SPI-1 calculations based on different data sets

As the first step of our analysis, we compare the SPI-1 values obtained from station- based precipitation observations with those derived from the two candidate data sets, CAMS and ERA-Interim (based on the respective neighbouring grid point at the orig- inal higher spatial resolution of these data sets of 0.5◦ × 0.5◦). Figure 5.2 shows the local correlation values between the time series of station-based and gridded SPI-1 values for all boreal summer months, obtained by computing the classical Pearson cor- relation coefficient between each station and the respective closest grid point and spa- tially interpolating the obtained values over the study region. Note that the particular values shown in the resulting maps are affected by the spatial interpolation, especially in regions with sparse station coverage, as well as by the fact that precipitation is a small-scale property that can vary markedly between a particular station location and the location of the neighbouring grid point. In this spirit, the results shown should be considered only in qualitative terms, allowing us to assess the relative representation quality of observed monthly precipitation sums in the two data sets under considera- tion. However, high correlations can still be accompanied by strong bias.

It is observed that the typical local correlation values for the CAMS data set are clearly higher than those for ERA-Interim (Fig. 5.3). Table 5.1 summarizes the corresponding mean local correlations taken over the complete set of stations, where the correspond- ing correlation values have been obtained individually for each boreal summer month (June, July, August) during the period 1979-2014. In addition, the table displays the re- sults of two other common verification metrics already introduced in Section 3.5. The spatial field correlation describes the correlation between the SPI-1 values of all sta- tions and their respective closest grid point during the same month and is represented here by its mean value taken over all months. The reliability characteristic ρ is defined as the difference between the numbers of true and false classifications of SPI-1 values

5.1. Data and model 61

Figure 5.2: Spatial patterns of local (point-wise) correlations between SPI-1 values obtained from direct measurements made at meteorological stations across Russia and monthly precipitation data from the neighbouring grid points in the CAMS_CPC (left) and ERA–Interim (right) data sets.

in the gridded data as compared to the station-based reference data, divided by the total number of comparisons made, and is estimated using all stations and all years of observations. Here, a corresponding classification is obtained by coarse-graining the SPI-1 values into seven distinct classes defined by the WMO5 (Table 3.1). All three measures clearly demonstrate that CAMS reproduces the true precipitation patterns with higher accuracy than ERA-Interim. Consequently, for further SPI-1 calculations the monthly precipitation data from CAMS is used.

Next, the behaviour of SPI-1 values obtained from the raw NWP data taken from the SL-AV model output is studied. It is found that one-month lead-time SPI-1 forecasts based on direct precipitation estimates from hindcast simulations of SL-AV indicate relatively poor predictability (see Table 5.2 for details). As an example, Figure 5.4 displays the calculated SPI-1 values based on CAMS and SL-AV for one randomly selected month (August 2010), indicating a remarkable discrepancy in the resulting spatial patterns. In fact, when computing the local correlations between the SPI values derived from both data sets for the different boreal summer months (Fig. 5.5), we find correlation values distributed around zero, with absolute values rarely exceeding 0.3. While the SL-AV model is capable of capturing the timing of drought and wet events with reasonable confidence, the geographical extent of these events is not correctly represented. This observation again underlines the necessity of developing and apply- ing an alternative approach for seasonal precipitation forecasts that improves the poor accuracy of predictions for the Russia.

Figure 5.3: Box plots of local (point-wise) correlations between SPI-1 values obtained from direct measurements made at meteorological stations across Russia and monthly precipitation data from the neighbouring grid points in the CAMS and ERA–Interim data sets.

Table 5.1: Statistical characteristics of CAMS and ERA-Interim derived SPI-1 fields compared with station data.

Month Local Field Reliability correlation correlation characteristics ρ

CAMS June 0.76 0.72 0.59 July 0.71 0.68 0.53 August 0.75 0.73 0.57 ERA-Interim June 0.65 0.63 0.45 July 0.62 0.60 0.42 August 0.64 0.62 0.44

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Table 5.2: Summary of verification criteria for SL–AV based SPI-1 hindcasts and deterministic forecasts using the scheme proposed in this thesis (see Section 5.6.1 for details).

Month Local Field Reliability correlation correlation characteristics ρ

SL-AV hindcast June 0.14 0.13 0.11 July 0.12 0.11 0.07 August 0.15 0.14 0.09 Deterministic forecast June 0.58 0.56 0.54 July 0.52 0.51 0.48 August 0.55 0.53 0.50

Figure 5.4: Example of resulting SPI-1 estimates for August 2010 based on CAMS (left) and SL–AV hindcast data (right). For this month, the spatial field correlations between the different fields are 0.65 (CAMS vs. rain gauges), 0.61 (ERA- Interim - not shown here - vs. rain gauges), 0.14 (SL-AV hindcast vs. rain gauges), 0.21 (CAMS vs. SL-AV hindcast), illustrating the poor agreement between the SL-AV hindcasts and both the station-based precipitation records and the selected spatially homogeneous reference data set (CAMS).

Figure 5.5: Local (point-wise) correlations between CAMS and SL-AV hindcast- derived SPI-1 values during the boreal summer.