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NCAR’s	
  Wind	
  Forecas2ng	
  System	
  
                    	
  
                                                   	
  
                             Luca	
  Delle	
  Monache	
  et	
  al.	
  
                                       (lucadm@ucar.edu)	
  
    Na2onal	
  Center	
  for	
  Atmospheric	
  Research	
  (NCAR)	
  −	
  Boulder,	
  CO,	
  USA	
  
                                                   	
  
                                                   	
  
                                                   	
  

       Winterwind	
  –	
  Interna2onal	
  Wind	
  Energy	
  Conference	
  
                12-­‐13	
  February,	
  2013,	
  Östersund,	
  Sweden	
  
Outline	
  

•  The	
  U.S.	
  Na2onal	
  Center	
  for	
  Atmospheric	
  Research	
  (NCAR)	
  

•  Renewable	
  energy	
  forecas2ng	
  research	
  and	
  development	
  

•  NCAR-­‐Xcel	
  energy	
  project	
  

•  Probabilis2c	
  power	
  predic2ons	
  

  o    The	
  Analog	
  Ensemble	
  (AnEn)	
  

  o    Test	
  cases	
  

•  Summary	
  

                                                                                      2
What	
  is	
  the	
  US	
  Na2onal	
  Center	
  for	
  
         Atmospheric	
  Research	
  (NCAR)?	
  
•  NCAR	
  is	
  a	
  Federally	
  funded	
  research	
  and	
  
   development	
  center	
  sponsored	
  by	
  the	
  U.S.	
  
   Na2onal	
  Science	
  Founda2on	
  
•  NCAR	
  is	
  operated	
  by	
  the	
  University	
  
   Corpora2on	
  for	
  Atmospheric	
  Research	
  
   (UCAR),	
  a	
  non-­‐profit	
  corpora2on.	
  
•  UCAR	
  has	
  1400	
  employees	
  and	
  ~$250M	
  
   budget.	
  
•  Research	
  is	
  conducted	
  on	
  climate	
  and	
  
   weather	
  modeling,	
  air	
  chemistry,	
                     NCAR, Boulder, CO
   thunderstorms,	
  hurricanes,	
  icing,	
  turbulence,	
  
   societal	
  impacts	
  of	
  weather,	
  energy,	
  solar	
  
   physics,	
  etc.	
  
Power	
  Predic2on	
  


Goal:	
  
Accurate	
  power	
  forecasts	
  and	
  reliable	
  quan2fica2on	
  of	
  forecast	
  
uncertainty	
  
	
  
	
  
Mo+va+on:	
  
•  Wind	
  power	
  forecas2ng	
  is	
  necessary	
  for	
  effec2ve	
  grid	
  integra2on	
  
     o  Day-­‐ahead	
  forecas2ng	
  –	
  energy	
  trading	
  

     o  Short-­‐term	
  forecas2ng	
  –	
  grid	
  integra2on	
  &	
  stabiliza2on	
  

•  Thus,	
  an	
  effec2ve	
  forecas2ng	
  system	
  should	
  include	
  components	
  for	
  
     both	
  
	
  
Renewable	
  Energy	
  Forecas2ng	
  
Xcel	
  Energy	
  Service	
  Areas	
  
                                                        Wind Farms (50+)
                                                        3585 Turbines (growing)
                                                        4842 MW+ (wind)
                                                        ~10% Wind
                                                        (highest in continental US)




                                                        3.4 million customers
                                                           (electric)
                                                        Annual revenue $11B



Provides	
  good	
  geographical	
  diversity	
  for	
  research	
  and	
  tes2ng	
  
NCAR’s	
  Wind	
  Energy	
  Predic2on	
  	
  
        System	
  for	
  Xcel	
  Energy	
  
                                                  CSV	
  Data	
  
  NCEP Data          Wind Farm Data                                  Operator GUI
     NAM              Nacelle wind speed
     GFS               Generator power
     RUC                 Node power
  GEM (Canada)            Met tower
                         Availability
                                              Statistical
WRF RTFDDA                                   Verification
  System                Dynamic,
                                                                     Meteorologist
                       Integrated          Wind to Energy                GUI
  WRF+MM5               Forecast            Conversion
Ensemble System          System             Subsystem
                        (DICast®)

                                                                    WRF	
  Model	
  Output	
  
 Supplemental            VDRAS
Wind Farm Data          (nowcasting)
    Met towers
   Wind profiler
  Surface Stations   Expert System
  Windcube Lidar        (nowcasting)
WRF	
  RTFDDA	
  Model	
  Domains	
  
 Determinis2c	
  System	
         Ensemble	
  System	
  (30	
  members)	
  


             D3 = 3.3 km                          D2 = 10 km




D1 = 30 km 0-72 hrs           D1 = 30 km 0-48 hrs
D2 = 10 km 0-72 hrs           D2 = 10 km 0-48 hrs
D3 = 3.3 km 0-24 hrs
NCAR-Xcel Energy Project
Accurate prediction economical benefits
                            ~$1.9M	
  per	
  each	
  
                                       percent	
  	
  
                               improvement	
  
NCAR-Xcel Energy Project
      CO2 reduction due to accurate predictions


“The avoided generation occurred when Xcel cycled offline
baseload thermal units (coal or natural gas combined cycle) due
to extended periods of forecasted low loads and high winds.”

AVOIDED EMISSIONS DUE TO IMPROVED PREDICTIONS: 238,136 TONS OF CO2


MWh’s of avoided generation in 2011
Arapahoe 3 = 317
Arapahoe 4 = 6,941
Cherokee 1 = 11,606
Cherokee 2 = 13,772
Valmont 5 = 10,061
FSV CC = 93,626
RMEC CC = 308,989

                                                                 10
Probabilis2c	
  Power	
  Predic2on	
  


Goal:	
  
Accurate	
  power	
  forecasts	
  and	
  reliable	
  quan2fica2on	
  of	
  forecast	
  
uncertainty	
  
	
  
	
  
Mo+va+on:	
  
•  Wind	
  power	
  forecas2ng	
  is	
  necessary	
  for	
  effec2ve	
  grid	
  integra2on	
  
     o  Day-­‐ahead	
  forecas2ng	
  –	
  energy	
  trading	
  

     o  Short-­‐term	
  forecas2ng	
  –	
  grid	
  integra2on	
  &	
  stabiliza2on	
  

•  Thus,	
  an	
  effec2ve	
  forecas2ng	
  system	
  should	
  include	
  components	
  for	
  
     both	
  
	
  
Ensemble	
  (En)	
  Predic2on	
  

                                                                                                fi
The	
  single	
  determinis+c	
  forecast	
  f0	
  
fails	
  to	
  predict	
  the	
  TRUE	
  
	
                                                                                                   f0




                                                      Wind	
  Speed	
  
The	
  ini2al	
  probability	
  density	
  func2on	
  
PDF(0)	
  represents	
  the	
  ini2al	
  
uncertain2es	
  
	
                                                                                            	
          PDF(t)
                                                                                          TRUE
An	
  ensemble	
  of	
  perturbed	
  forecasts	
  
fi,	
  star2ng	
  from	
  perturbed	
  ini2al	
  
condi2ons	
  designed	
  to	
  sample	
  the	
  
ini2al	
  uncertain2es	
  can	
  be	
  used	
  to	
   PDF(0)
es2mate	
  the	
  probability	
  of	
  future	
  
states	
  PDF(t)	
  


                                                                          Forecast	
  +me	
  

                                                                                                                   12
Weather	
  analogs:	
  basic	
  idea	
  




                                           13
Weather	
  analogs:	
  basic	
  idea	
  



Today
Weather	
  analogs:	
  basic	
  idea	
  



Today




              One week ago?




                                            15
Weather	
  analogs:	
  basic	
  idea	
  



Today




              One week ago?




                                5 years ago?!?

                                                 16
Weather	
  analogs:	
  basic	
  idea	
  



Today




              One week ago?




                                5 years ago?!?

                                                 17
Weather	
  analogs:	
  basic	
  idea	
  



Today




              One week ago?




                                5 years ago?!?

                                                 18
Weather	
  analogs:	
  basic	
  idea	
  



Today




              One week ago?




                                5 years ago?!?

                                                 19
Weather	
  analogs:	
  basic	
  idea	
  



       Today                      	
  
           Can	
  we	
  use	
  this	
  informa2on	
  
         (i.e.,	
  both	
  obs	
  and	
  re-­‐analysis),	
  	
  
to	
  improve	
  forecasts	
  or	
  resource	
  es2mates?	
  
                           One week ago?

                                  	
  

                                             5 years ago?!?

                                                                   20
Analog	
  Ensemble	
  (AnEn)	
  




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0   Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                        21
Analog	
  Ensemble	
  (AnEn)	
  
                                                             PRED




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0      Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                           22
Analog	
  Ensemble	
  (AnEn)	
  
                                                             PRED
                                                             OBS




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0      Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                           23
Analog	
  Ensemble	
  (AnEn)	
  
                                                             PRED
                                                             OBS




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0      Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                           24
Analog	
  Ensemble	
  (AnEn)	
  
                                                             PRED
                                                             OBS




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0      Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                           25
Analog	
  Ensemble	
  (AnEn)	
  
                                                             PRED
                                                             OBS




          Mon    Tue   Wed    Thu    Fri   Sat   Sun
                                                       t=0      Time	
  




Analog search as in Delle Monache et al. (MWR 2011)
                                                                           26
Analog	
  Ensemble	
  (AnEn)	
  
                                                                       PRED
                                                                       OBS




          Mon            Tue   Wed   Thu   Fri   Sat   Sun
                                                                 t=0      Time	
  




          Wed            Fri   Sat   Tue   Sun   Mon   Thu
          farthest	
                                   closest	
  
           analog	
                                    analog	
         “Analog”	
  Space	
  
Analog search as in Delle Monache et al. (MWR 2011)
                                                                                                27
Analog	
  Ensemble	
  (AnEn)	
  
                                                                       PRED
                                                                       OBS




          Mon            Tue   Wed   Thu   Fri   Sat   Sun
                                                                 t=0      Time	
  




          Wed            Fri   Sat   Tue   Sun   Mon   Thu
          farthest	
                                   closest	
  
           analog	
                                    analog	
         “Analog”	
  Space	
  
Analog search as in Delle Monache et al. (MWR 2011)
                                                                                                28
Analog	
  Ensemble	
  (AnEn)	
  
                                                                       PRED
                                                                       OBS




          Mon            Tue   Wed   Thu   Fri   Sat   Sun
                                                                 t=0       Time	
  

                                                                       2-­‐member	
  AnEn	
  




          Wed            Fri   Sat   Tue   Sun   Mon   Thu
          farthest	
                                   closest	
  
           analog	
                                    analog	
         “Analog”	
  Space	
  
Analog search as in Delle Monache et al. (MWR 2011)
                                                                                                29
How skillful is AnEn?
•  AnEn generated with Environment Canada GEM (15 km),
  0-48 hours
•  Comparison with:
   o    Environment Canada Regional Ensemble Prediction System (REPS, next slide)
   o    Logistic Regression (LR) out of 15-km GEM
   o    LR our of REPS, i.e., Ensemble Model Output Statistics (EMOS)

•  Period of 15 months (verification over the last 3 months)
•  10-m wind speed
•  550 surface stations over CONUS (in two slides)
•  Probabilistic prediction attributes: statistical consistency,
  reliability, sharpness, resolution, spread-error consistency
                                                                                    30
Regional	
  Ensemble	
  Predic2on	
  System	
  (REPS)
•  Model: GEM 4.2.0 (vertical staggering)
•  20 members + 1 control run
•  72 hours forecast lead time
•  Resolution: ~33 km with 28 levels
•  Initial conditions (i.e., cold start) and 3-hourly boundary
   condition updates from GEPS (EnKF + multi-physics)
•  Physics:
 o    Kain et Fritsch (1993) for deep convection
 o    Li et Barker (2005) for the radiation
 o    ISBA scheme (Noilhan et Planton, 1989) for surface
•  Stochastic Physics: Markov Chains on physical tendencies




                                                                 31
Ground	
  truth	
  dataset	
  
•  550 hourly METAR Surface Observations
•  1 May 2010 – 31 July 2011, for a total of 457 days
•  10-m wind speed




                                                        32
Probabilis2c	
  forecast	
  ahributes:	
  Reliability	
  

Example:

①  An event (e.g., wind speed > 5 m/s) is predicted to happen
   with a 30% probability

②  We collect the observations that verified every time we made
   the prediction in 1

③  If the frequency of the event in the observation collected is
   30%, then the forecast is perfectly RELIABLE
Analysis	
  of	
  reliability	
  &	
  sharpness	
  
 Reliability and sharpness diagram: 10-m wind speed > 5 m s-1, 9-h fcst
                                     1                                             1
                                              (a)                                           (b)


      Observed Relative Frequency
                                              REPS                                          EMOS
                                    0.8                                           0.8

         Forecast Probability
                                    0.6                                           0.6


                                    0.4                                           0.4


                                    0.2                                           0.2


                                     0                                             0
                                          0          0.2   0.4    0.6   0.8   1         0          0.2   0.4    0.6   0.8   1




                                     1                                             1
                                              (c)                                           (d)
      Observed Relative Frequency




                                              LR                                            AnEn
                                    0.8                                           0.8
         Forecast Probability




                                    0.6                                           0.6


                                    0.4                                           0.4


                                    0.2                                           0.2


                                     0                                             0
                                          0          0.2   0.4    0.6   0.8   1         0          0.2   0.4    0.6   0.8   1
                                                      Forecast Probability                          Forecast Probability
                                                                                                                                34
Probabilis2c	
  forecast	
  ahributes:	
  Sharpness	
  
Sharpness refers to the degree of concentration of a forecast PDF’s
probability density, and is a property of the forecasts only.

Ideally, we want the forecast system, while mainly reliable, with as
many forecasts as possible close to 0% and 100%, corresponding to a
perfect deterministic forecast system. However, an improvement in
sharpness does not necessarily mean that the forecast system has
improved.




   0    5    10    15     20   T         0      5    10   15       20   T
       Sharper Forecast                      Less Sharp Forecast
Analysis	
  of	
  reliability	
  &	
  sharpness	
  
 Reliability and sharpness diagram: 10-m wind speed > 5 m s-1, 9-h fcst
                                     1                                             1
                                              (a)                                           (b)


      Observed Relative Frequency
                                              REPS                                          EMOS
                                    0.8                                           0.8

         Forecast Probability
                                    0.6                                           0.6


                                    0.4                                           0.4


                                    0.2                                           0.2


                                     0                                             0
                                          0          0.2   0.4    0.6   0.8   1         0          0.2   0.4    0.6   0.8   1




                                     1                                             1
                                              (c)                                           (d)
      Observed Relative Frequency




                                              LR                                            AnEn
                                    0.8                                           0.8
         Forecast Probability




                                    0.6                                           0.6


                                    0.4                                           0.4


                                    0.2                                           0.2


                                     0                                             0
                                          0          0.2   0.4    0.6   0.8   1         0          0.2   0.4    0.6   0.8   1
                                                      Forecast Probability                          Forecast Probability
                                                                                                                                36
ATTRIBUTES OF FORECASTSahributes:	
  
     Probabilis2c	
  forecast	
  
atic error     RESOLUTION – Different forecasts
             Resolu2on	
   observed events
               precede different




stConsider different classes of classes of fcst events
            Consider different forecast events.
bsIf all observed classes corresponds to different by
              If all observed classes are preceded forecast classes,
ons => probabilisticdifferent forecasts => RESOLUTION.
  then the     distinctly forecast has perfect
Y                      PERFECT RESOLUTION
orrected       Resolution CANNOT BE statistically                      37
Analysis	
  of	
  Resolu2on	
  (1)	
  
     Rela2ve	
  Opera2ng	
  Characteris2cs	
  skill	
  score,	
  10-­‐m	
  wind	
  speed	
  ≥	
  5,	
  10	
  m	
  s-­‐1	
  


                                      WSPD	
  >	
  5	
  m	
  s-­‐1	
                                      WSPD	
  >	
  10	
  m	
  s-­‐1	
  
                    0.8                                                               1
beNer	
  




                              AnEn    EMOS           REPS            LR        (a)                                                             (b)


                    0.7
                                                                                     0.9


                    0.6
            ROCSS




                                                                                     0.8

                    0.5


                                                                                     0.7
                    0.4
worse	
  




                                                                                     0.6
                          0    6     12     18     24      30      36     42   48          0   6     12      18      24     30     36     42   48
                                   Forecast Lead Time (hours)                                      Forecast Lead Time (hours)




                                                                                                                                                     38
AnEn	
  sensi2vity	
  
            Rela2ve	
  Opera2ng	
  Characteris2cs	
  skill	
  score,	
  10-­‐m	
  wind	
  speed	
  ≥	
  5	
  m	
  s-­‐1	
  

                          AnEn	
  with	
  a	
  shorter	
  	
                                    AnEn	
  built	
  with	
  a	
  coarser	
  
                   training	
  data	
  set	
  (15	
  à	
  9	
  months)	
                    dynamical	
  model	
  (15	
  à	
  33	
  km)	
  
beNer	
  




             0.8                                                                   0.8




            0.75

                                                                                   0.7
    ROCSS




                                                                           ROCSS
             0.7



                                                                                   0.6

            0.65
                                                                                              AnEn
                       AnEn
                                                                                              LR
worse	
  




                       LR
                                                                                                 Fine Resolution (15−km GEM)
                          Full Training
                          Short Training                                                             Coarse Resolution (33−km REPS mem. # 20)
             0.6                                                                   0.5
                   0     6     12     18    24    30     36    42     48                 0       6      12     18    24    30    36    42       48
                              Forecast Lead Time (hours)                                                Forecast Lead Time (hours)


                                                                                                                                                     39
Power predictions: Experiment design




•  Test site: Wind farm in northern Sicily − 9 turbines, 850 kW Nominal Power (NP)
•  Training period: November 2010 - October 2012
•  Verification period: November 2011 – October 2012
•  Probabilistic prediction systems: ECMWF EPS, COSMO LEPS, AnEn
Power	
  predic2ons	
  
Normalized Power




                         Forecast Lead Time



                                              41
RMSE	
  of	
  ensemble	
  means	
  
Probabilis2c	
  forecast	
  ahributes:	
  	
  
     Sta2s2cal	
  and	
  spread-­‐error	
  consistency	
  


①  The ensemble spread tell us how uncertain a forecast is.
   Ideally, large spread should be associate with larger
   uncertainties, low spread should indicate higher accuracy

②  If an ensemble is perfect, than the observations are
   indistinguishable from the ensemble members
Spread-­‐skill	
  relathionship	
  



           ECMWF	
  EPS	
  
Summary	
  
•  NCAR’s	
  wind	
  energy	
  research	
  and	
  development	
  

  o    Icing,	
  fine-­‐scale	
  &	
  boundary	
  layer	
  meteorology	
  research	
  

  o    Data	
  assimila2on,	
  sta2s2cal	
  learning	
  

  o    Wind	
  &	
  power	
  predic2ons,	
  wind	
  resource	
  assessment	
  

•  The	
  NCAR-­‐Xcel	
  Project,	
  a	
  successful	
  story	
  

•  The	
  analog	
  ensemble	
  provides	
  accurate	
  predic2ons/es2mates	
  and	
  
    reliable	
  uncertainty	
  quan2fica2on	
  (at	
  a	
  lower	
  computa2onal	
  cost)	
  	
  

•  The	
  analog	
  ensemble	
  can	
  be	
  used	
  for	
  dynamical	
  downscaling	
  and	
  wind	
  
    resource	
  assessment	
  
           	
  	
  
                                                                                                          45
THANKS!	
  
                                                                 (lucadm@ucar.edu)	
  



Collaborators	
  include:	
  
Bill	
  Mahoney,	
  Sue	
  Haupt,	
  Greg	
  Thompson,	
  Gerry	
  Wiener,	
  Bill	
  Myers,	
  David	
  Johnson,	
  
Yubao	
  Liu,	
  Jenny	
  Sun,	
  Tom	
  Hopson,	
  Branko	
  Kosovic,	
  Julie	
  Lundquist,	
  Stefano	
  
Alessandrini,	
  Seth	
  Linden,	
  Julia	
  Pearson,	
  Frank	
  McDonough,	
  …	
  


References	
  
•  Delle	
  Monache	
  et	
  al.,	
  2011:	
  Kalman	
  filter	
  and	
  analog	
  schemes	
  to	
  postprocess	
  numerical	
  weather	
  predic2ons.	
  Monthly	
  
   Weather	
  Review,	
  139,	
  3554−3570.	
  
•  Delle	
  Monache	
  et	
  al.,	
  2013:	
  Probabilis2c	
  weather	
  predic2ons	
  with	
  an	
  analog	
  ensemble.	
  Mon.	
  Weather	
  Review,	
  sub.	
  	
  
•  Vanvyve	
  and	
  Delle	
  Monache,	
  2013:	
  Wind	
  resource	
  assessment	
  with	
  an	
  analog	
  ensemble.	
  Journal	
  of	
  Applied	
  
   Meteorology,	
  in	
  prepara2on.	
                                                                                                                                 46

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Delle monache luca

  • 1.   NCAR’s  Wind  Forecas2ng  System       Luca  Delle  Monache  et  al.   (lucadm@ucar.edu)   Na2onal  Center  for  Atmospheric  Research  (NCAR)  −  Boulder,  CO,  USA         Winterwind  –  Interna2onal  Wind  Energy  Conference   12-­‐13  February,  2013,  Östersund,  Sweden  
  • 2. Outline   •  The  U.S.  Na2onal  Center  for  Atmospheric  Research  (NCAR)   •  Renewable  energy  forecas2ng  research  and  development   •  NCAR-­‐Xcel  energy  project   •  Probabilis2c  power  predic2ons   o  The  Analog  Ensemble  (AnEn)   o  Test  cases   •  Summary   2
  • 3. What  is  the  US  Na2onal  Center  for   Atmospheric  Research  (NCAR)?   •  NCAR  is  a  Federally  funded  research  and   development  center  sponsored  by  the  U.S.   Na2onal  Science  Founda2on   •  NCAR  is  operated  by  the  University   Corpora2on  for  Atmospheric  Research   (UCAR),  a  non-­‐profit  corpora2on.   •  UCAR  has  1400  employees  and  ~$250M   budget.   •  Research  is  conducted  on  climate  and   weather  modeling,  air  chemistry,   NCAR, Boulder, CO thunderstorms,  hurricanes,  icing,  turbulence,   societal  impacts  of  weather,  energy,  solar   physics,  etc.  
  • 4. Power  Predic2on   Goal:   Accurate  power  forecasts  and  reliable  quan2fica2on  of  forecast   uncertainty       Mo+va+on:   •  Wind  power  forecas2ng  is  necessary  for  effec2ve  grid  integra2on   o  Day-­‐ahead  forecas2ng  –  energy  trading   o  Short-­‐term  forecas2ng  –  grid  integra2on  &  stabiliza2on   •  Thus,  an  effec2ve  forecas2ng  system  should  include  components  for   both    
  • 6. Xcel  Energy  Service  Areas   Wind Farms (50+) 3585 Turbines (growing) 4842 MW+ (wind) ~10% Wind (highest in continental US) 3.4 million customers (electric) Annual revenue $11B Provides  good  geographical  diversity  for  research  and  tes2ng  
  • 7. NCAR’s  Wind  Energy  Predic2on     System  for  Xcel  Energy   CSV  Data   NCEP Data Wind Farm Data Operator GUI NAM Nacelle wind speed GFS Generator power RUC Node power GEM (Canada) Met tower Availability Statistical WRF RTFDDA Verification System Dynamic, Meteorologist Integrated Wind to Energy GUI WRF+MM5 Forecast Conversion Ensemble System System Subsystem (DICast®) WRF  Model  Output   Supplemental VDRAS Wind Farm Data (nowcasting) Met towers Wind profiler Surface Stations Expert System Windcube Lidar (nowcasting)
  • 8. WRF  RTFDDA  Model  Domains   Determinis2c  System   Ensemble  System  (30  members)   D3 = 3.3 km D2 = 10 km D1 = 30 km 0-72 hrs D1 = 30 km 0-48 hrs D2 = 10 km 0-72 hrs D2 = 10 km 0-48 hrs D3 = 3.3 km 0-24 hrs
  • 9. NCAR-Xcel Energy Project Accurate prediction economical benefits ~$1.9M  per  each   percent     improvement  
  • 10. NCAR-Xcel Energy Project CO2 reduction due to accurate predictions “The avoided generation occurred when Xcel cycled offline baseload thermal units (coal or natural gas combined cycle) due to extended periods of forecasted low loads and high winds.” AVOIDED EMISSIONS DUE TO IMPROVED PREDICTIONS: 238,136 TONS OF CO2 MWh’s of avoided generation in 2011 Arapahoe 3 = 317 Arapahoe 4 = 6,941 Cherokee 1 = 11,606 Cherokee 2 = 13,772 Valmont 5 = 10,061 FSV CC = 93,626 RMEC CC = 308,989 10
  • 11. Probabilis2c  Power  Predic2on   Goal:   Accurate  power  forecasts  and  reliable  quan2fica2on  of  forecast   uncertainty       Mo+va+on:   •  Wind  power  forecas2ng  is  necessary  for  effec2ve  grid  integra2on   o  Day-­‐ahead  forecas2ng  –  energy  trading   o  Short-­‐term  forecas2ng  –  grid  integra2on  &  stabiliza2on   •  Thus,  an  effec2ve  forecas2ng  system  should  include  components  for   both    
  • 12. Ensemble  (En)  Predic2on   fi The  single  determinis+c  forecast  f0   fails  to  predict  the  TRUE     f0 Wind  Speed   The  ini2al  probability  density  func2on   PDF(0)  represents  the  ini2al   uncertain2es       PDF(t) TRUE An  ensemble  of  perturbed  forecasts   fi,  star2ng  from  perturbed  ini2al   condi2ons  designed  to  sample  the   ini2al  uncertain2es  can  be  used  to   PDF(0) es2mate  the  probability  of  future   states  PDF(t)   Forecast  +me   12
  • 14. Weather  analogs:  basic  idea   Today
  • 15. Weather  analogs:  basic  idea   Today One week ago? 15
  • 16. Weather  analogs:  basic  idea   Today One week ago? 5 years ago?!? 16
  • 17. Weather  analogs:  basic  idea   Today One week ago? 5 years ago?!? 17
  • 18. Weather  analogs:  basic  idea   Today One week ago? 5 years ago?!? 18
  • 19. Weather  analogs:  basic  idea   Today One week ago? 5 years ago?!? 19
  • 20. Weather  analogs:  basic  idea   Today   Can  we  use  this  informa2on   (i.e.,  both  obs  and  re-­‐analysis),     to  improve  forecasts  or  resource  es2mates?   One week ago?   5 years ago?!? 20
  • 21. Analog  Ensemble  (AnEn)   Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 21
  • 22. Analog  Ensemble  (AnEn)   PRED Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 22
  • 23. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 23
  • 24. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 24
  • 25. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 25
  • 26. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Analog search as in Delle Monache et al. (MWR 2011) 26
  • 27. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Wed Fri Sat Tue Sun Mon Thu farthest   closest   analog   analog   “Analog”  Space   Analog search as in Delle Monache et al. (MWR 2011) 27
  • 28. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   Wed Fri Sat Tue Sun Mon Thu farthest   closest   analog   analog   “Analog”  Space   Analog search as in Delle Monache et al. (MWR 2011) 28
  • 29. Analog  Ensemble  (AnEn)   PRED OBS Mon Tue Wed Thu Fri Sat Sun t=0 Time   2-­‐member  AnEn   Wed Fri Sat Tue Sun Mon Thu farthest   closest   analog   analog   “Analog”  Space   Analog search as in Delle Monache et al. (MWR 2011) 29
  • 30. How skillful is AnEn? •  AnEn generated with Environment Canada GEM (15 km), 0-48 hours •  Comparison with: o  Environment Canada Regional Ensemble Prediction System (REPS, next slide) o  Logistic Regression (LR) out of 15-km GEM o  LR our of REPS, i.e., Ensemble Model Output Statistics (EMOS) •  Period of 15 months (verification over the last 3 months) •  10-m wind speed •  550 surface stations over CONUS (in two slides) •  Probabilistic prediction attributes: statistical consistency, reliability, sharpness, resolution, spread-error consistency 30
  • 31. Regional  Ensemble  Predic2on  System  (REPS) •  Model: GEM 4.2.0 (vertical staggering) •  20 members + 1 control run •  72 hours forecast lead time •  Resolution: ~33 km with 28 levels •  Initial conditions (i.e., cold start) and 3-hourly boundary condition updates from GEPS (EnKF + multi-physics) •  Physics: o  Kain et Fritsch (1993) for deep convection o  Li et Barker (2005) for the radiation o  ISBA scheme (Noilhan et Planton, 1989) for surface •  Stochastic Physics: Markov Chains on physical tendencies 31
  • 32. Ground  truth  dataset   •  550 hourly METAR Surface Observations •  1 May 2010 – 31 July 2011, for a total of 457 days •  10-m wind speed 32
  • 33. Probabilis2c  forecast  ahributes:  Reliability   Example: ①  An event (e.g., wind speed > 5 m/s) is predicted to happen with a 30% probability ②  We collect the observations that verified every time we made the prediction in 1 ③  If the frequency of the event in the observation collected is 30%, then the forecast is perfectly RELIABLE
  • 34. Analysis  of  reliability  &  sharpness   Reliability and sharpness diagram: 10-m wind speed > 5 m s-1, 9-h fcst 1 1 (a) (b) Observed Relative Frequency REPS EMOS 0.8 0.8 Forecast Probability 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 1 1 (c) (d) Observed Relative Frequency LR AnEn 0.8 0.8 Forecast Probability 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Forecast Probability Forecast Probability 34
  • 35. Probabilis2c  forecast  ahributes:  Sharpness   Sharpness refers to the degree of concentration of a forecast PDF’s probability density, and is a property of the forecasts only. Ideally, we want the forecast system, while mainly reliable, with as many forecasts as possible close to 0% and 100%, corresponding to a perfect deterministic forecast system. However, an improvement in sharpness does not necessarily mean that the forecast system has improved. 0 5 10 15 20 T 0 5 10 15 20 T Sharper Forecast Less Sharp Forecast
  • 36. Analysis  of  reliability  &  sharpness   Reliability and sharpness diagram: 10-m wind speed > 5 m s-1, 9-h fcst 1 1 (a) (b) Observed Relative Frequency REPS EMOS 0.8 0.8 Forecast Probability 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 1 1 (c) (d) Observed Relative Frequency LR AnEn 0.8 0.8 Forecast Probability 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Forecast Probability Forecast Probability 36
  • 37. ATTRIBUTES OF FORECASTSahributes:   Probabilis2c  forecast   atic error RESOLUTION – Different forecasts Resolu2on   observed events precede different stConsider different classes of classes of fcst events Consider different forecast events. bsIf all observed classes corresponds to different by If all observed classes are preceded forecast classes, ons => probabilisticdifferent forecasts => RESOLUTION. then the distinctly forecast has perfect Y PERFECT RESOLUTION orrected Resolution CANNOT BE statistically 37
  • 38. Analysis  of  Resolu2on  (1)   Rela2ve  Opera2ng  Characteris2cs  skill  score,  10-­‐m  wind  speed  ≥  5,  10  m  s-­‐1   WSPD  >  5  m  s-­‐1   WSPD  >  10  m  s-­‐1   0.8 1 beNer   AnEn EMOS REPS LR (a) (b) 0.7 0.9 0.6 ROCSS 0.8 0.5 0.7 0.4 worse   0.6 0 6 12 18 24 30 36 42 48 0 6 12 18 24 30 36 42 48 Forecast Lead Time (hours) Forecast Lead Time (hours) 38
  • 39. AnEn  sensi2vity   Rela2ve  Opera2ng  Characteris2cs  skill  score,  10-­‐m  wind  speed  ≥  5  m  s-­‐1   AnEn  with  a  shorter     AnEn  built  with  a  coarser   training  data  set  (15  à  9  months)   dynamical  model  (15  à  33  km)   beNer   0.8 0.8 0.75 0.7 ROCSS ROCSS 0.7 0.6 0.65 AnEn AnEn LR worse   LR Fine Resolution (15−km GEM) Full Training Short Training Coarse Resolution (33−km REPS mem. # 20) 0.6 0.5 0 6 12 18 24 30 36 42 48 0 6 12 18 24 30 36 42 48 Forecast Lead Time (hours) Forecast Lead Time (hours) 39
  • 40. Power predictions: Experiment design •  Test site: Wind farm in northern Sicily − 9 turbines, 850 kW Nominal Power (NP) •  Training period: November 2010 - October 2012 •  Verification period: November 2011 – October 2012 •  Probabilistic prediction systems: ECMWF EPS, COSMO LEPS, AnEn
  • 41. Power  predic2ons   Normalized Power Forecast Lead Time 41
  • 42. RMSE  of  ensemble  means  
  • 43. Probabilis2c  forecast  ahributes:     Sta2s2cal  and  spread-­‐error  consistency   ①  The ensemble spread tell us how uncertain a forecast is. Ideally, large spread should be associate with larger uncertainties, low spread should indicate higher accuracy ②  If an ensemble is perfect, than the observations are indistinguishable from the ensemble members
  • 45. Summary   •  NCAR’s  wind  energy  research  and  development   o  Icing,  fine-­‐scale  &  boundary  layer  meteorology  research   o  Data  assimila2on,  sta2s2cal  learning   o  Wind  &  power  predic2ons,  wind  resource  assessment   •  The  NCAR-­‐Xcel  Project,  a  successful  story   •  The  analog  ensemble  provides  accurate  predic2ons/es2mates  and   reliable  uncertainty  quan2fica2on  (at  a  lower  computa2onal  cost)     •  The  analog  ensemble  can  be  used  for  dynamical  downscaling  and  wind   resource  assessment       45
  • 46. THANKS!   (lucadm@ucar.edu)   Collaborators  include:   Bill  Mahoney,  Sue  Haupt,  Greg  Thompson,  Gerry  Wiener,  Bill  Myers,  David  Johnson,   Yubao  Liu,  Jenny  Sun,  Tom  Hopson,  Branko  Kosovic,  Julie  Lundquist,  Stefano   Alessandrini,  Seth  Linden,  Julia  Pearson,  Frank  McDonough,  …   References   •  Delle  Monache  et  al.,  2011:  Kalman  filter  and  analog  schemes  to  postprocess  numerical  weather  predic2ons.  Monthly   Weather  Review,  139,  3554−3570.   •  Delle  Monache  et  al.,  2013:  Probabilis2c  weather  predic2ons  with  an  analog  ensemble.  Mon.  Weather  Review,  sub.     •  Vanvyve  and  Delle  Monache,  2013:  Wind  resource  assessment  with  an  analog  ensemble.  Journal  of  Applied   Meteorology,  in  prepara2on.   46