Climatology of snow cover and snow water equivalent

• Chapter V: Building climatological time series of snow cover data


•  
• General remarks
• Cloud gap filling
• Extracting data for a particular location
• Validation techniques

Validation techniques

The accuracy of satellite-based snow cover data can be determined using in-situ data or high resolution satellite imagery as a reference. Statistical scores for data validation can be calculated using contingency table statistics. The contingency table shows the frequency of "yes" and "no" events (snow presence in satellite data) compared with in-situ data. The categories of the contingency table are:

• HIT — the snow was identified by satellite data and was observed in-situ;
• MISS — the snow was not identified in satellite data but was observed in-situ;
• FALSE_P — the snow was identified in satellite data but was not observed in-situ (false alarms);
• TRUE_N — the snow was not verified in satellite data and was not observed in-situ (correct negatives).

The contingency table is a useful way to show what type of errors occur when deriving snow cover days (SCD) from satellite measurements. A perfect snow product from a remote sensing system would produce only hits (HIT) and correct negatives (TRUE_N), and no misses (MISS) or false alarms (FALSE_P). The statistical scores for satellite products can be computed from contingency table values to describe the products' performance. A summary of different statistical scores is presented in Table 1.

Table 1: Statistical scores used for satellite data validation with in-situ measurements.

Statistical Score	Formula	Description
Accuracy		What fraction of satellite derived snow cover was correct? Range: 0 to 1. Perfect score: 1.
Bias		How satellite derived snow cover frequency compare with snow observed in-situ? Range: 0 to ∞. Perfect score: 1.
Probability of detection (POD)		What fraction of the in-situ snow events were determined by satellite data? Range: 0 to 1. Perfect score: 1.
False alarm ratio (FAR)		What fraction of the satellite derived snow cover was false (no snow was observed in-situ)? Range: 0 to 1. Perfect score: 0.
Probability of false detection (POFD)		What fraction of the observed "no snow" events were determined as snow in satellite data? Range: 0 to 1. Perfect score: 0.
6. Threat score (TS)(Critical success index)		How well did the satellite derived snow cover correspond to the observed snow cover in-situ. Range: 0 to 1. Perfect score: 1.

Additional statistical parameters can be calculated to show the different aspects of satellite data accuracy and performance:

1. the mean absolute difference (d)
2. standard deviation (SD)
3. correlation coefficient (c)
4. relative difference (d, %)
5. Climatological Skill Score (SSclim), which indicates whether satellite-based data was better than the in-situ station climatology:

	[1]
	[2]
	[3]

MSE - mean square error (satellite observations versus in-situ);
MSEclim - mean square error (climatological value versus satellite observations).

Contingency table statistics can be applied to evaluate snow cover presence, but it is not applicable for quantitative snow characteristics such as snow depth or SWE. Satellite-based SWE products can be evaluated using correlation coefficients, bias, and RMSE (Root-Mean-Square-Error).