Gauging Rainfall
By Andrew J. Newman and Paul A. Kucera, Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota, USA
Accurate precipitation measurements are required for many different applications such as river/flash flood forecasting, water resource management, and agriculture. Precipitation measurements are retrieved through the use of a variety of instruments located on the surface (e.g. rain gauges) or from remote observations such as satellites or ground-based radars. Despite advances in technology, rain gauges are still considered the standard for surface rainfall measurements. Often, these data are used for verification of remotely sensed rainfall estimates. Therefore, it is important to understand and quantify the errors associated with the in situ precipitation estimates.
Velocity magnitude for the rain gauge simulation |
Streamlines colored by velocity magnitude with static pressure contours on the rain gauge |
When using a rain gauge, a variety of errors can occur due to calibration and sighting problems and instrument failure. While these errors are mostly preventable, environmental conditions can lead to wind induced catchment errors that are not generally preventable. Catchment errors occur because a rain gauge modifies the environment in which data are collected. Field measurements show that these errors can range from 1-2% to over 10%, depending on the rainfall intensity and free stream wind velocity [1]. A few prior studies have used CFD to simulate precipitation gauges with results that have fairly good agreement with observations [2, 3]. Using FLUENT, similar instruments are now being modeled for comparison with previous studies, and for the development of a tool for evaluating the flow characteristics around new precipitation monitoring instrumentation.
Pressure contours on the camera and mount and pathlines colored by velocity |
As an example, the flow around a Qualimetrics tipping-bucket rain gauge mounted on a 1m pole has been studied. The computational domain is 1.8m x 1.2m x 2m and comprised of roughly 200,000 tetrahedral cells, with local refinement around the rain gauge to better resolve the detailed flow there. A steady wind velocity with a height profile was specified along with the standard k-ε turbulence model. The results were compared with one of the earlier studies [2], even though a slightly larger wind speed (4m/s versus 3m/s) was used. The results illustrate the acceleration of the flow over the top of the gauge along with the formation of a vortex in the gauge catchment area. A flow profile such as this can cause raindrops, especially small ones, to miss the rain gauge, giving rise to an underestimate of the actual rainfall.
Contours of velocity for the Rain Imaging System with the worst case wind scenario, in which the wind blows from behind the camera (left) towards the sample volume (right) |
The flow around an instrument that is used to image hydrometeors (raindrops or snowflakes, for example) has also been studied. The instrument, called a disdrometer or Rain Imaging System (RIS) and developed at NASA/Wallops Flight Facility by Dr. Larry Bliven, photographs hydrometeors that fall through a sample volume located between the camera and light source. The RIS is unique because it can provide measurements with less wind interference than other instruments of similar capability [3]. In a FLUENT simulation of this device, roughly 450,000 tetrahedral cells were used, again with a refined mesh in the vicinity of the instrumentation. The worst-case scenario has the wind coming from behind the camera housing and flowing toward the sample volume. The CFD results demonstrate that this condition creates a shadow effect in the sample volume that may cause under catchment of the hydrometeors. Wind flow conditions that are not along the camera line-of-sight do not affect the hydrometeor sampling as much.
References
- Sevruk, B.: Wind Induced Measurement Error for High- Intensity Rains. Proc. International Workshop on Precipitation Measurement, WMO Tech. Document 328, 199-204, 1989. [Available online at http://www.wmo.ch.]
- Nespor, V.; Sevruk, B.: Estimation of Wind-Induced Error of Rainfall Gauge Measurements Using a Numerical Simulation. J. Atmos. Oceanic Tech., 16, 450-464, 1999.
- Nespor, V.; Krajewski, W.F.; Kruger, A.: Wind-Induced Error of Raindrop Size Distribution Measurement Using a Two-Dimensional Video Disdrometer. J. Atmos. Oceanic Tech., 17, 1483-1492, 2000.
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