1. What is the importance of high spatial resolution in precipitation estimation?
High spatial resolution is crucial in precipitation estimation due to the high spatial and temporal variability of precipitation, especially in the case of intense events associated with convective phenomena. Precise quantitative estimation is challenging and subject to errors. Techniques like rain gauge measurements, meteorological radar measurements, and satellite estimates have limitations in providing satisfactory precision. Combining data from different techniques helps exploit their advantages and minimize weaknesses, leading to multi-source precipitation estimates used for quantitative precipitation estimation (QPE). Sub-hourly resolutions, such as 10-minute resolution, are increasingly used for nowcasting, flash flood forecasting, and analyzing precipitation extremes. There is also a growing demand for long-term precipitation estimates with high spatial resolution, which can be met by using algorithms for quality control and combining data into multi-source estimates. This approach is applied operationally to 10-minute data and also used for reanalyses, providing valuable insights for climatologists, agrometeorologists, and other researchers.
read more
2. What are the advantages of using manual rain gauges over telemetric rain gauges?
Manual rain gauges, specifically Hellmann type gauges, offer more accurate measurements compared to telemetric rain gauges. While telemetric gauges are subject to significant failure rates and are considered less accurate, manual gauges provide high-quality data with a delay of almost two months. This delay is mainly due to the human-made data quality control process. The dense network of manual rain gauges installed at IMGW, as shown in Figure 1 and Table 1, allows for a more accurate representation of precipitation data. Additionally, the use of manual rain gauges enables researchers to exploit the high-quality measurements for reanalysis purposes, which can enhance the overall understanding of precipitation patterns and contribute to improved weather forecasting and climate studies.
read more
3. What is the frequency of data generation by the Rainbow 5 system?
The Rainbow 5 system generates three-dimensional raw data and two-dimensional products every 10 minutes. However, there is an ongoing shift to a 5-minute measurement frequency. This system has a spatial resolution of 0.5 km and a range of 250 km. The POLRAD network, operated by the IMGW, consists of eight Doppler radars manufactured by Leonardo Germany. These radars are currently being replaced by new models with dual-polarised radar beams, and two new radars are being installed. The RADVOL-QC system is used for quality control of radar data, correcting the source 3D radar data and generating dynamic maps of the data quality index. Merging data from individual radars into radar composite maps is done by applying algorithms that consider the spatial distribution of the quality index for each time step. Quality control of satellite precipitation is also carried out by the RainGRS system, primarily considering the availability of NWC-SAF products at a given time. The quality of satellite precipitation is significantly lower at night-time due to the absence of visible range-based products analyzing the physical properties of hydrometeors.
read more
4. How does RainGRS combine precipitation data?
RainGRS combines precipitation data by using a conditional merging algorithm that enhances the strengths of individual inputs and reduces their weaknesses. It takes into account the quality information of rain gauge, radar, and satellite data. The algorithm first interpolates rain gauge values at radar pixel resolution using Ordinary Kriging. Then, the deviation between measured and interpolated radar values is computed and added to the interpolated rain gauge values. Satellite data is also interpolated using an analogous formula. The resulting precipitation fields are recombined using a weighted scheme that considers the quality indices of individual precipitation fields. The final quantitative precipitation estimate (GRS) is a combination of gauge-radar and gauge-satellite fields computed using a weighted formula that considers the quality of radar data as a function of distance to the nearest radar site.
read more