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

Transcription

1 16TH WRF USER WORKSHOP, BOULDER, JUNE 2015 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

2 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 2

3 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 Spatial and temporal scales 3

4 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 4

5 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 5

6 WRF set up 1. GFS initial and boundary conditions vertical levels hours spin-up 4. outputs saved every 5 minutes 5. 4 nested domains 34, 12, 4 and 1.3 km (evaluated) 6

7 6 Microphysics parameteriza#ons 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

8 WRF modeled cloud fraction, cloud occurrence, CBH, CTH and cloud cover 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.

9 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.

10 March km 4km 1.3 km Sky camera image ceilometer 10

11 CBH 4 km 12 km

12 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. WRF Ceilometer Y N Y A B N C D CBH, CTH and cloud cover 12

13 Evaluation procedure 3 spatial resolutions: 12, 4 and 1.3 km 1. Cloud occurrence 2. CBH 3. CTH 4. Cloud cover Temporal resolution: aggregations starting at 5 minutes to 6 hours. 6 microphysics parameterization 2 cloud fraction models 3 cloud overlapping methods

14 RESULTS: WRF CLOUD OCCURRENCE PREDICTION SKILL 5 minutes samples. XCF 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 middleand 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

15 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.

16 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 thicker

17 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 Scarce role of the MPs choice

18 RESULTS: WRF CLOUD COVER PREDICTION SKILL Evalua\on carried out in an area of km centered at the sta\on loca\on 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

19 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.

20 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.

21 Cloud cover SUMMARY 1. The model tends to over-predict cloud cover and produce estimates with RMSE values of 0.5 cloud cover unit 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 Arbizu- Barrena et al., Under review J. Geophys. Res. Atmospheres.

22 16 TH 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. 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