Rainfall Monitoring

Products for reliable rainfall monitoring

Reliable rainfall monitoring is key for any water system analysis and for operational water-system control. Traditionally rainfall is measured using rain gauges which are manually read or which automatically send real time data to a telemetry network using wired or wireless communication. It is a proven technology which works fine for monitoring when many rain gauges are available in the area to monitor.

Radar monitoring

However, heavy rainfall events often occur very locally, and with the use of rain gauges alone, there is a high probability that a rainfall event is missed by the rain gauges. To be able to detect such events, continuous spatial monitoring such as rainfall radar monitoring is a better alternative. Radars measure circularly and via an antenna send out radio signals which may or may not hit raindrops falling from clouds. When the signal hits such raindrops it reflects and part of the reflected signal is then detected by the radar dish. The travel time of the radio signal and the diffusion of the signal are measures for distance and intensity of rainfall monitored by the radar.

The embedded software of the radar system converts the signal into a rainfall intensity on a certain place and converts it from dBZ to mm of rainfall per minute. C-band radars which are most often applied for this purpose measure once every 5 minutes up to a distance of 100-200 km. Specific software such as SCOUT form hydro&meteo is used to combine the measurements of several radars and to transfer the radial measurements into a grid which can be used for water management analytics and operations.

The monitoring principle has a weakness in that radio signal is lost due to the collision with raindrops. This attenuates or lowers the intensity of the signal behind a rain front and consequently the radar underestimates rainfall intensity at such places.

The solution is to continually correct the radar signal using a correction factor field, which is determined by means of differences of the raw radar measurement with online point measurements of rain gauges.
By doing so, best of both worlds can be used: accuracy of local rainfall measurements with rain gauges and; spatial measurement of the radar. Practice shows that quite accurate spatial rainfall monitoring can be made available by combining both products. An example is the corrected HydroNET rainfall product, available for the Netherlands and parts of Germany, South-Africa and Australia.

Satellite rainfall monitoring

Apart from rainfall radar, satellite information can be used for rainfall monitoring. These types of measurements are available through polar and geo-stationary satellites. Polar satellites continually scan the atmosphere and the earth surface. The advantage of polar satellites is that the distance to the clouds monitored is small compared to geostationary satellites. Therefore, polar satellites monitor with the highest spatial resolution; however, the time resolution is usually sparse. Blended products of geostationary and fine polar satellites are available by NASA and ESA. New products appear at high frequency and some of them have more accurate results in certain regions of the world than others.

The obvious advantage of satellite rainfall monitoring is that it is available over large parts of the world, which is advantageous in sparsely monitored regions. The downside of satellite rainfall monitoring is that it is indirect and sometimes inaccurate. Clouds are monitored basically and coverage and temperature at specific heights are drivers for the computed rainfall estimates. Examples of such products are Hydro-Estimator and CHIRPS.

Atmospheric models

Weather monitoring organizations operating globally such as ECMWF and NOAA continually run weather models which simulate the entire atmosphere, using physically based dynamics equations. The models are assimilated with monitoring data e.g. from ground stations and weather balloons and in this way they can be regarded as smart interpolators of monitored and unmonitored weather variables. The power of these models in monitoring is that they can also be a source of the blended products, described above.