ASSESSMENT OF THE CAPABILITY OF WRF MODEL TO ESTIMATE CLOUDS AT DIFFERENT TEMPORAL AND SPATIAL SCALES

Clara Arbizu-Barrena, David Pozo-Vázquez, José A. Ruiz-Arias, Joaquín Tovar-Pescador

SOLAR RADIATION AND

ATMOSPHERE MODELLING GROUP (MATRAS)

DEPARTMENT OF PHYSICS UNIVERSITY OF JAEN

SPAIN University of Jaén

Spain

16TH _{WRF USER WORKSHOP, BOULDER, JUNE 2015}

ASSESSMENT OF THE CAPABILITY OF WRF MODEL TO ESTIMATE CLOUDS AT DIFFERENT TEMPORAL AND SPATIAL SCALES

Mo#va#ons  of  this  work  

Ø

Improvement of solar radiation forecasting reliability: a

key issue for solar energy grid integration

Ø

Reliability of the cloud forecasts is the most important

factor that limits this accuracy

Ø

Scarce works evaluating the reliability of the WRF cloud

estimates at high temporal and spatial resolution (i.e. site

locations), needed in solar energy applications

Ø

Evaluation studies are important to improve cloud

representation in the WRF

Motivations of this work

Evaluation of the cloud representation reliability in the WRF model:

a complex task

Ø

Clouds are characterized by microscopic and macroscopic

parameters

Ø

Different types of clouds an cloud-related processes

Ø

Clouds parameters difficult to measure

Ø

Discrete nature of clouds: double penalty effect

Ø

The evaluation of the cloud representation in WRF involves

the analysis of the role of:

•

Microphysics, cumulus and PBL parameterizations

•

Cloud fraction models

•

Cloud overlapping approaches

Aims of this work

In this work we aim to evaluate the role of the:

Ø

Microphysics parameterizations

Ø

Cloud fraction models

Ø

Cloud overlapping approaches

Ø

Spatial and temporal scales

..in the reliability of the WRF model cloud macroscopic

characteristics representation, i.e.:

Ø

Cloud occurrence

Ø

CBH and CTH

Ø

Cloud fraction

Evalua#on  loca#on  and  data  

UNIV. JAEN METEO STATION:

Ceilometer (Jenoptik 15k-Nimbus)

Ø  CBH and CTH estimates based on LIDAR technique Ø  Up to 5 cloud layers simultaneously

Ø  Accuracy of ±5 m, range 5 m to 15 km

Ø  Measurement every 15 seconds: 5 minutes average

TSI-880 Sky camera

Ø  Hemispheric cloud cover

measurements every 30 seconds Study period: 21 days along 2013 Ø  Different types of sky conditions

WRF set up

1.  GFS initial and boundary conditions

2.  50 vertical levels

3.  24 hours spin-up

4.  outputs saved every 5 minutes

5.  4 nested domains 34,

12, 4 and 1.3 km (evaluated)

Acronym   Microphysics  Scheme   Reference  

WSM6   WRF  Single-­‐Moment  6-­‐class  scheme     [Hong  and  Lim,  2006]  

THOM   New  Thompson  et  al.  scheme     [Thompson  et  al.,  2008]  

MILB   Milbrandt-­‐Yau  Double-­‐Moment  7-­‐class  

scheme    

[Milbrandt  and  Yau,   2005]  

MORR   Morrison  double-­‐moment  scheme     [Morrison  et  al.,  2009]  

SBLI   Stony  Brook  University  (Y.  Lin)  scheme     [Lin  and  Colle,  2011]  

NSSL   NSSL  2-­‐moment  scheme     [Mansell  et  al.,  2010]  

Other physics prescribed for all the simulations:

•  YSU PBL (Hong et al., 2006), RRTMG short- and long-wave radiation (Iacono et al., 2008), Noah land surface parameterization (Tewari et al., 2004).

•  The parameterization for the cumulus scheme was disabled

Cloud-fraction (CF) is used to verify cloud structures simulated

by the WRF against ground observations (ceilometer and sky

camera).

Two CF parameterizations have been evaluated:

1: Binary CF (BCF), based on a threshold over the cloud

liquid water and ice mixing ratios. Only values 0 and 1 are

allowed

2: Xu and Randall [1996] CF (XCF), continuous CF value

between 0 and 1 are allowed.

WRF modeled cloud fraction, cloud occurrence, CBH, CTH

and cloud cover

Cloud occurrence in the model is here considered whenever the

modeled CF >0

The WRF-modeled CBH (CTH) estimates are derived from the

height of the lowest (highest) model layer with CF>0

Modeled cloud cover is derived from the CF values using a

cloud overlapping scheme. Here, we have evaluated 3:

1.  maximum overlap,

2.  random overlap,

3.  maximum-random overlap.

WRF modeled cloud fraction, cloud occurrence, CBH, CTH

and cloud cover

10  

ceilometer   Sky  camera  image  

12km   4km   1.3  km  

12  km  

4  km   CBH  

Evaluation procedure

Cloud occurrence:

contingency table

Frequency bias:

𝐹𝐵

=

​𝐴

+

𝐵/𝐴

+

𝐶 

Cloud occurrences predicted by WRF divided by the total number of cloud occurrences reported by the ceilometer. Perfect model FB=1.

CBH, CTH and cloud cover

        Ceilometer           Y   N   WRF   Y   A   B   N   C   D  

Evaluation procedure

6 microphysics parameterization 2 cloud fraction models

3 cloud overlapping methods 3 spatial resolutions: 12, 4 and 1.3 km

1.  Cloud  occurrence  

2.  CBH  

3.  CTH  

4.  Cloud  cover      

RESULTS: WRF CLOUD OCCURRENCE PREDICTION SKILL

•  WRF over-predict the number of observed cloud occurrence events, except for low level clouds at 4 and 1 km

•  FB values for high-level clouds are considerably higher than for middle- and low-level clouds.

•  All MPs performs similarly, except WSM6 , that shows the best FB.

•  FB slightly better for the 4 and 1.3 km resolutions, caused by the low levels clouds

RESULTS: CBH AND CTH PREDICION SKILL

5  minutes  samples.    XCF  

•  Overall,  scarce  dependence  of  the  results  on  MPs,  except  for  the  BIAS   •  Spa\al  resolu\on  only  important  for  low  level  clouds  BIAS  

•  Model  tends  to  yield  too  low  CBHs  and  too  high  CTHs,  irrespec\vely  of  the  cloud   level  considered.  Thus,  it  tends  to  produce  thicker  clouds  than  the  observed  ones.    

RESULTS: CBH and CTH prediction skill

5  minutes  samples.    XCF  

High  level  clouds  

•  Model systematically underestimates the CBH of high-level

clouds by ≈1100 m, regardless MP and the domain spatial

resolution (model locates cloud bases below observed values)

•  Contrarily, CTH of high-level clouds are overestimated (≈700 m )

•  As a consequence, the WRF-modeled high-level clouds appear

RESULTS: CBH AND CTH PREDICTION SKILL

5  minutes  samples.    XCF  

Low  level  clouds  

•  Low-level clouds shows lower BIAS and RMSE values for both

CBH and CTH compared to high and middle level clouds

(vertical resolution!!)

•  Significant dependence of the BIAS on the spatial resolution.

•  CBH RMSE lower for 4 and 1.3 km

RESULTS: WRF CLOUD COVER PREDICTION SKILL

•  WRF tend to over-predict cloud fraction, positive bias

•  In general, 4 and 1 km experiments, more reliable cloud cover estimates. •  XCF: WSM6/NSSL MPs best/worst estimates

•  BCF: lower RMSE values, MORR the best performing MPs •  Differences in RMSE values are lower than 10%

•  There is little dependence on the choice of CF overlapping scheme Evalua\on  carried  out  in  an  area  of  12  ×  12  km  centered  at  the  sta\on  loca\on  

RESULTS: WRF CLOUD COVER PREDICTION SKILL

Time  aggrega\on  experiment  

The  modeled  and  observed    cloud  covers  are  averaged  for  aggrega\ng  \me  intervals   in  the  range  from  5  minutes  to  ~5  hours,  by  5  minutes  \me  increments  

•  For shorter averaging time intervals, the experiments with finer spatial resolutions provide lower RMSE values.

•  As the averaging time interval increases, RMSE decreases at a rate of ≈0.03 cloud cover unit per hour.

•  For averaging time intervals longer than 4 hours, the RMSE decreasing rate slows down and RMSE does not appear to depend on the spatial resolution anymore •  BCF lower RMSE values than XCF.

SUMMARY  

Cloud occurrence

1.  WRF over predicts cloud occurrence of high-level clouds while tends to under-predict the cloud occurrence of low-level clouds for the domains with 4 and 1 km cell spacing.

2.  Better prediction skill of the 4 and 1.3 km experiments specially for low level clouds

3.  Scarce role of the MPs and the cloud fraction parameterization

CBH and CTH

1.  Model tends to yield too low CBHs and too high CTHs, irrespectively of the cloud level considered. Thus, it tends to produce thicker clouds than the observed ones.

2.  The role of the domain spatial resolution has proven to be only important for low-level clouds, with decreasing CBH error for increasing spatial resolution.

3.  The choice of MPs has little influence in the model performance, except for high-level clouds.

SUMMARY  

Cloud cover

1.  The model tends to over-predict cloud cover and produce estimates with RMSE values of ≈0.5 cloud cover unit.

2.  4 km and 1 km experiments higher reliability 3.  Better performance of the WSM6 MPs

4.  Scarce role of the cloud overlapping schemes

5.  Temporal aggregation analysis has shown a nearly linear decrease of RMSE as the size of the averaging window increases.

6.  Maximum WRF reliability has been observed for averaging time intervals longer than 4 hours. RMSE reduces from about 0.48 to 0.35.

Arbizu-­‐Barrena  et  al.,    

Clara  Arbizu-­‐Barrena,  David  Pozo-­‐Vázquez,   José  A.  Ruiz-­‐Arias,  Joaquín  Tovar-­‐Pescador  

SOLAR  RADIATION  AND    

ATMOSPHERE  MODELLING  GROUP  (MATRAS)  

DEPARTMENT  OF  PHYSICS   UNIVERSITY  OF  JAEN    

SPAIN   University of Jaén _Spain

16TH  _{WRF  USER  WORKSHOP,  BOULDER,  JUNE  2015}  

ASSESSMENT OF THE CAPABILITY OF WRF MODEL TO ESTIMATE CLOUDS AT DIFFERENT TEMPORAL AND SPATIAL SCALES

Arbizu-­‐Barrena  et  al.,  Under  review     J.  Geophys.  Res.  Atmos.