Commercial microwave links (CMLs) have emerged as a valuable source of rainfall information that can complement existing observations. In Germany, we acquire attenuation data from 4000 CMLs with a temporal resolution of one minute. In this contribution we present our results of deriving country-wide rainfall information from these CML data and show the first long-term application of CML data for adjusting the radar rainfall field.
We present results of a large-scale analysis of our country-wide dataset for one full year (Graf et al. 2020) and compare it with the gauge adjusted radar product RADOLAN-RW from the German Weather Service and the climatologically corrected radar product RADKLIM-YW. Our analysis also compares several different methods for processing CML data, including our recent improvements for the separation of dry and rainy periods in noisy CML attenuation time series based on a convolutional neural network (Polz et al. 2020). We show seasonal and diurnal variations of the performance of CML-derived rainfall data. Promising results are achieved year-round except for periods with solid precipitation. Pearson correlations for the comparison of the hourly rainfall sums reach up to 0.7 for summer months.
Furthermore, we present results from using the CML rainfall estimates to adjust radar rainfall fields. We extended the RADOLAN-method for radar-gauge adjustment for this purpose. The path-averaged CML rainfall information is compared to the gridded radar rainfall information at the path-intersecting grids. This information is then used in addition to the adjustments derived from rain gauges. We show first results of an hourly adjustment over several months. We further discuss the envisaged operational system for this application and give an outlook on the potential for radar rainfall field adjustments with higher temporal resolutions.
Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020
Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020
How to cite: Chwala, C., Winterrath, T., Graf, M., Polz, J., and Kunstmann, H.: Country-wide CML rainfall estimation and CML-Radar combination in Germany, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-499, https://doi.org/10.5194/ems2021-499, 2021.
