## 4 Experiments and Analysis
### Experimental Section  
We took the day's atmospheric pressure, relative humidity, dry and wet bulb temperature, and wind speed observations at 20 o'clock (Beijing Time) as input conditions. Before the experiments, we classified the input meteorological conditions into different levels according to their rating, which is shown in Table 2. Atmospheric pressure falls into four levels shown in the first two columns of Table 2; dry and wet bulb temperature falls into four levels shown in the third and fourth columns; relative humidity falls into five levels, shown in the fifth and sixth columns; and wind speed falls into five levels shown in the last two columns.  
We tested our case-based FCTF reasoning systems with different numbers of input conditions in these experiments to discover the effects of number of input conditions on the case-based FCTF reasoning systems' predicting accuracy. Table 3 shows the detailed experiment section: in experiment number one, we took only one input condition which is atmospheric pressure; in experiment number two, we took two input conditions which are atmospheric pressure and relative humidity; in experiment number three, we took three input conditions which are atmospheric pressure, relative humidity, and dry and wet bulb temperature; and in experiment number four, we took four input conditions which are atmospheric pressure, relative humidity, dry and wet bulb temperature, and wind speed. Since the same meteorological premises may lead to several different conclusions, which are different meteorological levels. In those cases, we choose the most probable level.  
We also took the accumulated precipitations in the next 24 h as outputs for the weather prediction models, and used the precipitation observations to verify the prediction results of Case-based FCTF Reasoning Systems'. Numbers from zero to four inclusively is given to represent light rain, moderate rain, heavy rain, rainstorm, and heavy rainstorm, respectively. Thus, precipitation values falls into five levels shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Precipitation Rating** & **Precipitation Value (mm)** \\ \hline Light rain & (0,10.0) \\ Moderate rain & [10.0,24.9) \\ Heavy rain & [24.9,49.9) \\ Rainstorm & [49.9,99.9) \\ Heavy rainstorm & [99.9,249.0) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Levels classification of precipitation outputs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Atmospheric Pressure**} & \multicolumn{4}{c}{**Dry and Wet Bulb**} & \multirow{2}{*}{**Relative Humidity**} & \multirow{2}{*}{**Wind Speed**} \\ \cline{3-3} \cline{5-6}  & & **Temperature** & & & & \\ \hline Rating & Value (hPa) & Rating & Value (\({}^{\circ}\)C) & Rating & Value (\%) & Rating & Value (MPH) \\ Moderate & \(>\)940 & Lowest & \(<\)–10 & Dry & [0,30) & Calm & (0,2) \\ Lower & [930, 940) & Lower & [–10,5) & Less dry & [30,50) & Light Air & [2,4) \\ slightly & Moderate & [5,30) & Less & & Light Brezeze & [4,7) \\ Lower & [920,930) & Higher & [30,45) & humid & & Gentle Brezeze & [7,11) \\ Lowest & \(<\)920 & Highest & \(>\)45 & Humid & [70,100) & Moderate Breeze & [11,17) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Levels classification of input conditions.  
Since people concern forecasting rainfalls more than forecasting sunny days [33; 34], we defined _Hitrate_ as:  
\[\textit{Hitrate}=\textit{Correctp}/\textit{Totalp} \tag{2}\]  
where _Correctp_ is times of correct prediction in observed rainfall days and number of observed rainfall days as _Totalp_. We defined _Error_ as:  
\[\textit{Error}=\textit{Predictp}-\textit{Observp} \tag{3}\]  
where _Predictp_ is the predicted precipitation values and the observed precipitation values as _Observp_. \(|\textit{Error}|\) denotes the absolute value of _Error_. For the same reason as above, we also only considered observed rainfall days in calculating average \(|\textit{Error}|\).