5.1 Met-subdivision mean forecast for the monsoon season 2020.
Before the met-subdivision forecast skills are analysed it is useful to see the ERF skill over India as a whole. In order to see the quantitative verification of real-time ERF over the country as a whole, the observed weekly rainfall departure for country as a whole during the period of 2020 monsoon season is correlated with the corresponding ERF rainfall up to four weeks lead time. The observed weekly rainfall departures along with the corresponding ERF rainfall departure for the country as a whole with different lead time is shown in Fig. 10 along with the correlation coefficients (CC). As seen from Fig. 10 the ERF did capture the observed intra-seasonal variability of monsoon rainfall during different phases of monsoon such as onset, active & break phases and also the withdrawal phase in most of the period during the season with significant CC between observed and forecast rainfall departure is found and is skilful up to 3 weeks.
Though the ERF of monsoon on all India level is very useful, due to the spatial inhomogeneity of the rainfall distribution for agricultural application two factors are necessary to be considered viz., (i) forecast over 36 met-subdivisions of India as shown in Fig. 11a and secondly (ii) the skill in terms of rainfall category will be more useful compared to the actual rainfall departure. Thus, due to increased variability, the forecast skill over smaller spatial domain in terms of actual rainfall departure is not expected to provide meaningful results particularly for the Agricultural applications. Thus, for verification at meteorological subdivisions level in place of actual rainfall departure, the category of the met-subdivision based on the observed rainfall departure is compared with the ERF category of the met-subdivision for different lead times. The forecast about the active-break cycles of monsoon two to three weeks in advance is of great importance for agricultural planning (sowing, harvesting, etc.), which can enable tactical adjustments to the strategic decisions that are made based on the longer-lead seasonal forecasts, and also will help in timely review of the ongoing monsoon conditions for providing outlooks to farmers. As discussed by Pattanaik (2014) the met-subdivision wise category forecasts for monsoon 2012 was very skilful for application in issuing agrometeorological advisories. Similarly, a recent study by Robertson et al., (2019) also demonstrated the two weeks ERF issued in real time during June–September 2018 monsoon period for the four districts of the state of Bihar in India (Sub division no 9 in Fig. 11a) could predict the monsoon onset and break phase forecasts related to episodes of the Madden-Julian Oscillation quite well at 1–2 week lead.
In order to see the skill of ERF for smaller spatial domains the 36 met-subdivisions (Fig. 11a) of India the observed weekly rainfall departure over each met- subdivisions the met subdivision is categorised in to five categories like (i) Large Excess (LE), Excess (E), Normal (N), Deficient (D) and Large Deficient (LD) or no rain as per the rainfall departure given in Table 1a. For agricultural applications some of the categories are merged and formed into 3 broad categories with ‘LE’ and ‘E’ categories combined into above normal’ and ‘D’ & ‘LD’ into below normal as shown in Table 1a. For the verification purpose over met-subdivision level the 3 categories [(Above Normal, AN); (Normal, NN); (Below Normal, BN)] are considered for preparing the contingency Table 1b. As seen in Table 1b the forecast met subdivision is considered to be correct (C) if the forecast category matches with the observed category, partially correct (PC) if it is one category out and wrong if it is two or more categories out. Based on this contingency Table 1b the verification skill score of met-subdivision level forecast in terms of correct (C), partially correct (PC) and wrong (W) forecast for the entire monsoon season 2020 is shown in Fig. 11b. Similarly, the spatial distribution of met-subdivision level mean forecast skill in terms of correct (C), partially correct (PC) and wrong (W) forecast for the 36 subdivisions during the entire monsoon season of 2020 is shown in Figs. 12a-l.
Table 1a-b : (a) Classification of met-subdivision as normal, above normal and below normal based on rainfall departure in a week (b) Contingency table considered for verification of sub-division level forecast.
(a)
Categories
|
Subdivision Rainfall Departure in a week
|
Classification
|
Excess ( E)
Large Excess (LE)
|
+ 20% or more
+ 60% or more
|
Above Normal (AN)
|
Normal (NN)
|
-19 % to + 19 %
|
Normal (NN)
|
Deficient (D)
Large Deficient (LD)
No Rain (NR)
|
- 20% or less
- 60% or less
-100 %
|
Below Normal (BN)
|
(b)
Forecast Categories
Observed Categories
|
Above Normal (AN)
|
Normal (NN)
|
Below Normal (BN)
|
Above Normal (AN)
|
Correct (C)
|
Partially Correct (PC)
|
Wrong (W)
|
Normal (NN)
|
Partially Correct (PC)
|
Correct (C)
|
Partially Correct (PC)
|
Below Normal (BN)
|
Wrong (W)
|
Partially Correct (PC)
|
Correct (C)
|
As seen in Fig. 11b, the correct percentage gradually decreases and is 58% for week 1 forecast, 45% for week 2 forecast, 38% for week 3 forecast and 34% for week 4 forecasts. It may be mentioned here that in case of normal ERF (when all the metsubdivisions are considered to be of normal (NN) category in the ERF for the entire season), the mean percentage of correct forecast during the 18-week period for 2020 is found to be about 22%. Thus, in terms of statistical score the ERF at met-subdivision level forecast is better than the climatology forecast till 4 weeks. It is also seen in Fig. 11b that the partially correct category forecast is 31% of the met-subdivisions in week 1, 38% in week 2, 42% in week 3 and 44% in case of week 4 forecast. With regard to the wrong forecast, it is 11% in week 1 forecast to 22% in week 4 forecast. Thus, it is very clear from Fig. 11b that the mean percentage of correct to partially correct (one category out) forecast for the total number of met-subdivisions is found to be 89% in week 1 forecast, 83% in week 2 forecast, 80% in week 3 forecast and 78% in week 4 forecast, which is found to be very skilful for issuing the agrometeorological advisories to farmers.
The corresponding percentage of forecast categories (C, PC and W) at met-subdivision level in the decreasing order of correct categories percentage are shown in Figs. 13a-d for week 1 to week 4 forecast respectively. As it is seen from Fig. 12a most of the met-subdivisions over the south peninsula, central India and north-western parts of India show higher percentage of correct forecast (> 60%) with lower skill (≤ 40%) over the four Met subdivisions viz., Vidarbha; East Madhya Pradesh; East Uttar Pradesh; and Nagaland, Manipur, Mizoram & Tripura (Sub divisions no. 26, 20, 10 and 4 in Fig. 11a respectively) with remaining parts the correct category is between 40 to 60% (Fig. 12a). The week 1 forecast under the partially correct (PC) categories show highest value (> 50%) for the met subdivision east Madhya Pradesh (Sub div no. 20 in central CEI in Fig. 11a) followed by the values between 30 to 50% over the other five met-subdivisions of central India including met-subdivision no 19, 22, 25, 26 and 27; three subdivisions over the NWI including the subdivisions no. 10, 11 and 15 and all the 7 met-subdivisions over the NEI shown in Fig. 11a. The lower values (< 30%) of partial categories are found in the remaining met-subdivisions over the eastern coastal regions, northwest India and western coastal parts of India (Fig. 12b). With regard to the week 1 forecast in the wrong categories (Fig. 12c) it is mainly below 20% over most of the sub-divisions except Vidarbha and east Rajasthan (Sub div no. 26 and 18 in Fig. 11a). The same can also been seen in Fig. 13a with decreasing order of correct % categories, which can also be seen from Fig. 13a with 6 meteorological subdivisions viz., J & K, West Rajasthan, Gujarat, Madhya Maharashtra, Tamil Nadu & Pondicherry and North Interior Karnataka (Sub divisions no 16, 17, 21, 24, 31 and 33 in Fig. 11a) indicating correct categories of higher than 70% with lowest being the sub-divisions Vidarbha (No. 26 in Fig. 11a) with 33 %.
Similarly, the week 2 forecast as shown in Fig. 12b indicated most of the sub-divisions with correct to partially correct categories between 30–50%, whereas subdivisions over northwest India indicated slightly lower percentage (< 20%). Similarly, the wrong categories in week 2 are up to 30% except some met-subdivisions over northwest India (No. 18, 13, 11 and 8 in Fig. 11a) also exceeding 30%. It is also seen from Fig. 13b that except met subdivision J & K and Himachal Pradesh (Sub division no. 16 and 15 in Fig. 11a) the correct percentage for met subdivisions are less than 70% with lowest being the west Uttar Pradesh (Sub division no 11 in Fig. 11a) with 17%. It is also seen from Fig. 12b and Fig. 13b that in week 2 forecast the lower % of partially correct categories (< 30%) are mainly seen over north, north-western and south-eastern coastal states of India and higher values over the remaining regions. Comparing the week 1 and week 2 forecast in Fig. 12a and Fig. 12b it is seen that the partially correct % are indicated almost identical like the correct % categories in week 2 forecast, whereas in week 1 forecast it is dominated with higher values of correct % over many subdivisions of India.
When the week 3 and week 4 forecasts are considered, the higher values are dominated with partially correct categories over most of the met-subdivision followed by the correct categories (Figs. 12c-d & Fig. 13c-d). It is also seen from Fig. 13c that the correct categories week 3 forecasts show much higher values (> 50%) over some met-subdivisions of India such as Himachal Pradesh, Arunachal Pradesh, Odisha, Saurashtra & Kutch (Subdivisions division no. 15, 2, 7, 22 and 31 in Fig. 11a respectively). Similarly, in case of week 4 forecast the correct categories show much higher values (≈ 50%) over the three met-subdivisions viz., Himachal Pradesh, J & K and Saurashtra & Kutch (Subdivisions division no. 15, 16 and 22 in Fig. 11a respectively). Thus, the above analysis indicated that the categories forecasts at meteorological subdivisions show skilful result for applications in Agriculture planning, where it is not the quantum of rainfall that is important but the category forecast can also give useful inputs for Agricultural advisories. It may be mentioned here that the soil moisture along with precipitation and temperature anomalies can play a major role to estimate the agricultural and hydrological droughts severity and areal extent (Saha and Mishra 2017; Pattanaik et al., 2019).