boulder, February Object-oriented methods  evaluate attributes of features

WRF Verification Toolkit Workshop, Boulder, February Entity-based approach (CRA) Ebert and McBride (2000) Define entities using threshold (Contiguous Rain Areas) Horizontally translate the forecast until a pattern matching criterion is met:  minimum total squared error between forecast and observations  maximum correlation  maximum overlap The displacement is the vector difference between the original and final locations of the forecast. Observed Forecast

WRF Verification Toolkit Workshop, Boulder, February CRA information Gives information on: Location error RMSE and correlation before and after shift Attributes of forecast and observed entities Error components  displacement  volume  pattern

WRF Verification Toolkit Workshop, Boulder, February MODE* Davis et al. (2006) *Method for Object-based Diagnostic Evaluation Two parameters: 1.Convolution radius 2.Threshold

WRF Verification Toolkit Workshop, Boulder, February MODE object matching/merging Compare attributes: - centroid location - intensity distribution - area - orientation - etc. When objects not matched: - false alarms - missed events - rain volume - etc. 24h forecast of 1h rainfall on 1 June 2005

WRF Verification Toolkit Workshop, Boulder, February MODE methodology Identification Merging Matching Comparison Measure Attributes Convolution – threshold process Summarize Fuzzy Logic Approach Compare forecast and observed attributes Merge single objects into composite objects Compute interest values Identify matched pairs Accumulate and examine comparisons across many cases

WRF Verification Toolkit Workshop, Boulder, February Cluster analysis approach Marzban and Sandgathe (2006) MM5 precipitation forecasts 8 clusters identified in x-y-p space Goal: Assess the agreement between fields using clusters identified using agglomerative hierarchical cluster analysis (CA) Optimize clusters (and numbers of clusters) based on  Binary images (x-y optimization)  Magnitude images (x-y-p optimization) Compute Euclidean distance between clusters in forecast and observed fields (in x-y and x-y-p space)

WRF Verification Toolkit Workshop, Boulder, February Cluster analysis example Stage IV COAMPS Error = average distance between matched clusters in x-y-p space log e error

WRF Verification Toolkit Workshop, Boulder, February Composite approach Nachamkin (2004) Goal: Characterize distributions of errors from both a forecast and observation perspective Procedure:  Identify events of interest in the forecasts  Define a kernel and collect coordinated samples  Compare forecast PDF to observed PDF  Repeat process for observed events Forecast Observation x Event center

WRF Verification Toolkit Workshop, Boulder, February Composite example Compare kernel grid-averaged values Average rain (mm) given an event was predicted FCST-shade OBS-contour Average rain (mm) given an event was observed

WRF Verification Toolkit Workshop, Boulder, February Field verification  evaluate phase errors

WRF Verification Toolkit Workshop, Boulder, February Feature calibration and alignment (Hoffman et al., 1995; Nehrkorn et al., 2003) Error decomposition e = X f (r) - X v (r) where X f (r) is the forecast, X v (r) is the verifying analysis, and r is the position. e = e p + e b + e r where e p = X f (r) - X d (r)phase error e b = X d (r) - X a (r)local bias error e r = X a (r) - X v (r)residual error Original forecast X f (r) 500 mb analysis X v (r) Adjusted forecast X a (r) Forecast adjustment Residual error e r

WRF Verification Toolkit Workshop, Boulder, February Forecast quality measure (FQM) Keil and Craig (2007) Combines distance measure and intensity difference measure  Pyramidal image matching (optical flow) to get vector displacement field  e distance  Unmatched features are penalized for their intensity errors  e intensity  Forecast quality measure satelliteorig.modelmorphed model

WRF Verification Toolkit Workshop, Boulder, February Conclusions What method should you use for model verification?  Depends what question(s) you would like to address Many spatial verification approaches  Scale decomposition – scale-dependent error  Fuzzy (neighborhood) – credit for "close" forecasts  Object-oriented – attributes of features  Field verification – phase error