Regional center for northern africa, middle east and europe of the wmo sds-was



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Forecast evaluation

The forecasts of dust optical depth (DOD) are compared with the total aerosol optical depth (AOD) provided by the AERONET network for 42 selected dust-prone stations located in Northern Africa, Middle East and Southern Europe that are listed in Appendix A. Evaluation scores are computed in order to assess the performance of the median multi-model on a monthly, seasonal and annual basis.


Since AERONET retrievals include the contribution of different types of particles, not only mineral dust, it is intended to restrict the comparison to situations in which dust is the dominant aerosol type. Threshold discrimination is made by discarding observations with an Ångström exponent 440-870 higher than 0.6 (Pérez et al., 2006). Although observations with an Ångström exponent 440-870 higher than 0.6 are discarded in order to restrict the evaluation to situations in which mineral dust is the dominant aerosol type, other particles are always present (anthropogenic aerosol, products from biomass burning, etc.). Therefore, negative bias can be expected.
The evaluation system is applied to instantaneous forecast values of DOD ranging from the initial day (D) at 15:00 UTC to the following day (D+1) at 12:00 UTC. Rather than time-interpolated, AERONET observations are assigned to the nearest multiple-of-3 hour. In case more than one observation is assigned to the same hour, only the closest-in-time is considered.
  1. Results

The scores of the evaluation are presented in Tables 5, 6 and 7.




Period

Mean Bias

Root Mean Square Error

Correlation coefficient

Fractional Gross Error

Number of cases

Sep 2012

-0,14

0,37

0,2

0,7

993

Oct 2012

-0,12

0,31

0,41

0,81

991

Nov 2012

-0,16

0,4

0,29

0,96

730

Dec 2012

-0,12

0,26

0,51

1,05

866

Jan 2013

-0,11

0,23

0,61

0,99

961

Feb 2013

-0,12

0,27

0,62

0,75

1075

Mar 2013

-0,13

0,3

0,57

0,82

1561

Apr 2013

-0,12

0,28

0,62

0,65

1566

May 2013

-0,15

0,33

0,57

0,62

1545

Jun 2013

-0,11

0,31

0,58

0,46

1567

Jul 2013

-0,08

0,31

0,45

0,41

1317

Aug 2013

-0,14

0,36

0,35

0,52

1051

Sep 2013

-0,12

0,28

0,48

0,63

1060

Oct 2013

-0,1

0,31

0,49

0,69

1215

Nov 2013

-0,09

0,27

0,4

0,83

858

Dec 2013

-0,15

0,35

0,59

0,94

713

Jan 2014

-0,11

0,25

0,53

1,02

854

Feb 2014

-0,12

0,27

0,7

0,88

1122

Mar 2014

-0,14

0,32

0,63

0,75

1213

Apr 2014

-0,14

0,31

0,47

0,7

1225

May 2014

-0,12

0,38

0,53

0,66

1168

Jun 2014

-0,1

0,29

0,67

0,49

1218

Jul 2014

-0,1

0,29

0,62

0,54

1055

Aug 2014

-0,15

0,36

0,55

0,63

872


Table 5. Monthly scores for the median multi-model


Period

Mean Bias

Root Mean Square Error

Correlation coefficient

Fractional Gross Error

Number of cases

autumn 2012

-0,14

0,36

0,3

0,81

2714

Winter 2012

-0,12

0,26

0,59

0,92

2916

Spring 2013

-0,13

0,3

0,59

0,7

4672

Summer 2013

-0,11

0,33

0,49

0,46

3965

Autumn 2013

-0,1

0,29

0,49

0,71

3133

Winter 2013

-0,12

0,29

0,64

0,94

2689

Spring 2014

-0,13

0,33

0,57

0,69

3706

Winter 2014

-0,11

0,31

0,62

0,54

3094


Table 6. Seasonal scores for the median multi-model



Period

Mean Bias

Root Mean Square Error

Correlation coefficient

Fractional Gross Error

Number of cases

2013

-0,13

0,30

0,56

0,67

14525


Table 7. Annual scores for the median multi-model
The evolution of the scores with time is shown in figures 2 to 5

Figure 2. Monthly (left) and seasonal (right) mean bias of the median multi-model



Figure 3. Monthly (left) and seasonal (right) root mean square error of the median multi-model

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