Upload
jocelin-jacobs
View
218
Download
1
Embed Size (px)
Citation preview
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
NWP precipitation forecasts: Validation and Value
Deterministic Forecasts
Probabilities of Precipitation
Value
Extreme Events
François Lalaurette, ECMWF
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification
Deterministic:
one cause (the weather today - the analysis),
one effect (the weather in n days - the forecast)
Verification of the forecast using observations
categorical (e.g. verify events when daily rainfall > 50mm)
continuous (needs a definition or norm for errors)
» - e.g. (RRforec.-RRobs)2 (Root Mean Square Errors)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: Biases
Bias=mean(observation-forecast)
Diurnal cycle (too much convective rain by 12h, too little by 00h - local time)
T3194D-var T511
60 levels + new precipitation scheme
New PhysicsNew microphysics3D-var
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: Bias maps
(DJF 2001)
Overestimation of orographic precipitation
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: scatter plots
Error distribution
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: Frequency Distribution
Small amounts of precipitation are much more frequent in the forecast than in SYNOP observations
39%
58%
% of days <0.1 mm
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: Heavy rainfall
Higher resolution has brought more realistic distributions of heavy rainfall
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic Verification: Does all this make sense?
Synop observations catchment area (raingauge) = O(10-1 m2)
Model grid catchment area = O(1000 km2)
a large number of independent SYNOP observations per model grid are required for the assessment of the precipitation fluxes in a model grid box.
high resolution climatological data - O(10 per model grid box)- are not exchanged in real time, but can be used for a-posteriori verification
two studies recently explored the sensitivity of ECMWF verification to the upscaling of observations (Ghelli and Lalaurette, 2000 used data from Meteo-France while Cherubini et al. used data from MAP)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic verification: Super-observations
Synop data collected from the GTS
Climatological network (Météo-France)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Deterministic verification: Super observations (2)
The bias towards too many light rain events is to a large extend a representativity artifact
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Probabilities of Precipitation
PoP can be derived following 2 strategies:
To derive the PDF from past error (conditional) statistics (MOS, Kalman Filter) e.g. using scatter diagrams
To transport a prescribed PDF for initial errors into the future (dynamical or “ensemble” approach)
• ECMWF runs 50 perturbed forecasts at T255L40 (+ 1 control)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Probabilities of Precipitation (EPSgram)
Forecast for Prague, Base time 10/5/2001 12UTC
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Probabilistic Verification
What do we want to verify?
Whether probabilities are biased…
• e.g., when an event is forecast with probability 60%, it should verify 6 times out of 10 (no more, no less!)
– but then forecasting with the probability=climate frequency is a “perfect” forecast
… or whether the probabilistic product is useful
• compared, for example, with a single, deterministic forecast
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Probabilistic Verification: 1) Reliability Diagrams
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Probabilistic Verification: 4) Brier Scores
BS=(1/N) (p-o)2
p is the probability forecast (relative number of EPS members forecasting the event)
o is the verification (=0 if the event did occur, =1 otherwise)
the Brier score varies from 0 (perfect, deterministic forecast) to 1 (perfectly wrong, deterministic forecast)
the Brier Skill Score measures the relative performance with respect to the climate (for which p=pc, the relative frequency of occurrence in the long term climate)
BSS=1-(BS/BSC)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Proba. Verification: Brier Skill Scores Time Series
Rnorm+Bugfix
T255
Rnorm -Stochastic Phys
60 levels + new precipitation
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Forecast Value: Brier Scores partition
The BS can be split into the sample climate uncertainty, the forecast reliability (BS_REL), and the forecast resolution (BS_RSL):
resolution tells how informative the probabilistic forecast is; it varies from zero for a system for which all forecasted probabilities verify with the same frequency of occurrence to the sample uncertainty for a system for which the frequency of verifying occurrences takes only values 0 or 100% (such a system resolves perfectly the forecast between occurring and non-occurring events);
reliability tells how close the frequencies of observed occurrences are from the forecasted probabilities (on average, when an event is forecasted with probability p, it should occur with the same frequency p);
uncertainty varies from 0 to 0.25 and indicates how close to 50% the occurrence of the event was during the sample period (uncertainty is 0.25 when the event is split equally into occurrence and non-occurrence).
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Forecast Value: Categorical Forecasts
Categorical forecast - Step 1: event definition
e.g.: will rain exceed 10mm over the 24h period H+72/H+96?
Step 2: gather verification data
H=number of good forecasts of the event occurring
M=number of misses (no-forecast but the event occurred)
F=number of false alarms (yes-forecast of a no-event)
Z=number of good forecasts of a no-event
False Alarm Rate=F/(F+Z)
Hit Rate=H/(H+M)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Value of Probabilistic Categorical Forecast: Relative Operative CharacteristicsForecast of the event can be made at different probability levels
(10%, 20%, etc…)
P>0
P>10%
P>20%
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Categorical Forecast Economic Value (Richardson, 2000)
Cost/loss ratio (C/L) decision model can be based on several decision-making strategies:
1. To take preventive action (with cost C) on a systematic basis;
2. To never take action (and therefore facing loss L when the event occurs);
3. taking action when the event is forecast by the meteorological model;
4. taking action when the event occurs (this strategy is based on the availability of a perfect forecast model)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Categorical Forecast Economic Value (Richardson, 2000)
Strategies 1 and 2 can be combined
always take action if the cost/loss ratio is smaller than the climatological frequency of occurrence of the event, and not to take action otherwise.
The economic value of the meteorological forecast is then computed as the reduction of the expense made possible by the use of the meteorological forecast:
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Categorical Forecast Economic Value (Richardson, 2000)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Refinements of the EPS verification procedures
Address the skill over smaller areas (need to gather several events categories - CRPS)
Specifically target extreme events (need climatological data)
Refine the references (“Poor Man Ensembles”)
Show the ensemble forecast of Z500 is not more skillful than cheaper alternatives (distributions of errors over the previous year and/or multi-model ensemble) (Atger, 1999; Ziemann, 2000)
The ensemble maximum skill seems to be achieved for abnormal situations
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: Recent examples
A) November French Floods (12-13/11/1999)
Inches
252015105
150km
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: November floods
TL319 precip. Acc 72-96h
>80mm[40, 80]mm
1100km
TL159 EPS proba precip. >20mm (0.8”)
>5%>35%
>65%
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: November floods
Verification against SYNOP data
0%<p
10%<p
20%<p
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: An EPS Climate
3 years (January 1997 to December 1999)
constant horizontal resolution (TL159)
Monthly basis, valid. 12UTC
Europe Lat/Lon grid (0.5x0.5 - oversampling )
T2m, Precip (24, 120, 240h acc.), 10m-wind speed
50 members (D5+D10) + Control (D0, D5+D10)
around 10,000 events per month
post-processing is fully non-parametric (archived values are all 100 percentiles + 1‰ and 999‰)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: An EPS Climate (2)
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: EPS Climate (November)
24h rain rates exceeded with frequency:
1% 1‰
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events : Proposals
A better definition of events worth plotting
e.g.: Number of EPS members forecasting values of 10m-wind speeds exceeding the 99% threshold in the “EPS Climate”
A non-parametric “Extreme Forecast Index”?
Based on how far the EPS distribution is from the Climate distribution
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events : Extreme Forecast Index
•By re-scaling using the climate distribution, we can create a dimensionless, signed measure:
)])([sgn()(31
0
lim
1
0
2lim
dppxxppdppxxppEFI cEPScEPS
•The Extreme Forecast Index is:•0% when forecasting the climate distribution,
•25% for a determinist forecast of the median,
•100% for a deterministic forecast of an extreme
•A CRPS-like distance between distributions:
dxpxpCRPS CEPS2
lim )]()([
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: EFI Maps for November Floods
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Extreme Events: Verification issues
The proposal is to extend the products from physical parameters
• (e.g. amounts of precipitation) to the forecast of climatological quantiles
• (e.g. the forecast to day is for a precipitation event that was not occurring more than one time out of 100 in our February climatology)
Need local climatologies to rescale the observed values
What to do with major model changes?
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Summary
Data currently exchanged on the GTS (SYNOP) can only address very crude measures of precipitation forecast performance (biases) or on scales much broader than resolved by the model (e.g. hydrologic basins)
High resolution networks are needed to upscale the data from local to model grids;
Ensemble forecasts have shown some skill in assessing the probabilities of occurrence in the medium range; an optimum combination of dynamical and statistical PoP remains to achieve
ECMWFWWRP/WMO Workshop on QPF Verification - Prague, 14-16 May 2001
Summary (2)
Value of probability forecast compared to pure deterministic forecast of precipitation are easy to establish
Some idea of extreme events can be found in the model direct output... provided it is seen from a model perspective
A framework for the verification of these extreme events forecasts has been established, but needs gathering long climatological records from a range of stations