**5. Regional rainfall forecasts**

The rainfall forecasts discussed so far correspond to specific locations within a region. The limitation in selection of those sites depends on the availability of sufficient historical data for adequately training and testing. In some cases, shorter data sets may be adequate, and shorter data duration may be offset by inclusion of additional climate indices [20].

Provided there are sufficient well-distributed sites within a geographical region, it may be possible to construct a regional rainfall forecast map, as shown in **Figures 4** and **5**. This Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies… http://dx.doi.org/10.5772/intechopen.72619 41

**Figure 3.** Skill scores for monthly forecasts for Victoria Mill (central Queensland), including the comparison between single-month optimization for short duration (reduced-time) and long duration (extended-time) forecasts [20].

There are many more climate indices available, but most are of much shorter published duration, often only beginning in the 1950s. These 'short' duration indices are listed in **Table 2**, specifically the south-eastern Indian Ocean index (SEIO), Western Indian Ocean Index (WIO), The Southern Annular Mode (SAM) and the Quasi-Biannual Oscillation (QBO). Data for these indices were variously sourced from the Bureau, KNMI Climate Explorer, and also the UK Met Office for the recent publication [20] in which we tested the trade-off between more cli-

The skill of an ANN is dependent on finding patterns or relationships in data, and we intuitively thought that input series of less than 100 years would be unlikely to provide adequate information. In a recent study for a region in central Queensland it was apparent that the skill of the forecast depended on the particular month for which the forecast was being made, as shown in **Figure 3**. Nevertheless, it was apparent that of more importance than inputting long-duration series, was single-month optimization. When single month optimization was applied to both long and short duration series, the long duration series gave superior forecasts, as shown in **Figure 3**. More information on the definition of, and methodology used, to calculation the skill score is provided in the technical paper [20] and under the following

The rainfall forecasts discussed so far correspond to specific locations within a region. The limitation in selection of those sites depends on the availability of sufficient historical data for adequately training and testing. In some cases, shorter data sets may be adequate, and shorter

Provided there are sufficient well-distributed sites within a geographical region, it may be possible to construct a regional rainfall forecast map, as shown in **Figures 4** and **5**. This

section: Deterministic versus Probabilistic Forecasts, and Skill Scores.

data duration may be offset by inclusion of additional climate indices [20].

mate indices and shorter series as input.

40 Engineering and Mathematical Topics in Rainfall

**5. Regional rainfall forecasts**

**Figure 4.** Forecast rainfall (mm) for December 2010 for the south-east Queensland region. A: bar chart for individual sites; and B: isohyet map with 50 mm interval spacing [21].

enables forecasts to be generated for specific locations within the geographical region that do not correspond to sites with rainfall data. It also enables rainfall forecasts to be made corresponding to a defined geographical area within the region, such as a water catchment area.

As an example, **Figure 4**, is a specific sub-region of south-eastern Queensland defined by longitudes 151.0°E and 153.5°E, and latitudes 24.4°S and 28.5°S based on rainfall data from 54 sites. The region extends about 300 km southwards along the Queensland Coast from Bundaberg to the Gold Coast, and extended inland from the coast to include the towns of Dalby, Inglewood and Mundubbera, a distance of about 200 km. This sub-region therefore approximates the same area as a single grid forecast area used by POAMA (250 km × 250 km).

This example is provided to show that it is possible to develop an isohyet map for such a region derived from the individual series to predict rainfall for a period of intense rainfall, as occurred in December 2010, and also a relatively dry month such as December 2005. The isohyet map was constructed using *Teraplot* software – and the forecast are 12 months in advance using single-month optimization [21].

This is illustrated in **Figure 6** showing the seasonal forecast issued by the BOM for the period December 2010 to February 2011 for the entire continent of Australia (Bureau of Meteorology, Archive of rainfall forecasts). Although the forecast indicates that there is a probability in the range of 60–70% above median rainfall for the south-east Queensland region, there was no warning of the magnitude of the impending heavy rainfall. Furthermore, the extensive flooding that affected much of costal Queensland beyond the south-eastern region where the forecast is a 50% probability of above median. This contrasts with the specific quantities forecast

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies…

http://dx.doi.org/10.5772/intechopen.72619

43

Not only is the information content of probabilistic forecasts less than corresponding deterministic forecasts, where actual numerical values of predicted rainfall are provided, studies have demonstrated that the general public and specific classes of end-users, such as farmers from south-east Queensland, often have difficulty interpreting the meaning of the probabilistic forecasts [33, 34]. The distinction between median and average was often not understood. In addition, the statement that there was a 30% probability of above the median rainfall was often misinterpreted to mean a forecast of a specific quantity of rainfall 30% higher than the median. There is confusion between a probabilistic and deterministic forecast that can be understood by the public as the expectation of higher rather than lower rainfall as intended by the forecaster. The Bureau bases its probabilistic forecasts on the simulation of actual physical climatic processes through general circulation models, specifically POAMA [36–38]. For the period from

2002 through until 2011, seasonal rainfall predictions were made using POAMA 1.5.

In July 2011, the Bureau provided us with monthly forecasts for the period to March 2011 for 17 individual sites as simple bilinear interpolations of surrounding grid points which were calculated from the ensemble mean which in turn had been calculated from many runs of this

**Figure 6.** Seasonal rainfall forecast for Australia by the BoM issued in November 2010. http://www.bom.gov.au/climate/

ahead/archive/rainfall/20101123.national.hrweb.gif [35].

for the isohyet maps shown in the previous section.

**Figure 5.** Forecast rainfall (mm) for December 2005 for the south-east Queensland region. A: bar chart for individual sites; and B: isohyet map with 50 mm interval spacing [21].

In **Figures 4** and **5**, it is evident that rainfall varies with topography, and was highest at the places of some altitude in both the relatively dry December of 2005, and also the flood month of December 2010. It is also evident that the ANN correctly predicted December 2010 as experiencing relatively higher rainfall across the sub-region, peaking at over 700 mm.
