Climatology of daily rainfall semi-variance in The Netherlands

Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted

Preliminary Study on the Feasibility of Performing Quantitative Precipitation Estimation Using X-band Radar

IRCTR has built an experimental X-band Doppler po-larimetric weather radar system aimed at obtaining high temporal and spatial resolution measurements of precipitation, with particular interest in light rain and drizzle. In this paper a first analysis of the feasibility of obtaining accurate quantitative precipitation estimation from the radar data performed using a high density network of rain gauges is presented

Seasonal semi-variance of Dutch rainfall at hourly to daily scales

Using 30 years (1979–2009) of data from 33 automatic rain gauges in the Netherlands, a study of the space–time variability of rainfall is performed. This study uses 90-day averaged semi-variograms to find seasonal signals in the fitted spherical semi-variogram parameters of rainrates in the Netherlands, for accumulation intervals between 1 and 24 h. These signals can be well-described by simple cosine functions. The dependence of these cosine functions on the accumulation interval is modeled in two different ways: (1) power-law relations between the variogram parameters and the accumulation interval, and (2) power-law relations between the parameters of the cosine functions and the accumulation interval. For the first method the cosine function at the 24-h accumulation interval is also needed. The second of these methods has more parameters, but is shown to model the temporal scaling best. The space–time scaling relations found in this paper are compared to those found by others for similar and contrasting climates. Seasonality is shown to play an important role in determining rainfall spatial variability

Climatology of daily rainfall semi-variance in The Netherlands

Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted

Seasonal semivariance of Dutch rainfall at hourly to daily scales

Using 30 years (1979–2009) of data from 33 automatic rain gauges in the Netherlands, a study of the space–time variability of rainfall is performed. This study uses 90-day averaged semi-variograms to find seasonal signals in the fitted spherical semi-variogram parameters of rainrates in the Netherlands, for accumulation intervals between 1 and 24 h. These signals can be well-described by simple cosine functions. The dependence of these cosine functions on the accumulation interval is modeled in two different ways: (1) power-law relations between the variogram parameters and the accumulation interval, and (2) power-law relations between the parameters of the cosine functions and the accumulation interval. For the first method the cosine function at the 24-h accumulation interval is also needed. The second of these methods has more parameters, but is shown to model the temporal scaling best. The space–time scaling relations found in this paper are compared to those found by others for similar and contrasting climates. Seasonality is shown to play an important role in determining rainfall spatial variability